Business Technology Perspectives – Details, episodes & analysis

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Business Technology Perspectives

Neil C. Hughes

Business

Technology

Frequency: 1 episode/17d. Total Eps: 32

Business Technology Perspectives is a podcast from the Tech Talks Network that explores how digital innovation is influencing business strategy across industries.

Hosted by Neil C. Hughes, creator of the Tech Talks Daily Podcast, the series features conversations with leaders who are shaping the future of enterprise technology.

Each episode takes a closer look at how organisations are aligning technology with business outcomes. From AI and cloud to data strategy and digital skills, we speak with those navigating complex decisions that drive transformation.

These are honest, grounded discussions about what's working, what isn't, and what business leaders are learning as they adopt and adapt new technologies. Whether you're steering strategy, leading innovation, or simply trying to keep up with the pace of change, this podcast offers a balanced view of the possibilities and pitfalls that come with building a digital-first business.

Search Tech Talks Network to discover more shows in the series.

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🇬🇧 Great Britain - Management
24/09/2026
#100

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From Transactions to Interactions in Banking With CSI

Episode 32

Wednesday, September 23, 2026 • Duration 26:44

What if your bank could recognize that you needed help before you had to ask for it?

In this episode of Business Technology Perspectives, I speak with Michel Jacobs, Chief Strategy Officer at CSI, about how artificial intelligence and customer intelligence are changing the relationship between financial institutions and the people they serve.

Community banks and regional financial institutions have traditionally competed through personal relationships rather than scale. But digital banking, fintech competition, and changing consumer expectations are putting that advantage under pressure. Michel argues that the future of banking will increasingly be defined by interactions rather than transactions.

That means understanding why a customer is contacting the bank, what may be happening in their life, and which service could provide value at that moment. It also means preparing for interactions initiated through APIs, open banking services, and AI agents, rather than assuming every customer journey begins with a person opening a banking app.

Michel explains how CSI’s Customer Intelligence Suite combines transaction information, card activity, merchant category data, and digital behavior to identify signals and changing patterns. According to Michel, these signals can help a financial institution understand when a customer’s circumstances may have changed instead of relying on the demographic category assigned when the account was opened.

This creates opportunities for relevant financial support, but it also introduces serious questions about privacy, consent, accuracy, and customer trust. Inferring that somebody has changed jobs, bought a home, become a parent, or encountered financial difficulty can be useful when the response genuinely helps. The same capability can feel intrusive when it produces poorly timed sales offers or conclusions the customer cannot question.

We discuss how banks can balance personalization with regulatory responsibilities and why younger consumers are less likely to remain loyal when a provider offers little practical value. As Michel puts it, the era of offering everybody the same account and throwing in a free beach ball has probably run its course.

Customer retention is another major part of our conversation. Michel explains how declining digital activity, money leaving an account, late payments, or changes in income can reveal that a relationship is weakening. Used responsibly, this information could allow a bank to offer assistance before missed payments or financial strain become harder to address.

For community banks, the answer is unlikely to be copying every product offered by the largest global institutions. Michel believes they should decide where they can provide distinctive value, then use technology and data to support that position. CSI’s stated aim is to give smaller financial institutions access to customer analytics capabilities that would otherwise require considerable internal investment.

Can AI help banks become useful partners in their customers’ lives without crossing the line from personalization into intrusion? Listen to the conversation and share your thoughts with me.

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Closing the Technology Confidence Gap With Babble

Episode 31

Saturday, September 12, 2026 • Duration 30:34

Why do 88 percent of UK SMB leaders see technology as important to business performance while fewer than half feel highly confident about adopting it effectively?

In this episode of Business Tech Perspectives, I speak with Mark Weait, Chief Revenue Officer at Babble, about the company’s inaugural Technology Performance Index. The research draws on responses from 1,000 UK SMB leaders and C-suite executives and examines a growing technology confidence gap between recognizing what digital tools could achieve and knowing how to identify, implement, and expand them successfully.

Mark describes a pattern many businesses will recognize. A security incident prompts an urgent software purchase. A board request to “do something with AI” leads to a batch of licenses. A new cloud application arrives without a clear business problem, owner, or adoption plan. The company gains another tool, but employees may lack the training, processes, or time needed to turn it into a result.

