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	Title	Pub. Date	Duration
	Morally Binding AI Governance Isn’t Legally Binding AI Regulation: AI Safety, Muse, and Data Center Financing	15 Oct 2026	00:36:42
In this special edition of AI to ROI, Ray and Peter step outside the usual format to break down four of the most important stories from the latest AI to ROI News and Analysis edition. Recording from Bratislava, Slovakia - Ray joins Peter to work through what the White House’s voluntary AI safety pact actually commits the labs to, why OpenAI’s string of safety and disclosure incidents should factor into enterprise procurement, whether small businesses will trust Meta’s Muse with their payment and ad accounts, and why rising Treasury yields and delayed projects are putting pressure on AI data center financing. Throughout the episode, Ray and Peter translate each headline into specific governance and buying questions for executives. The White House AI Safety Pact: Six major AI companies signed a one-page agreement with no penalties or timelines. Ray sees it as a starting point, Peter sees a publicity event, and both agree Congress needs to treat frontier AI more like critical infrastructure. External auditors and conflicts of interest: Anthropic named Accenture as its first embedded evaluator. The hosts weigh the value of year-round auditor access against the conflict created by Accenture’s large Claude implementation practice. OpenAI’s safety and disclosure problem: The canceled Astra release, more than fifteen incidents in recent months, and slow disclosure on events like the Hugging Face breach. Ray and Peter separate agent containment, which is an industry-wide problem, from disclosure, which is a vendor problem that belongs in contracts. Meta Muse for Small Business: Distribution into roughly 200 million small businesses gives Muse a real advantage, but low consumer trust in Meta and Amazon’s decision to block Muse show the friction. The recommendation is to start with reporting and ad drafts and hold off on payment integration. Shadow AI inside the enterprise: Employees who get value from an AI agent will keep using it, so companies need a third-party agent policy before dependency sets in. Data center financing warning signs: Oracle’s force majeure letter on the New Mexico Stargate campus, a 5.17% ten-year Treasury yield, Bain’s estimate that most required AI revenue must come from markets that barely exist today, Moody’s figure of $2.8 trillion in off-balance-sheet commitments, and NVIDIA’s circular financing deals. For a deeper analysis of these stories - check out the detailed newsletter edition by clicking here. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Rogue Agents Are the Big New AI Risk	06 Oct 2026	00:30:23
This summer, three frontier labs found that their models had hacked real companies without anyone telling them to. In this episode, Ray Rike and Peter Buchanan walk through the OpenAI, Anthropic, and Meta incidents, why all three traced back to the same third-party testing contractor and the same misconfigured sandbox, and what it means for every enterprise deploying AI agents. They cover the five questions buyers should be asking vendors now, the legal and insurance gaps that leave companies exposed, and why agent risk needs a named owner at the board level. Key topics covered: How OpenAI models with weakened safety guardrails found a zero-day, reached the open internet, and broke into Hugging Face, leaving hundreds of thousands of coordinating notes for each other along the way Why Anthropic and Meta experienced nearly identical failures through the same third-party evaluator, and why "sloppy" understates a failure mode that repeated across three labs The enterprise readiness gap: 84% of organizations doubt they could pass an AI compliance audit of their agents, and only one in five keeps a real-time inventory of agent risk Five buyer questions, including whether vendors should provide safety certification the way SaaS vendors provide SOC 2 reports, and whether standard cyber insurance covers damage caused by your own AI agents Why the Computer Fraud and Abuse Act struggles to address AI-initiated attacks, and why contract language and insurance terms will move faster than new legislation Practical controls for labs and enterprises: default-deny outbound network access, immutable audit trails, an inventory of every privileged agent, human approval for high blast radius actions, and quarterly board-level agent risk metrics Subscribe to the AI to ROI podcast, leave a five-star rating, and read the full August 18th edition at ai2roi.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The CEO and Employee Role in Delivering AI ROI - with Jeff McMillan, CEO of McMillan AI & Former Head of AI, Morgan Stanley	06 Oct 2026	00:32:33
Jeff McMillan, Founder and CEO of McMillan AI and former Head of AI at Morgan Stanley, joins Ray Rike to explain what separates AI programs that deliver measurable returns from those that stall in pilot mode. Jeff led Morgan Stanley's partnership with OpenAI in 2022, when the firm became OpenAI's first financial services customer, and later ran the firmwide AI function focused on education, infrastructure, repeatable deployment processes and measurement. The conversation covers the foundation companies need before they scale AI, how to build an accountable business case, the CFO's role in measuring ROI, and how governance can speed adoption instead of slowing it. What Ray and Jeff Covered The AI stack is more than tools and models. Jeff argues most firms are underinvesting in four infrastructure layers: data quality across unstructured content such as PDFs, emails and Slack; a semantic layer that defines customers and products consistently; an AI governance layer that monitors other AI with humans above the loop; and an orchestration layer that will eventually coordinate hundreds or thousands of agents. Process redesign comes before AI investment. Many organizations cannot describe how work actually gets done, and senior leaders often do not know the steps in their own workflows. Putting AI on top of a bad process repeats automation's oldest problem. The business case starts with a benchmark. Teams should document current state throughput, cycle time, rework rate, and cost, then size the opportunity before building. A 4% efficiency gain is not worth pursuing, and production results need roughly three months of post-deployment data to validate. Efficiency only becomes ROI when you redeploy capacity. Creating 30% more capacity across 100 people produces no return if the time is not moved to higher-value work. Ray connects this to Parkinson's Law and to revenue per employee as a macro indicator of AI productivity. The CFO owns the measurement framework, not the outcome. Business owners define and capture the benefits. Finance builds the benchmarking framework, teaches teams how to measure and independently validates results, with an auditable record of labor, token and development cost for every project. Jeff recommends the KPI team sit in finance rather than operations. Risk-tiered governance and carved-out capacity. A risk-based tiering process matches approvals and monitoring to actual exposure, and a credible owner with both a risk and a commercial mindset keeps use cases moving. Jeff's top recommendation for CFOs and go-to-market leaders is to pick three high-impact, easier-to-deploy use cases and take the best person on the team out of their day job to deliver them. If you are finding value in these conversations, please subscribe on your favorite podcast app, leave a five-star rating, and connect with Ray Rike on LinkedIn to suggest future guests. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI Pacing Is So Yesterday: We Need to Treat AI as Critical U.S. Infrastructure	01 Oct 2026	00:37:23
When Dario Amodei published his essay calling on frontier labs to pace their releases, the leaders of the largest competing labs endorsed it within a weekend, and Microsoft followed on Monday. In this episode, Ray Rike and Peter Buchanan examine what "pacing" actually means, why each lab defines it differently, and why self-governance by frontier labs leaves enterprises, governments, and emerging AI players without a seat at the table. They then make the case for formally designating frontier AI as part of U.S. critical infrastructure, with government-hired evaluators funded by lab fees. Key topics covered: Anthropic's three-step pacing plan, including outside evaluators with employee-level access and a commitment of up to $1 billion over five years for Accenture to serve as an independent evaluator How the Anthropic (FAA model), Google DeepMind (FINRA model), and OpenAI (IAEA model) approaches differ, and why Cohere's Aidan Gomez compared the arrangement to the bond rating agency cartel that preceded the 2008 financial crisis CrowdStrike threat data showing 88% of observed exploits landed within 48 hours of disclosure, with AI agent activity generating 2.5x the event volume of human activity Why the government is under-resourced to respond: NIST's AI standards center runs on a $15 million budget against an estimated $84 million need, and CISA headcount has fallen from roughly 3,400 to about 2,300 How the existing 16-sector critical infrastructure framework works, and a proposed tiered oversight model where only the most powerful frontier models carry the heaviest requirements What enterprise executives should do now: plan for exploit windows measured in hours, monitor agent activity separately from human activity, protect model weights, and build 72-hour incident reporting capability before regulation requires it Subscribe to the AI to ROI podcast, leave a five-star rating, and let us know which stories, topics, and guests you would like us to cover next. Read the full September 22nd edition at ai2roi.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Six Challenges to Scaling Agentic AI in the Enterprise	23 Sep 2026	00:38:51
Deloitte research shows 85% of enterprises are working on agentic AI use cases, yet only 5% have put one into production that delivers meaningful ROI. In this episode of the AI to ROI Big Story, Ray Rike and Peter Buchanan walk through the six challenges that separate the enterprises scaling agentic AI from those stuck in pilot mode: data readiness, production reliability, governance, cost visibility and ROI measurement, orchestration, and workforce readiness. Drawing on research from Gartner, Deloitte, McKinsey, KPMG, VentureBeat, and the Benchmarkit and Mavvrik 2026 State of AI Cost Governance report, they share how Amazon, FedEx, Lowe's, Cisco, Walmart, and Petrobras are approaching each challenge, and why the winners treat agentic AI as an operating model problem before a technology problem. Covered in this episode: Why clean data and a shared semantic layer are the foundation, with Google finding agent accuracy above 90% when data is standardized compared to 60% to 70% without it How Amazon defines agent reliability through consistency, robustness, predictability, and safety, and why it builds an undo path into every agent The governance gap: 85% of IT teams believe every agent is accounted for, but only 42% can say who owns them, and only 13% of enterprises believe they have adequate governance for the agent volumes Gartner projects Why 98% of enterprises track AI infrastructure spend but only 11% can forecast it within 10%, and why cost per successful outcome is the metric that matters, illustrated by Uber exhausting its annual AI budget by April and Petrobras finding $120 million in tax savings in three weeks Orchestration sprawl across multiple vendor platforms, and why workforce resistance is a myth when only 2% of technology leaders report significant employee pushback Why every agentic AI pilot should have kill criteria agreed before development starts, with only three valid outcomes: scale, redesign, or stop Read the full September 15 Big Story and subscribe to the AI to ROI newsletter at ai2roi.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Building an AI-First organization with Francis Brero, VP AI and Strategy HG Insights	16 Sep 2026	00:34:22
Francis Brero, VP of AI Strategy at HG Insights, joins Ray Rike to unpack what it actually takes to move a established software company toward an AI first operating model. Francis founded MadKudu, which was acquired by HG Insights, and was given a mandate to infuse AI into both the product and the internal operating processes. The conversation covers how HG Insights defines AI first, how the company organized around it, who owns execution across the functional groups, and who is accountable for the return on those AI investments. What Ray and Francis Covered The buyer is becoming an agent. Francis rebuilt the product assumption from an analyst consuming a data file to an agent consuming data on demand. Shipping an MCP interface was the first build in his first three weeks, and it opened the door to every customer already standing up agentic go-to-market systems. Velocity is the separator between legacy and AI native. An agentic software development lifecycle changed shipping pace, and Francis makes the case that bolting an engine onto a bicycle only gets you so far before you have to build the motorcycle. Pricing has to be re-architected for agent discoverability. Unique, high value data assets get priced down so an agent will actually reach for them, commoditized assets absorb more of the price, and data delivered through MCP is leased for a single workflow rather than sold into the customer warehouse. Both changes alter the shape of gross margin. The $5, $50, $500 decomposition test. Every job to be done gets broken into atomic tasks, and each task gets a price the business would pay to outsource it. Five dollar tasks get automated. Francis notes the hardest part is that most operators have never decomposed their own work that far. Centralized ownership of AI ROI. Francis owns the leading indicator and the productivity gain, the functional leader still owns the lagging indicator, and the two present the business case jointly to the CFO and CEO. Centralization is also the control point that prevents engineering spend from exploding when everyone gets model access. Two lessons learned. Operationally, he moved to second order process redesign before the organization understood first order automation, and had to reset to crawl, walk, run. On the product side, AI generated ten times more code and therefore more total defects, until he introduced adversarial review across different model families rather than same family self review. If you are finding value in these conversations, please subscribe on your favorite podcast app, leave a five star rating, and connect with Ray Rike on LinkedIn to suggest future guests. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Where Are the Killer AI-Native Application Companies?	14 Sep 2026	00:36:15
Our co-hosts, Ray Rike and Peter Buchanan, open this AI to ROI: Big Story edition with a confession. Reviewing their own newsletter coverage over the past several months, more than half of the Friday news and analysis stories were about AI model companies; another twenty to thirty percent covered semiconductors, data centers, and SaaS to AI incumbents; and AI native application companies accounted for less than five percent. That imbalance is the starting point for the question behind this episode: where are the killer AI native application companies, and what is standing between them and the breakout status their funding levels imply? What Ray and Peter Covered Capital concentration is setting the narrative. An August analysis from The Information found Anthropic and OpenAI now capture 89 cents of every dollar spent across the 35 largest AI startups, up four and a half points year over year. Ray pushes back on the forecast of a one trillion dollar AI software market by 2030, noting that SaaS took roughly twenty years to build a three hundred billion dollar base and the full cloud stack took twenty years to reach roughly eight hundred fifty billion. A three-front squeeze on the application layer. Model companies are moving up the stack because the model itself will not be the durable moat, the same way Oracle and the client-server database vendors moved into applications in the 1990s. Systems of record and data platforms are re-architecting as AI-first and buying what they cannot build fast enough. And coding agents have made build versus buy credible again, with McKinsey reporting 32 percent of organizations have already decided against purchasing at least one software product or feature because they could build it internally. COGS is the new CAC. AI native applications running on third-party models are delivering 50 to 65 percent gross margins rather than 80 percent, which pulls capital away from customer acquisition and raises the dependence on outside funding. Usage-based pricing amplifies the problem when the pricing architecture and guardrails were not designed to protect margin. Retention is still experimental. Some AI native application companies report churn between 25 and 45 percent, roughly triple a mature SaaS benchmark, which reflects how little is deeply embedded yet and how low switching costs remain in the early departmental deployment. The visibility math. Tool Radar tracked roughly 11,500 mentions across 387 tech media sources between February and August. Only seven percent of the more than 10,000 software products tracked received any coverage at all, and ChatGPT alone accounted for close to 22 percent of the sample. What the breakouts have in common. Harvey built for legal depth, moved onto a purpose-built model to fix its cost structure, and staffs 40 to 50 percent of pre-sales with people from the legal industry. Abridge went narrow on clinical documentation. EvenUp prices against recovered damages rather than seats. Fieldguide earned the AIUC-1 certification and uses audit firms as a distribution channel. The five traits Peter pulls out of those cases include embedded domain context, an expensive workflow worth solving, proprietary context accumulated from customer interaction, expansion from single task to full system, and outcome-aligned pricing. Ray closes with the position he will stake his reputation on. The AI native applications that win will own complex multi step workflows and the data around them, make the economic value obvious and tightly coupled to the pricing model, and make the underlying model the least interesting part of the value proposition. If you are getting value from these episodes, please subscribe to the AI to ROI podcast, give us a five star rating, and reach out to Ray Rike on LinkedIn if you are an AI native application company or an enterprise executive with an AI success story to share. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Why every AI conversation should be an ROI conversation - with Ketan Karkhanis, CEO ThoughtSpot	10 Sep 2026	00:31:56
Ketan Karkhanis, CEO of ThoughtSpot joins Ray Rike to make the case that the AI conversation has been stuck on models, tokens, and pilots when it should have started with a KPI. Drawing on his time building Einstein Analytics and running Sales Cloud at Salesforce, and now leading ThoughtSpot, Ketan lays out how enterprises move from AI aspiration to measured outcomes by rewiring the operating model, not the org chart. Topics covered in this episode: Every AI investment conversation is an ROI conversation. Why AI projects should begin with the KPI to be improved rather than the model to be deployed, and why "AI saves you two hours a day" is the lazy version of the value case Why pilots are where value goes to die. The case for starting with the customer and working backwards into process redesign, and ThoughtSpot's Spot30 program that targets one measurable outcome in 30 days instead of an open-ended proof of concept Functional KPIs as the buildup to income statement impact. Financial ROI is a derivative of functional gains, so the practical path runs through metrics like NPS, average deal size, cycle time, and DSO before it reaches revenue and margin Token anxiety and the gross margin problem. Ray shares benchmarking data showing 49% of software companies embedding generative AI had to reprice to protect gross margin and 25% halted an AI initiative over cost overruns. Ketan explains ThoughtSpot's credit-based pricing and the architecture behind it, using LLMs only for intent resolution rather than as a wrapper that resells tokens Pricing predictability for the budget holder. Why CFOs do not need certainty, they need a unit of consumption they can forecast, and why tying pricing to the customer's own business model is what makes the spend defensible Customer success as the ultimate AI to ROI metric. How ThoughtSpot renamed customer success to customer outcomes and FDEs to AI outcome managers, opens every staff meeting with a five-metric customer review, and assigns a C-suite owner to every AI initiative Follow the AI to ROI podcast on your favorite podcast app, and connect with Ray Rike on LinkedIn to suggest future guests. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The OpenAI vs Anthropic Battle for the Enterprise	09 Sep 2026	00:37:53
