#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](decisionintelligencepodcast@gmail.com)