The Decision Intelligence Lab

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 accuracyIf 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 eraTimestamps0:00 - Preview1:01 - Meet Jeff Camm1:40 - Why "decision scientist," not "data scientist"4:20 - The research outcome: academia versus 1.4M job ads 9:10 - Why data projects fail12:50 - Building Wake Forest's Masters in Business Analytics from scratch14:42 - The bookends: problem framing and influencing18:22 - Teaching problem framing: the practicum and "Mess to Model"20:35 - Team charters and learning to work in teams22:50 - A new undergrad core: AI → BI → Decision Intelligence29:10 - Problem-centric teaching and just-in-time technique33:30 - "Nobody likes to be told what to do": choices, not answers34:50 - LLMs, families of solutions, and the risk/return trade-off38:40 - Jeff's papers and where to learn moreResources: - "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 showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect 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-07600a1⁠About the podcastThe 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 ⁠[email protected]⁠

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.Timestamps0:00 - Preview1:01 - Meet Cyrus Safaie2:00 - What people underestimate about DoorDash: hyper-local markets3:28 - Scaling by doing things that don't scale6:48 - Optimizing a three-sided marketplace & the trade-offs11:20 - Who resolves conflicts between dasher, merchant, and consumer teams13:00 - Balancing supply/demand and dasher incentives in real time16:48 - Reactive Real-time decisions and the Netflix Tyson fight21:30 - Human input vs. human override: how to automate the right way27:27 - Fixing ETAs by predicting your own model's errors29:35 - Cyrus's new venture: AI-native operationally heavy companies33:38 - Where the human stays in the loop: one-expert companies36:36 - 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 systemFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestCyrus Safaie: ⁠https://www.linkedin.com/in/hsafaie/Connect with hostsProf. 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-07600a1⁠About the podcastThe 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 ⁠[email protected]⁠

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.Timestamps0:00 - Preview0:44 - Vijay's Caddy Master Turned Airline Veteran5:10 - What deregulation actually changed8:00 - The overnight flood of new entrants9:30 - Pan Am's $99 cabin and ~120 bankruptcies11:43 - The fall of TWA and Pan Am; American's innovations 14:23 - Network design: Hub-and-spoke vs. point-to-point16:32 - Southwest and the Southwest Effect19:40 - 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 COVID29:05 - Airfares never covered the cost of flying30:45 - Basic economy as a weapon against Spirit and Frontier33:50 - The end of the value airline sector36:55 - Wrap-upFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestWilliam 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-07600a1⁠About the podcastThe 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 ⁠[email protected]⁠

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.Chapters0:00 - Preview & Introduction1:00 - Meet Geoffrey De Smet, Co-founder Timefold1:20 - What is field service routing?3:05 - Non-disruptive replanning and uncertainty10:25 - What-if simulations11:35 - Who uses Timefold Users14:45 - The telecom case: ROI and trade-offs of optimization18:05 - Spreadsheets and scheduling problems19:10 - From OptaPlanner to Timefold: The Origin story26:23 - Customising the model28:20 - Explainability deep dive30:28 - Where LLMs fit 31:50 - Trust, feasibility, constraints and the operator's never-ending world35:23 - The Leap: leaving a steady job, six months of burning savings38:07 - Go-to-market challenges40:40 - Closing thoughtsFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestGeoffrey De Smet: ⁠https://www.linkedin.com/in/ge0ffrey/Timefold: https://timefold.aiTimefold Solver: https://timefold.ai/solverConnect with hostsProf. 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-07600a1⁠About the podcastThe 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 ⁠[email protected]⁠

. 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/2509.16402Timestamps0:00 - Preview & Introduction0:57 - Meet Connor Lawless and Madeleine Udell2:04 - OR's accessibility problem vs. ML3:07 - The six stages of building an optimization model5:38 - Iteration as a flywheel: the "99% of the time" finding7:45 - Why is ML so much easier than OR? 10:45 - The role of visualization and pattern recognition12:00 - Optimization as a distribution-shift problem13:24 - The big surprise: the human bottleneck, not the math15:32 - Implications for teaching in the age of AI18:00 - Handling uncertainty: data-scarce vs. data-rich problems20:00 - Solver friction: Gurobi, CPLEX, and parameter tuning22:20 - "Good enough" beats optimal24:16 - Practitioner innovations: data and constraint validators26:00 - Separating data from the model27:37 - Preventing silent wrong answers29:00 - Documentation, debugging, and LLMs as intermediaries30:30 - Connor's next chapter: Percepta and the "last mile" of analytics32:00 - If not us, then who?Follow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect 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-07600a1⁠About the podcastThe 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 ⁠[email protected]⁠