The Decision Intelligence Lab

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.Chapters0:00 — Preview 0:40 — Meet Dr. Ram Bala's background1:11 — What is "contextual AI"? Why generic AI falls short5:45 — AI as cross-functional coordinator, not just individual productivity tool7:20 — Where is context today? Stuck in heads or unread docs11:46 — Procurement + legal alignment: AI surfacing historical contract patterns14:00 — Org redesign & change management16:20 — Agentic AI replacing information-handoff roles19:10 — Agentic chaos & AI slop23:25 — Contextual AI vs. traditional business rules and hard-coded dashboards27:37 — Pharma sales territory optimization33:40 — Human value-add & accountability38:33 — The possibility of explorating options with AIFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestRam Bala: ⁠https://www.linkedin.com/in/ram-bala-61560a5/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/⁠.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.Chapters0:00 — Preview & Introduction1:00 — Meet Tim Varelmann2:50 — ML + optimization: general trends3:50 — Three ways to combine ML and optimization6:06 — Solver landscape evolution9:45 — ML-optimization integration examples13:35 — Maintainable optimization code principles16:20 — ML integration challenges with algebraic modeling17:30 — Downsides: nonlinearity and scaling issues18:50 — Is "Start linear" advice still valid?21:35 — Drift case study: inventory optimization27:29 — Why closed-form inventory formulas fail29:50 — Engineering the full solution, demand adjustments32:00 — Future: Warren Powell framework, policy-based optimization35:15 — Closing RemarksFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestDr. Tim Varelmann: ⁠https://www.linkedin.com/in/timvarel/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/⁠.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.Chapters0:00 - Preview & Introduction1:05 - Meet Stephen Wunker1:50 - AI and The Octopus Organization8:21 - Centralize vs. Decentralize Decision Science11:00 - The AI Magic Dust Problem12:58 - Jobs-To-Be-Done Framework16:30 - HelloFresh Case Study20:40 - Skills for the Future22:40 - When is Central Coordination Necessary24:35 - Building an Experimental Muscle26:55 - Governance & Metrics Alignment30:05 - Figma Destroyed Adobe31:35 - The VC Playbook33:35 - What’s NOT Going to Happen34:30 - Closing & ResourcesFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with StephenLinkedIn: ⁠https://www.linkedin.com/in/stephenwunker/AI and the Octopus: https://www.newmarketsadvisors.com/books/ai-and-the-octopus-organizationJobs to be Done: https://www.newmarketsadvisors.com/services/jobs-to-be-done-frameworkConnect 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/⁠.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- GovernanceChapters0:00 - Preview & Introduction 0:41 - Meet Justin Trombold 5:58 - Readiness Assessment Explained7:51 - Strategic Alignment Deep Dive10:06 - Leadership Blind Spots & Overestimating Alignment12:49 - GenAI Strategy vs Reality17:28 - Experimentation & Guardrails21:00 - Real Risks (Hallucinations & Poor Inputs)24:21 - Biggest Organizational Blind Spot27:33 - GenAI as R&D, not IT30:23 - Don’t Approach Vendors without Defined Problems36:30 - Closing ThoughtsAre 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-3Follow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with the guestJustin Trombold: ⁠https://www.linkedin.com/in/trombold/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/⁠.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.Chapters0:00 - Preview & Introduction0:48 - Meet Evan and BCGX's Overview3:09 - The Scale of Rio Tinto’s Mining Operations5:15 - Tackling Large-Scale Scheduling Problems7:04 - The 70-20-10 Rule of AI Projects11:30 - Combining Technical and Consulting Teams14:28 - Proving Business Value Before Building Tools17:36 - Escaping AI Pilot Purgatory20:40 - Deploy, Reshape, and Invent Framework22:45 - Balancing Speed and Transformation25:35 - Maintaining AI Systems Long Term28:48 - The Problem with Cheap Consulting31:41 - Building Better Algorithms When Value Is Clear33:44 - Retail Pricing Optimization Case Study38:38 - Book Recommendations and Closing ThoughtsFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with the guestEvan Shellshear: ⁠https://www.linkedin.com/in/eshellshear/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/⁠.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.Chapters0:00 - Preview & Introduction 1:00 - Meet Rich Savoie 1:40 - Overview of Adiona3:30 - Why the Last Mile Is So Hard6:10 - The Optimization Stack: MIP, ML & Clustering9:40 - Who Buys Optimization Software? 