Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠https://decisionintelligencelab.substack.com/⁠.

This episode explores how organizations can successfully adopt generative AI by focusing less on tools and more on operating models, decision-making, and alignment.

Justin Trombold, President & Founder, Antesyn Advisors, shares his journey from academia to consulting and explains why most companies struggle with GenAI—not because of technology—but due to misaligned strategy, poor processes, and unrealistic expectations.

The conversation centers on a GenAI readiness framework with five dimensions:

- Strategic alignment

- Cross-functional collaboration

- End-user proficiency

- Scalability & adaptability

- Governance

Chapters

0:00 - Preview & Introduction

0:41 - Meet Justin Trombold

5:58 - Readiness Assessment Explained

7:51 - Strategic Alignment Deep Dive

10:06 - Leadership Blind Spots & Overestimating Alignment

12:49 - GenAI Strategy vs Reality

17:28 - Experimentation & Guardrails

21:00 - Real Risks (Hallucinations & Poor Inputs)

24:21 - Biggest Organizational Blind Spot

27:33 - GenAI as R&D, not IT

30:23 - Don’t Approach Vendors without Defined Problems

36:30 - Closing Thoughts

Are you ready to unlock the transformative potential of Generative AI (GenAI) for your organization? Test your organization’s GenAI Readiness at - https://www.antesynadvisors.com/blank-3

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with the guest

Justin Trombold: ⁠https://www.linkedin.com/in/trombold/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠https://decisionintelligencelab.substack.com/⁠.

In this episode of the Decision Intelligence Lab Podcast, Vijay Mehrotra and Michael Watson sit down with Evan Shellshear, Principal at BCGX (BCG’s technology innovation arm) and co-author of Why Data Science Projects Fail.

Evan shares lessons from working on large-scale AI and optimization projects across industries like mining, supply chains, and retail, including a fascinating case study with Rio Tinto’s massive autonomous mining operation in Western Australia.

The conversation dives into why most AI and data science projects fail, the critical role of organizational change, and how companies can move beyond “pilot purgatory” to deliver real business value from AI.

Evan also explains BCG’s 70-20-10 rule for AI transformations, why executives should focus on value before technology, and how successful organizations redesign their operating models to fully leverage AI.

If you work in data science, AI, operations research, or digital transformation, this episode offers practical insights from real-world deployments at a global scale.

Chapters

0:00 - Preview & Introduction

0:48 - Meet Evan and BCGX's Overview

3:09 - The Scale of Rio Tinto’s Mining Operations

5:15 - Tackling Large-Scale Scheduling Problems

7:04 - The 70-20-10 Rule of AI Projects

11:30 - Combining Technical and Consulting Teams

14:28 - Proving Business Value Before Building Tools

17:36 - Escaping AI Pilot Purgatory

20:40 - Deploy, Reshape, and Invent Framework

22:45 - Balancing Speed and Transformation

25:35 - Maintaining AI Systems Long Term

28:48 - The Problem with Cheap Consulting

31:41 - Building Better Algorithms When Value Is Clear

33:44 - Retail Pricing Optimization Case Study

38:38 - Book Recommendations and Closing Thoughts

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with the guest

Evan Shellshear: ⁠https://www.linkedin.com/in/eshellshear/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Lab podcast, Zohar Strinka discusses the concept of meta-problems in problem solving, emphasizing the importance of selecting the right problem to tackle. She shares real-world examples of how solving the wrong problem can lead to disastrous outcomes and highlights the iterative nature of problem-solving in analytics. Zohar also explores the distinction between problem selection and problem definition, the challenges of consulting, and the significance of focusing on outcomes in analytics. She provides valuable insights for young professionals in the field and discusses her involvement in the Analytics Plus Conference and the Edelman competition.

Chapters

0:00 - Preview

0:31 - Meet Zohar Strinka

1:32 - Understanding Meta Problems in Problem Solving

2:31 - The Importance of Problem Selection vs. Problem Definition

3:45 - Navigating Client Expectations in Consulting

5:36 - The Dilemma of Problem Scope in Decision Making

10:41 - Transitioning to Independent Consulting

12:46 - Budgeting for Iterative Problem Solving

14:28 - Iterative Problem Solving and Agile Methodologies

16:57 - The Role of Analytics Professionals in Client Relationships

18:55 - Value of Analytics Plus Conference and Edelman Competition

20:50 - Advice for Young Analytics Professionals

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with the guest

Zohar Strinka: https://www.linkedin.com/in/zohar-strinka/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson engage with Warren Lieberman, President, Veritec Solutions, to explore the evolution of revenue optimization, particularly in the self-storage industry. Warren shares his journey from high school computer science to operations research at Yale, and his pivotal role in revenue management at American Airlines. He discusses the founding of Veritec Solutions, its transition from consulting to software development, and the unique challenges and strategies involved in pricing for self-storage. The conversation highlights the importance of understanding consumer psychology, the need for user-friendly systems, and the balance between product development and customer needs.

Chapters

0:00 - Preview

0:42 - Meet Warren Lieberman

1:28 - Warren's Journey into Optimization and Analytics

6:16 - Revenue Management at American Airlines

8:40 - The Birth of Veritec Solutions

11:35 - Transitioning to Self-Storage Revenue Management

13:10 - Innovations in Self-Storage Revenue Management

20:16 - Pricing Strategies

22:40 - Value Pricing

29:40 - User Adoption and System Integration

34:54 - Balancing Product Development and Customer Needs

37:39 - Transition from Services to Software

42:00 - Closing Thoughts and Recommendations

Key Takeaways:

- Self-storage revenue management differs significantly from traditional industries due to low transaction volumes.

- Value pricing in self-storage has transformed the industry.

- Consumer psychology plays a crucial role in pricing strategies.

- The transition from services to software requires careful consideration of customer needs.

