The Decision Intelligence Lab

The Decision Intelligence Lab
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  • ريادة الأعمال
  • يتم التحديث كل شهرين

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. قبل ٦ ساعات

    #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

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  2. ٦ مايو

    #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

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  3. ٢٢ أبريل

    #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

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  4. ٨ أبريل

    #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

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  5. ٢٥ مارس

    #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

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  6. ١١ مارس

    #24 Evan Shellshear: Why Many Data Science & AI Projects Fail

    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

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  7. ٢٥ فبراير

    #23 Richard Savoie: Solving the Hardest Problem in Logistics

    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

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  8. ١١ فبراير

    #22 John Brandon Elam: Building a Decision Factory in Large Organizations

    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

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حول

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.

المعلومات

  • صناع العمل
    The Decision Intelligence Lab
  • سنوات النشاط
    ٢٠٢٥ - ٢٠٢٦
  • الحلقات
    ٣٠
  • التقييم
    ملائم
  • حقوق النشر
    © The Decision Intelligence Lab
  • موقع البرنامج على الويب
    The Decision Intelligence Lab

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