11:45 - Customer-Led Approach for Product Development14:04 - The SaaS Dilemma of Modularization & Revenue Optimization19:00 - Building Trust & Overcoming Resistance in Non-tech Operations Environments21:41 - From Commute Optimization to Logistics AI28:30 - Founder-Market Fit & Getting Real Data34:10 - Lessons from the Medical Devices Industry36:10 - Perception & Selling to Enterprise38:15 - Tools, Books & ResourcesFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with the guestRich Savoie: ⁠https://www.linkedin.com/in/richsavoie/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/⁠.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.Chapters0:00 - Preview 0:45 - Meet John Brandon Elam1:55 - What are “orphaned” decision systems?4:55 - Why decision ownership breaks down in large companies7:28 - Who should own decision systems? The case for product ownership10:21 - Preparing cross-functional leaders for analytics-driven decisions15:20 - Lessons from AT&T’s FirstNet and mission-critical systems20:05 - Adoption and change management at Toyota: “go and see”25:01 - Trust, influence, and why being likable matters27:01 - KISS 2.0: Keep it simple to start30:02 - Rethinking technical debt31:32 - Aligning incentives between operations and transformation teams37:15 - From data to decisions: building a “Decision Factory”39:29 - Bit Bros, books, and connect with John40:42 - Closing remarksFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestJohn Brandon Elam: ⁠https://www.linkedin.com/in/johnbelam/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/⁠.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.Chapters0:00 - Preview & Introduction 0:46 - Meet Linda & Benjamin3:50 - Understanding DecideWise6:40 - Bridging Gaps in Decision Intelligence9:49 - The Role of Community in Technology Marketing15:25 - The Evolution of Decision Intelligence Across Industries18:15 - Vendor Landscape & Categorization21:56 - Common Implementation Mistakes26:00 - Audibility, Traceability, and Risk33:00 - Lessons Learned from Early Community Engagement39:00 - Conclusion and Call to ActionFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with the guestBenjamin Baer: ⁠https://www.linkedin.com/in/benjaminbaer/Linda Crowe: https://www.linkedin.com/in/llcrowe/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/.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.Chapters0:00 Preview Reflecting on the Journey of Decision Intelligence Podcast3:09 The Birth of the Podcast: A Personal Story5:52 Exploring Decision Intelligence: Insights from Guests8:47 The Role of Data in Decision Making11:58 Project Risks and Failures in Data Science15:14 Testing and Implementation of Models17:57 The Value Chain of Decision Intelligence20:53 Innovations in AI and Decision Making24:08 Looking Ahead: The Future of Decision Intelligence27:03 The Importance of Trust in AI29:57 The Role of Humans in Decision Processes32:49 The Upcoming Book: Capturing Business Value from Decision IntelligenceFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect 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-07600a1⁠Mike's blog: https://miketalksai.substack.com/Resources: Advent of OR - https://adventofor.comAbout 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]⁠

In this episode, Dr. Lorien Pratt discusses the concept of Decision Intelligence (DI), its importance in bridging the gap between data and decision-making, and how it evolves from decision engineering. She emphasizes the need for stakeholder alignment, the role of data in decision-making, and the importance of understanding the context of decisions. The conversation also touches on the integration of various technologies and the future of DI in a rapidly changing world.Chapters0:00 - Preview 0:28 - Meet Dr. Lorien Pratt1:43 - Defining Decision Intelligence2:28 - The Gap Between Data and Decision-Making5:05 - The Evolution from Decision Engineering to Decision Intelligence9:28 - Integrating Operations Research with Decision Intelligence14:14 - Pricing optimization, diffuse objectives & cross-silo interactions16:44 - Two Meanings of “Decision”18:55 - Incorporating Uncertainty in Decision-Making20:54 - The Dangers of Over-Engineering Models25:25 - Lack of a Shared Blueprint & The Need for Alignment33:20 - Learn about Quantellia 33:55 - The Inflection Point40:55 - Resources: DIHandbook.com & GettingStartedWithDI.comFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with the guestDr. Lorien Pratt: ⁠https://www.linkedin.com/in/lorienpratt/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]⁠