Veritech's approach is to customize software to fit the client's business processes.

- Importance of user adoption and understanding system recommendations, continuous innovation, and enhancement of their software.

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Warren Lieberman: ⁠https://www.linkedin.com/in/warren-lieberman-bb783/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Laboratory podcast, Dr. Filippo Focacci, co-founder and CEO of DecisionBrain, shares insights into the world of optimization, the evolution of their DB Gene platform, and the impact of generative AI on decision-making processes. The conversation covers Filippo's journey into optimization, the strengths of Decision Brain's team, and the technological approaches they employ, including constraint programming and mixed integer programming. Real-time applications and case studies illustrate the practical implications of their work, while discussions on team dynamics and the future of AI highlight the evolving landscape of decision intelligence.

Chapters

0:00 - Preview

0:44 - Meet Filippo Focacci

4:30 - Learn about Decision Brain

6:30 - The Strengths of Decision Brain

9:13 - Technological Approaches in Optimization

11:38 - The Evolution of Gene DB

16:45 - Real-Time Applications of Decision Brain

21:12 - Team Roles & Responsibilities in Optimization Projects

23:00 - Evolving the Gene Platform

27:30 - Implementing Optimization in Practice

30:30 - The Impact of Generative AI on Business

32:52 - Filippo's Recommendations

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Filippo Focacci: ⁠https://www.linkedin.com/in/filippo-focacci-35a5091/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

Recommendations:

Follow Yves Caseau: https://www.linkedin.com/in/ycaseau/

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson interview Jon Petersen, Vice President of Data at Archer, an aerospace company specializing in electric vertical takeoff and landing (EVTOL) aircraft. The conversation explores Archer's mission to revolutionize urban mobility, the critical role of data science in aircraft design and operations, and the collaborative efforts between data science and engineering teams. Petersen shares insights on the challenges of data-driven decision-making, the importance of building a diverse and effective team, and strategies for securing support for data science initiatives.

Chapters

0:00 – Preview

0:33 – Meet Jon Petersen

1:37 – Revolutionizing Urban Mobility

2:38 – Managing Aircraft Noise

4:23 – The Role of Data Science at Archer

9:38 – Collaborating with Engineering Teams

13:50 – Trip Density Distribution & Destination Origins

17:53 – Challenges in Data-Driven Decision-Making

19:48 – Team Building & Collaboration

28:53 – Scaling the Manufacturing Facility

30:33 – Funding & Justifying Data Science Initiatives

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with the guest

Jon Petersen: ⁠https://www.linkedin.com/in/jon-petersen-ba623950/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

Recommendations from Guest:

- Misbehaving: The Making of Behavioral Economics by Richard H. Thaler

- Thinking, Fast and Slow by Daniel Kahneman

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson discuss the concept of DecisionOps with Carolyn Mooney, Co-Founder & CEO of Nextmv. They discuss the evolution of decision-making systems, the importance of simulation and testing, and how to build trust within organizations. Carolyn shares insights on hierarchical optimization, the responsibilities of different roles in decision systems, and the latest trends in decision-making technologies, including the impact of generative AI. The conversation emphasizes the need for clear communication and collaboration between technical teams and operational users to enhance decision-making processes.

Chapters

0:00 - Preview

0:38 - Meet Carolyn Mooney

1:38 - What is DecisionOps?

3:44 - The Journey of Nextmv

6:20 - Applications of DecisionOps in Various Industries

8:30 - Simulation and Testing in DecisionOps

12:37 - Building Trust Through Testing and User Interaction

14:40 - Hierarchical Optimization and Decision Making

18:21 - Ownership and Responsibilities in DecisionOps

22:11 - Working on the Product With Clients

31:28 - Trends in Decision-Making Technologies

33:54 - Final Thoughts and Resources

Takeaways

* Decision Ops is essential for getting decisions live in organizations.

* Simulation helps in understanding the impact of decisions.

* Testing capabilities should be integrated with CI/CD platforms.

* Hierarchical optimization allows for balancing cost and service levels.

* Ownership of decision-making processes should involve operational teams.

* Prototyping and user feedback are key to successful decision systems.

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Carolyn Mooney: ⁠https://www.linkedin.com/in/carolyn-m-mooney/

Nextmv: https://www.nextmv.io/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson engage with Karthik Gada, a CFO and author, to explore the accelerating rate of change in technology and its implications for businesses, economies, and individuals. Gada discusses how technological advancements are interconnected and how they impact various sectors, including healthcare and education. He emphasizes the importance of adapting to rapid changes, fostering an entrepreneurial mindset, and leveraging individual strengths to navigate the complexities of the modern world. The conversation also touches on the challenges posed by institutional rigidity and the need for effective leadership in an era of constant disruption.

Chapters

0:00 - Preview

0:40 - Meet Kartik Gada

1:40 - Understanding Technological Disruption & Accelerating Pace of Change

4:05 - The Impact of Cost Reduction on Technology

7:40 - Bottlenecks in Healthcare and Education

12:30 - How Individuals Respond to Change

15:48 - Embracing Change and Entrepreneurial Mindset

20:50 - Education and Career Pathways in a Changing World

24:15 - Managing Economic Complexity with AI

28:20 - Leadership in an Era of Rapid Change

31:50 - Closing Thoughts

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Kartik Gada: https://www.linkedin.com/in/kartikgada/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠/

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Laboratory podcast, Borja Menéndez, Lead Operations Research Engineer at Trucksters who also earned his PhD in OR, joins us to discuss his journey into the field of operations research, the impact of his work at Trucksters, and the importance of integrating data science with traditional optimization methods. He shares insights on the relay optimization problem, the challenges of implementing algorithms in logistics, and the significance of user-centric design in developing effective solutions. Borja also reflects on the transition of his blog, Feasible, to a paid model and the future of operations research in the context of Gen.AI.

