Skip to content
Sign in
Artwork for The Decision Intelligence Lab
BusinessEntrepreneurship

The Decision Intelligence Lab

The Decision Intelligence Lab

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.

Play
Open in
  • 24 episodes
  • fortnightly
  • Avg 39 min
  • English

Episodes 24

NewestOldestLongest
All20Unplayed20In progress0Played0
Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.
  • #39
    Wednesday · 41 min

    #39 Jerry Yurchisin: The Skill Data Scientists Ignore — Companies Save Millions With It

    Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠. What happens after your ML model makes a prediction? When Jerry Yurchisin asks that at data science conferences, the answer is usually "I don't know." In this episode, Gurobi's senior data science strategist joins Vijay and Mike to talk about the tool that turns predictions into decisions: optimization. They cover why you don't need a PhD to deliver real value, why solvers never forget a constraint the way an LLM can, how GenAI helps experts model faster (while confidently getting things wrong) and why finance may be leaving optimization value on the table. If you're wondering what comes after prediction, this one's worth listening to. Timestamps: 0:00 - Preview 0:42 - Meet Jerry Yurchisin 4:25 - What a Senior Data Science Strategist does at Gurobi 5:45 - Spreading the word beyond OR conferences (Gartner, Databricks, ODSC) 7:00 - Statistics + OR at UNC 9:30 - The pitch to ML practitioners: "What happens next?" 11:11 - Optimization as a career differentiator 13:40 - Is optimization too math-heavy? 17:42 - "I tried MIP ten years ago and it was too slow" - try again 21:02 - When "optimization" means different things to ML people 22:35 - Building Optimization 101 for data scientists 25:10 - Do executives confuse data science and optimization? 26:30 - Teaching with realistic (but solvable) examples 29:10 - Applying GenAI and optimization together 32:10 - Advice for the OR community: join the AI conversation 35:40 - Hard constraints vs. context windows 37:15 - Underutilized frontier: optimization in finance 39:20 - Learn more at gurobi.com/learn 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 - Jerry's LinkedIn: https://www.linkedin.com/in/mathwithjerome/ - Gurobi: https://www.gurobi.com - Gurobi learning resources: https://www.gurobi.com/learn 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

  • #38
    October 7 · 40 min

    #38 Dr. Rich Staats: 41 Years in Uniform, OR in Every Job

    Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠. What does it take to lead when the stakes are literally life and death? In this episode, Vijay and Mike sit down with Dr. Rich Staats — retired U.S. Army Major General, MIT Operations Research PhD, Hertz Fellow, and former Commanding General of the U.S. Army Reserve's 75th Innovation Command. Rich shares the three leadership principles he published in every command he held, the story of dining across the table from a man who threatened to kill him — and the one sentence that defused it — and why operations research sat at the center of every decision in his 41-year career. The conversation covers how much data a leader really needs, his "law of risk", lessons from running MITRE's OR department on briefing senior decision makers, and a candid take on generative AI: where it genuinely helps, and why he worries young analysts are losing their analytic muscle memory. Timestamps 0:00 - Preview 0:47 - Meet Dr. Rich Staats 2:00 - How Rich and Mike met at an INFORMS conference 3:05 - What ties a career across combat, think tanks, and startups together 8:25 - "If it hadn't been for the war, we'd have retired several ranks earlier" 11:03 - The Iraq dinner story: "I could just kill you now" 14:14 - Double down on strengths or fix your weaknesses? 15:50 - Innovation in the military & the 75th Innovation Command 19:43 - OR at the center of 41 years of decisions 22:30 - How often did he actually build a model? 24:06 - The Red Pen Award: rewarding the best mistakes 25:23 - How much data do you need? The 80/20 lesson 28:10 - Advising senior executives: decision space, ranges, and the ORmadillos 32:40 - Generative AI: biggest hopes and red flags 37:30 - IP ownership and liability: the next 15–20 year challenge 38:58 - Where to find Rich What You'll Learn - The three leadership principles (Perrin, Stafford, Peterson) that work in combat units, think tanks, and startups alike - Why respect and relationships (not command-and-control) kept Rich alive in Iraq - How an OR mindset can shrink 2-hour meetings to 15 minutes - How much data you actually need before deciding and why waiting for certainty is its own risk - How to brief analysis to senior senior decision makers: decision space, ranges, and the value of information - Where generative AI helps and how it can quietly erode analytic "muscle memory" and creativity 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 - Rich's Website: https://zhalindor.com/ - Rich's Music: https://zhalindor.com/dreams - Rich's Books: https://zhalindor.com/books 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
    September 23 · 41 min