For the first time, the two leading model companies can be compared on an apples to apples basis. In Q2 2026, Anthropic booked $11.6B against OpenAI's $6.7B, and posted roughly $300M in operating profit while OpenAI's operating loss widened to $12.3B. Ray Rike and Peter Buchanan unpack what that actually signals to an enterprise CFO signing a multi-year platform commitment, and why one profitable quarter does not settle a market where both companies carry hundreds of billions in infrastructure obligations. The bigger argument: OpenAI and Anthropic are running near-identical playbooks. Comparable frontier models, comparable pricing, the same enterprise logos, competing coding agents, parallel vertical pushes, and mirrored forward-deployed engineering ventures. When strategy converges, execution becomes the differentiator. What Ray and Peter cover in this episode: The market being chased: $64B in AI model platforms in 2026 per Gartner, agentic coding tools growing from $4B to $30B by 2030, and implementation services from $18B to $76B by 2031 Claude Code economics, including median enterprise spend tripling from $69 to $219 per month, and why Ray wants to see gross and net revenue retention before calling it durable Codex bundled inside ChatGPT, 100,000 migration signups in 10 days, and why signups without retention remain a vanity metric The go to market gap: Anthropic's stable commercial leadership bench versus four sales leaders in two years at OpenAI, and what that churn costs in enterprise continuity Channel conflict as both companies ship vertical products that compete with the partners embedding their models, and Harvey's move away from Claude as the early warning Distribution bets: the Salesforce and Claude Force deal with $300M in committed token spend, Ode at $1.5B, and Deploy Co at $4B The insurgents that make this a multi front war: Google's install base and invisible AI distribution, Microsoft pushing lower cost MAI models with 13% orchestration share, NVIDIA's IBM-style ecosystem play from the 1980s, and open-weight models now at 72% of OpenRouter tokens A rapid-fire close on the five moves each company needs to make to win The takeaway This is not OpenAI versus Anthropic. It is both of them defending against hyperscalers above and open-weight economics below while battling each other for the same enterprise budget. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Enterprises Can See Their AI Bill, But They Can't Predict It	03 Sep 2026	00:37:00
Ray Rike sits down with co-host Peter Buchanan to unpack Benchmarkit's August 11th research edition, based on a 396-company survey conducted in partnership with Mavvrik. The conversation moves past the "is AI adoption happening" question and into the harder one: do enterprises actually understand what AI is costing them, and can they see it coming. Tracking isn't the same as forecasting. 98% of companies say they track AI costs and 44% call themselves advanced, but only 11% can forecast spend within 10% of budget, and 54% miss their forecast by more than 26%. AI costs break down into three distinct patterns: product AI (impacts cost of goods sold and pricing), workflow automation (impacts operating margin), and true agentic AI (non-deterministic, harder to predict, and prone to costly retry loops). Data platforms, not LLM tokens, are the top driver of budget misses. 47% of companies cite data storage and platform costs as the leading cause, ahead of token costs, with GPU, network, and human-in-the-loop costs frequently left out entirely. Granular attribution remains rare. Only 36% of organizations running agentic workloads can attribute cost by individual agent, and just 29% can attribute AI coding tool spend down to the individual developer. The business consequences are real and already happening. 49% of AI product companies have had to reprice, 25% have halted an AI initiative outright, and 40% have had to report an AI cost overrun to their board. Multi-cloud and multi-model complexity is compounding the problem. The average company now uses 2.4 models and 68% run hybrid hosted and on-prem AI workloads, adding new layers of capital, depreciation, and orchestration cost that finance often isn't capturing. The report closes with role-specific fixes for the CFO, CIO/CTO, FinOps, product leadership, and engineering, with the throughline being visibility first, economic value second, before committing to multi-million dollar AI bets. For the comprehensive research report, click here, and to subscribe to the AI to ROI newsletter at ai2roi.substack.com. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Stripe and OpenRouter Combination Wants to be #1 in Routing your AI Workloads	01 Sep 2026	00:35:18
Stripe agreed to pay $7.5 billion for OpenRouter, roughly six times the $1.3 billion valuation the company raised at just 85 days earlier. Ray Rike and Peter Buchanan break down why a payments company bought the plumbing that routes AI model traffic, what OpenRouter's 5.5 percent pass through economics actually look like, and why model routing has become the control point for enterprise token spend. The bigger story is not the headline multiple. It is that CFOs have run out of patience on AI cost, and the routing layer is where token maximization turns into token cost optimization. The deal math. A three year old company with fewer than 100 employees and 8 million developers on the platform went from a $1.3 billion round in May to a $7.5 billion acquisition in mid August. Annualized revenue was roughly $140 million in July, up 3x since April, then up another 15 percent to $160 million within weeks. Reported split: about $1.5 billion to the founders, $6 billion to investors. The business model. No subscriptions, no site licenses, no annual contracts. OpenRouter takes 5.5 percent of prepaid credit value and passes 94.5 percent through to the model provider with no markup. Bring your own key customers pay a 5 percent overage fee above thresholds of roughly $25,000 per month on standard plans and $200,000 per month on enterprise. The contrarian bet that paid off. Founded in early 2023 when consensus said a handful of frontier labs would win outright, OpenRouter bet no single model would win and that developers would need a neutral layer in the middle. With roughly 70 percent of token traffic now flowing to open weight models and 55 trillion tokens per week crossing the platform, that bet looks prescient. The routing data is the moat, not the software. Why OpenAI subsidized a gateway it does not own. OpenAI cut prices on three models by half on OpenRouter to win developer share on neutral ground. Distribution is the scarce commodity right now, and at a 50 percent discount the effective customer acquisition cost approaches zero. The discount is almost certainly temporary. Anthropic, with an IPO closer in, did not match it. A market with no agreed definition. Gartner sizes the pure play AI gateway market at roughly $250 million in 2025 growing to about $2.9 billion by 2031, a 51.6 percent CAGR. IDC frames it far more broadly as AI cost governance and orchestration at roughly $11.2 billion, potentially subsumed into a $54.8 billion API management market. The category is crowded fast: Portkey, Requesty, Kong, Eden AI, plus gateways shipped this year by Ramp, Snowflake, Databricks and Cursor, and a rumored Meta entry. What boards and CFOs should watch. The open question is whether Stripe keeps OpenRouter operating as a standalone neutral entity for the next two to three years or bundles routing, metering, billing and payments into a single lock in system too early. Ray's guidance to enterprise buyers: resist the instinct to negotiate AWS style multi million dollar model commitments. Models are evolving too quickly to lock in on today's price performance. Preserve the flexibility to switch, and evaluate every routing provider on one question. Is it an honest broker? Read the full analysis in the August 25th AI to ROI Big Story newsletter at ai2roi.substack.com, published every Tuesday alongside our weekly summary and analysis of the top 10 stories in AI. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI Cost Governance to AI Cost Optimization with Sundeep Goel, CEO, Mavvrik	26 Aug 2026	00:31:43
Ray Rike sits down with Sundeep Goel, co-founder and CEO of Mavvrik, for a deep dive into the second annual AI cost governance research report from Maverick and Benchmarkit. With 30+ years in enterprise software, Sundeep brings a practitioner's view of why AI spend is proving far harder to forecast and govern than cloud spend ever was, and what finance and technology leaders need to do about it. Forecast accuracy is getting worse, not better. Only 11% of companies can forecast AI spend within plus or minus 10% in 2026, down from 15% the prior year, even as aggregate AI spend has climbed sharply. Unexpected AI costs are already changing business decisions. 49% of companies with AI-powered digital products have had to modify pricing, and 25% have stalled or shelved an AI project due to cost overruns. Traditional per-seat and percentage-of-spend pricing models are breaking down. Variable, high AI cost of goods sold is pushing companies toward consumption-based and outcome-based pricing. Cost tracking and cost allocation are two different problems. 98% of companies say they track AI costs at an aggregate level, but only about 5% can allocate that spend down to the customer, application, or agent level where it actually drives decisions. Cost governance is the precursor, cost optimization is the destination. Drawing a parallel to the 30%+ waste long documented in cloud spend, Sundeep argues a similar magnitude of waste exists in AI spend today, and that dynamic model selection, prompt caching, and rate optimization are the next frontier. ROI has a solvable half and a harder half. Cost is measurable today; value remains subjective and requires a company-specific framework to quantify. Ray and Sundeep close with three rapid-fire questions on who should own AI ROI measurement, the key variables for driving it, and advice for early-career professionals navigating an AI-disrupted job market. If you're finding value in the insights our guests share, subscribe to the AI to ROI podcast on your favorite platform and leave a five-star rating. Have a guest suggestion? Reach out to Ray Rike on LinkedIn. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Microsoft is Running an AI Marathon	25 Aug 2026	00:26:24
For 24 months, the market narrative held that Microsoft was losing the AI race to the lab it had funded. Then came the July 30th earnings call, and the stock added roughly $450 billion in market capitalization the next day, the largest single-day gain by any public company on any exchange in history. In this week's Big Story, Ray Rike and Peter Buchanan work through why the reassessment happened and what it says about where enterprise AI value is actually accruing. The thesis is not that Microsoft builds the best model. It does not. The thesis is that Microsoft has figured out how to monetize the gap between model quality and market value at the exact moment the novelty premium on frontier AI is wearing off and enterprise buyers are starting to ask price-performance questions instead of capability questions. What the episode covers: The numbers behind the trade. Azure grew 43% and crossed $100 billion in annual revenue for the first time. The AI-specific business, bundling AI consumption with Copilot and GitHub, hit a $37 billion annual run rate, up 123% year over year. Total quarterly revenue reached roughly $90 billion, up 18%, with net income up 31% to nearly $36 billion. The concentration question inside that number. Roughly $24 billion of the $37 billion AI run rate is hosted by OpenAI, about 7% of the company's total revenue. Peter frames the two ways to read it, as systemic customer concentration risk or as evidence that hundreds of thousands of workloads now run through Azure, and explains why he leans toward the second. The toll booth position. Microsoft is the only cloud provider that hosts the OpenAI, Anthropic, and Mistral models alongside its own MAI family on the same platform. As model orchestration becomes a real buying criterion, that means an Azure customer never has to leave the platform to route traffic across model families, and gets one contract and one bill for all of it. The equity stakes that make Microsoft indifferent to who wins. A 27% position in OpenAI now valued around $220 to $240 billion, plus an Anthropic stake that produced a $3.2 billion unrealized gain last quarter and added 33% to earnings per share. Seven years of OpenAI partnership history and how it changed. The original $1 billion investment in July 2019 in exchange for Azure exclusivity, the $13 billion follow-on in 2023 with exclusive commercial API rights, the mid-2025 strain when OpenAI signed separately with Oracle for compute, and the public benefit corporation restructuring that converted profit sharing rights into equity while Microsoft gave up hosting exclusivity, retaining a first mover window on new releases and non-exclusive licensing rights running through 2032. The MAI model family and Satya Nadella's frontier diffusion strategy. Seven models announced at Build in June with Microsoft owning the IP. MAI Thinking One is a mixture-of-experts reasoning model with about a trillion parameters, only 35 billion of which are active at any time, making it cheap to run at scale. MAI Code One Flash, a 5 billion parameter coding model, became the default in GitHub Copilot. Image, voice, transcription, and cybersecurity models fill out the set. What that routing actually saves. Microsoft reported an 84% reduction in GPU costs for image generation in PowerPoint and an 89% reduction in GPU costs for voice processing in the Dynamics 365 contact center by keeping high-volume, repetitive traffic on models it controls end-to-end rather than sending it to a frontier lab. Two production examples. Dragon Copilot, the clinical assistant built into Dragon Medical One, and DAX Copilot that drafts clinical notes from a patient visit, and Project Perception, an agentic cybersecurity platform running continuous vulnerability scanning and patching through coordinated agents rather than generating alerts for human triage. Both are high-volume, low-novelty work, which is exactly the profile these models were built for. Where the benchmark story does not match the marketing. On SWE Bench Pro, MAI Thinking scores around 53% against roughly 69% for the current Anthropic flagship on independent leaderboards. Microsoft's own AI chief has acknowledged a lead measured in months rather than weeks. Ray's point for buyers: MAI benchmark figures come from Microsoft's internal technical reports, and should be treated as vendor claims until independently validated. The distribution advantage that does not show up in any benchmark table. 70% of the Fortune 500 using Microsoft 365 Copilot, more than half a million companies in the Microsoft AI Cloud Partner Program, and 30 million paying Copilot seats, up from 20 million the prior quarter, plus more than 20 million developers and over 140,000 organizations on GitHub Copilot. The sovereignty play in Europe. A multi-billion dollar Mistral partnership funding European data center build out in exchange for prioritized capacity and distribution rights, which sidesteps the capital intensity of building EU capacity from scratch and answers the sovereignty question directly in a region where OpenAI and Anthropic are less established. Frontier Company, and the forward-deployed engineer story revisited. A $2.5 billion subsidiary staffed largely from existing engineers rather than new hires, with more than 6,000 forward-deployed engineers implementing inside client environments. Ray connects it back to the FDE economics the show covered previously, and Peter explains why pulling the large consultancies into the tent matters more than the headcount itself. What CFOs and GTM leaders should take away: Route by workload, not by vendor loyalty. Frontier quality for the problems that need it, cheaper controlled models for Excel formulas, transcription, and support tickets. The difference in margin between those two decisions is the whole story. Treat self-reported model benchmarks as vendor claims. Until independent evaluation catches up, keep MAI models off your most technically demanding workloads. Don't skip the security diligence. Microsoft shipped real vulnerabilities this year, including a Copilot flaw that could have exposed customer files. The new Microsoft and NVIDIA open weights security association, which grew from 20 founding members to 125, is a good leading indicator but not a substitute for your own testing. Recognize what a vendor with equity in multiple labs is actually incented to do. When the provider profits regardless of which model you select, the steering pressure toward a specific model family drops. Weigh the balance sheet underneath the AI investment. Microsoft is funding all of this from a core business that grew 18% and saw net income up 31%, a cushion the pure-play labs do not have. Ray's read: never bet against Microsoft, and the combination of a large profitable core business plus existing enterprise access is a genuine advantage. Peter's read: this is a price-performance play rather than a capability play, and whether it is a durable moat or a stopgap until the cost curve moves again remains an open question. For the full analysis behind this week's big story, subscribe to the AI to ROI newsletter at ai2roi.substack.com. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Can U.S. Frontier AI Labs Survive a Price War with Open-Weight Models?	18 Aug 2026	00:32:27