His analogy is simple: a business can buy a Ferrari, but that does not mean its people know how to drive it. Technology adoption depends on clarity about the desired outcome, the skills needed to deliver it, and the connections between systems, people, security, and data. Mark argues that treating every program as an IT responsibility can overlook the management support and operational change required across the wider business.

The Babble research groups SMBs into Tech Vanguards, Emerging Adopters, and Tech Bystanders. According to figures discussed in the interview, 84 percent of Tech Vanguards report strong processes for technology-led change, compared with 3 percent of Tech Bystanders. The research also reports that 57 percent of Tech Vanguards are receiving measurable results from AI and automation, compared with 21 percent of Tech Bystanders.

Those figures suggest that confidence may build through experience. Companies that begin with a defined problem, equip their people, and measure a deployment can use that success to support the next decision. Those buying technology through fear of missing out risk adding cost, security exposure, and another disconnected application to an already complicated environment.

Mark does not believe smaller businesses have missed their opportunity with AI. He says many companies remain near the beginning, which means an SMB can still catch up without attempting to apply AI everywhere. His recommended approach is to assess readiness, train employees, confirm that networks and data controls are suitable, identify a process where AI can produce a worthwhile result, and expand gradually from there. He also distinguishes AI from established automation, noting that some business problems can still be solved without a generative system.

Moving AI Pilots Into Production With QualityAI

Episode 30

Thursday, August 20, 2026 • Duration 42:34

What evidence would persuade your board that a successful AI pilot is ready to become part of everyday business operations?

In this episode of Business Technology Perspectives, I speak with Andrew Duncan, CEO of QualityAI, formerly Qualitest. Andrew previously served as CEO of Infosys Consulting and has spent over 30 years advising global organizations across technology and business change.

Andrew believes the AI era differs from previous technology cycles because adoption is moving faster than many organizations can govern it. Fear of missing out is also creating pressure for companies to appear further along than they really are.

This creates a growing gap between AI activity and operational value. A company may have numerous pilots, prototypes, and employee experiments without possessing enough evidence to deploy any of them across the business.

Andrew describes the enterprise AI conversation as moving from “can we build it?” toward “can we trust it?” Answering that question requires companies to assess three areas. Does the technology work? Is it producing the expected business outcome? Will it continue behaving within agreed boundaries as its data, model, users, and environment change?

We discuss the questions boards should ask before approving AI at scale. What business result is the company pursuing? What level of risk is acceptable? Who becomes accountable when the system makes an unexpected decision? Has it been tested under real operating conditions? What evidence demonstrates that people can rely on it?

Andrew explains why a successful pilot should never be confused with production readiness. Controlled tests cannot fully represent real data, human behavior, connected applications, operational processes, or downstream consequences. A system that performs well in isolation may behave very differently once it reaches customers and employees.

This is why Andrew advocates continuous AI assurance. Testing cannot end when an application enters production. Organizations must monitor whether the system continues functioning technically, produces the intended result, and remains within defined guardrails.

Our conversation also addresses AI washing and the growing tendency to measure progress through the number of pilots or features launched. Andrew suggests asking a much simpler question: what has materially improved because AI is involved?

How Larridin and Hunton Andrews Kurth Assess Enterprise AI Risk

Episode 29

Sunday, August 2, 2026 • Duration 28:50

Could your company produce a complete inventory of every AI tool, model, agent and embedded feature operating across the business today?

In this episode of Business Tech Perspectives, I speak with Russ Fradin, founder and CEO of Larridin, and Michael Levine, partner at Hunton Andrews Kurth, about the enterprise AI visibility gap and the growing legal, financial and insurance consequences for companies that cannot identify their AI systems.

Russ has experienced several major periods of technology adoption across digital advertising, mobile and workforce communication. He launched Larridin after recognizing that standard measurement systems could not account for the speed and variety of AI tools entering businesses. The company helps enterprises understand which AI systems employees use, what they cost, which models they rely on and where adoption is taking place.