Chapters

00:00 Introduction to Operations Research and Borja Menendez

01:14 The Journey into Operations Research

04:39 Real-World Applications of Operations Research

07:03 Understanding Trucksters and Relay Optimization

12:48 Challenges in Relay Optimization

16:21 Non-Technical Aspects of Implementing OR

20:15 Testing and Quality Assurance in Optimization Models

25:22 User-Centric Design in Optimization Solutions

27:47 Transitioning to a Paid Model for Feasible

29:32 Integrating Data Science with Operations Research

35:52 The Future of Operations Research and Gen.AI

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Check out Borja Menéndez's Newsletter: https://www.feasible.club/

Connect with guest

Borja Menéndez: ⁠https://www.linkedin.com/in/borjamenendezmoreno/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson engage with Ben Alamar, a PhD economist and sports analytics expert. They explore the evolution of sports analytics, the impact of motion tracking technology, and the challenges of integrating data into decision-making processes in sports. Ben shares personal anecdotes about his experiences with teams, the importance of understanding risk, and the role of leadership in fostering a data-driven culture. Ben offers advice for aspiring sports analysts and concludes with recommendations for further reading.

Chapters

00:00 - Preview

1:30 - Meet Ben Alamar

3:21 - The Evolution of Sports Analytics

7:04 - Motion Tracking and Its Impact

10:03 - Translating Sports Analytics to Business

12:00 - Behavior Change and Mindset Shifts

14:40 - Risk Perception in Decision Making

19:16 - Fan Engagement and Business Objectives

20:20 - Draft Decisions: Case Studies

28:00 - Fostering Evidence-Based Culture in Sports

33:43 - Advice for Aspiring Sports Analysts

36:46 - Recommendations for Further Reading

Book Recommendations:

- "Basketball Beyond Paper" by Dean Oliver

- "Ballistic" by Henry Abbott

Check out P3 Applied Sports Science: https://www.p3.md/

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Benjamin Alamar: ⁠https://www.linkedin.com/in/benalamar/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode, Mike & Vijay sit down with Peter Cacioppi, Principal Operations Research Scientist at Decision Spot to dive into the intersection of model building and production software development. Peter shares insights on the tools he's developed, the role of Python in the Gurobi ecosystem, and the transformative impact of LLMs on software development workflows, particularly in real-time optimization. Tune in to explore the challenges and innovations in bringing production models to life.

Chapters

0:00 - Preview

1:20 - Meet Peter Caccioppi

1:47 - Integrating Computer Science in OR

7:16 - Tidy, Tested, and Safe

16:55 - The History & Utitlity of Ticdat

31:21 - GenAI's Role in OR and Software Development

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Peter Cacioppi: ⁠https://www.linkedin.com/in/peter-cacioppi-86519210/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Lab podcast, host Vijay Mehrotra and co-host Michael Watson engage with Zahir Balaporia, an expert in analytics and decision-making. They explore the concepts of the 'last mile' and 'extra mile', and the politics of analytics. Zahir also shares real-world examples of how organizational dynamics can impact the success of analytics initiatives. The conversation highlights the significance of beliefs in decision-making, the necessity of building trust, and the challenges of change management in the face of evolving technologies like AI.

Chapters

0:00 - Preview

1:00 - Meet Zahir Balaporia

2:00 - The Last Mile & Extra Mile Problems in Supply Chain

7:24 - Real-World Examples of Analytics Failures

11:54 - Building Trust and Relationships in Analytics

21:25 - Cultural Dynamics in Technology Adoption

29:15 - The Future of Work with AI and Change Management

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Zahir Balaporia: ⁠https://www.linkedin.com/in/zahirbalaporia/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠https://decisionintelligencelab.substack.com/⁠.

In this episode of the Decision Intelligence Lab, hosts Vijay Mehrotra and Michael Watson sit down with Rich Savoie, CEO and co-founder of Adiona, to explore one of the toughest problems in modern logistics: last-mile delivery optimization.

Rich shares his unconventional journey from electrical engineering and medical devices to logistics technology. He discusses the intricate challenges of last-mile delivery, emphasizing how data science and AI are used to make supply chains more cost-effective and environmentally friendly. The conversation dives into the realities of building and commercializing enterprise software, navigating customer demands, and managing the trust gap with non-technical users in the logistics sector.

Beyond logistics, Rich reveals his journey from medical device engineering to startup founder, and the lessons he learned about sales, perception, cybersecurity, and enterprise-grade reliability.

Chapters

0:00 - Preview & Introduction

1:00 - Meet Rich Savoie

1:40 - Overview of Adiona

3:30 - Why the Last Mile Is So Hard

6:10 - The Optimization Stack: MIP, ML & Clustering

9:40 - Who Buys Optimization Software?

11:45 - Customer-Led Approach for Product Development

14:04 - The SaaS Dilemma of Modularization & Revenue Optimization

19:00 - Building Trust & Overcoming Resistance in Non-tech Operations Environments

21:41 - From Commute Optimization to Logistics AI

28:30 - Founder-Market Fit & Getting Real Data

34:10 - Lessons from the Medical Devices Industry

36:10 - Perception & Selling to Enterprise

38:15 - Tools, Books & Resources

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with the guest

Rich Savoie: ⁠https://www.linkedin.com/in/richsavoie/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Lab podcast, Ganesh Ramakrishna joins our hosts Vijay & Mike to share his journey from co-founding Opex Analytics to launching Lyric. He discusses the challenges of building a software company, the importance of fundraising with scaled ambitions, and the critical role of product-market fit. Ganesh emphasizes the need for an AI-first mindset in supply chain intelligence and the responsibilities that come with venture capital. He also highlights Lyric's innovative approach to solving supply chain problems through a comprehensive platform that integrates data, algorithms, and user experience.