In this special holiday episode of the Decision Intelligence Lab Podcast, Vijay welcomes back the show’s first-ever returning guests: Borja Menéndez, creator of Advent of OR, and Zohar Strinka, a lead contributor to the INFORMS Analytics Framework. Together, they explore two major initiatives released in 2024 that aim to reshape how the next generation of operations research (OR) and analytics professionals learn and practice.Together, the guests and host dive deep into why OR is NOT just math, why curiosity is essential, how user engagement changes everything, and how practitioners learn to build systems that actually solve decision problems — not just elegant models.Timestamps 0:00 - Preview & Intro1:22 - What Is the Advent of OR?3:45 - Introducing the INFORMS Analytics Framework6:06 - OR isn't Just Math 10:10 - Skills Developed in the 2025 Advent of OR12:10 - How Advent of OR Supports CAP Prep14:21 - Why OR Must Teach Systems & Engineering16:20 - The Consultant View: Building Real Systems18:00 - Experiencing & Becoming a User Yourself22:53 - Curiosity as a Core OR Skill23:55 - How Borja Designs Advent of OR Challenges26:00 - How to Learn More About the INFORMS Framework & CAP27:22 - How to Join Advent of OR 202529:00 - Closing ThoughtsFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Resources Mentioned: 1. Advent of OR - Sign-up page for the annual 24-day challenge - https://adventofor.com2. INFORMS Analytics Framework - https://www.informs.org/Professional-Development/Professional-Development-Classes/INFORMS-Analytics-Framework3. Certified Analytics Professional (CAP) Certification - https://www.certifiedanalytics.org/4. Decision Intelligence Lab Podcast (Substack) - https://decisionintelligencelab.substack.comConnect with the guestBorja Menéndez Moreno: ⁠https://www.linkedin.com/in/borjamenendezmoreno/Zohar Strinka: https://www.linkedin.com/in/zohar-strinka/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]⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Mike Watson engage with guests Warren Powell, Co-Founder of Optimal Dynamics, and Adam DeJans Jr., Technical Account Manager at Gurobi Optimization, to explore the intricacies of decision-making in uncertain environments. The conversation delves into the importance of sequential decision optimization, the role of uncertainty in supply chain management, and the need for democratizing optimization techniques. The guests share their experiences in bridging the gap between academic theories and practical applications, emphasizing the significance of building trust and understanding in decision-making processes. They also discuss the potential of LLMs in enhancing accessibility to optimization tools, ultimately aiming to empower a broader audience in making informed decisions.Chapters0:00 - Preview 0:31 - Meet Warren Powell & Adam DeJans Jr.4:45 - Understanding Sequential Decision Optimization9:30 - Common Sense in Decision-Making11:50 - Incorporating Uncertainty in Models15:45 - The Importance of Trust in Decision-Making19:18 - Framework for Sequential Decision Analytics22:44 - Collaboration Between Academia and Industry24:49 - State Transitions & State Variables29:40 - Human Decision-Making Challenges34:40 - Democratizing Optimization & The Role of LLMsTakeaways- Uncertainty must be incorporated into decision-making frameworks.- Formalizing common sense can enhance understanding of complex problems.- The role of state transitions is vital in decision-making processes.- Democratizing optimization can empower non-experts to engage with complex problems.- LLMs can assist in modeling and interpreting optimization problems.- Understanding metrics, decisions, and uncertainties is foundational for effective problem-solving.------Follow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠-----Connect with guestWarren Powell: ⁠https://www.linkedin.com/in/warrenbpowell/Adam DeJans Jr.: https://www.linkedin.com/in/addejans/Referenced: - On State Variables: https://castle.princeton.edu/statevariables/- "Sequential Decision Analysis" by Warren Powell (2022)- "Reinforcement Learning and Stochastic Optimization" by Warren Powell- "You Got Your Data Job, Now What?" co-authored by Adam DeJans Jr.----- 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-07600a1⁠------ About 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]⁠