The scale of the problem can be surprising. Russ describes an early customer that expected to find approximately 70 AI tools operating across the company but discovered 220. Most were benign, including five separate AI meeting assistants, but the discovery revealed unnecessary spending and fragmented adoption.

Another customer found an AI agent built by a former employee that continued running at a cost of $1,800 each month. Nobody remaining at the company understood what the agent did. The example shows why an enterprise AI inventory concerns business spending and accountability alongside security and compliance.

Mike explains how embedded AI can expose a company to legal action without leaders realizing that AI was involved. He discusses a class action concerning an HR screening tool that allegedly used AI-assisted lie detection without the required disclosure.

We also examine the growing patchwork of US AI regulation. Mike discusses the Texas Responsible Artificial Intelligence Governance Act and the Colorado AI Act, along with the difficulties faced by businesses operating across several states. A web-based company may need to comply with a state law because it serves residents there, even when the company is based elsewhere.

Board accountability is another focus. Russ says boards increasingly want to know which tools and models are being used, what employees use them for, what data agreements apply and how much the company is spending. Mike adds that inaccurate statements about AI capabilities can create directors and officers exposure through shareholder litigation.

Why Investing in AI Without Investing in People Is a Costly Business Mistake

Episode 28

Monday, July 13, 2026 • Duration 30:27

Why do companies spend millions on AI and digital transformation while overlooking the people expected to make those investments successful?

In this episode of Business Technology Perspectives, I speak with Elena Varo, founder of Elevatio Tech & Culture, about why the biggest barriers to technology adoption are often human rather than technical, what leaders misunderstand about organizational change, and why companies need to invest in people as seriously as they invest in technology.

Elena brings more than a decade of experience across the technology industry, including work with Microsoft, Red Hat, CyberArk, and WSO2. Having worked across Spain, Ireland, and the UK, she has seen how leadership, workplace culture, communication, and organizational structures influence whether technology investments produce meaningful business outcomes.

A major theme throughout our conversation is the gap between buying technology and preparing a company to use it effectively. Elena argues that many businesses begin with the wrong question. They focus on which AI platform, cybersecurity product, or cloud provider to purchase before identifying the problem they need to solve and understanding whether employees have the skills, training, information, and support required to adopt new technology.

We discuss why AI is creating a leadership challenge that may be harder to solve than the technology itself. Employees want answers to practical questions about training, skills, changing roles, and expectations. When companies fail to communicate clearly or listen to their workforce, uncertainty grows, experienced employees leave, and technology adoption becomes harder.

Talent retention is another major part of the conversation. The technology industry frequently talks about skills shortages and recruiting new employees while paying far less attention to why talented people leave. Elena explains why salary alone rarely solves problems caused by unclear responsibilities, poor communication, limited career development, internal competition, and workplaces where employees do not feel safe speaking openly.

We also examine the relationship between employee well-being and long-term business performance. Constant technology change, economic uncertainty, demanding targets, and pressure to do more with fewer resources affect employees and managers alike. Companies that want people to collaborate, share knowledge, question assumptions, and develop new ideas need to create working environments where people feel supported and heard.

AI, Open Source, and the Security Challenges Few Leaders See Coming

Episode 27

Monday, June 8, 2026 • Duration 35:49

What happens when AI can write software in seconds but lacks the context to understand whether the code it creates is built on secure foundations?

In this episode of Business Tech Perspectives, I speak with Brian Fox, Co-Founder and CTO of Sonatype, about the growing pressure facing software teams as AI accelerates development while cyber threats continue to evolve. Brian brings a unique perspective from his work overseeing Maven Central and helping organizations understand the risks hidden inside modern software supply chains.

Our conversation begins with a challenge that many organizations may not fully appreciate. While AI coding assistants are becoming increasingly capable, the information they rely on can already be months old. Brian explains why that matters when selecting open source dependencies and how outdated recommendations can leave security risks buried inside applications long after they are deployed.

We also discuss the role open source software now plays in almost every application. Developers can build products faster than ever by using existing components, but that speed comes with responsibility. Brian shares why understanding what is inside your software has become a business issue as much as a technical one.

Another major topic is the expected surge in vulnerability discoveries driven by new AI capabilities. Brian warns that both attackers and security researchers now have access to tools that can identify weaknesses at unprecedented speed. The result could be a flood of vulnerability reports that challenges maintainers, vendors, and security teams alike.