Chapters

00:00 Introduction to Ganesh Ramakrishna

03:05 The Birth of Lyric: Vision and Challenges

06:05 Navigating the Startup Landscape: Fundraising Insights

08:59 Building a Software Company: Early Stages and Funding

12:07 Achieving Product-Market Fit: Lessons Learned

14:58 The Role of Venture Capital: Control and Responsibility

17:58 Supply Chain Challenges: Lyric's Approach

21:02 AI-First Mindset: The Future of Supply Chain Intelligence

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Ganesh Ramakrishna: ⁠https://www.linkedin.com/in/ganeshatalk/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this podcast episode, Michael Watson & Vijay Mehrotra interviews Harley Davis, Founder and CEO of Athena Decision Systems. They discuss the rise of generative AI and its integration with business rules, emphasizing the importance of reliable decision-making in mission-critical applications. Harley explains the limitations of LLMs, referring to them as 'stochastic parrots,' and highlights the challenges of scaling these technologies while ensuring privacy and compliance. The conversation also covers the role of RAG in enhancing LLM performance and shares real-world use cases where AI can improve administrative processes.

Chapters

0:00 - Introduction & Preview

1:19 - Meet Harley Davis & Athena Decision Systems

4:30 - Business Rules in Mission-Critical Applications

9:23 - Understanding LLMs and Their Limitations

12:05 - Integrating LLMs with Business Rules

14:12 - Challenges in Scaling LLMs and Privacy Concerns

17:34 - The Role of RAG in Enhancing LLM Performance

20:15 - Deploying LLMs in Real-World Applications

24:24 - Improving Administrative Processes with AI

27:10 - Final Thoughts and Recommendations

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Harley Davis: ⁠https://www.linkedin.com/in/harleydavis/

Athena Decision Systems: https://athenadecisions.com/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes. For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

We live in a world filled where our lives and livelihoods increasingly depend on complex systems.  In this episode, Professor Laura Albert from the University of Wisconsin discusses a recent paper on the “Grand Challenges in Industrial and Systems Engineering” and offers some suggestions on how educational programs must evolve in the age of Artificial Intelligence.

Timestamps

0:00 - Preview & Introduction

3:20 - Definition & Importance of Resiliency

5:38 - The Need for Big Thinking in the Field

6:45 - Significance of case studies in bridging academia and industry needs9:00 - Rethinking educational approaches in light of GenAI advancements

11:23 - The Threat of Irrelevance for Industrial & Systems Engineering

12:55 - Challenges of Teaching with GenAI

13:55 - Lessons from COVID-19: Supply Chain Resiliency

16:32 - AI in Decision-Making and Governance

18:15 - Identifying and managing trade-offs

21:24 - Barriers to Impact in Data Science and OR Fields

23:45 - Cybersecurity as an Opportunity for Industrial and Systems Engineering & Operational Research

26:10 - The 'Equifax Breach Story'

29:10 - Laura Albert’s blog

Follow the show

Apple: ⁠⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠⁠

Spotify: ⁠⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠⁠

Connect with guest

Laura Albert: https://www.linkedin.com/in/lauraalbertphd

Laura's Blog: https://punkrockor.com/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠⁠

Prof. Michael Watson (Northwestern University): ⁠⁠https://www.linkedin.com/in/michael-watson-07600a1⁠⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at  decisionintelligencepodcast@gmail.com

Keywords:

Industrial Engineering, Grand Challenges, Resiliency, Education, AI, Cybersecurity, Supply Chain, Governance, Decision-Making, Operations Research, Industrial and Systems Engineering, Operations Research, Resiliency, Complex Systems, Cybersecurity, Supply Chain, Education, Case Studies, Artificial Intelligence, Generative AI (GenAI), AI Governance, Engineering Curriculum, AI Ethics, Human-AI Teaming, Systems Thinking, Optimality, AI Audits, Workforce Training, Trade-offs, Data Science Projects, Impact Measurement, Public Outreach, Software Supply Chains, Power Systems, Digital Transformation, Decision-Making, Risk Management, Academic Blogging

In this episode’ of the Decision Intelligence Lab podcast we have Doug Gray, Director of Data Science at Walmart Global Tech and co-author of Why Data Science Project Fail. Whether you are a business leader trying to capture value from your analytic investments, a practicing data scientist, or a student in a technical field aspiring to bring your skills to solve real world problems, this episode will provide you with valuable insights, examples, suggestions, and inspiration.

Chapters

0:00 - Preview, & Introductions

2:30 - Birth of Why Data Science Projects Fail?

4:36 - Top 5 People-Related Factors in Data Science Project Failure

13:00 - Cost and Complexity Beyond the Model

20:34 - Business User Resistance to “Black Box” Models

26:17 - Doug’s Estimate of an Optimal Failure Rate

31:20 - Organizational Maturity and Its Impact on Success

36:50 - Doug's Recommendations

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Doug Gray: https://www.linkedin.com/in/doug-gray-06bb4a4/

Books:

1. Why Data Science Projects Fail2:  The Art of Data Science: A Practitioner's GuideConnect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

Additional Notes:

BOOK: All in on AI

ARTICLE:  "$100 of Implementation for $1 of modeling"

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at decisionintelligencepodcast@gmail.com

Keywords

Project failure, analytics, organizational maturity, change management, Doug Gray, decision science, business value, success rates, data science projects, AI implementation, analytics failure rates, organizational maturity, change management, communication challenges, project management, unrealistic expectations, technology focus, business value, key performance indicators (KPIs), stakeholder engagement, decision intelligence, model deployment, production systems, data pipelines, integration challenges, testing and validation, airline operations research, supply chain optimization, cost avoidance, AI adoption in business, center of excellence, subject matter experts, user interface design, interactive optimization, prompt engineering, business process alignment, success rates in analytics, learning from failure

In the first episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson interview Dr. Irv Lustig from Princeton Consultants about the Princeton 20, a framework for identifying and mitigating AI and Optimization project risks. This evaluation framework includes both business and technical factors, and is a valuable methodology for project discovery, effective selling, and building and maintaining trust with clients.