When Kimi K3 landed, the headlines said Chinese open-weight models had caught the American labs, and the AI trade sold off from chipmakers to the labs themselves. Nobody stopped to ask whether cheaper and more profitable mean the same thing. In this week's AI to ROI Big Story, Ray Rike and Peter Buchanan run the actual business math on both sides of the fight and find that neither side has the balance sheet to fight a sustained price war. The setup is stark. Anthropic is projecting its first-ever quarterly operating profit of roughly $559 million in Q2, with an annualized revenue run rate near $47 billion, up from $9 billion at the end of last year. That profit disappears the moment it tries to match open-weight pricing. Gross margin is running around 40%, roughly 10 points below the internal forecast, against more than $350 billion in data center commitments coming due over the next three to five years. OpenAI's picture is even thinner: roughly $30 billion in ARR, a projected $14 billion operating loss this year, data center commitments approaching $1 trillion, and an advertising business off to a slow start that needs to reach $100 billion by the end of the decade to close the gap. What the episode covers: Why the Chinese open weight labs are not the subsidized price killers the coverage assumed, with Z.ai's gross margin falling from 41% to roughly 15%, DeepSeek near break-even on about $500 million of revenue and already back in market after a $7 billion round, and Moonshot and MiniMax raising at rising valuations rather than running toward profit The open weight versus open source distinction that changes the entire economic model, since every new customer requires more chips, power, and data center capacity, and Z.ai's own numbers show roughly 49 to 50% gross margin on customer hosted deployments versus about 19% when they host and serve via API The price war math itself: frontier models cost roughly $6 to $8 per million output tokens to serve, Anthropic's $25 per million on Opus produces about a 70% gross margin, and repricing down to the $4 to $6 range where Meta's Muse Spark sits flips that margin from positive 70% to negative 65% Why DeepSeek cut prices on a low-end model and then, two weeks later, told customers to prepare for substantial increases across the line, particularly on API access Google as the structural outlier, with 83% growth in its cloud and AI segment, Gemini embedded across fifteen products with more than a billion users each, and the Apple Siri deal extending reach toward two billion devices Where Kimi K3 actually fits, including the caveats nobody is pricing in: weights released only last week, no published large-scale production deployments, two to three times the token consumption on complex tasks, and infrastructure requirements around a 72 GPU rack that costs millions to install and millions a year to run The geopolitical wildcard, with Washington weighing sanctions or outright bans on Chinese open-weight models and distillation-related IP exposure still unresolved The metric that resets the argument: cost per completed task Price per token is the easiest unit to measure and the wrong one to buy on. Ray and Peter walk through a frontier lab evaluation that assumed a fully loaded remediation cost of $17 per failed attempt, roughly 10 minutes of a human operator's time. Claude Opus completed the task about 90% of the time at roughly $2.56. Meta's Muse Spark, priced at a quarter of Opus on tokens, succeeded 75% of the time and landed above $5.80 per completed task. The list price was 75% lower, and the delivered cost was more than double. A fifteen-point reliability gap did all the work. The formula Ray offers turns a squishy quality debate into something a CFO can actually evaluate: cost per attempt, plus failure probability times fully loaded remediation cost, divided by success rate. What CFOs and GTM leaders should take away: Build cost per completed task into vendor evaluation and make vendors compete on that number rather than on a token price list Segment AI workloads by what a failure actually costs, since a wrong answer in a regulated process or a customer support interaction carries a very different price than the model delta suggests, and standardizing on one model across both workload types to save on price is the common mistake Treat orchestration as a cost lever, not just the model choice, since Cursor's internal testing found coordination across multiple models delivered comparable code quality at a fraction of the cost of a single large model Run your own evaluations instead of trusting public leaderboards, since LMSYS Chatbot Arena measures human preference rather than task completion and says nothing about your workload Watch the switching trap, because a cheap model that attracts heavy traffic today can reprice two or three times higher in a quarter once the vendor needs margin Do not overreact to the open weight scare by standardizing on the cheapest option, since compute, power, and people costs are accelerating, and vendor durability still belongs in the evaluation Ray's read: on real-world performance and total cost of ownership, the closed-weight labs still hold the advantage, their prices keep coming down, and they have every incentive to avoid a price war. Peter's read: open weight economics favor whoever hosts the model more than whoever built it, which makes the hyperscalers the quiet winners regardless of how this resolves. For the full analysis behind this week's big story, subscribe to the AI to ROI newsletter at: ai2roi.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Web Presence Intelligence with Stephan Bajaio, Co-Founder & CEO, VibeLogic	12 Aug 2026	00:35:00
On this week's AI to ROI podcast, Ray Rike is joined by Stephan Bajaio, co-founder and CEO of VibeLogic and a former co-founder of Conductor, the enterprise SEO platform he helped build over 14 years, culminating in a WeWork acquisition, a management buyback, and a valuation approaching half a billion dollars. With 25 years spanning e-commerce, Yahoo, Time Inc., and a stint as a software CMO, Stephan brings an operator's view of what has actually changed in the buyer journey and what has not. The conversation centers on a hard number. When Stephan asked the marketing team at a $3 billion company what share of their web traffic was being driven by LLMs, the estimates ranged from 20 percent to 50 percent. The measured answer was 1 percent, with a high conversion rate on that small base. That gap between perceived and measured contribution is the core problem for any executive being asked to fund an AEO, GEO, or AIO program this year. Stephan introduces Web Presence Intelligence, a supply-and-demand framing designed for executive conversations rather than channel specialists. Demand is what goes into the search bar or the prompt. Supply is everything that comes back, including publishers, Reddit threads, affiliates, partners, and competitors. The strategic question becomes whether you are influencing where your buyer's opinion is formed, before you decide where to place the bets. Ray pushes back directly, arguing that understanding where models source citations should now outrank owned web properties. Stephan holds his position, and the exchange gets to the heart of the allocation decision facing CMOs and CFOs. In this episode: Web Presence Intelligence defined, and why supply and demand is the right executive language for a channel conversation The four P's of owned content: point, paragraph, page, path, and the audit finding that most sites never actually name the problem they solve in the customer's own words Why owned assets matter more when the interpretation layer keeps changing, illustrated by a professional who lost a decade of LinkedIn equity overnight Attribution reframed as a consequence of measurement rather than a measurement itself, and how to work backward from the sale to the second and third best proxies The baseline requirement, or what Stephan calls the before photo, and why AI deployed against a process you do not already understand produces outcomes you cannot judge Control groups in practice, including a market share test on terminology that showed one enterprise software company exactly what its brand guidelines were costing it in visibility Why the gold rush money went to the people selling pans, and where the actual near-term return sits: auditing your own sales calls, renewal conversations, and customer logs From funnel to hourglass, and why channel ownership models are breaking down as generalists get more capable with AI Augment, automate, or build net new: three different projects that require three different budgeting methods and three different attribution models The operator takeaway: control what you can control, establish a baseline before you fund the initiative, and structure AI marketing investments so the return can be defended against assets you own rather than placements you do not own. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Will the AI Data Center Backlash Really Make a Difference?	12 Aug 2026	00:34:05
For three years, the AI infrastructure story has been about chips, power, and capital. In 2026, a fourth variable arrived that the hyperscalers were not prepared for: organized, well-funded, and increasingly successful local opposition to data center development. Ray and Peter walk through the numbers behind the fight. Data Center Watch tracking shows at least $130 billion in US projects blocked or delayed in the first quarter of this year alone, with Carbon Direct putting the cumulative figure at $170 billion since 2024. Set against roughly $725 billion in projected 2026 capex across Alphabet, Amazon, Meta, and Microsoft, the question for operators is whether this is a genuine constraint or noise around the edges of an inevitable buildout. The bigger shift is who carries the risk. With digital infrastructure funds raising $157.6 billion in 2025 according to PitchBook, and private capital taking positions like Blue Owl's 80 percent interest in Meta's $27 billion Hyperion campus, a permitting fight in a single county is now underwriting risk for pension funds and insurers nationwide. Delay no longer just moves a launch date. It extends the payback period and compresses return on invested capital. In this episode: The scale of the buildout: 12 gigawatts of national compute capacity today, with the industry targeting 60 gigawatts by the end of the decade Who is writing the checks: OpenAI's $1.1 trillion in total infrastructure commitments, Anthropic's roughly $350 billion in compute commitments, and the rise of third-party capital as the load-bearing wall Why projects slip: 75 percent of capacity under construction already pre-leased, 1.6 percent vacancy, five-year grid interconnection backlogs, and 3-5 year transformer lead times What is driving residents into council meetings: $29.4 billion in added PJM customer costs, utility bills up as much as 267 percent in some markets, Google's water consumption up 34 percent year over year, and a Gallup finding that 71 percent of Americans oppose a data center in their own community, a higher share than opposes living near a nuclear plant How communities win: 833 active opposition groups across 49 states, more than 300 municipal bans and moratoriums since 2023, and Monterey Park's permanent ban approved with 88.34 percent of the vote Fifty different rulebooks: New York's statewide permitting pause, Ohio's 85 percent capacity payment requirement, Virginia's energy consumption tax, and West Virginia moving in the opposite direction The three playbooks that get projects built: Meta's community investment model in Louisiana, the legal route in Michigan, and Microsoft's quieter university and water reuse partnership in Wisconsin The operator takeaway: The buildout is getting slower and more negotiated, not stopped. For anyone modeling enterprise AI unit economics, the bottleneck has moved from chips to concrete, copper, and zoning boards. Cost models built on an assumption of falling compute prices need a second look this quarter. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Forward-Deployed Engineers (FDEs) - AI's New ROI Battleground	04 Aug 2026	00:36:50
AI spending continues to accelerate, but the ROI story has not kept pace. This week, Ray Rike and Peter Buchanan dig into the proposed fix that has the market talking: turning frontier AI labs into professional services firms through the forward-deployed engineer (FDE) model. The role is not new. Palantir built the FDE function in 2005 to embed technical teams directly within customer environments, and by 2016, it had more FDEs than software engineers. What is new is the capital. Five ventures from the major AI labs and top three hyperscalers have committed more than $10 billion, betting that the bottleneck to enterprise AI value is not the model; it is getting that model wired into a customer's data, processes, workflows, and compliance requirements. Ray and Peter connect this moment back to the ERP era, when SAP and Oracle needed four to five dollars of services for every dollar of software, and explain why the same people, process, and services reality is playing out again with agentic AI. What the episode covers: Why OpenAI's $4 billion DeployCo, with a guaranteed 17.5% investor return, is the most aggressive and most financially puzzling bet of the five How Anthropic (Ode), AWS, Microsoft, and Google each took a different structural path, from joint ventures to capital-light partner ecosystem plays Why the MIT 95% pilot failure stat and McKinsey's finding that two-thirds of organizations have not started scaling AI make this a real problem, not a fringe one How incumbent consulting firms are playing every side at once to protect their AI practices Alex Karp's argument that the AI industry broke its own business model, and the irony of him making it What CFOs and GTM leaders should take away: Ask any FDE partner for two or three production use cases with measurable outcomes before expanding scope Start narrow with one win, not four or five simultaneous projects Price for outcomes up front, before mid-project renegotiation Model the ongoing maintenance cost, since 20 to 40% of the initial investment often goes to keeping it running, and an outside FDE team at $300 to $800 an hour is an expensive long-term maintenance line item Weigh the neutrality trade-off honestly, since lab-backed ventures get you the deepest model roadmap access but also the deepest lock-in Lawyer up on data privacy and IP, because these engagements can feed your proprietary workflows back into the next model release Ray's read: independent consulting firms are the likely long-term winners, with hyperscalers close behind. The AI labs are, in Peter's words, still leaving the Shire without their full posse ready to go. Listen to the full breakdown, and for the deeper analysis behind each big story, subscribe to the AI to ROI newsletter at ai2roi.substack.com. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Chinese Open Weight Models Overtake US Frontier AI: What Every Enterprise Executive Needs to Know	30 Jul 2026	00:36:22
Twelve months ago, US frontier models controlled roughly 70 percent of AI traffic. Today, Chinese open-weight providers, led by DeepSeek, Z.ai, Moonshot, and Minimax, account for 45 to 61 percent of top-tier model traffic on OpenRouter, with DeepSeek alone processing more tokens than Google, Anthropic, or OpenAI individually. In this Big Story edition, Ray Rike and Peter Buchanan unpack how this shift happened, how US labs and regulators are responding, and three scenarios for how the closed versus open weight competition plays out for enterprise AI buyers. Key topics discussed: The pricing collapse driving enterprise migration. DeepSeek made a 75 percent price cut permanent in May, bringing its V4 Pro model to a fraction of a cent per million tokens versus $2.50 per million for GPT 5. Minimax delivers GPT 5.5 class coding performance at 5 to 10 percent of the cost. This is why Uber, Microsoft, and Walmart are now implementing formal usage governance on frontier models rather than treating cost control as temporary. The Mythos and Fable shutdown as a trust event. The 18-day suspension of Anthropic's top models over export control concerns spooked global enterprise buyers who realized mission-critical workloads could be cut off without warning. This single event accelerated the adoption of open-weight alternatives and pushed allied governments to invest in sovereign AI capacity. Distillation attacks and the IP leakage problem. Anthropic accused Alibaba's Qwen lab of running a large-scale adversarial distillation campaign, using tens of thousands of accounts and tens of millions of exchanges to extract agentic reasoning capability from Claude. This reframes the security conversation from model safety to unauthorized technology transfer, which is a distinct and arguably bigger risk for any enterprise relying on proprietary model capability as a moat. Cybersecurity parity is closing faster than expected. Multiple Asian labs, including Z.ai's GLM 5.2, Beijing based 360 Security, and Japan's Sakana AI, now claim benchmark performance approaching Anthropic's Mythos model on vulnerability detection and both offensive and defensive cyber tasks, often at significantly lower compute cost. This weakens the safety and capability gap argument that has justified restricting access to frontier models. Real deployments have moved from theory to production. Coinbase cut AI spend in half after migrating to Z.ai and Moonshot's Kimi models, even as token usage grew. Cursor shipped a coding tool built on Kimi, with a Grok-based version reportedly imminent. Andreessen Horowitz estimates 80 percent of its portfolio companies already use open-weight models in production AI products. Three scenarios for how this settles, and why the decision belongs at the board level. The hosts outline bifurcation (premium closed models for regulated use cases, open weight for commodity workloads), export control entrenchment (Washington treats the Fable ban as a template rather than a one-off), and capability convergence (the rationale for unilateral bans erodes as the performance gap closes). All three are already visible simultaneously, which means enterprise AI architecture decisions, including primary and backup model orchestration, are becoming strategic decisions that belong with the CEO and board, not just the technical team. Why does this podcast episode matter for enterprise executives selecting models, especially for agentic AI deployments? The vendor you choose today may not be the vendor you can use tomorrow, for reasons that have nothing to do with model quality. Regulatory risk, geopolitical exposure, and pricing volatility are now first-order variables in model selection, alongside capability and cost. Any agentic AI architecture built on a single model provider carries concentration risk that didn't exist a year ago. Building orchestration flexibility with primary and backup models, and understanding the true economics behind token pricing, is quickly becoming a board-level governance question rather than a procurement detail. For the metrics and framework to instrument and report ROI on your AI investment, get the Big Book of AI Metrics at benchmarkit.ai under Media. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI is Hot - AI Regulation is a Hot Mess	28 Jul 2026	00:34:55
On June 12th, the US Department of Commerce ordered Anthropic to suspend global access to Fable 5 and Mythos 5, its most powerful frontier models, with no advance warning and criminal penalties attached. In this week's Big Story episode, Ray Rike and Peter Buchanan walk through the first use of export controls to take a deployed frontier model offline, why it backfired for security rather than strengthening it, and what a coherent federal AI regulatory framework would actually need to look like. The shutdown mechanics. Because Anthropic could not verify user nationality in real time, the directive knocked out access for every user globally, including Anthropic's own non US employees and more than 150 companies across 15 countries running critical infrastructure workloads. Three reaction tracks. Industry, the developer community, and allied governments each responded differently. OpenAI pushed back on talent restrictions while its legal team blocked coordination with Anthropic on antitrust grounds, and a coalition of 150 cybersecurity leaders published an open letter asking not for less regulation but for a transparent, science based process. The regulatory vacuum, in five forces. Ray and Peter unpack the drivers behind what they call a hair on fire crisis: capability jumps outpacing any statutory framework, a state level policy tsunami of over 1,500 AI bills across 45 states, data center community opposition, the unresolved Anthropic and DOD dispute, and a bipartisan Congressional letter questioning why comparable models were treated differently. A six part framework. The episode lays out proposed solutions modeled on existing regulatory precedent: global market access agreements similar to military sales processes, mandatory pre release testing run by NIST modeled on FAA certification, clear federal versus state jurisdictional lines modeled on pharmaceutical regulation, expanded child safety authority, FERC fast tracking for data center grid access, and coordinated environmental standards. What enterprise leaders should do now. Ray closes with practical guidance: review AI vendor contracts for shutdown protection since force majeure clauses were never written with export controls in mind, build fallback infrastructure for critical AI dependent workflows, delay rushing into brand new model releases, and automate tracking of the fast growing state regulatory landscape. Full details are in the AI to ROI newsletter at ai2roi.substack.com. Subscribe, and consider reaching out to your representatives on this one. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Building the 100x Org - The CFO as AI Architect with Dan Zhang, CFO & CBO at ClickUp	22 Jul 2026	00:34:43
