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. 8月19日

    #35 William Swelbar: How Airlines Became the Ultimate OR Playground

    Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠. Airline veteran William Swelbar joins Vijay Mehrotra and Mike Watson for a tour through nearly 50 years of airline economics(from 1978 deregulation to today's premiumization era). William Swelbar's path is one of a kind: flight attendant at North Central in 1979, local union president in Detroit within a few years, and part of a 1982 employee coalition that had all but raised the $400 million it needed in a bid to buy Republic Airlines. The conversation digs into the analytics behind the industry: why hub-and-spoke networks turn 10 routes into 230 sellable city pairs, how Pan Am's $99 cabin became an early pricing wake-up call, why "capacity is a bad drug," and how Delta and United decommoditized flying through cabin segmentation, basic economy, and credit card revenue - while the end of labor arbitrage killed the low-cost carrier era. Timestamps 0:00 - Preview 0:44 - Vijay's Caddy Master Turned Airline Veteran 5:10 - What deregulation actually changed 8:00 - The overnight flood of new entrants 9:30 - Pan Am's $99 cabin and ~120 bankruptcies 11:43 - The fall of TWA and Pan Am; American's innovations 14:23 - Network design: Hub-and-spoke vs. point-to-point 16:32 - Southwest and the Southwest Effect 19:40 - High-speed rail: too late for the US? 22:15 - Capacity is a "bad drug" 25:15 - How Delta and United segmented the cabins & won during COVID 29:05 - Airfares never covered the cost of flying 30:45 - Basic economy as a weapon against Spirit and Frontier 33:50 - The end of the value airline sector 36:55 - 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 guest William Swelbar: ⁠https://swelbar.substack.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

  2. 8月5日

    #34 Geoffrey De Smet: Solving Real-Time Scheduling Problems

    Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠. Geoffrey De Smet built a scheduling solver as a side project and ended up with a NASA supplier, big telcos, and pest control companies running on it. The Timefold co-founder and OptaPlanner creator joins Vijay and Mike to talk field service routing: hundreds of technicians, tens of thousands of jobs, and the chaos of a real day in the field. They also discuss why plans are useless but planning is essential, why a 90% feasible schedule is 100% useless, why operators reject schedules they can't interrogate and what it took to leave a steady job when his life's work became roadkill after the IBM–Red Hat acquisition. If you've ever wondered why the world still runs on scheduling spreadsheets, this one's for you. Chapters 0:00 - Preview & Introduction 1:00 - Meet Geoffrey De Smet, Co-founder Timefold 1:20 - What is field service routing? 3:05 - Non-disruptive replanning and uncertainty 10:25 - What-if simulations 11:35 - Who uses Timefold Users 14:45 - The telecom case: ROI and trade-offs of optimization 18:05 - Spreadsheets and scheduling problems 19:10 - From OptaPlanner to Timefold: The Origin story 26:23 - Customising the model 28:20 - Explainability deep dive 30:28 - Where LLMs fit 31:50 - Trust, feasibility, constraints and the operator's never-ending world 35:23 - The Leap: leaving a steady job, six months of burning savings 38:07 - Go-to-market challenges 40:40 - 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 Geoffrey De Smet: ⁠https://www.linkedin.com/in/ge0ffrey/ Timefold: https://timefold.ai Timefold Solver: https://timefold.ai/solver 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

  3. 7月22日

    #33 Connor Lawless & Madeleine Udell: What Really Slows Optimization Down

    . Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠. Optimization powers decisions in everything from healthcare to logistics, but for most practitioners it stays a "magical box": powerful, opaque, and locked behind PhD-level expertise. So what actually gets in the way of putting these models to work? In this episode, hosts Vijay Mehrotra and Michael Watson sit down with Stanford's Postdoctoral Researcher Connor Lawless and Madeleine Udell (Assistant Professor at Management Science and Engineering Department, Stanford University) to unpack their paper "It Was a Magical Box": Understanding Practitioner Workflows and Needs in Optimization. Based on interviews with 15 optimization model developers, the research uncovers a surprising truth: the hardest part of operations research usually isn't the math. It's the people, the data, and the endless back-and-forth. We dig into the six-stage workflow of building an optimization model, why nearly every project becomes an iterative "flywheel," why machine learning feels so much easier than OR, when "good enough" beats provably optimal solutions, and how LLMs might finally close the accessibility gap. Connor also shares what's next as he joins Percepta to work on the "last mile" of analytics. Whether you build models for a living or just wonder why so many great models never make it into the real world, this conversation will change how you think about optimization in practice. The Research Paper: "It Was a Magical Box": Understanding Practitioner Workflows and Needs in Optimization (2025) - https://arxiv.org/abs/2509.16402 Timestamps 0:00 - Preview & Introduction 0:57 - Meet Connor Lawless and Madeleine Udell 2:04 - OR's accessibility problem vs. ML 3:07 - The six stages of building an optimization model 5:38 - Iteration as a flywheel: the "99% of the time" finding 7:45 - Why is ML so much easier than OR? 10:45 - The role of visualization and pattern recognition 12:00 - Optimization as a distribution-shift problem 13:24 - The big surprise: the human bottleneck, not the math 15:32 - Implications for teaching in the age of AI 18:00 - Handling uncertainty: data-scarce vs. data-rich problems 20:00 - Solver friction: Gurobi, CPLEX, and parameter tuning 22:20 - "Good enough" beats optimal 24:16 - Practitioner innovations: data and constraint validators 26:00 - Separating data from the model 27:37 - Preventing silent wrong answers 29:00 - Documentation, debugging, and LLMs as intermediaries 30:30 - Connor's next chapter: Percepta and the "last mile" of analytics 32:00 - If not us, then who? 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 - Connor Lawless (Postdoctoral researcher, Stanford University): https://www.linkedin.com/in/connorlawless/ - Madeleine Udell (Assistant Professor, Management Science & Engineering, Stanford University): https://www.linkedin.com/in/madeleine-udell/ 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

  4. 7月8日

    #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

  5. 6月17日

    #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

  6. 6月3日

    #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

  7. 5月20日

    #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

  8. 5月6日

    #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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番組について

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.

情報

  • クリエイター
    The Decision Intelligence Lab
  • 配信期間
    25年 - 26年
  • エピソード
    36
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © The Decision Intelligence Lab
  • 番組のWebサイト
    The Decision Intelligence Lab

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