Chapters

00:00 - Preview, & Introductions

02:20 - The Princeton Twenty Framework Overview

3:24 - How the Princeton Twenty Is Used In Practice

7:10 - Client Perspectives on Risk Management

8:27 - Overselling in Optimization and AI Projects

11:11 - Understanding the Decision Scope (Environmental Factor 5)

16:43 - Exploring Value Proposition (Environmental Factor 7)

21:45 - Process Maturity (Technical Factor 10) and Ongoing Maintenance

25:00 - Last Mile Problems in Decision Making

30:33 - Broader Impact & Risk Tolerance

35:35 - Irv's Recommendations

Follow the show

Apple: https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064

Spotify: https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b

Connect with guest

Irv Lustig: https://www.linkedin.com/in/irv-lustig/

Princeton 20: https://princetonoptimization.com/ai-project-management/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): https://www.linkedin.com/in/vijay-mehrotra-ba9498/

Prof. Michael Watson (Northwestern University): https://www.linkedin.com/in/michael-watson-07600a1

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at decisionintelligencepodcast@gmail.com

Keywords

Princeton 20, AI deployment, optimization, risk management, decision making, data science, consulting, project success, value proposition, process maturity, decision science, project management, technical factors, sales tool, client engagement, cost benefit analysis, value proposition, decision scope, process maturity, machine learning, change management, agile methodology, human-in-the-loop optimization, black box optimization, business problem framing, analytics framework, certified analytics professional, project delivery, solution deployment, operational scheduling, strategic consulting, informs analytics.

The Decision Intelligence Lab podcast explores the practical challenges of implementing data science, analytics, and AI projects, shining a light on many diverse links in the information value chain that transform raw data into real business outcomes. 

Each episode features conversations with leaders from technology firms, consultancies, enterprise data science teams, and academia.

Hosted by Professor Michael Watson (Northwestern University) and Professor Vijay Mehrotra (University of San Francisco) — both seasoned entrepreneurs, consultants, and researchers — this podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data for smarter decision-making.

Subscribe and follow to join us as we uncover how decision intelligence is reshaping the future of business and technology. You can connect with us at  decisionintelligencepodcast@gmail.com

Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠https://decisionintelligencelab.substack.com/⁠.

In this episode of the Decision Intelligence Lab Podcast, hosts Michael Watson and Vijay welcome John Brandon Elam, Decision Systems Leader at Toyota and co-founder of Bit Bros. John shares deep, practical insights on why decision systems in large organizations often become “orphans,” how fragmented ownership across business, IT, and analytics creates risk, and what it takes to build scalable, repeatable decision-making systems. Drawing on experience at Toyota and AT&T (including work on FirstNet for first responders), the conversation explores decision ownership, incentives, change management, technical debt, and why simplicity must be earned.

This episode is a must-listen for leaders, product managers, data scientists, and anyone working at the intersection of analytics, technology, and real-world decision-making.

Chapters

0:00 - Preview

0:45 - Meet John Brandon Elam

1:55 - What are “orphaned” decision systems?

4:55 - Why decision ownership breaks down in large companies

7:28 - Who should own decision systems? The case for product ownership

10:21 - Preparing cross-functional leaders for analytics-driven decisions

15:20 - Lessons from AT&T’s FirstNet and mission-critical systems

20:05 - Adoption and change management at Toyota: “go and see”

25:01 - Trust, influence, and why being likable matters

27:01 - KISS 2.0: Keep it simple to start

30:02 - Rethinking technical debt

31:32 - Aligning incentives between operations and transformation teams

37:15 - From data to decisions: building a “Decision Factory”

39:29 - Bit Bros, books, and connect with John

40:42 - Closing remarks

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

John Brandon Elam: ⁠https://www.linkedin.com/in/johnbelam/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠https://decisionintelligencelab.substack.com/⁠.

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson welcome Linda Crowe and Benjamin Baer from DecideWise, a community focused on data, decision intelligence, and AI. The conversation explores the purpose of DecideWise and the importance of community in bridging gaps between vendors and buyers in the decision intelligence space. They discuss the challenges of implementing decision intelligence, the evolving landscape of vendors, and the significance of audibility and compliance in decision-making processes. The episode concludes with insights on the future of decision intelligence and the value of community engagement.

Chapters

0:00 - Preview & Introduction

0:46 - Meet Linda & Benjamin

3:50 - Understanding DecideWise

6:40 - Bridging Gaps in Decision Intelligence

9:49 - The Role of Community in Technology Marketing

15:25 - The Evolution of Decision Intelligence Across Industries

18:15 - Vendor Landscape & Categorization

21:56 - Common Implementation Mistakes

26:00 - Audibility, Traceability, and Risk

33:00 - Lessons Learned from Early Community Engagement

39:00 - Conclusion and Call to Action

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with the guest

Benjamin Baer: ⁠https://www.linkedin.com/in/benjaminbaer/

Linda Crowe: https://www.linkedin.com/in/llcrowe/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at https://decisionintelligencelab.substack.com/.

In this episode, hosts Vijay Mehrotra and Michael Watson reflect on the first year of the Decision Intelligence podcast, discussing the evolution of the podcast, key themes from their guests, and insights into decision intelligence, data science, and AI. They explore the importance of trust and collaboration in decision-making processes, the role of data in shaping decisions, and the challenges of project management in data science. The conversation also touches on the future of decision intelligence and the impact of generative AI on business practices.