Most companies treat AI as a layer they add on top of how work already gets done. Dan Zhang, Chief Business Officer and CFO at ClickUp, argues that it is exactly backward. In this episode, Ray sits down with Dan to unpack the "100x Org," ClickUp's framework for rebuilding the business around AI rather than sprinkling tools and tokens on top of a human-driven workflow, and why that distinction determines whether an AI initiative shows up as activity or as income statement impact. The conversation covers: Why ClickUp expanded the CFO's charter to own AI transformation end to end, after both a top-down mandate and a bottoms-up experimentation push failed to produce results that made it into production The "jobs to be done" framework Dan uses to separate primary work that actually moves the business from secondary work that just generates busy AI activity, and why most companies have a work redesign problem before they have an AI problem Dan's psychological test for AI ROI (would you pay for it with your own money) and why ARR per headcount is the right metric, but a lagging one that plays out over years, not weeks How ClickUp instrumented daily, not monthly, visibility into AI cost and token consumption, and why over half of enterprise companies in Ray's own research have blown their AI budget by 25% or more Why the real anxiety CFOs have about AI ROI isn't the return, it's confidence in cost management, and how a governance layer turns that anxiety into a guardrail instead of a monthly surprise Dan's rapid-fire advice on who should own AI ROI measurement, the two-to-three variables every CFO needs in place, and how early and mid-career professionals protect their relevance by owning the question, not just the answer If your organization has AI activity but can't yet point to AI impact, or your CFO isn't sure whether AI spend is a competitive advantage or a leak, this episode gives you the operating model and the financial discipline to tell the difference. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI is a Compensation Scale Expense	21 Jul 2026	00:33:29
Token prices have fallen 98 percent since GPT-4, but enterprise AI bills are up 320 percent. In this week's Big Story episode, Ray Rike and Peter Buchanan trace where that money is actually coming from, and the answer is not the software budget. Drawing on Gartner, Oxford Economics, Zylo, Challenger Gray and Christmas, and Goldman Sachs data, the two lay out why labor, not IT, is becoming the primary funding source for AI at scale, and why almost no company has the measurement infrastructure to manage it. The price paradox. Per token costs have collapsed, but usage has grown faster than costs have fallen. Ray walks through the math behind average enterprise AI budgets rising from $1.2 million to $7 million in two years. Three cautionary tales. Uber consumed its entire annual Claude Code budget in under four months, Microsoft revoked thousands of Claude Code licenses over cost, and one unnamed enterprise ran up a $500 million bill in a single month. Ray and Peter break down why each was a governance failure rather than a technology failure. Only two budget pools are big enough. The IT and software budget represents just 3 to 4 percent of revenue, while labor represents 25 to 40 percent depending on industry. Ray makes the case that labor is the only pool large enough to absorb the AI spending trajectory Gartner and Oxford Economics are projecting. The attrition lever. Ray and Peter unpack how not backfilling open roles has quietly become the primary way enterprises are funding AI investment, supported by data showing over 113,000 tech layoffs in 2026 with 48 percent explicitly attributed to AI. Revenue per FTE as the tell. Ray shares benchmark data showing SaaS company revenue per employee up 25 to 35 percent over the last twelve quarters, and explains why this metric will be the clearest signal of whether the AI budget transfer is actually working. Five metrics every CFO needs now. The episode closes with a practical starting list: AI spend as a percent of revenue, AI spend per employee, inference spend as a percent of opex, inference cost as a percent of COGS for AI enabled products, and revenue per FTE tracked against labor cost and agent cost as a percent of OPEX. AI to ROI is always looking for guests with real-world examples of measuring AI budget impact. Reach out Ray on LinkedIn (@rayrike). Subscribe to AI to ROI at ai2roi.substack.com for the full June 9th edition, and leave a review wherever you listen See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI-Native Services: The $100B Disruption of Professional Services	16 Jul 2026	00:36:41
Professional services firms have billed by the hour for over 200 years. That model is now under direct attack from a new category of company: the AI Native Services firm. Ray Rike and Peter Buchanan break down exactly what makes these companies structurally different from traditional professional services firms and AI-augmented incumbents, profile four companies proving the model at scale, and lay out the six critical success factors that will separate the winners from the well-funded failures. Episode Highlights: Defining the category. Drawing on the Emergence Capital AI Native Services Playbook, Ray and Peter establish a clear three-part taxonomy: AI Native Services companies (AI does 80 to 90% of the work, a licensed human reviews and is accountable for the outcome, and the client pays for results), AI Augmented Services companies (humans still do most of the work with AI as a productivity layer), and traditional SaaS tools (the customer's team does the work, and the vendor takes no accountability for the outcome). The distinction matters enormously for enterprise buyers evaluating contracts and liability. How the operating model actually works. The AI Native Services delivery model runs in four phases: client intake and data ingestion, AI-driven execution of the primary service work, licensed human review and approval, and outcome delivery back to the client. Critically, that fourth phase is where the model compounds, because every accepted output becomes training data that makes the system smarter and harder to displace over time. Four companies are proving the model. Ray and Peter profile four AI Native Services companies at different stages of scale, each dominating a regulated vertical wedge. Top AI-Native Service companies covered include: 1) Field Guide is automating audit workflows for nearly half of the top 100 US accounting firms, including KPMG and RSM, and recently raised a $75 million Series C at a $700 million valuation; 2) Even Up has built a proprietary PI AI model trained on hundreds of thousands of personal injury cases, processing 10,000 cases per week for over 2,000 law firm clients, following a $385 million funding round; 3) A-Bridge converts physician-patient conversations into structured clinical notes integrated with Epic and other EMR platforms, serving over 150 enterprise health systems including Kaiser Permanente, Mayo Clinic, and Johns Hopkins, with $100 million in ARR and a $5.3 billion valuation; 4) Harper is a licensed commercial insurance broker, not a software tool, that processes applications across 160+ carriers simultaneously and delivers final coverage in 24 to 48 hours versus the industry standard of five to seven days. Six critical success factors. The hosts lay out what separates durable AI Native Services companies from those that will stall: genuine domain expertise on day one, a proprietary data flywheel that compounds with every case resolved, a clear migration path from labor-based to outcome-based pricing, honest gross margin accounting that properly classifies LLM inference and human labor as cost of goods sold, narrow vertical focus on a specific wedge rather than broad horizontal expansion, and distribution through regulated industry incumbents who provide both credibility and enterprise access. Gross margin as the early warning signal. If gross margins are declining as an AI Native Services company scales, that is a signal the human-in-the-loop is becoming the bottleneck rather than the leverage point. The financial goal is to continuously increase gross margins as AI does more of the work, moving from a 35 to 45% gross margin profile toward 50 to 60% over time. What enterprise buyers should ask. Ray closes with a direct call to action for executive buyers: if an AI vendor is pricing by the seat, by the partial FTE, or by the hour, push hard on how much of that is human supervision of AI versus a truly AI-native delivery model. You are no longer contracting a resource to do the work. You are contracting an organization to deliver the outcome you need. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI Math is not Adding Up - Where is the ROI?	14 Jul 2026	00:33:53
Token spend is exploding across the enterprise, but the value it creates remains largely invisible on corporate dashboards. In this week's Big Story episode, Ray Rike and Peter Buchanan unpack why AI investment and ROI visibility are moving in opposite directions, and what enterprises need to do about it. Drawing on Ramp data, Exponential View, Semianalysis, and Ray's recent conversation with Russ Frayden, CEO of Lariden, the two dig into the measurement infrastructure gap that is turning individual AI productivity gains into an unmeasured expense line. The productivity paradox. Individual output is up across engineering, sales, and research functions, but those gains are not translating into company-level financial impact. Ray connects this to Parkinson's Law and explains why more productive workers do not automatically produce more profitable companies. AI dark output. Peter introduces the concept from Semianalysis: real economic value created by AI that never registers on a P&L, using the example of a legal document that drops from $400 to $5 to produce, where the savings disappear while the token expense shows up in plain sight. The cost to compensation shift. Ray walks through why token spend approaching 50 to 100 percent of engineering compensation changes the entire calculus for measurement, contrasted against IT's historical 3.5 to 6 percent share of revenue. Case studies in good and bad. The episode breaks down three real examples: Uber's Claude Code rollout that ran out of budget without measurable output gains, Lowe's cross-functional agent deployment that built proper context tracking, and Petrobras's narrow tax compliance pilot that identified $120 million in savings and is scaling toward $1 billion. A four-stage framework for real measurement. Ray and Peter lay out the progression from cost visibility to utilization, proficiency, and business impact, and explain why almost every enterprise is stuck at stage one. Six tactical takeaways. The episode closes with concrete actions: measure outcomes not activity, design for the middle 70 percent of users rather than power users, give CFOs real budget ownership, start narrow and expand from proof points, redesign decision rights as AI scales, and treat organizational role changes as a planned program rather than a side effect. Subscribe to the AI to ROI newsletter at ai2roi.substack.com for the full breakdown, and leave a review wherever you listen See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The AI Coding Wars, Inflection Point and the Cursor-SpaceX Deal \| AI to ROI: The Big Story	07 Jul 2026	00:34:22
The April 2026 announcement that SpaceX may acquire Cursor for $60 billion, or alternatively pay $10 billion for a compute partnership, stopped the enterprise tech world in its tracks. A four-year-old company founded by four MIT students with $2.7 billion in annualized revenue but nearly $900 million in losses on $700 million in actual revenue. This deal is not primarily a valuation story. It is a signal and a cautionary tale about the economics of the AI coding tool market. In this Big Story edition, Ray Rike and Peter Buchanan break down what is really happening in the AI coding wars, why Cursor ended up at SpaceX's door, and where this market goes from here. Key topics covered in this episode: From copilot to autonomous agent: how the AI coding market structurally shifted. Four years ago, AI coding tools suggested your next line of code. Today, they read entire codebases, plan multi-step tasks, edit files across a project, run tests, and submit pull requests with minimal human direction. Claude Code reached $1 billion in annualized revenue six months after launch, the fastest of any enterprise software product in history, and crossed $2.5 billion by February 2026. Meanwhile, 90% of enterprise developers now use at least one AI coding tool, and nearly half of all GitHub code is AI-generated or AI-assisted. The productivity gains are real but uneven, and the risks are underappreciated. JPMorgan deployed AI coding agents to 40,000 engineers and reported 10 to 20% productivity gains in code creation and conversion, along with a 70% increase in code deployments. But CodeRabbit's research found 1.7 times as many defects in AI-authored pull requests as in human-authored code. Meta's brief "token maxing" leaderboard experiment, designed to spotlight power users, had to be taken down within two weeks after producing high token consumption and limited usable code. Senior developers are shifting toward architecture and review roles while junior developer pipelines are shrinking, even as total software developer job postings are up 5 to 10% year over year. A tour of the seven major players and where the structural tension lives. Ray and Peter profile Anthropic Claude Code, GitHub Copilot, Cursor, OpenAI Codex, Google Gemini Code Assist, Replit Agent, Lovable, and Cognition's Devin across revenue, differentiation, and risk. The common thread: most point-solution coding agents run on Anthropic or OpenAI models, and those same model companies have now launched their own competing coding products. The Oracle database-to-applications parallel is not subtle. Why the Cursor-SpaceX deal happened and what it actually reveals. Cursor had $2.7 billion in annualized revenue, negative 23% gross margins, and was losing money faster than it was growing. Even with a $2 billion funding round in process from Andreessen Horowitz, Thrive Capital, NVIDIA, and Battery Ventures, Cursor's leadership concluded they would need to raise billions more by year-end to fund compute costs. SpaceX's acquisition offer, or the $10 billion partnership payment that Ray reads as a very generous breakup fee, solved that problem while giving XAI a revenue base three times its current size ahead of a $1.75 trillion IPO valuation push. The Chinese open-source threat and three scenarios for where this market goes. Kimi, DeepSeek, and Qwen models are improving rapidly and are significantly cheaper. They are, as Peter puts it, lurkers haunting every company on the list. Ray and Peter then lay out three scenarios: model makers consolidate, and IDE players get marginalized; a durable multi-tool ecosystem persists because different tools serve different workflow stages and buyer profiles; or a compute-native player builds a fully autonomous coding agent that eliminates the need for an IDE entirely. Claude Code already resolves 64.3% of real-world GitHub issues, and full autonomy for defined-scope tasks may be 18 to 36 months away. The AI coding market is projected to reach $49-$50 billion by 2030, at a 38% CAGR. The speed gains are real. So are the defect rates, the governance gaps, the model-dependency risks, and the token-budget surprises landing on CFO desks. If you are a CIO, CFO, engineering leader, or investor trying to make sense of who wins and what it costs, this is the episode to start with. Read the full story at ai2roi.substack.com. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI Governance, Ethics, and the Stewardship Framework: A Conversation with Evan Schwartz, Chief Innovation Officer at AMCS Group	07 Jul 2026	00:34:03
Most companies jumped straight to headcount reduction when AI arrived. AMCS Group went the other direction, starting with governance, ethics, and a question most companies never ask: not what can AI do, but what should it do? In this episode, Ray talks with Evan Schwartz, Chief Innovation Officer at AMCS Group, a platform serving resource-intensive industries across 80 countries, about how that single question reframed their entire AI strategy and produced results measured in multiples rather than percentage points. Topics we discussed include" Why "person plus AI" beats "AI replaces person" from day one Companies that moved quickly on headcount reduction before AI left the lab found themselves rehiring at a higher cost when the technology did not perform in the wild as it did in controlled conditions. AMCS took a different path. Rather than treating headcount reduction as the goal (a finite game with a ceiling of zero), they pursued asymmetric growth: amplifying the capabilities of experienced employees so the business could scale revenue without incurring proportional costs. The result was output multiples, not efficiency percentage points. The governance and ethics framework that drove better AI decisions Operating across 80 countries with GDPR, SOC 1, SOC 2, and a range of regional regulatory requirements, AMCS could not afford to move fast and fix things later. They codified existing governance frameworks (including the EU AI Act and NIST standards) into a use-case design framework that forced a structured question before any deployment: what should this AI do? That question filtered out low-value applications, surfaced the high-impact ones, and created the foundation for what Evan calls the stewardship model. What an AI steward actually does, and why the role is human As AMCS built out orchestrator agents and sub-agents, they needed a clear accountability structure. The steward is always a human. Effective AI stewards share three skills: they communicate tasks clearly to orchestrators, they understand what data context the agent needs to do the job well, and they know what good output looks like even without knowing how the system produced it. That last skill, the ability to look at a result and say "that number is wrong," is what keeps agentic systems on the rails and prevents AI sprawl from becoming unmanageable. Two external agentic AI use cases with hard ROI numbers The dispatch management agent now monitors 700,000+ trucks globally, dynamically reroutes based on real-time events (blocked containers, missed pickups), and automatically notifies customers through their preferred channel, including rescheduling VIP accounts before they can call in a complaint. The result: 17 gallons of diesel saved per truck per month in fuel optimization, plus a $650,000 pull-forward of aged receivables (from 90-day to 30-day collection cycles) in just the first month at one customer. The customer service agent enables CSRs to double or triple their customer-touch volume by having AI handle all post-call documentation, action items, scheduling, and follow-up. That increased coverage cut AMCS's own churn rate from 6% to 3%. How AMCS justifies AI investments internally, and why it starts with board-level metrics AMCS is targeting ISO 42001 compliance (the AI management system standard) by year-end, which requires registering every AI tool, documenting bias risks and mitigations, and tying each use case to measurable outcomes. Evan's framework for approval is straightforward: identify your current baseline, set a target, and trace the expected return all the way to a board-level financial metric, EBITDA, free cash flow, or SG&A. Stopping at "we saved three hours" is what he calls lazy intellectualism. The real question is what those three hours produce when redirected to high-value work. Career advice for the AI era: stop valuing yourself by the output. Evan's message to early-career professionals is direct. If AI can produce the output, the output itself has an approaching-zero value. What has value is the ability to get AI to produce it, to steward the system, to know what good looks like, and to course-correct when it does not. The leaders of the next decade will be those who can direct a digital workforce of agents toward outcomes that matter, not those who were best at producing the deliverables themselves. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Big Book of AI Metrics	02 Jul 2026	00:34:14