The discussion also covers the changing state of vulnerability intelligence. With NIST narrowing its focus and public resources under strain, organizations may need to rethink how they gather information, assess risk, and prioritize remediation efforts.

Throughout our conversation, Brian offers practical advice for leaders who want to prepare their organizations for what comes next. From understanding software bills of materials to improving patch management processes, he explains why preparation today could make all the difference tomorrow.

Despite the challenges, Brian remains optimistic about the future. He believes AI will help developers create software faster and help the industry address years of accumulated security issues. The question is whether organizations can adapt quickly enough to keep pace with the changes already underway.

How TWG AI Is Turning Enterprise AI Into Real Business Outcomes

Episode 26

Sunday, May 31, 2026 • Duration 31:43

What happens when the biggest barrier to AI success isn't the technology itself, but the way organizations are structured to adopt it? In this episode, I sit down with Milan Cooper, Head of Product at TWG AI, a company working alongside Palantir to help enterprises rebuild core business processes around AI. With previous leadership roles at JPMorgan Chase and Accenture, Milan brings a rare perspective from the intersection of AI, risk, governance, and large-scale transformation.

Our conversation moves beyond chatbots, pilots, and proofs of concept to examine what it actually takes to make AI part of mission-critical operations. Milan explains why so many organizations remain stuck in what he calls "AI theater," measuring success through use cases rather than business value. He shares how TWG AI approaches enterprise adoption by focusing on entire value streams, helping organizations move from isolated experiments to AI-native operations that directly influence revenue, efficiency, and decision-making.

We also discuss the growing challenge of AI concentration risk, why switching between AI models could become the equivalent of performing brain surgery on an enterprise, and how organizations can avoid locking themselves into a single provider. Milan offers insights from projects with companies including Guggenheim Investments, where AI is being embedded into investment workflows to increase deal throughput and remove operational bottlenecks.

Along the way, we tackle governance, compliance, AI accountability, the future of SaaS, and why leadership conviction may be the single biggest factor determining whether an AI transformation succeeds or stalls. Milan also shares why trust remains the missing ingredient in enterprise AI adoption and what organizations need to do before employees are comfortable using AI with their most sensitive information.

If you've ever wondered why some companies are turning AI into measurable business outcomes while others remain trapped in endless experimentation, this conversation offers a candid look at what separates the two. What do you think is holding back AI adoption in your organization, technology, culture, or leadership? Share your thoughts with me.

Veritone on the Next Frontier of AI and Monetizing Multimodal Data

Episode 25

Wednesday, May 27, 2026 • Duration 24:35

What if the most valuable AI asset your organization already owns is sitting untouched inside years of video, audio, and unstructured content?

In this episode, I sit down with Sean King from Veritone to explore how organizations are transforming massive archives of content into searchable, licensable, and revenue-generating assets for the AI economy. As the executive leading Veritone’s commercial business, Sean works directly with major organizations, including the NCAA and ESPN, to help unlock value hidden inside decades of multimedia data.

We discuss why the next phase of AI will be defined by multimodal data rather than text alone, and why businesses are dramatically underestimating the value locked inside their video, audio, and image archives. Sean explains how AI is turning what was once treated as a passive storage problem into an active business asset, making unstructured content searchable, contextual, and commercially valuable at scale.

The conversation also looks at how organizations can modernize decades-old archives without becoming overwhelmed by the sheer volume of data involved. Sean shares how companies can approach AI transformation by first building scalable workflows for incoming content before tackling historical archives. From sports media and broadcasting to enterprise knowledge management, we explore how searchable multimedia data is creating entirely new opportunities for storytelling, fan engagement, licensing, and monetization.

We also get into the growing debate around intellectual property and AI training data. Sean offers a thoughtful perspective on why trust, transparency, and rights-cleared content are becoming increasingly important as AI models evolve. He explains why sustainable AI ecosystems will depend on respecting creators, fairly compensating rights holders, and ensuring enterprises know exactly where their training data originates.