Chapters

0:00 Preview

Reflecting on the Journey of Decision Intelligence Podcast

3:09 The Birth of the Podcast: A Personal Story

5:52 Exploring Decision Intelligence: Insights from Guests

8:47 The Role of Data in Decision Making

11:58 Project Risks and Failures in Data Science

15:14 Testing and Implementation of Models

17:57 The Value Chain of Decision Intelligence

20:53 Innovations in AI and Decision Making

24:08 Looking Ahead: The Future of Decision Intelligence

27:03 The Importance of Trust in AI

29:57 The Role of Humans in Decision Processes

32:49 The Upcoming Book: Capturing Business Value from Decision Intelligence

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

Mike's blog: https://miketalksai.substack.com/

Resources:

Advent of OR - https://adventofor.com

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode, Dr. Lorien Pratt discusses the concept of Decision Intelligence (DI), its importance in bridging the gap between data and decision-making, and how it evolves from decision engineering. She emphasizes the need for stakeholder alignment, the role of data in decision-making, and the importance of understanding the context of decisions. The conversation also touches on the integration of various technologies and the future of DI in a rapidly changing world.

Chapters

0:00 - Preview

0:28 - Meet Dr. Lorien Pratt

1:43 - Defining Decision Intelligence

2:28 - The Gap Between Data and Decision-Making

5:05 - The Evolution from Decision Engineering to Decision Intelligence

9:28 - Integrating Operations Research with Decision Intelligence

14:14 - Pricing optimization, diffuse objectives & cross-silo interactions

16:44 - Two Meanings of “Decision”

18:55 - Incorporating Uncertainty in Decision-Making

20:54 - The Dangers of Over-Engineering Models

25:25 - Lack of a Shared Blueprint & The Need for Alignment

33:20 - Learn about Quantellia

33:55 - The Inflection Point

40:55 - Resources: DIHandbook.com & GettingStartedWithDI.com

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with the guest

Dr. Lorien Pratt: ⁠https://www.linkedin.com/in/lorienpratt/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this special holiday episode of the Decision Intelligence Lab Podcast, Vijay welcomes back the show’s first-ever returning guests: Borja Menéndez, creator of Advent of OR, and Zohar Strinka, a lead contributor to the INFORMS Analytics Framework. Together, they explore two major initiatives released in 2024 that aim to reshape how the next generation of operations research (OR) and analytics professionals learn and practice.

Together, the guests and host dive deep into why OR is NOT just math, why curiosity is essential, how user engagement changes everything, and how practitioners learn to build systems that actually solve decision problems — not just elegant models.

Timestamps

0:00 - Preview & Intro

1:22 - What Is the Advent of OR?

3:45 - Introducing the INFORMS Analytics Framework

6:06 - OR isn't Just Math

10:10 - Skills Developed in the 2025 Advent of OR

12:10 - How Advent of OR Supports CAP Prep

14:21 - Why OR Must Teach Systems & Engineering

16:20 - The Consultant View: Building Real Systems

18:00 - Experiencing & Becoming a User Yourself

22:53 - Curiosity as a Core OR Skill

23:55 - How Borja Designs Advent of OR Challenges

26:00 - How to Learn More About the INFORMS Framework & CAP

27:22 - How to Join Advent of OR 2025

29:00 - Closing Thoughts

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Resources Mentioned:

1. Advent of OR - Sign-up page for the annual 24-day challenge - https://adventofor.com

2. INFORMS Analytics Framework - https://www.informs.org/Professional-Development/Professional-Development-Classes/INFORMS-Analytics-Framework

3. Certified Analytics Professional (CAP) Certification - https://www.certifiedanalytics.org/

4. Decision Intelligence Lab Podcast (Substack) - https://decisionintelligencelab.substack.com

Connect with the guest

Borja Menéndez Moreno: ⁠https://www.linkedin.com/in/borjamenendezmoreno/

Zohar Strinka: https://www.linkedin.com/in/zohar-strinka/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Mike Watson engage with guests Warren Powell, Co-Founder of Optimal Dynamics, and Adam DeJans Jr., Technical Account Manager at Gurobi Optimization, to explore the intricacies of decision-making in uncertain environments. The conversation delves into the importance of sequential decision optimization, the role of uncertainty in supply chain management, and the need for democratizing optimization techniques. The guests share their experiences in bridging the gap between academic theories and practical applications, emphasizing the significance of building trust and understanding in decision-making processes. They also discuss the potential of LLMs in enhancing accessibility to optimization tools, ultimately aiming to empower a broader audience in making informed decisions.

Chapters

0:00 - Preview

0:31 - Meet Warren Powell & Adam DeJans Jr.

4:45 - Understanding Sequential Decision Optimization

9:30 - Common Sense in Decision-Making

11:50 - Incorporating Uncertainty in Models

15:45 - The Importance of Trust in Decision-Making

19:18 - Framework for Sequential Decision Analytics

22:44 - Collaboration Between Academia and Industry

24:49 - State Transitions & State Variables

29:40 - Human Decision-Making Challenges

34:40 - Democratizing Optimization & The Role of LLMs

Takeaways

- Uncertainty must be incorporated into decision-making frameworks.

- Formalizing common sense can enhance understanding of complex problems.

- The role of state transitions is vital in decision-making processes.

- Democratizing optimization can empower non-experts to engage with complex problems.

- LLMs can assist in modeling and interpreting optimization problems.

- Understanding metrics, decisions, and uncertainties is foundational for effective problem-solving.

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Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

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Connect with guest

Warren Powell: ⁠https://www.linkedin.com/in/warrenbpowell/

Adam DeJans Jr.: https://www.linkedin.com/in/addejans/

Referenced:

- On State Variables: https://castle.princeton.edu/statevariables/

- "Sequential Decision Analysis" by Warren Powell (2022)

- "Reinforcement Learning and Stochastic Optimization" by Warren Powell

- "You Got Your Data Job, Now What?" co-authored by Adam DeJans Jr.