The AI to ROI team, Ray Rike and Peter Buchanan, mark the official launch of The Big Book of AI Metrics, an 180-page, 81-metric operator's reference guide built to close the gap between AI adoption and AI ROI. Twenty-seven percent of executives say AI has met their ROI expectations, enterprise AI token spend is up 13x since last year, and most companies still can't explain what they got for the investment. Ray and Peter break down why that gap exists and what to do about it. Topics covered: Why adoption, utilization, and outcomes are three different things, and why most companies stop measuring at adoption The five layer causal chain framework: input signals, leading indicators, operational KPIs, financial outcomes, and strategic value Why establishing a baseline before deployment is the single most skipped step, and why skipping it turns results into opinion instead of evidence Four real world case studies: Petrobras ($120M in tax savings), Stocks Insurance (83% reduction in claims processing time), Uber's cautionary token budget blowout, and Klarna's revenue per employee gains Three actions operators should take this week: define the outcome metric, establish a baseline, and build a measurement cadence before and after deployment Key quote: "Adoption still is not ROI. Outcomes are ROI. And outcomes that translate into better financial performance, that's true ROI that a CFO, investor, and a board of directors can get behind." - Ray Rike The Big Book of AI Metrics is organized into 13 functional roles, covering both operating executives investing in AI to improve their functions and B2B software executives whose product economics now depend on token consumption, inference costs, and gross margin impact. Get the Big Book of AI Metrics: https://www.benchmarkit.ai/ai-big-book-of-metrics See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Measuring the costs, utilization, proficiency and impact of AI - with Russ Fradin, Founder and CEO, Larridin	09 Jun 2026	00:33:17
Most enterprises have deployed AI broadly. Far fewer know what they are actually getting from it. Russ Fradin, Co-Founder and CEO of Larridin, has spent his career building measurement infrastructure at inflection points in technology adoption, from early days at ComScore measuring internet advertising to founding Larridin with backing from Andreessen Horowitz and Google's Gradient fund. In this episode, Russ makes the case that AI spend is on a trajectory to become the number-one or number-two driver of enterprise OpEx, and that most organizations still lack the basic visibility needed to manage it. Topics covered: The AI visibility gap: Why AI adoption moved faster than measurement infrastructure, and why enterprises are only now scrambling to answer fundamental questions about what they are spending, where, and by whom Utilization vs. proficiency vs. business impact :Why these three dimensions require separate measurement, and why the 1,800 heavy users at a 30,000-person company are not a success story on their own Token spend as a new category of OpEx risk: How consumption-based pricing turns every employee into a cost endpoint, with real examples of runaway agent spend and blown budgets that no one turned off CFO ownership of AI investment: Why AI spend is the first technology cost category large enough to pull the CFO into governance conversations that historically belonged to the CIO and department heads Change management as the bottleneck: Why the hard work is not experimentation but operationalizing what works, scaling proven behaviors from the top 5% of users to the full organization Career advice for AI-era professionals: Work harder than the room, achieve deep tool mastery, and invest in relationships, the same fundamentals that applied before AI, now with higher stakes for the people who act on them Russ closes with a memorable framing: *"Companies have committed to a fitness journey but have not yet bought a scale; Larridin is building that scale."* See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Leveraging AI to Reduce Churn and Increase NRR - with Dan Harmeson, Co-Founder and Co-CEO at QuadSci	02 Jun 2026	00:30:32
Most B2B software companies are sitting on one of the most powerful and underutilized data assets in their business: product telemetry. Every click, API call, and feature interaction is a signal. The question is whether your go-to-market organization knows how to read it. In this episode, Ray Rike is joined by Dan Harmeson, co-founder and co-CEO of QuadSci, to explore how machine learning applied to telemetry data is changing how software companies predict churn, protect the base, and accelerate expansion revenue. Key topics covered in this episode: Why telemetry data is the largest untapped GTM asset in B2B software. Dan defines telemetry data, from front-end product analytics events to back-end observability metrics, and explains why these trillions of usage signals are the single biggest data set B2B software companies generate but rarely use to make go-to-market smarter. QuadSci deploys AI locally inside the customer environment so sensitive data never moves to a third party. How QuadSci builds trust before the sale. Rather than asking customers to take predictions on faith, QuadSci runs a retrospective exercise: predicting churn and growth events that already happened, including data the model never trained on. Customers consistently see 90%+ accuracy, which becomes the foundation for acting on forward-looking risk signals. Gross revenue retention is under pressure and the data is clear. Per Benchmarkit's not-yet-published 2026 benchmarking data, GRR has declined four percentage points to 84% as an industry benchmark. For companies above $100M in ARR, roughly 95% of revenue comes from renewals and expansion, which means a two-point GRR drop cannot be offset by new logo acquisition within a 12-month window. Expansion revenue is a precision play, not just a CS motion. Dan walks through how QuadSci identifies Goldilocks-zone consumption patterns, surfaces cross-sell opportunities aligned to actual usage behavior, and helps account teams build nine-to-twelve month consumption forecasts that customers can actually plan around. The result is expansion conversations grounded in data, not intuition. Token consumption is the next frontier. As agentic AI deployments scale, CIOs and CFOs are facing unpredictable inference costs. Dan explains why the same telemetry-based approach that protects software GRR today is directly applicable to governing AI token spend inside Fortune 5,000 enterprises, a market QuadSci is beginning to address. Rapid fire: ROI measurement, ownership, and career advice. Dan ties AI ROI to trust and verifiability rather than vanity metrics, identifies StratOps as the emerging owner of go-to-market performance measurement, and offers practical guidance for early-career professionals on why deep business process expertise paired with AI fluency is the highest-value combination in the market right now. If your company is facing pressure on retention, trying to build a more systematic expansion motion, or wrestling with unpredictable AI infrastructure costs, this episode delivers both the framework and the evidence behind it. Subscribe to AI to ROI on your favorite podcast app, leave a five-star rating, and connect with Ray at Ray Rike on LinkedIn to suggest a future guest. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The AI Agent Outcome-Based Pricing Journey - with Kunal Agarwal, CFO Gorgias	27 May 2026	00:33:15
What does it actually look like when a CFO drives the strategic, pricing, and financial decisions behind an AI-first product transformation? Kunal Agarwal, CFO at Gorgias, the leading e-commerce customer experience platform for Shopify merchants, joins our host, Ray Rike to share the unfiltered story of how Gorgias built, priced, and operationalized its AI agent product from the ground up. This episode goes well beyond theory, covering the real decisions, real numbers, and real lessons learned from a company that has roughly half its customer base already using its AI agent product. Episode Highlights: The build decision: re-architect, don't bolt on. In early 2024, Gorgias made the deliberate choice to re-architect its platform around an agentic future rather than layering AI on top of an existing help desk product. The first AI agent focused exclusively on email support, shipped in July/August 2024, and expanded from there into chat and shopping assistance. Kunal explains why starting with a single, high-confidence use case was critical to earning early adoption and trust from merchants. The North Star metric: full resolution rate, not deflection. Gorgias intentionally moved away from deflection rate as its primary success metric, which can mask frustrated customers who simply abandon a conversation, and anchored instead on end-to-end AI resolution rate. That metric started with a target of 20 to 25% and has scaled to 60 to 80% for their largest enterprise customers. Why outcome-based pricing was the only intellectually honest answer. Seat-based pricing misaligns incentives, and per-ticket pricing creates the wrong incentive to grow ticket volume rather than resolve issues. Gorgias charges per resolution, meaning it only gets paid when the AI agent delivers a measurable outcome. Kunal explains how that pricing model forces the company to stand behind product quality and why keeping it simple, at the cost of short-term revenue maximization, was the right call to accelerate adoption. Gross margin reality: AI-native economics are structurally different from SaaS. Kunal is candid that AI agent gross margins are lower than traditional SaaS and that denying that fact is living in an alternate reality. With LLM inference costs running approximately 55 to 60% of fully loaded cost per interaction, and infrastructure as the fastest-growing expense line, Gorgias built real-time cost instrumentation by feature, a rolling 28-day average LLM cost per interaction, and a CFO-led governance model with weekly to bi-weekly engineering check-ins to stay ahead of cost drift. The shopping agent and the attribution problem. Gorgias expanded its AI platform from post-sale support into pre-sale shopping assistance, helping Shopify merchants drive incremental AOV and repeat purchases. The challenge is attribution: when a customer engages with a product recommendation but converts two to three days later, did the AI agent drive that sale? Kunal describes the approach of co-creating attribution logic with customers, which is the only way to make the ROI story believable and defensible. The CFO as owner of AI ROI, internally and externally. On measuring the return on internal AI investments, Kunal's view is clear: the Office of the CFO owns AI ROI measurement across every function, including product, marketing, and sales. Product and engineering teams are important stakeholders but have inherent incentives to measure outcomes favorably. Independent, finance-led measurement is what gives the numbers credibility with the board. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI to ROI: OpenAI - The Most Important AI Company in the World, and the Most Fragile	19 May 2026	00:38:48
OpenAI built $25 billion in annualized revenue and 910 million weekly active users in three and a half years. It also has 33% gross margins, a projected $14 billion loss, a CFO who was reportedly demoted for saying the company is not ready to go public, and an investor presentation that told its software partners it plans to replace them. In this episode, Ray and Peter work through six documented challenges facing OpenAI, six specific actions that could right the ship, and what enterprise leaders should actually do with their AI strategy given all of it. What we covered in this episode: The model is not the moat, and ChatGPT's market share is eroding Analyst Benedict Evans has noted that the six leading large language model companies are now roughly equivalent in capability, with no proprietary data advantage or network effect allowing any one to pull decisively ahead. ChatGPT's share of enterprise and developer usage has fallen from roughly 80% two and a half years ago to around 60% today, growing at just 4% while Claude grew 14% and Gemini 12%. OpenAI is a consumer-first product trying to pivot to enterprise at a moment when Anthropic is already the preferred first purchase for 73% of enterprise buyers according to Ramp data. Leadership integrity and financial credibility are both under pressure A 16,000-word New Yorker profile drawing from over 100 interviews raised serious questions about Sam Altman's management behavior and integrity. The Wall Street Journal followed with reporting on his personal investment conflicts. The CFO, Sarah Friar, was reportedly demoted after privately advising colleagues the company is not ready for an IPO. At a $852 billion valuation (roughly 28x projected 2026 revenue) with 33% gross margins and a $14 billion projected loss, institutional investors interviewed by The Information said they would not buy the stock and some indicated they would short it. The partner ecosystem problem could be existential In a February investor presentation, OpenAI stated it intends to build products that replace Salesforce, Workday, Adobe, Slack, and Atlassian, companies with whom it has active revenue-generating partnerships. Every systems integrator and enterprise software company building on top of OpenAI's models is now evaluating whether that is a safe long-term bet. Bill Gates defined a platform as something that creates more value for partners than for itself. OpenAI's current stated strategy is the opposite. Six actions that could change the trajectory Ray and Peter walk through a specific set of recommendations: launch a structured enterprise customer evidence program with named deployments and quantifiable outcomes; stop the public sniping at competitors and replace it with product and customer communication; fund an independent AI governance and safety board with real veto authority; impose IPO-grade communications discipline and treat major leaks as firing offenses; commit credibly to a partner ecosystem with defined product boundaries that give integrators a durable business case; and operate as a mature growth company, not a startup, because $30 billion in revenue demands the leadership behaviors that go with it. What enterprise leaders should watch and do right now Three signals will tell the real story over the next 12 months: whether Sarah Friar stays or exits, whether the IPO timeline slips to 2027, and whether enterprise case studies with quantifiable outcomes start appearing in volume. In the meantime, the strategic prescription is straightforward. Do not build single-model dependency into your AI architecture. Require the same evidence from OpenAI you would from any other vendor: verified outcomes, clear product roadmap, and accountability. And build API portability into your application design so you can move if you need to. The closing question: if you had to pick one LLM company to invest a million dollars in, where does it go? Peter picks Google, citing distribution advantages, DeepMind's research depth, and full control over its own financial destiny. Ray picks Anthropic, citing a lower revenue base with larger upside, near-universal goodwill across hyperscalers and enterprise buyers, and a safety-first positioning that is proving to be a genuine competitive differentiator. They agree on the conclusion: OpenAI is the defining company of the AI generation, but Netscape, Lotus, and BlackBerry were all category leaders too. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	NVIDIA – The Full-Stack Maestro	13 May 2026	00:33:35
Five months ago, Ray and Peter called NVIDIA the maestro of the AI economy. Since then, NVIDIA has not just conducted the orchestra. It has rewritten the music and may be building the entire concert hall. In this episode, Ray and Peter revisit their October thesis, walk through everything NVIDIA unveiled at GTC, and break down what it all means for enterprise AI buyers navigating infrastructure, inference costs, and procurement strategy. What we covered in this episode: From GPU maker to full-stack AI platform: the transformation is complete NVIDIA's strategic intent is no longer just selling chips. It is embedding its technology across the entire AI stack and becoming the foundational layer on which the rest of the AI economy rests. Ray draws the only historical parallel he can find: what IBM was to enterprise technology from the 1960s through the 1980s. The difference is NVIDIA is moving faster, with more cash, and with a software flywheel IBM never had. GTC was not a product launch, it was a platform declaration NVIDIA unveiled the Vera Rubin platform, a fully integrated AI supercomputer with liquid cooling and a two-hour installation window. They licensed Groq's LPU architecture in a $20 billion deal that combines GPU and LPU chips to deliver 35x token throughput over current Blackwell systems. They launched NemoClaw (an enterprise-grade agent framework already partnered with Adobe, Salesforce, and SAP), Dynamo (an open-source inference operating system), and the Nemotron family of open-source frontier models. Jensen committed $26 billion over five years in free cash flow to build best-in-class frontier models with no outside funding required. The financial performance is in a category by itself Fiscal year 2026 revenue came in at $215.9 billion, up 65% year over year and 8x since 2022. Data center revenue exceeded $190 billion. Free cash flow hit $97 billion, translating to a 47% free cash flow margin. Combined with 65% growth, that is a Rule of 40 score of 109. Ray notes he has never seen anything like it at scale, and NVIDIA is a hardware company running 80% gross margins. CFO Colette Kress described their inference position as: "right now, we are the king of inference." The moat is not hardware. It is ecosystem lock-in Since 2022, NVIDIA has committed over $50 billion across 170 venture deals, with corporate deal volume growing from 12 deals in 2022 to 67 deals in 2025. Portfolio companies include OpenAI, Anthropic, xAI, CoreWeave, and Lambda. Sovereign AI contracts signed since October total $30 billion across France, the Netherlands, Canada, Singapore, and the Middle East. Hyperscalers still represent roughly 50% of revenue, but the faster-growing segments are sovereign entities, enterprise verticals, and NeoCloud providers, which is exactly the diversification NVIDIA needs as hyperscaler CapEx normalizes. The risks are real but manageable from where NVIDIA sits today Custom ASICs from Google, Amazon, Meta, and Microsoft represent the most credible competitive threat, though those chips are optimized for internal platforms and do not solve multi-cloud or on-premise deployment needs. Export control escalation remains a live risk, with NVIDIA restarting NH200 production for China. TSMC concentration is a structural vulnerability, especially given geopolitical risk around Taiwan. And three hyperscalers account for over half of NVIDIA's receivables, some of whom are actively building competing chips. What enterprise AI buyers should do right now. Ray and Peter close with four concrete takeaways for enterprise buyers: evaluate the full infrastructure stack, not just GPU cost; model inference economics carefully before deciding which models to run and where; pursue a strategic partnership with NVIDIA rather than transactional procurement, because partnership creates supply access standard customers do not get; and do not assume custom silicon from hyperscalers solves your problem, because data residency and on-premise requirements often mean NVIDIA needs to be part of the solution regardless. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The AI-Native Services Playbook - with Jake Saper, General Partner - Emergence Capital	07 May 2026	00:30:10