    #37 Jeff Camm: Why the ROI Is in the Decision

    Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠. Jeff Camm, Professor and Inmar Presidential Chair in Analytics at Wake Forest University School of Business joins Vijay and Mike to explain why so many data science projects fail to deliver ROI, and what his analysis of over 1.4 million job postings reveals about the skills that actually drive decisions. He shares how he built Wake Forest's MSBA around "the bookends" (problem framing and influence), his blueprint for a new undergrad core(Artificial Intelligence, Business Intelligence, Decision Intelligence) and why managers want choices, not answers. check against transcript for factual accuracy If your models aren't changing decisions, this one's for you. What You'll Learn - The measurable skill-set differences between data science and decision science roles, based on a decade of job posting data - Why data science projects fail and why the reasons echo OR project failures from decades past - How to design an analytics program around "the bookends": problem framing and influence - How to actually teach problem framing (experiential learning, real client messes, no data up front) - A three-course blueprint for the undergrad business core: Artificial Intelligence, Business Intelligence, Decision Intelligence - Why prescriptive analytics should deliver families of solutions, not single answers - Why you should ask an LLM for choices, not answers and what that means for teaching analytics in the AI era Timestamps 0:00 - Preview 1:01 - Meet Jeff Camm 1:40 - Why "decision scientist," not "data scientist" 4:20 - The research outcome: academia versus 1.4M job ads 9:10 - Why data projects fail 12:50 - Building Wake Forest's Masters in Business Analytics from scratch 14:42 - The bookends: problem framing and influencing 18:22 - Teaching problem framing: the practicum and "Mess to Model" 20:35 - Team charters and learning to work in teams 22:50 - A new undergrad core: AI → BI → Decision Intelligence 29:10 - Problem-centric teaching and just-in-time technique 33:30 - "Nobody likes to be told what to do": choices, not answers 34:50 - LLMs, families of solutions, and the risk/return trade-off 38:40 - Jeff's papers and where to learn more Resources: - "Data Science and Decision Science Skills: Are They Different and Does It Matter?" (Camm, Fry & Shafer, Harvard Data Science Review, Summer 2025): ⁠https://hdsr.mitpress.mit.edu/pub/9ir6e1j6/release/1⁠ - The INFORMS Journal on Applied Analytics: ⁠https://pubsonline.informs.org/authored-by/Camm/Jeffrey+D⁠ - "Stop Modeling Just the Data, Start Modeling Decisions" (forthcoming in MIT Sloan Management Review digital at time of publishing) 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 - Jeff Camm: ⁠https://www.linkedin.com/in/jeff-camm-395b366/ 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
    September 9 · 42 min

    #37 Cyrus Safaie: Inside DoorDash's Three-Sided Optimization Problem

    Make better business decisions with data and AI—subscribe to The Decision Intelligence Lab Newsletter at ⁠⁠https://decisionintelligencelab.substack.com/⁠⁠. What does it actually take to run a marketplace across thousands of micro-markets in real time? Cyrus Safaie, Former Director of Engineering and AI/ML at DoorDash joins Mike and Vijay to pull back the curtain on how DoorDash balances supply and demand, pays dashers, and predicts delivery times at massive scale. Cyrus shares why the company started with spreadsheets instead of algorithms, how a surprise Netflix boxing match drove a Super Bowl-sized demand spike with zero warning, and why the best automated systems treat human judgment as an input rather than an override. He also previews his new venture: using AI to build operationally heavy companies run by a single domain expert or tiny team. Timestamps 0:00 - Preview 1:01 - Meet Cyrus Safaie 2:00 - What people underestimate about DoorDash: hyper-local markets 3:28 - Scaling by doing things that don't scale 6:48 - Optimizing a three-sided marketplace & the trade-offs 11:20 - Who resolves conflicts between dasher, merchant, and consumer teams 13:00 - Balancing supply/demand and dasher incentives in real time 16:48 - Reactive Real-time decisions and the Netflix Tyson fight 21:30 - Human input vs. human override: how to automate the right way 27:27 - Fixing ETAs by predicting your own model's errors 29:35 - Cyrus's new venture: AI-native operationally heavy companies 33:38 - Where the human stays in the loop: one-expert companies 36:36 - Advice for students: curiosity and "torturing your brain" What You'll Learn - Why DoorDash is really thousands of tiny, semi-isolated markets and why it optimizes locally before globally - How DoorDash scaled by starting with spreadsheets and manual judgment before building algorithms - How a three-sided marketplace balances dashers, merchants, and consumers - The difference between proactive and reactive dasher incentives, and how real-time mobilization works - Why human input into automated systems beats human overrides of them - How DoorDash improved ETAs by building models that predict their own errors - Cyrus's vision for AI-native, operationally heavy companies run by a single domain expert (or tiny team) plus an AI operating system 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 Cyrus Safaie: ⁠https://www.linkedin.com/in/hsafaie/ 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

  • August 19 · 39 min

    #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

  • #34
    August 5 · 41 min

    #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

  • #33
    July 22 · 36 min

    #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

  • #32
    July 8 · 40 min

    #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

  • #31
    June 17 · 40 min

    #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

  • June 3 · 27 min

    #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

  • #29
    May 20 · 46 min

    #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

  • #28
    May 6 · 41 min

    #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

  • #27
    April 22 · 36 min

    #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

  • #26
    April 8 · 35 min

    #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

  • #25
    March 25 · 39 min

    #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

  • March 11 · 41 min

    #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

  • #23
    February 25 · 41 min

    #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

  • #22
    February 11 · 41 min

    #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

  • #21
    January 28 · 41 min

    #21 Linda Crowe & Benjamin Baer: Building a Community for Decision Intelligence

    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

  • January 14 · 38 min

    #20 Dr. Vijay & Dr. Mike: Reflections from the First Innings

    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

Showing 1–20 of 24 episodes