How Call Centers Shape Patient AccessIn this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson welcome Peyton Fry, founder of Glass Raven, to discuss the intricacies of healthcare operations, particularly focusing on call centers and patient access. Peyton shares his journey from working in call centers to consulting healthcare systems on optimizing operations. The conversation delves into the importance of metrics, the challenges of forecasting and staffing, and how call centers can significantly impact patient outcomes. Peyton emphasizes the need for clarity in metrics and the importance of building relationships in the healthcare industry for those looking to enter the field.Chapters0:00 - Preview & Introduction 1:03 - Meet Peyton Fry 5:17 - Understanding Metrics in Call Center Operations7:40 - Challenges in Forecasting and Staffing15:04 - Navigating Bureaucracy & Driving Change in Healthcare21:50 - Connecting Call Centers to Patient Outcomes26:01 - Advice for Entering the Healthcare Field30:11 - The Role of Medical Records in Call Center Efficiency32:45 - Case Study: 40-Minute Hold Time Eliminated34:49 - Final Thoughts and Key TakeawaysFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestPeyton Fry: ⁠https://www.linkedin.com/in/peyton-fry-mha-a4356862Connect 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]⁠

In this episode of the Decision Intelligence Lab podcast, Zohar Strinka discusses the concept of meta-problems in problem solving, emphasizing the importance of selecting the right problem to tackle. She shares real-world examples of how solving the wrong problem can lead to disastrous outcomes and highlights the iterative nature of problem-solving in analytics. Zohar also explores the distinction between problem selection and problem definition, the challenges of consulting, and the significance of focusing on outcomes in analytics. She provides valuable insights for young professionals in the field and discusses her involvement in the Analytics Plus Conference and the Edelman competition.Chapters0:00 - Preview 0:31 - Meet Zohar Strinka1:32 - Understanding Meta Problems in Problem Solving2:31 - The Importance of Problem Selection vs. Problem Definition3:45 - Navigating Client Expectations in Consulting5:36 - The Dilemma of Problem Scope in Decision Making10:41 - Transitioning to Independent Consulting12:46 - Budgeting for Iterative Problem Solving14:28 - Iterative Problem Solving and Agile Methodologies16:57 - The Role of Analytics Professionals in Client Relationships18:55 - Value of Analytics Plus Conference and Edelman Competition20:50 - Advice for Young Analytics ProfessionalsFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with the guestZohar Strinka: https://www.linkedin.com/in/zohar-strinka/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]⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson engage with Warren Lieberman, President, Veritec Solutions, to explore the evolution of revenue optimization, particularly in the self-storage industry. Warren shares his journey from high school computer science to operations research at Yale, and his pivotal role in revenue management at American Airlines. He discusses the founding of Veritec Solutions, its transition from consulting to software development, and the unique challenges and strategies involved in pricing for self-storage. The conversation highlights the importance of understanding consumer psychology, the need for user-friendly systems, and the balance between product development and customer needs.Chapters0:00 - Preview0:42 - Meet Warren Lieberman1:28 - Warren's Journey into Optimization and Analytics6:16 - Revenue Management at American Airlines8:40 - The Birth of Veritec Solutions 11:35 - Transitioning to Self-Storage Revenue Management 13:10 - Innovations in Self-Storage Revenue Management20:16 - Pricing Strategies 22:40 - Value Pricing29:40 - User Adoption and System Integration34:54 - Balancing Product Development and Customer Needs37:39 - Transition from Services to Software42:00 - Closing Thoughts and RecommendationsKey Takeaways:- Self-storage revenue management differs significantly from traditional industries due to low transaction volumes.- Value pricing in self-storage has transformed the industry.- Consumer psychology plays a crucial role in pricing strategies.- The transition from services to software requires careful consideration of customer needs.Veritech's approach is to customize software to fit the client's business processes.- Importance of user adoption and understanding system recommendations, continuous innovation, and enhancement of their software.Follow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestWarren Lieberman: ⁠https://www.linkedin.com/in/warren-lieberman-bb783/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]⁠