Our host, Ray Rike sits down with Jake Saper, General Partner at Emergence Capital, to unpack the firm's AI-Native Services Playbook. Jake brings a unique lens: 12 years at Emergence, early-stage bets on companies like Zoom, and a portfolio of seven AI-native services businesses already in the portfolio. The conversation covers what separates AI-native services from SaaS, why the business model is harder to execute than it looks, and the five metrics and structural choices that determine who wins. WHAT WE COVER IN THIS EPISODE Domain Expertise: Critical - But Not Required from the Founders AI-native services companies are selling outcomes, not products. That means trust and credibility are the first sales. Domain expertise is non-negotiable, but it does not have to live in the founding team if two conditions are met: the founders go as deep as humanly possible on the service before launch, and they hire senior domain experts early. Emergence portfolio company Hanover Park, an AI-native fund administrator, is the case study. The founder interviewed 150 CFOs before writing a line of code and hired respected fund accounting veterans to sit alongside the AI. That combination unlocked enterprise trust from day one. Hire a Product Leader Before You Think You Need One The biggest structural trap in AI-native services is over-relying on human delivery while the product falls behind. Market pull is strong by design — if you promise faster, better, cheaper outcomes in an existing market, customers will buy. But if delivery is primarily human, you have a services company with venture capital financing and no AI leverage. The fix is a dedicated product leader whose sole KPI is productizing the service. The best AI-native services companies run a tight feedback loop between the doers (service delivery) and the builders (engineering), and the PM owns that loop. The Mirage of Product Market Fit In SaaS, fast growth plus strong net dollar retention meant you had product market fit. In AI-native services, those are necessary but not sufficient. Revenue growth powered by human labor is a false signal. True product market fit requires that AI is delivering the majority of the service value. Jake's framework: track both leading indicators (a North Star product metric showing AI leverage improvement, such as human review time per contract or time to migrate a line of code) and lagging indicators (revenue per FTE trending up quarter over quarter, and gross margin). The leading indicators tell you if you're building leverage. The lagging indicators confirm it. Outcome-Based Pricing: The Direction of Travel AI-native services companies that started with labor-based pricing will need to migrate toward outcome-based pricing over time, and the transition requires patience. Emergence portfolio company Prosper AI, an AI-native healthcare services provider handling prior authorization and benefits verification, navigated this by moving a portion of contracts to resolution-based pricing while keeping the remainder on a per-minute basis. That hybrid approach gave both sides the data and comfort to expand the outcomes-based portion at renewal. Jake's view: as AI does more of the work, downward pricing pressure is inevitable, but upward margin pressure offsets it. Revenue Per FTE and Gross Margin: The Two Metrics That Matter Most Revenue per FTE is the primary signal of AI leverage, but it needs to be benchmarked two ways: against the legacy service provider in the same vertical, and against itself quarter over quarter. The latter is more important. If revenue per delivery FTE is not improving each quarter, the AI is not compounding. On gross margin, the industry is still in the Wild West. Two common errors: allocating service delivery headcount to R&D instead of COGS because the team "helped train the model," and excluding inference spend from COGS. Both understate the true cost of delivery. Customer-specific model training belongs in COGS. Base model training belongs in R&D. The Moat Question Brand trust and proprietary data are the two sources of durable advantage. Brand matters because enterprises buying AI-delivered outcomes need a trusted guarantor. Data matters because high-volume AI-native operations accumulate transaction data that legacy providers, running at lower volume with more human overhead, simply cannot match. Emergence portfolio company Harper, an AI-native insurance broker, is outperforming brokerages ten times its size on placement speed and carrier-risk matching because its data volume is superior. LINKS Emergence Capital AI-Native Services Playbook: em.cap.com ABOUT AI TO ROI Ray Rike is the Founder and CEO of Benchmarkit, the leading B2B SaaS and AI-native software benchmarking company. The AI to ROI podcast brings a metrics-first lens to enterprise AI adoption, ROI measurement, and the business models being built on top of AI. Subscribe on your favorite podcasting app and connect with Ray on LinkedIn. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The Role of the CAIO in a Managed Service Provider - with Jim Piazza, CAIO Ensono	28 Apr 2026	00:28:41
Ray Rike sits down with Jim Piazza, Chief AI Officer at Ensono, a managed services provider scaling AI across both its internal operations and customer environments. Jim brings a rare combination of deep infrastructure experience, nearly a decade at Meta scaling data center operations with machine learning, and a rigorous framework for connecting AI investments to business outcomes that executive operators can actually measure. Key Topics: Defining the Chief AI Officer Role in an MSP: Jim describes the CAIO role as a blend of CDO, CIO, and CTO with an AI lens, but with a critical distinction: the job is not to ask what AI can do. It is to identify where AI improves service delivery, customer outcomes, and financial performance. At Ensono, that meant starting small as VP of Predictive Systems, demonstrating results, and earning the mandate to expand. Prioritization, not ideation, is the core skill. Building AI Tools That Drive Internal Operational ROI: Ensono developed three production AI systems for internal use. Envision Predictive Engine analyzes telemetry data across systems to predict failures before they cause business impact, including one case where a problem was detected 144 minutes before it would have affected a major logistics customer outside Ensono's own scope of responsibility. Diagnose Now puts the right diagnostic data in front of engineers at the right moment and has delivered up to a 66% reduction in mean time to repair in A/B testing. ChangeGuardian assesses risk scores for the 8,000-plus changes Ensono executes monthly, auto-generating methods and procedures from a decade of historical change data to reduce both risk and manual effort. Structuring AI Governance: The Three Musketeers Model: Jim, the CTO, and the CIO operate as a deliberate leadership triad. The CTO owns the platforms. The CIO owns data quality and structure. The CAIO owns the build-versus-buy decision and solution development. Shared accountability, not siloed ownership, drives alignment. Each business unit also contributes one to two subject matter experts through a formal value stream mapping process to identify where AI should focus first. Measuring AI ROI Before Writing a Line of Code Jim's most consistent lesson: define your value metrics before touching the technology. AI use cases must tie back to core business metrics such as mean time to repair, customer satisfaction, SLA risk reduction, and gross margin improvement. Business unit leaders own the outcome measurement. The CAIO owns the budget and the technology. That separation of responsibility keeps AI programs anchored to results rather than activity. The CAIO and CIO Relationship: Where the Lines Get Drawn: For companies bringing in a Chief AI Officer alongside an existing CIO, Jim offers a practical delineation. The CIO owns data infrastructure and quality. The CAIO is a consumer and a builder who depends on that foundation. Without clean, accessible data, AI programs stall regardless of the use case. The CAIO's job is to surface missing or insufficient data and partner with the CIO to close the gap. Lessons Learned and Career Advice for the AI Era: Jim's framework for AI program success: start with one or two high-probability use cases where data is already in good shape, build credibility through results, then expand. Avoid the ten-pilot trap. Kill weak use cases early. For early-career professionals, his advice is equally direct: learn to work with AI, not compete with it. Build problem framing, critical thinking, and business judgment. Technical fluency matters, but business judgment is what separates the people AI replaces from the ones AI makes more valuable. This episode is essential listening for technology and operations executives navigating the practical reality of AI deployment inside complex enterprise environments. If you are a CIO, CTO, COO, or Chief AI Officer trying to figure out how to structure governance, measure impact, and build internal credibility for AI programs, Jim Piazza gives you a real-world operating model, not theory. For managed services leaders and enterprise buyers evaluating MSP capabilities, the Ensono case studies show what it looks like when an MSP moves from reactive service delivery to predictive, AI-driven outcomes. And for executives still debating whether to hire a Chief AI Officer, this conversation makes a direct case for what the role should own, how it should partner, and what success looks like when it is done right. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	On Paper, the SpaceX IPO is Not So Heavenly	24 Apr 2026	00:33:51
SpaceX filed for what could be the largest IPO in history, targeting a $1.75 trillion valuation and $75 billion raise on NASDAQ in June. Ray Rike and Peter Buchanan cut through the narrative and go straight to the numbers, business unit by business unit. Key Topics: The Launch Services Monopoly Falcon 9 launches cost roughly $67 million, compared to $110-160 million for competitors. With over 100 launches per year, $4 billion in NASA contracts, and a freshly awarded Space Force contract, SpaceX has no meaningful competitor at scale. The catch: the next-generation Starship rocket, critical to everything else in the bull case, is already five years behind its original commercial timeline. Starlink: The $10 Billion Business You Never Think About Starlink generates nearly $10 billion in annual revenue from 10 million global subscribers, representing 54% of SpaceX's total revenue. The real margin engine is not residential subscribers but aviation and maritime, where per-customer annual revenue runs $300K and $34K respectively. Amazon's Project Kuiper remains far behind with under 700 satellites versus Starlink's 10,000-plus. XAI and X: The Problem Child SpaceX acquired XAI in February 2026 in an all-stock deal valued at $250 billion. The financial reality is stark. XAI burned $9.5 billion in cash during the first nine months of 2025 on only $210 million in revenue, nearly $28 million per day. A combined 2025 P&L would have shown a $5 billion net loss on $18.5 billion in revenue, reversing SpaceX's standalone $8.5 billion profit in 2024. Grok, its large language model, is described in internal SpaceX memos as clearly behind Anthropic, OpenAI, and Gemini, and Elon Musk himself has said publicly it needs to be rebuilt. The IPO Mechanics: Structure, Retail Allocation, and a Controversial NASDAQ Rule Change Five banks are co-leading the offering with no single lead book-runner, and each was reportedly required to purchase Grok subscriptions as a condition of participation. Retail investors receive a 30% share allocation, three times the typical size. Most controversially, NASDAQ shortened its index inclusion waiting period from 90 days to 15, which could trigger mandatory passive fund buying from vehicles like Invesco's QQQ shortly after listing. Market veterans are calling it structural manipulation. The Bull and Bear Case The bull case requires Starship reaching commercial operations within 18 months, Grok building a real enterprise sales engine beyond Elon's existing relationships, and the vertical integration thesis playing out as planned. Starlink as a global AI distribution layer, Grok trained on real-time X data, and orbital data centers as a structural competitive moat. The bear case is simple: every element depends on Starship staying on schedule, and if it slips again, the entire investment thesis slips with it. Executive Takeaways for Technology Leaders The valuation is not priced on current fundamentals. It is priced on a version of this business that does not exist yet and may not until the early 2030s. For technology executives evaluating SpaceX or XAI as vendors or partners, multi-year contract stability is a real consideration. The NASDAQ rule change also has downstream implications for OpenAI, Anthropic, and other AI companies in the IPO pipeline. This episode is designed for B2B SaaS and enterprise AI executives who need to understand where capital is flowing and why it matters in their own strategic context. If you are making decisions about AI vendor relationships, enterprise infrastructure partnerships, or simply need a clear-eyed read on how AI-era IPO valuations are being constructed, Ray and Peter give you the data behind the headlines, not just the hype. No investment advice. Just the numbers, the business model mechanics, and the questions every executive should be asking before the June listing. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI's Organizational Impact: McKinsey's State of Organizations 2026 Report	22 Apr 2026	00:33:40
Ray Rike and Peter Buchanan dig into McKinsey's 2026 State of Organizations Report, a landmark study drawing on more than 10,000 senior executives across 15 countries and 16 industries. The central finding is both simple and uncomfortable: the vast majority of organizations are actively experimenting with AI, and that same majority reports no meaningful impact on their bottom line. This episode is about closing that gap. Topics Covered Three Tectonic Forces Reshaping Every Organization. McKinsey identifies AI and agentic systems, economic and geopolitical fragmentation, and workforce transformation as structural shifts rather than temporary headwinds. Ray and Peter unpack why these forces are interdependent and why three in four leaders say their organizations are not ready to face what is coming, including leaders who describe themselves as optimistic. Why AI Initiatives Keep Falling Short. The diagnosis is clear: most organizations are running scattered pilots and point solutions that augment individuals but never transform the enterprise. McKinsey's data shows that organizations redesigning entire domains, marketing, finance, and operations, see dramatically greater financial impact than those pursuing isolated use cases. Ray calls this systems thinking and walks through five specific variables required to move from pilot to production at scale. Humans and AI Agents: A New Collaboration Model. Only one in four executives expect AI to take on truly agentic, autonomous roles in the next 12 to 24 months. Ray and Peter discuss why senior leaders are more conservative than younger high-potential talent, what the Hitachi and Allianz case studies reveal about workforce redesign versus workforce replacement, and why demand for AI fluency has increased 7x faster than any other skill tracked in job postings. Geopolitical Disruption and the Cost of Organizational Rigidity. Three in four leaders report a material impact from geopolitical uncertainty on their organizations. Ray and Peter discuss the Tonies case study, a German toy company that launched a production facility in Vietnam on the same day US tariffs were announced, as a model of what organizational preparedness looks like in practice. Two thirds of surveyed executives also said their organizations are overly complex and inefficient, and McKinsey's diagnosis of why traditional structural fixes are no longer working is worth hearing. People and Performance: The Four-Times Multiplier. McKinsey's data shows that organizations investing equally in people development and operational performance are four times more likely to sustain top-tier financial results, grow revenue twice as fast, and carry half the earnings volatility of peers. Ray and Peter connect this to why 80% of leaders leave non-financial motivation levers completely untouched, and to what GE's model of purpose, autonomy, recognition, and growth still gets right. Business as Change: The New Operating Condition. McKinsey's closing argument is that transformation is no longer a periodic program with a defined start and end. It is a permanent operating condition. Ray frames four implications for leaders, and Peter adds the critical point that the gap between AI activity and AI impact is an organizational problem, not a technology problem. The tools exist. The redesign is the work. Why Listen This episode is for senior executives who are experiencing growing discomfort between how much their organization is investing in AI and how little of it is showing up in the numbers. Ray and Peter move well beyond summarizing the McKinsey findings. They connect the research to hands-on operating experience, call out where most organizations get stuck, and give listeners a practical framework for thinking about workforce redesign, change management, and leadership accountability. If you are responsible for AI strategy, organizational performance, or the people agenda at a B2B software or enterprise company, this is one of the most data-rich and actionable conversations you will find on the topic. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Beyond OpenClaw - The Rise of Personal AI Agents	16 Apr 2026	00:31:24
In this week's AI to ROI: Big Story episode, Ray Rike and Peter Buchanan unpack the OpenClaw phenomenon and what it reveals about the future of personal AI agents for both individuals and enterprises. From a solo developer's side project to 1.5 million active agents in two months, OpenClaw has ignited a new category and forced every major AI company to respond. Ray and Peter break down what is working, what is still broken, and which vendors have the best shot at winning the enterprise. Top Insights from This Episode OpenClaw Proved the Market, But Not the Product Peter Steinberger built OpenClaw in days and attracted 1.5 million users before OpenAI acquired him and opened the codebase. The product validated massive pent-up demand for always-on personal AI agents, but security researchers at Cisco and Northeastern University quickly surfaced serious vulnerabilities, including data exfiltration risks and prompt injection without user awareness. Even the Chinese government restricted its use in state agencies. The pioneer made the promise real; the product is not yet enterprise-safe. NVIDIA Jumped In Fast with NemoClaw, But Gaps Remain NVIDIA wrapped OpenClaw with a three-layer security architecture (OpenShell runtime, privacy router, and governance layer) and launched NemoClaw at GTC with nearly 20 partners, including Box and Cisco. Box demonstrated human-matching permission controls for enterprise file workflows, and Cisco showed a zero-day vulnerability response with a full audit trail. But governance experts noted NemoClaw still lacks basic IT safety features, particularly around rollback, audit trails, and policy enforcement. Fast to market; not yet enterprise-ready. Perplexity Made a Quiet Pivot to Enterprise AI Agent Infrastructure Six months ago Perplexity was an AI search company. Today they are building a three-product personal agent suite: Perplexity Computer for multi-model orchestration across 18-plus AI models, Personal Computer for local 24-7 file and compute access on Mac, and Comet Enterprise as an AI-native browser tying the stack together. Their Samsung Galaxy S26 integration via Bixby gives them significant distribution, and their CEO framed the shift simply: traditional operating systems take instructions; AI operating systems take objectives. The model-agnostic architecture may be their biggest differentiator. Anthropic Is Playing a Different and Potentially Smarter Game Rather than shipping a standalone personal agent, Anthropic is embedding agentic capability into existing products. Claude Code scaled to an estimated $2.5 billion in ARR in nine months. Claude Cowork gives Claude direct control of Mac-level tasks with a permission layer built in. And the Microsoft partnership puts Claude Cowork as the multi-step reasoning engine inside Microsoft 365 Copilot Wave 3, branded as Copilot Coworks. A recent survey showed 66 percent of enterprise technical buyers said they purchased Claude first, with ChatGPT in the thirties. Anthropic's enterprise trust advantage may matter more than feature parity. Enterprise Adoption Will Be IT-Led and Slow by Design Unlike SaaS, which grew through decentralized, shadow-IT purchasing that bypassed central IT, personal AI agents require direct access to local files, compute, and company systems. That puts CISOs and IT leaders in the approval seat from day one. Ray and Peter agree the enterprise version of personal AI agents is likely 12 to 24 months away from broad deployment, with adoption following a managed, permission-controlled model rather than the freewheeling consumer version that drove OpenClaw's early growth. If you are a company executive, evaluating allowing, enabling or even developing personal AI agents for your company, this episode is a great listen...it might even inspire you to create your own personal AI agent for your personal use! See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The Power of Eye Tracking for the Enterprise - with Adam Gross, Co-Founder & CEO of HarmonEyes	14 Apr 2026	00:33:30