The discussion then shifts toward the emerging “agentic web,” where AI systems move beyond research and begin handling tasks, workflows, and decisions autonomously. Sean argues that future competitive advantage will belong to organizations with access to the highest-quality licensed data, because the difference between an average AI agent and a truly effective one will come down to the quality, structure, and accessibility of the information they can use.

The AI Visibility Gap: Why Enterprises Still Cannot Measure What They Are Using

Episode 24

Thursday, April 9, 2026 • Duration 29:07

How can businesses make smart AI bets when they cannot even see the full picture of what is already happening inside their own organization?

In this episode of Business Tech Perspectives, I sit down with Russ Fradin, CEO of Larridin, for a conversation about one of the biggest blind spots in enterprise AI right now. While many leaders are focused on adoption, experimentation, and speed, Russ argues that a more fundamental issue is being overlooked. Companies are investing in AI at scale, but many still lack a clear view of which tools are being used, who is using them, and whether any of it is delivering measurable value.

What made this conversation so timely for me was Russ’s perspective as someone who has lived through several major waves of technology change. From digital advertising and mobile to cloud and now AI, he has seen what happens when innovation moves faster than the systems designed to manage it. In this case, the challenge is what he calls the AI visibility gap, where tools are spreading across teams faster than IT, finance, and leadership can track. That creates questions around governance and cost, but it also raises a more practical business issue. If you do not know what is being used, how do you know what is working?

We also get into why Russ believes experimentation is not the problem. In fact, he makes a strong case that organizations should be trying lots of tools right now. The issue is when those experiments happen without measurement, without accountability, and without a framework for understanding productivity and return on investment. I particularly liked his point that this is not about shutting innovation down. It is about building the right measurement, governance, and data foundations so businesses can experiment with confidence instead of chaos.

Another part of the conversation that stayed with me was the idea of identifying the people inside an organization who are already becoming dramatically more productive with AI. Russ talks about how some employees are already figuring out what great looks like, while others are still staring at a blank prompt box unsure where to begin. That creates an opportunity for leaders to stop treating AI adoption as a vague aspiration and start turning real employee behavior into repeatable playbooks that can help the wider workforce improve.

Denodo CTO Alberto Pan On The Next Evolution Of Business Intelligence

Episode 23

Monday, April 6, 2026 • Duration 21:34

What if enterprise AI could move beyond answering questions and start explaining why things are happening in your business?

In this episode of Business Tech Perspectives, I sat down with Alberto Pan, Chief Technology Officer at Denodo, to explore how AI is shifting from surface-level responses to deeper, reasoning-driven insights. As organizations wrestle with fragmented data, governance challenges, and growing expectations around AI, this conversation gets to the heart of what meaningful progress actually looks like.

At the center of our discussion is Denodo’s DeepQuery, an AI reasoning agent designed to perform complex, open-ended research across an organization’s data landscape. Alberto explains how it goes far beyond traditional approaches like retrieval augmented generation by creating research plans, analyzing patterns, and even refining its own process along the way. The result is not just faster answers, but a more complete understanding of what is really happening beneath the surface.

We also unpack what this means for business intelligence teams. Rather than manually building dashboards and reports, analysts are stepping into a new role as guides, working alongside AI systems that can gather, analyze, and present insights in minutes. It raises an interesting question about how skills, roles, and expectations will evolve as these tools become more widely adopted.

A big part of the conversation focuses on data itself. Alberto shares how Denodo’s logical data layer allows organizations to access and govern data across multiple systems in real time, without creating new silos. That foundation becomes even more important as AI adoption accelerates, especially when accuracy, context, and explainability are all under increasing scrutiny.

We also touch on the growing importance of transparency in AI. With concerns around black box decision making continuing to rise, Alberto explains how DeepQuery provides full traceability, showing exactly how insights are generated and where the underlying data comes from. It is a practical step toward building trust in AI systems at scale.

Looking ahead, this episode offers a clear view into how research-driven AI could reshape decision making across industries. From finance to healthcare, the ability to move from static reports to dynamic, AI-assisted investigation has the potential to change how organizations operate on a daily basis.

Business Technology Perspectives – Details, episodes & analysis

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Business Technology Perspectives – Details, episodes & analysis

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