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. 23h ago

    #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

  2. Oct 7

    #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

  3. Sep 23

    #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

  4. Sep 9

    #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

  5. Aug 19

    #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

  6. Aug 5

    #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

  7. Jul 22

    #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

  8. Jul 8

    #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

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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.

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  • Creator
    The Decision Intelligence Lab
  • Years Active
    2025 - 2026
  • Episodes
    40
  • Rating
    Clean
  • Copyright
    © The Decision Intelligence Lab
  • Show Website
    The Decision Intelligence Lab

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