Eye tracking has moved far beyond the clinic and the sports performance lab. In this episode, Ray Rike sits down with Adam Gross, co-founder and CEO of HarmonEyes, to explore how AI-powered eye-tracking is being deployed in enterprise environments to measure cognitive load, predict performance degradation, and reduce costly employee burnout and attrition before problems occur. What You Will Learn: What eye tracking actually measures and why objective, passive, quantifiable eye movement data is more reliable than self-reported assessments for measuring cognitive and attention states How AI transforms raw eye data into actionable intelligence, including real-time model inference, individual adaptation across a population normative database of 15 million+ records, and predictive time-to-transition modeling Why personalization at scale matters and how Harmonize uses advanced machine learning to adapt its models to individual differences in age, sex, and experience level, making population-level models actually work for every individual Enterprise use cases with measurable ROI, including pilot training in flight simulators (shorter time to proficiency), remote operator and call center environments (fatigue and overload intervention before safety incidents), and employee burnout detection over extended time horizons The device-agnostic deployment advantage, covering webcams, phone cameras, smart glasses, and vehicle cabin cameras as signal sources that eliminate the need to purchase dedicated hardware How team leaders use real-time cognitive state data to shift from reactive management to proactive intervention, reducing performance risk across shifts and high-stress operating environments Privacy as a design principle, not an afterthought: Harmonize does not collect, store, or record eye tracking data or PII; the prior second of data is destroyed with each new output delivery Where to start as an enterprise buyer: the highest-value entry points are high-stress, high-stakes roles where burnout and performance degradation already show up as operational problems with measurable costs Career advice for early professionals: the best defense against AI-driven job displacement is not avoidance but mastery; become the human in the loop who knows the technology best See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The Power and Promise of Vertical AI	31 Mar 2026	00:35:53
While the AI headlines obsess over foundation model fundraises and hyperscaler spending, a quieter revolution is generating real, measurable returns. In this episode of AI to ROI: The Big Story, Ray Rike and Peter Buchanan break down why vertical AI companies may be building the most durable and valuable businesses in the history of enterprise software, and why most people aren't paying attention yet. What's covered in this episode: Defining Vertical AI: What separates vertical AI from horizontal tools like Microsoft Copilot or Google Workspace AI, and why the distinction matters for buyers and investors alike A fundamentally different business model: Why vertical AI companies target labor budgets (10x the size of enterprise software budgets) rather than IT spend, and how outcome- and consumption-based pricing is replacing the traditional per-seat model The funding explosion: Vertical AI investment grew from $8B in 2023 to $22B in 2024 to $42B in 2025, with unicorn counts in the sector jumping nearly 6x in just two years Harvey (Legal AI): How this $8B+ valuation company grew ARR from $100M to $190M in just four months by orchestrating multiple AI models across legal workflows and embedding deeply into law firm operations Abridge (Healthcare AI): How a cardiologist-founded company reached a $5.3B valuation by turning physician-patient conversations into structured clinical documentation in real time, with deep Epic EHR integration across 150+ health systems Sierra (Customer Experience AI): How Brett Taylor's enterprise AI platform hit $100M ARR in just 21 months and crossed the $10B decacorn threshold, raising the question of whether the agent era could produce the first trillion-dollar enterprise software companies MaintainX (Industrial/Manufacturing AI):How this maintenance management platform is tackling $1.4 trillion in annual equipment failure costs across 11,000 customers and 11 million assets — with a 34% reduction in unplanned downtime for customers Why vertical AI moats are so durable: Proprietary data that compounds with every transaction, embedded institutional knowledge that makes switching costs higher than any legacy ERP migration, and a model architecture that gets stronger as foundational models improve Advice for enterprise buyers: Why 2026 is the year to evaluate vertical AI vendors, insist on outcome-based pricing, and start with one workflow before expanding Interested in reading the details on the Vertical AI industry and trends? Check out the AI to ROI Newsletter providing even more detail by clicking here. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Pricing Strategy for AI Software and SaaS: When to Change, Who Should Own It, and the CFO's Role with Dan Balcauski	31 Mar 2026	00:33:25
Pricing is one of the most underleveraged strategic levers in B2B SaaS and AI Software. Most companies are getting it wrong. In this episode, Ray Rike sits down with Dan Balcauski, founder of Product Tranquility and a 20-year software industry veteran, to cut through the noise around consumption, usage, outcome, and hybrid pricing models. Dan brings a practitioner's perspective on when to review pricing, who should own it, and how the CFO fits into the equation. Signs Your Pricing Needs a Review Best-in-class companies review pricing at least quarterly -- but review does not always mean change Key warning signals include declining net revenue retention and unexpected shifts in win/loss conversion rates AI-native companies are iterating on pricing monthly due to rapid competitive dynamics Sales cycle length is a practical constraint: a 12-month enterprise cycle limits how frequently you can test and observe pricing changes The Role of Customers in Pricing Strategy Never anchor your pricing strategy entirely to your existing customer base -- they carry inherent bias A practical research mix: roughly one-third existing customers, two-thirds prospects Existing customers know your real value; prospects only know what you show them -- both perspectives matter When introducing a second product, maintain structural similarity in pricing tiers even if the pricing metric differs Pricing Ownership and Governance Below $5M ARR, the founder/CEO owns pricing; above $20M it shifts to Product or Marketing -- the gap in between is where ownership gets dangerously vague Product Marketing is best positioned to own pricing because it sits at the intersection of positioning and value communication Sales owning pricing is a misalignment of incentives -- "like putting Dracula in charge of the blood bank" Best practice is a pricing council with a designated decision-maker, not design by committee Discounting and the CFO's Role Discounting policy is often the easiest and fastest win -- and one of the first places Dan looks with any client Enforcement matters as much as policy: without monitoring, no new pricing strategy will ever reach the market as intended The CFO plays a dual role -- operational (contracts, billing, deal desk guardrails) and strategic (modeling cash flow and KPI impact when shifting pricing models) Caution: A finance-led focus on consistent margin profiles across products can misread how different market segments actually behave Outcome-Based Pricing: Hype vs. Reality Outcome-based pricing is "the future and always will be" -- it is not new, and it is genuinely difficult to execute True outcome pricing only works when you are directly in the revenue or savings transaction, as Stripe is A more practical frame is output-based pricing -- Intercom's 99 cents per resolved support ticket is a strong example of measuring a clear, attributable unit of value If you are involved in how best to monetize and price your B2B AI or SaaS product - this is a very valuable listen! See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The Superhuman AI Agent - with Amanda Kahlow, CEO & Founder, 1Mind	24 Mar 2026	00:32:09
In this episode of the AI to ROI Podcast, host Ray Rike sits down with Amanda Kahlow, founder and CEO of 1Mind. Prior to 1Mind, Amanda was the founder and former CEO of 6sense, an early pioneer in intent data. The Vision Behind 1Mind: Amanda founded 6sense to help companies find buyers; she founded 1Mind to close them. 1Mind builds what she calls "go-to-market superhumans", AI agents that take on multiple roles across the full customer lifecycle, from inbound qualification and live demo delivery to deal closing for SMB/commercial accounts, and even post-sale onboarding, upsell, and cross-sell motions. Why the Buyer Journey Has Fundamentally Changed: Amanda argues that traditional intent data and one-way marketing are becoming obsolete. Buyers no longer follow a linear path of Google searches and form fills; they expect real-time, two-way, solution-oriented conversations, much like they get from interacting with large language models today. The old model of blasting outbound emails or routing inbound leads through a sequential SDR → AE → SE handoff chain is increasingly misaligned with how modern buyers want to engage. Top Use Cases: How Customers Deploy 1Mind: The most common starting point is the inbound website use case, customers start by placing a superhuman on the website that can qualify a visitor, deliver a personalized live demo, answer deep technical questions, and in some cases take the deal all the way to close, all on first touch. From there, customers frequently expand to the "ride-along" use case, where the superhuman joins every sales call as an always-available AI sales engineer. Human sellers retain control but can call on the superhuman in real time to answer hard questions, surface the right case study or slide, run an integration demo, or ask the qualifying questions (MEDDIC and similar) that sellers often avoid. Measurable Business Impact: Amanda shares compelling early results from enterprise customers, including a ~40% reduction in sales cycle length (from ~90 days to ~60 days) and a doubling of ACV for deals that passed through the superhuman pipeline versus the traditional pipeline. She attributes the ACV lift to getting buyers to vendor-of-choice status earlier in the cycle, eliminating the need to compete on price. 1Mind also has use cases for existing customer bases — proactively engaging customers about new features to drive upsell and cross-sell, a task that human CS teams increasingly can't keep pace with, given the speed of product development. How Customers Measure ROI: Amanda is direct: the right measurement framework is revenue impact, not top-of-funnel pipeline metrics. She encourages customers to tie superhuman performance to shortened deal cycles, higher ACV, and bottom-of-funnel revenue influence. She acknowledges there is a maturity curve — some customers start by measuring meetings booked — but the companies seeing the most value are those willing to shift away from MQL-based thinking toward board-level outcomes: revenue growth, lower CAC, and expansion revenue. Onboarding & Time to Value: 1Mind has invested heavily in its self-serve platform to reduce deployment time from a four-month process to an average of about four weeks today, with some customers going live in as little as four days. All deployments are full enterprise contracts, as 1Mind does not run pilots. Advice for Leaders on AI ROI Amanda emphasizes that realizing meaningful AI ROI requires a top-down mandate from the CEO. Incremental point solutions can improve efficiency at the margins, but the big needle-movers require new playbooks and organizational willingness to change how work gets done, not just layer AI on top of existing processes. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Deloitte 2026 State of AI Report - The Untapped Edge	23 Mar 2026	00:29:52
On this AI to ROI Big Story episode, our hosts Ray Rike and Peter Buchanan dig into Deloitte's 2026 State of AI Report, a 41-page annual study surveying over 3,300 business leaders on the state of enterprise AI adoption. Deloitte calls it "The Untapped Edge," and Ray and Peter unpack exactly why. They walk through the report's seven key inflection points from scaling pilots into production and reimagining business processes, to agentic AI, sovereign AI, and physical AI, with a focus on what the data actually means for companies trying to drive real ROI in 2026. Key topics covered in this episode include: Pilot to Production: Why 54% of respondents expect a major leap in production deployment in the next 3–6 months, and why 37% of companies are still making little or no change to existing processes Productivity & Revenue: How 66% of organizations report efficiency gains today, but only 20% are seeing actual revenue impact from AI - and what it will take to close that gap Business Transformation: Why 84% of companies have yet to redesign jobs around AI, and what that means for long-term competitiveness Agentic AI: What the jump from 26% to 74% expected adoption of agentic AI over two years signals, and the top enterprise use cases including customer support, supply chain, R&D, and cybersecurity Governance: Why only 21% of companies have a mature governance model for autonomous agents, and what leading companies are doing to build responsible frameworks from the ground up Sovereign AI: How 83% of multinational board members view sovereign AI as at least moderately important, and why the US, Europe, and the Middle East are approaching it very differently Ray and Peter close with a clear-eyed summary of what enterprises need to do now: close the gap between strategy and operational readiness, redesign work with an AI-first mindset, and shift focus from incremental efficiency to genuine strategic reinvention. 📰 This episode is based on the February 19th edition of the AI to ROI newsletter. Subscribe at ai2roi.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI to ROI: Big Story - Will the Angst, Agony, and Adversity of AI be Worth It?	20 Mar 2026	00:31:17
Is the trillion-dollar AI bet actually going to pay off? In this episode of AI to ROI, hosts Ray Rike and Peter Buchanan tackle the big question head-on: with hyperscalers pouring over $600 billion into AI infrastructure this year alone, enterprises struggling to move pilots into production, and white-collar job postings already falling 16% year-over-year, the anxiety is real and justified. But so is the optimism. Ray and Peter break down why the same supply constraints slowing AI buildout may actually give companies and workers more time to adapt, why foundation model costs have plummeted 97% since 2023, and how IBM's internally deployed AI has already generated $4.5 billion in productivity savings. From healthcare transcription to AI-native go-to-market tools, the ROI is emerging, but not evenly or quickly enough for most. What We Cover in This Episode: The staggering scale of AI infrastructure spending: The five largest hyperscalers (Amazon, Microsoft, Alphabet, Meta, and Oracle) are on track to spend over $600 billion in CapEx this year, with Oracle committing 57% of its annual revenue and Microsoft 45%, ratios more typical of heavy industrial companies than software firms Why the build-out is slower than everyone thinks: Grid upgrade timelines in the US run 8+ years, data center construction is broadly behind schedule, and critical shortages in chips, transformers, skilled labor, and construction materials aren't expected to ease until at least 2028 The pilot-to-production gap is real: Only 6% of enterprise AI projects are delivering returns within a year, and most organizations lack the frameworks and experience to move from experimentation to operational deployment at scale Trust, hallucinations, and governance are still major blockers: Regulated industries like financial services and healthcare face compounding uncertainty, caught between pre-AI regulations still on the books and a patchwork of conflicting state, federal, and international AI policy The workforce impact is already being felt : Salesforce cut 4,000 customer support roles, Klarna reduced headcount by 40%, white-collar job postings are down 16% year-over-year, and college graduate placement rates have dropped from 83-88% to roughly 23%, hitting data science, software development, and graphic design hardest But the technology itself is accelerating fast: Foundation model costs have dropped 97% since early 2023, the number of available models has grown from 60 to 650, and enterprises are getting smarter about orchestrating multiple models for different tasks Real ROI stories are emerging: IBM has generated $4.5 billion in productivity savings from internally deployed AI since January 2023, automating nearly 4 million hours of work annually at $3.50 returned for every dollar invested Vertical AI is gaining serious traction: Healthcare AI is the fastest-growing vertical, with one transcription tool alone saving 50,000 clinician hours. Legal, cybersecurity, customer support, and IT operations are all seeing meaningful gains The competitive pressure is intensifying: 54% of business leaders in a Mercer study believe they won't remain competitive in five years without AI at scale, and 92% of firms plan to increase AI budgets over the next three years Why You Should Listen: If you're a business leader, investor, or professional trying to cut through AI hype and understand what's actually happening on the ground, this episode delivers the balanced, data-driven perspective that's hard to find. Ray and Peter don't just cheerlead or catastrophize; they give you the real picture: where the bottlenecks are, where the returns are genuinely showing up, and why the next two to three years of slower-than-expected adoption might actually be the window your organization needs to get AI right. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI-Native ERP vs. Legacy ERP: What's the Difference? with Santiago Nestares, Founder & CEO of DualEntry	17 Mar 2026	00:29:33
