The Decision Intelligence Lab

The Decision Intelligence Lab
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The Decision Intelligence Lab explores practical challenges of applying data science, analytics, and AI to drive real-world business outcomes. Hosted by Prof. Michael Watson (Northwestern University) and Prof. Vijay Mehrotra (University of San Francisco) — both seasoned entrepreneurs, consultants, and researchers — this podcast delivers real-world insights for data professionals, business leaders, & anyone seeking to leverage data for smarter decision making. Each episode features leaders sharing how smarter decisions are reshaping business and technology. Subscribe to join the conversation.

  1. -17 h

    #32 James Taylor: Why Your Decisions (Not Your Data) Are the Problem

    Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠. What if the biggest mistake organizations make isn't in their data or their AI, but in never actually defining the decision they're trying to make? In this episode of the Decision Intelligence Lab, hosts Vijay Mehrotra and Michael Watson sit down with James Taylor, the author of Digital Decisioning, and Executive Partner at Blue Polaris. James traces the idea back 25+ years to a simple observation in financial services: fraud, origination, and collections systems all ran on the same technology stack yet were treated as entirely separate things. Naming that pattern changed how a whole industry approaches automation. The conversation digs into why decisions should be isolated as their own unit of work, why you should aim to automate everything to discover the parts that truly need people, and how a disability-claims team once saved 15 months of work simply by understanding the decision before building the model. James also shares where decisioning is going next, why he partners so closely with IBM, and his three durable principles for anyone trying to improve how their organization decides. Chapters 0:00 - Preview & Introduction 1:00 - Meet James Taylor & the origins of "decision management" 3:20 - Economies of scale and cross-team learning 5:25 - Task automation vs. true decisioning 11:35 - Decision maps and who successfully adopts them 13:25 - What to do when companies say "our data is no good" 18:26 - Why data science teams get undermined presenting without business input 19:35 - Automating decisions with uncertainty and multiple objectives 23:05 - The case for "automate first" as a default mindset 28:30 - Beyond financial services: healthcare payers and personalized care plans 32:45 - The Blue Polaris business story 38:15 - 3 principles for aspiring decision leaders 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 James Email: james@bluepolaris.com LinkedIn: https://www.linkedin.com/in/jamestaylor/ Website: https://bluepolaris.com/ James's Book: https://www.amazon.de/stores/James-Taylor/author/B001IOH7UI 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

    41 min

  2. 17 juin

    #31 Meinolf Sellmann: Decision-Making Under Uncertainty

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

    41 min

  3. 3 juin

    #30 Mike Watson: Preparing Students for Real-World Problem Solving

    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

    28 min

  4. 20 mai

    #29 Carlos Zetina: AI Is Only as Smart as Your Documentation

    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

    47 min

  5. 6 mai

    #28 Ram Bala: Why Context Is the Missing Layer in Enterprise AI

    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

    42 min

  6. 22 avr.

    #27 Dr. Tim Varelmann: Primal Solvers, Inventory Agents & the ML-Optimization Stack

    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

    37 min

  7. 8 avr.

    #26 Stephen Wunker: Building Distributed, Adaptive Companies

    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

    36 min

  8. 25 mars

    #25 Justin Trombold: The Biggest Mistake Companies Are Making with GenAI

    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

    39 min
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The Decision Intelligence Lab explores practical challenges of applying data science, analytics, and AI to drive real-world business outcomes. Hosted by Prof. Michael Watson (Northwestern University) and Prof. Vijay Mehrotra (University of San Francisco) — both seasoned entrepreneurs, consultants, and researchers — this podcast delivers real-world insights for data professionals, business leaders, & anyone seeking to leverage data for smarter decision making. Each episode features leaders sharing how smarter decisions are reshaping business and technology. Subscribe to join the conversation.

Informations

  • Créateur
    The Decision Intelligence Lab
  • Années d’activité
    2025 - 2026
  • Épisodes
    33
  • Classement
    Tout public
  • Copyright
    © The Decision Intelligence Lab
  • Site Web de l’émission
    The Decision Intelligence Lab

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