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Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

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About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

How Call Centers Shape Patient Access

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson welcome Peyton Fry, founder of Glass Raven, to discuss the intricacies of healthcare operations, particularly focusing on call centers and patient access. Peyton shares his journey from working in call centers to consulting healthcare systems on optimizing operations. The conversation delves into the importance of metrics, the challenges of forecasting and staffing, and how call centers can significantly impact patient outcomes. Peyton emphasizes the need for clarity in metrics and the importance of building relationships in the healthcare industry for those looking to enter the field.

Chapters

0:00 - Preview & Introduction

1:03 - Meet Peyton Fry

5:17 - Understanding Metrics in Call Center Operations

7:40 - Challenges in Forecasting and Staffing

15:04 - Navigating Bureaucracy & Driving Change in Healthcare

21:50 - Connecting Call Centers to Patient Outcomes

26:01 - Advice for Entering the Healthcare Field

30:11 - The Role of Medical Records in Call Center Efficiency

32:45 - Case Study: 40-Minute Hold Time Eliminated

34:49 - Final Thoughts and Key Takeaways

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Peyton Fry: ⁠https://www.linkedin.com/in/peyton-fry-mha-a4356862

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Meinolf Sellmann, computer scientist, entrepreneur, and founder/CEO of InsideOpt, joins the Decision Intelligence Lab podcast with hosts Mike Watson and Vijay Mehrotra. Meinolf built MIP solvers at Bell Labs, IBM, and GE before launching his startup, InsideOpt.

The conversation starts with a 1998 story: George Nemhauser named three open challenges in operations research- timely decision support, multi-objective optimization, and decision-making under uncertainty. Nearly 30 years later, according to the 2025 NSF DECIDE workshop, the same problems are critical to national security and competitiveness.

Meinolf explains why machine learning "hit a nerve" but couldn't deliver perfect forecasts, why optimization is seeing renewed interest, and how primal solvers attack highly combinatorial problems under uncertainty. He covers jettisoning dual bounds, scaling to 1,000 cores with ML-guided search operators, and beating Gurobi by a factor of 1,000 on quadratic assignment (Taillard instances). He shares competition wins (MaxSAT 2016, AI for TSP 2021 at IJCAI), a real coffee-roasting scheduling story, and three principles for better decision-making.

What You'll Learn

- Why George Nemhauser's 1998 challenges remain unsolved today.

- The difference between primal solvers and dual solvers, and why dual bounds limit you.

- Why perfect forecasts are impossible and what to do with residual uncertainty.

- How machine learning guides search

- Why a primal solver scales to 1,000 cores while MIP heuristics stall.

- How Seeker beat Gurobi by 1,000x on quadratic assignment (Taillard instances).

- Why the right tool beats raw algorithmic improvement.

- Bridging the gap between a well-shaped technical problem and the business customer's real problem.

- The coffee-roasting scheduling story — why MIP failed and a primal solver won.

- Three rules for good decision-making

- Why risk mitigation matters more than expected value (gambler's ruin, UPS fleet scenarios).

Timestamps

0:00 - Preview & Introduction

0:52 - Meet Meinolf Sellmann, InsideOpt

1:29 - The 1998 George Nemhauser story: Three OR challenges

3:21 - Multi-objective optimization: the three canonical approaches and why they fail

6:20 - Why renewed focus on decision-making after the AI/ML wave

7:43 - Perfect forecasts are impossible: the sushi example

9:50 - Solving combinatorially complex problems under uncertainty

12:10 - What is a primal solver vs. a dual solver?

14:16 - Technical problem vs. the business customer's real problem

16:45 - Jettisoning bounds; 1,000 cores; ML-guided search

19:22 - Machine learning as counting cards in blackjack

22:05 - Hardware vs. algorithms; beating Gurobi 1,000x on quadratic assignment

23:43 - Why leave the big labs and start a company

25:27 - MaxSAT 2016 win; the self-learning solver

27:29 - Evolving view of good decision-making: three principles

31:20 - Where to find InsideOpt and Seeker

35:40 - The Coffee-Roasting Scheduling Story

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Meinolf Sellmann: ⁠https://www.linkedin.com/in/meinolf-sellmann-a349636/

InsideOpt: https://insideopt.com/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠.

Three years ago, a student without a coding background couldn't ship a prototype. Today they're starting companies.

Mike Watson and Vijay Mehrotra have spent years putting students in front of real clients with real problems and no safety net. In this conversation, Mike pulls back the curtain on Northwestern's Client Project Challenge: how he hunts down the right projects, why he wants a client's fourth most important problem, and why he refuses to sit in on a single client call.

Then it gets interesting. AI didn't just speed things up. It moved the whole game. Building is cheap now. Knowing what to build is everything. They unpack how LLMs became 24/7 tutors, why TAs are suddenly idle, and the one skill no course has figured out how to teach. Plus straight-talk pitches for any professor or company thinking about jumping in.

Chapters

0:00 - Preview & Introduction

0:43 - The Client Project Challenge class explained

3:21 - How Mike sources projects

3:58 - The "fourth most important project" rule

5:12 - Getting clients for the project

6:28 - Weekly meetings

8:06 - Assigning students to projects

11:28 - Why Mike still gets nervous

12:16 - Student anxiety and peer feedback

13:30 - Learning from success vs failure

15:32 - Student backgrounds and self-teaching

16:40 - LLMs as tutors & delivering working prototypes

18:53 - The shrinking role of TAs

21:34 - What organizations get right and wrong with AI

23:08 - Teaching students to identify problems

24:20 - Pitch to faculty colleagues

25:32 - Pitch to industry partners

26:40 - Wrap-up

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Dr. Carlos Zetina — industrial engineer, ex-Amazon research scientist, and pre-sales consultant at FICO — walks through how he thinks about problems before solving them. Drawing on his PhD in optimization, years in risk consulting, and three intense years at Amazon, Carlos shares the frameworks he uses to make sure organizations work on the right problems, not just the loudest ones. The conversation covers what pre-sales engineering actually is, why documentation is the foundation of good AI adoption, and how the rise of generative AI is shifting the most valuable work from authoring to monitoring.