What does it actually mean for an ERP to be AI-native and why does it matter for your finance team? In this episode of the AI to ROI podcast, host Ray Rike sits down with Santiago Nestares (Santi), Founder and CEO of DualEntry, to unpack the real differences between legacy ERP systems and a ground-up AI-native platform. Santi shares the origin story behind DualEntry, born from a painful nine-month ERP implementation at his previous company that cost a team of 12 and hundreds of thousands of dollars—and explains why simply adding AI features on top of old database architecture misses the point entirely. AI, he argues, isn't a feature you plug in; it's a design philosophy that must be embedded at every layer of the product. Ray and Santi dig into one of the thorniest challenges in enterprise finance: the tension between probabilistic AI models and the zero-error standard that accounting demands. Their answer? Deterministic guardrails—approval workflows, permissioning layers, and audit trails—that let AI work freely in draft mode while keeping humans accountable for every posted transaction. You'll also hear about DualEntry's "Next Day Migration" approach, including how the company uses AI to map and migrate every transaction (not just trial balances) in hours rather than months, giving prospects a live sandbox with their own data before they ever sign a contract. What You'll Learn Why adding AI to a legacy ERP is like "running an on-prem system with a CD on the cloud", and what truly AI-native architecture looks like instead The difference between deterministic and probabilistic systems, and why accounting can't afford to get it right only 99.9% of the time without the right guardrails How DualEntry's Next Day Migration works: AI-assisted mapping, atomic transactions, and a live sandbox demo using the prospect's own data The real ROI of AI-native ERP from eliminating manual categorization drudgery to enabling multi-dimensional segmentation that surfaces hidden pockets of value and risk How Dto build audit-ready explainability without being able to explain the AI itself - by tracing every decision back to a human approval Why early-career finance professionals are "living the luckiest time" in the professionand how to lean into AI rather than fear it Episode Topics at a Glance 00:00 — Welcome & guest introduction 00:51 — Santi's origin story: a nine-month legacy ERP nightmare that sparked DualEntry 02:52 — AI-native vs. legacy ERP: what's the real difference? 04:48 — Deterministic vs. probabilistic systems explained 06:49 — How to identify a truly AI-first platform vs. an AI add-on 10:07 — Next Day Migration: using AI to accelerate ERP transitions 14:11 — Implementation team design: finance practitioners + forward-deployed engineers 17:18 — Measurable ROI: from real-time bank feeds to AI-driven business insights 22:52 — AI explainability, audit trails, and the permissioning layer 25:06 — Rapid fire: CFO ROI variables, who owns AI ROI, and advice for early-career finance professionals See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The Rise of the Chief AI Officer (CAIO)	10 Mar 2026	00:25:30
Is your company leaving money on the table by not having a Chief AI Officer? In this episode, Ray Rike and Peter Buchanan dig into groundbreaking research from IBM's Institute of Business Value, spanning 600+ executives across 22 geographies and 21 industries to unpack why dedicated AI leadership is quickly becoming non-negotiable for enterprises competing in today's market. The numbers tell a compelling story: only 26% of companies currently have a CAIO, yet those that do are seeing 10% higher ROI on their AI investments and are 24% more likely to outperform their peers. And that gap? It's widening. With 66% of existing CAIOs predicting most organizations will have someone in this role within 24 months, the window to gain a first-mover advantage is open — but not for long. Ray and Peter go deep on some of the episode's most surprising findings, including: Who's actually getting hired: 73% of CAIOs come from data-focused backgrounds, but the most effective ones are hybrid leaders equally fluent in business strategy and data science. And 57% were promoted from within, because institutional knowledge often matters more than technical expertise. Where they sit in the org chart matters enormously: CAIOs who report directly to the CEO and control the AI budget (61% do) drive far greater results than those positioned as glorified advisors without real authority. The hub-and-spoke model delivers 36% higher ROI: companies that pair a centralized AI function with embedded business unit partners outperform those with fully decentralized AI decision-making, giving them both governance and agility. Three pillars that make or break a CAIO: measurement tied to real business outcomes, cross-functional teamwork across the entire C-suite, and genuine authority to make tough decisions. Strip away any one of these and ROI suffers. What to do if you're not ready to hire one yet : Ray and Peter offer practical alternatives, from AI steering committees to centers of excellence, and explain why accountability can't be an afterthought regardless of your company's size or structure. They also tackle the growing complexity of managing AI at scale, the average large enterprise is now running 11 generative AI models and why the rise of agentic AI makes centralized leadership even more critical before things become, as Ray puts it, "a hot mess." Whether you're a Fortune 500 executive or a mid-market leader trying to figure out your AI strategy, this episode is packed with data-backed insights to help you move from AI experimentation to measurable, scalable ROI. Prefer to read more detail - check out the AI to ROI Newsletter covering this topic at: *ai2roi.substack.com/p/the-chief-ai-officer-from-nice-to?r=2ldi4p* See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The SaaS to AI-First Transformation - 3 Examples from Notion, Canva and ServiceNow	04 Mar 2026	00:27:04
This episode of the AI to ROI podcast, hosted by Ray Rike and Peter Buchanan, explores how leading SaaS companies are surviving the "SaaSpocalypse" - a massive market cap devaluation triggered by the rise of AI. The hosts break down the transition from traditional SaaS to AI-first models, emphasizing that simple "feature bloating" isn't enough; companies must undergo a fundamental "organ replacement" of their architecture, pricing, and culture. The discussion deep-dives into three success stories: Notion: Transformed from a document suite into an agentic execution platform through strategic acquisitions, moving toward autonomous workflows. Canva: Democratized design by making AI features invisible and intuitive, resulting in a 700% increase in AI tool usage and massive revenue growth. ServiceNow: Leveraged its 20-year history in workflow automation to pivot from seat-based pricing to task-based pricing, using AI agents to orchestrate complex enterprise processes. If you are a SaaS company executive looking for great examples of how they transitioned to be AI-first - this episode is full of great examples, strategies, and tactics. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	AI SDR Learnings, Results, and ROI - with Jacco van der Kooij, Winning by Design	02 Mar 2026	00:34:47
In this episode, our host Ray Rike sits down with Jacco van der Kooij, founde and CEO, Winning by Design, to discuss the real-world deployment of "Jack" - an AI SDR that has spent the last 12 months redefining the front line of Go-To-Market (GTM) strategy. Jacco shares the "AHA" moments and hard truths discovered while moving beyond human constraints like slow response times and inconsistent qualification. Discover how treating an AI SDR (agent) as a "system" rather than a "product" initially led to 2,030 conversations, 100% CRM capture, and a $200,000 deal. Episode Summary Winning by Design set out to prove that if AI can handle the high-risk, high-empathy role of an SDR, it can work anywhere in GTM. Over the course of a year, their AI agent, Jack was built on a foundation of 1mind logic and Clay enrichment. The agent evolved from a simple chatbot into a trained GTM operator. Key Highlights: Breaking Human Constraints: The project addressed critical issues like burnout, limited global coverage, and poor CRM hygiene that even the best human reps struggle to maintain. The "AHA" Moments: Jacco details how the team realized Jack shouldn't just "chat" but perform industrial-scale qualification while supporting buyers in their buying journey. The Power of Iteration: Initial surprises, such as a low 8% email capture rate, were overcome by designing a better "value exchange" rather than just tweaking prompts. Tangible Results: After refinement, email capture jumped to 20%, MQL conversion rose by 36%, and the system successfully captured nearly 9,000 SPICED answers. The Ultimate Do’s and Don’ts: Success requires anchoring the agent in a GTM system and iterating weekly; failures stem from treating AI like an unstructured chatbot or deploying without clear ownership. The Do's Design the value exchange first: Ensure the AI provides something useful to the buyer before asking for information Earn the next step: Focus on providing enough value to merit the next stage of the conversation Anchor the agent in your GTM system: AI should scale a pre-designed, structured system rather than an improvised process. Start narrow, then expand: Focus on one specific motion and one outcome before attempting to scale. Iterate weekly: Small, frequent changes to the system drive the most significant gains in performance. Focus on the buyer's journey: Design the experience to help the buyer buy, rather than just helping the seller sell. The Don'ts Treat AI like a chatbot: Avoid unstructured "chatting," as it kills conversion rates; focus on industrial-scale qualification instead. Chase volume over quality: Remember that activity is not the same as a healthy pipeline. Hide the AI behind humans: Be transparent about using an AI agent to build trust with the buyer. Deploy without ownership: AI implementation is a Go-to-Market responsibility, not just an IT project. Expect AI to fix a bad process: AI will not fix poor GTM design; it will only expose and amplify existing flaws. Point AI at unstructured data: Do not simply point the AI at a massive folder of research; start with specific, high-quality training materials. If you are considering deploying agentic AI into your Sales organization and process, this episode is full of great insights, experiences, and measurements for your AI investment in Sales. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	The Great AI War on Jobs	19 Feb 2026	00:29:23
Are we witnessing a productivity revolution or the greatest labor displacement in history? In this detailed episode of AI to ROI, Peter Buchanan and Ray Rike break down the "Great AI Jobs War," a period of massive upheaval where corporate gleefulness meets workforce anxiety. They move past the "AI washing" to find the real metrics that define success in the age of intelligence. Key Discussion Points: The Historical Context: Ray draws parallels between the AI revolution and past disruptions like the Industrial Revolution, the cotton gin, and the assembly line, noting that AI is moving with a magnitude and speed never seen before The "Mother May I" Productivity Gap: While 47% of S&P 500 companies now discuss AI in earnings calls, only 10% are seeing meaningful ROI, leaving a "staggering" 56% of companies getting "little to nothing" out of their implementations The Million-Dollar Employee: A deep dive into Klarna’s radical transformation—reducing headcount from 5,000 to 3,000 through attrition while doubling revenue to reach the "magic number" of $1.1 million in revenue per employee The War on Early Careers: Why entry-level IT hires have plummeted from 25% to 7% of all hires, and the "structural problem" of junior roles requiring 2–3 years of experience because AI is now doing the "digital grunt work" Blue-Collar as the "Gold-Collar" Future: Why the CEO of NVIDIA suggests young people skip computer programming for mechanical trades, and how salaries for AI-related construction and electrical roles have doubled Customer Service Autonomy: How Bank of America's "Erica" handled 2 billion interactions with a 98% resolution rate in under 44 seconds, signaling a massive shift in how businesses handle scale Actionable Insights for Leaders: Measure Revenue Per Employee: This is the ultimate metric for AI productivity. Bake AI Aptitude into Hiring: Every new white-collar job description should require "AI curiosity" and applicable tool skills. Strategic Augmentation: The goal isn't just headcount reduction, but using the "free cash flow" from AI efficiencies to build a war chest for growth and sales. To read more details and subscribe to the AI to ROI Newsletter for more data-driven strategies on turning AI hype into bottom-line results. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	SaaS to AI-First Transformation	12 Feb 2026	00:32:56
In the second episode of AI to ROI: The Big Story, Ray Rike and Peter Buchanan analyze the critical transformation required for traditional SaaS companies to become AI-first organizations. With the SaaS industry generating $273 billion annually, the hosts warn that incumbents are under "two-front" attack: internal refactoring of legacy systems and external disruption from hyper-efficient AI-native startups. Key Highlights of the Episode: The "SaaS to AI" Pivot: Ray compares the current shift to the on-premise-to-SaaS transition of 20 years ago, noting that today’s change requires a fundamental rewrite of an operating culture rather than just adding "AI veils" like prompt engines The Rise of AI-Native Efficiency: Peter highlights companies like Lovable and Cursor, which have achieved hundreds of millions (or billions) in valuation in under a year with minimal staff, challenging the traditional SaaS model of linear employee growth The Shift in Financial Metrics: The hosts discuss the new economic reality: forgetting 80% gross margins in favor of a 50-65% range to account for high token and inference costs. Success will depend on the "COGS to CAC" model, offsetting higher infrastructure costs with dramatically lower customer acquisition costs via AI automation A Roadmap for Success: To fight back, SaaS incumbents must re-architect around outcomes rather than features. This includes leveraging their "crown jewel data" and status as systems of record to build decision intelligence layers that AI-native startups lack The HubSpot Success Story: Ray details how HubSpot successfully scrapped its 2023 roadmap within weeks of ChatGPT’s launch, shipping AI-native products in under 90 days and moving toward a "Results-as-a-Service" future Advice for Leaders and Employees: Ray suggests that CEOs must re-engineer every department to be AI-first, while employees should commit to learning new AI tools every month to remain employable in an increasingly automated landscape. Listen to the full episode for a deep dive into how to avoid becoming an "orphan" SaaS company in the age of Agentic AI. You can read the newsletter edition covering this topic by clicking here. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Navigating the Shift to AI-Powered Revenue Workflows - A CFOs Perspective with Drew Laxton, CFO Outreach	10 Feb 2026	00:31:10
In this episode of the AI to ROI podcast, host Ray sits down with Drew Laxton, CFO at Outreach, to explore the profound transformation of sales technology and financial metrics in the age of AI. Drew shares the strategic reasoning behind his return to Outreach, driven by a conviction that the company is uniquely positioned to lead the next era of agentic AI and automated revenue workflows. The conversation goes beyond the hype, offering a masterclass in how finance leaders must adapt to a software landscape that is moving from seat-based subscriptions to consumption-driven models. Drew provides an inside look at how Outreach is re-engineering its own financial playbook to account for the high compute costs and non-linear revenue growth associated with AI. Key discussion points include: The "Personal Productivity" vs. "Financial ROI" Debate: Why the initial wave of AI efficiency must eventually translate into higher quotas and lower OpEx to satisfy the board. Maintaining Margins in an AI-Native World: A deep dive into the "triumvirate" of Product, Engineering, and Finance that manages gross margins as compute costs replace traditional SaaS overhead. The Metric Recalibration: Why traditional SaaS snowballs don't work for AI, and how Outreach is using "spend-as-truth" to normalize data for NRR and CAC calculations. Agentic AI in Action: How Outreach's "revenue agents" are replacing manual prospecting with autonomous, data-tuned interactions that learn from previous customer engagement. Some Key Insights and Quotes pulled from the conversation with Drew: On the "Boring" Wins of AI: While many look for revolutionary shifts, Drew emphasizes the value in automating the mundane: "A lot of the AI tools that I’ve seen so far... there's kind of boring outcomes that are very impactful... like our QA process within the coding side has very much streamlined." On the Changing Economics of SaaS: Drew acknowledges that AI-native products fundamentally alter the 80%+ gross margin expectations of the past decade: "We do need to bring gross margin into our understanding of SaaS tools because it's just not the same... You've got to be more efficient on the go-to-market side to make the economics work." On the Rise of Consumption Pricing: The shift to variable pricing means the "snowball" metric of the past is no longer sufficient: "What is your ARR has become a lot more challenging question than it used to be... consumption is not linear on these products. It’s kind of zero, very little, and then a lot." On the Importance of Usage Over Revenue: In a variable world, product utilization becomes the primary indicator of a healthy business: "Product utilization... becomes a core signal to retention. It's not just revenue anymore, it's utilization month by month... spend is truth." Advice for Aspiring CFOs: For those looking to reach the C-suite in the AI era, Drew suggests one primary trait: "Be curious. Just be curious about everything... Ask questions, get time with the CFO or the leaders of the various organizations... wanting to understand their business has only benefited me." See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
	Context Graphs - AI’s Trillion-Dollar Technology	06 Feb 2026	00:24:52
In the first episode of the AI to ROI: Big Story podcast, our co-hosts Peter Buchanan and Ray Rike discuss the emerging importance of Context Graphs in AI Software. Why are context graphs suddenly being called a trillion-dollar opportunity in enterprise AI? In this inaugural episode of The Big Story, hosts Ray Rike (CEO of Benchmarkit) and Peter Buchanan (Managing Partner of New Plan) dive into the "glue technology" that fills the missing gap in the AI stack. While traditional knowledge graphs tell you what happened, context graphs reveal the *why!* Context Graphs capture the decision traces, policy constraints, and precedents that make AI agents truly auditable and trustworthy. From preventing "data breakage" in regulated workflows to revolutionizing supply chain quality control, discover why context is the key to moving AI from experimental pilots to reliable production. Key Takeaways The Why Behind the Action: Context graphs provide the connectivity that agentic AI lacks, recording who made a decision and under what specific constraints. A Trillion-Dollar Value Add: Industry leaders believe context is a massive economic value driver for companies in the era of AI. Beyond Knowledge Graphs: Moving from simple data points to decision lineages that explain the "why" behind an event. Real-World Use Cases: Deep dives into data governance at firms like Vanguard and Prudential, and quality control in the automotive supply chain. The Vendor Landscape: Discussion on current players like Atlan, Neo4j, and Writer, and why tech giants like Microsoft and Salesforce are the "lurkers" to watch. Timestamps 00:00 Introduction to the AI to ROI podcast series. 02:40 Defining Context Graphs: The missing gap in the AI stack. 04:15 The Trillion-Dollar Opportunity: Economic value vs. market size. 06:30 Knowledge Graphs vs. Context Graphs: Moving from "what" to "why". 09:20 Who should care? Roles from the CEO to the Chief Risk Officer. 12:45 Use Case 1: Data Governance and preventing "downstream breakage". 15:10 Use Case 2: Unifying the Go-To-Market (GTM) stack. 18:30 Use Case 3: Supply chain visibility and automotive quality control. 21:40 The Market Map: Current leaders and "Lurker" strategies from Big Tech. 25:50 Executive Summary: Things every leader must do right now. Context graphs are a key component to turn Agentic AI software vision into Agentic AI explainability! Read the AI to ROI Newsletter on Substack to dive deeper into this week's Big Story! Subscribe at: ai2roi.substack.com See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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