Chapters

0:00 - Preview

1:00 - Meet Carlos Zetina & career overview

5:23 - What working in Amazon is actually like

7:26 - How to identify & prioritize the right problems before building anything

10:22 - Operational planning cadence

14:13 - Decision framing: Why Carlos's first ML model completely missed the mark

17:32 - What is pre-sales engineering?

19:30 - Push vs pull systems

23:59 - Should you join pre-sales?

27:41 - Post-sale knowledge transfer

33:50 - Gen AI & why writing culture becomes a strategic asset

37:45 - The future of OR and data science with GenAI

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Carlos Zetina: ⁠https://www.linkedin.com/in/cazetina/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Dr. Ram Bala (Professor at Santa Clara University's Leavey School of Business, author of The AI-Centered Enterprise, and founder of Samvid.ai) joins Vijay Mehrotra and Michael Watson on the Decision Intelligence Lab podcast. They unpack "contextual AI" — why generic LLM answers fail enterprises, how role-aware AI aligns procurement and legal teams, the real danger of "agentic chaos," and why organizational structure will evolve on its own once information flows improve.

Chapters

0:00 — Preview

0:40 — Meet Dr. Ram Bala's background

1:11 — What is "contextual AI"? Why generic AI falls short

5:45 — AI as cross-functional coordinator, not just individual productivity tool

7:20 — Where is context today? Stuck in heads or unread docs

11:46 — Procurement + legal alignment: AI surfacing historical contract patterns

14:00 — Org redesign & change management

16:20 — Agentic AI replacing information-handoff roles

19:10 — Agentic chaos & AI slop

23:25 — Contextual AI vs. traditional business rules and hard-coded dashboards

27:37 — Pharma sales territory optimization

33:40 — Human value-add & accountability

38:33 — The possibility of explorating options with AI

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Ram Bala: ⁠https://www.linkedin.com/in/ram-bala-61560a5/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠https://decisionintelligencelab.substack.com/⁠.

What happens when the optimization rules you learned no longer apply?

Dr. Tim Varelmann, Founder of Bluebird Optimization, an expert for mathematical modeling, algorithms and software development, joins Vijay Mehrotra and Michael Watson to unpack the real mechanics of combining machine learning with optimization. Not the textbook version. The practitioner version.

Dr. Tim breaks down how ML and optimization actually combine in practice — beyond just demand forecasting. Three integration patterns, the rise of primal solvers, why "start linear" is outdated advice, and a case study where simulation-based inventory optimization saved millions. Plus: maintainable optimization code, Pareto fronts for business stakeholders, and Warren Powell's policy framework.

Chapters

0:00 — Preview & Introduction

1:00 — Meet Tim Varelmann

2:50 — ML + optimization: general trends

3:50 — Three ways to combine ML and optimization

6:06 — Solver landscape evolution

9:45 — ML-optimization integration examples

13:35 — Maintainable optimization code principles

16:20 — ML integration challenges with algebraic modeling

17:30 — Downsides: nonlinearity and scaling issues

18:50 — Is "Start linear" advice still valid?

21:35 — Drift case study: inventory optimization

27:29 — Why closed-form inventory formulas fail

29:50 — Engineering the full solution, demand adjustments

32:00 — Future: Warren Powell framework, policy-based optimization

35:15 — Closing Remarks

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with guest

Dr. Tim Varelmann: ⁠https://www.linkedin.com/in/timvarel/

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠

Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠https://decisionintelligencelab.substack.com/⁠.

In this episode, Vijay Mehrotra and Michael Watson sit down with Stephen Wunker, a strategy advisor for innovative leaders and Managing Director at New Markets Advisors, to explore transformative frameworks for navigating the AI era. Drawing from his book AI and the Octopus Organization—co-authored with Amazon futurist Jonathan Brill—Wunker shares actionable insights on how managers and executives can redesign their organizations for distributed decision-making, agile experimentation, and sustainable competitive advantage.

Chapters

0:00 - Preview & Introduction

1:05 - Meet Stephen Wunker

1:50 - AI and The Octopus Organization

8:21 - Centralize vs. Decentralize Decision Science

11:00 - The AI Magic Dust Problem

12:58 - Jobs-To-Be-Done Framework

16:30 - HelloFresh Case Study

20:40 - Skills for the Future

22:40 - When is Central Coordination Necessary

24:35 - Building an Experimental Muscle

26:55 - Governance & Metrics Alignment

30:05 - Figma Destroyed Adobe

31:35 - The VC Playbook

33:35 - What’s NOT Going to Happen

34:30 - Closing & Resources

Follow the show

Apple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠

Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠

Connect with Stephen

LinkedIn: ⁠https://www.linkedin.com/in/stephenwunker/

AI and the Octopus: https://www.newmarketsadvisors.com/books/ai-and-the-octopus-organization

Jobs to be Done: https://www.newmarketsadvisors.com/services/jobs-to-be-done-framework

Connect with hosts

Prof. Vijay Mehrotra (University of San Francisco): ⁠https://www.linkedin.com/in/vijay-mehrotra-ba9498/⁠

Prof. Michael Watson (Northwestern University): ⁠https://www.linkedin.com/in/michael-watson-07600a1⁠

About the podcast

The Decision Intelligence Lab podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data & AI for smarter decision-making & successful business outcomes.

For business inquiries, email at ⁠decisionintelligencepodcast@gmail.com⁠