In this episode of the Decision Intelligence Laboratory podcast, Dr. Filippo Focacci, co-founder and CEO of DecisionBrain, shares insights into the world of optimization, the evolution of their DB Gene platform, and the impact of generative AI on decision-making processes. The conversation covers Filippo's journey into optimization, the strengths of Decision Brain's team, and the technological approaches they employ, including constraint programming and mixed integer programming. Real-time applications and case studies illustrate the practical implications of their work, while discussions on team dynamics and the future of AI highlight the evolving landscape of decision intelligence.Chapters0:00 - Preview 0:44 - Meet Filippo Focacci 4:30 - Learn about Decision Brain6:30 - The Strengths of Decision Brain9:13 - Technological Approaches in Optimization11:38 - The Evolution of Gene DB16:45 - Real-Time Applications of Decision Brain21:12 - Team Roles & Responsibilities in Optimization Projects23:00 - Evolving the Gene Platform27:30 - Implementing Optimization in Practice30:30 - The Impact of Generative AI on Business32:52 - Filippo's Recommendations Follow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestFilippo Focacci: ⁠https://www.linkedin.com/in/filippo-focacci-35a5091/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-07600a1⁠Recommendations: Follow Yves Caseau: https://www.linkedin.com/in/ycaseau/About 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]⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson interview Jon Petersen, Vice President of Data at Archer, an aerospace company specializing in electric vertical takeoff and landing (EVTOL) aircraft. The conversation explores Archer's mission to revolutionize urban mobility, the critical role of data science in aircraft design and operations, and the collaborative efforts between data science and engineering teams. Petersen shares insights on the challenges of data-driven decision-making, the importance of building a diverse and effective team, and strategies for securing support for data science initiatives. Chapters0:00 – Preview0:33 – Meet Jon Petersen1:37 – Revolutionizing Urban Mobility2:38 – Managing Aircraft Noise4:23 – The Role of Data Science at Archer9:38 – Collaborating with Engineering Teams13:50 – Trip Density Distribution & Destination Origins17:53 – Challenges in Data-Driven Decision-Making19:48 – Team Building & Collaboration28:53 – Scaling the Manufacturing Facility30:33 – Funding & Justifying Data Science InitiativesFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with the guestJon Petersen: ⁠https://www.linkedin.com/in/jon-petersen-ba623950/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-07600a1⁠Recommendations from Guest: - Misbehaving: The Making of Behavioral Economics by Richard H. Thaler- Thinking, Fast and Slow by Daniel KahnemanAbout 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]⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson discuss the concept of DecisionOps with Carolyn Mooney, Co-Founder & CEO of Nextmv. They discuss the evolution of decision-making systems, the importance of simulation and testing, and how to build trust within organizations. Carolyn shares insights on hierarchical optimization, the responsibilities of different roles in decision systems, and the latest trends in decision-making technologies, including the impact of generative AI. The conversation emphasizes the need for clear communication and collaboration between technical teams and operational users to enhance decision-making processes.Chapters0:00 - Preview0:38 - Meet Carolyn Mooney 1:38 - What is DecisionOps?3:44 - The Journey of Nextmv6:20 - Applications of DecisionOps in Various Industries8:30 - Simulation and Testing in DecisionOps12:37 - Building Trust Through Testing and User Interaction14:40 - Hierarchical Optimization and Decision Making18:21 - Ownership and Responsibilities in DecisionOps22:11 - Working on the Product With Clients31:28 - Trends in Decision-Making Technologies33:54 - Final Thoughts and ResourcesTakeaways* Decision Ops is essential for getting decisions live in organizations.* Simulation helps in understanding the impact of decisions.* Testing capabilities should be integrated with CI/CD platforms.* Hierarchical optimization allows for balancing cost and service levels.* Ownership of decision-making processes should involve operational teams.* Prototyping and user feedback are key to successful decision systems.Follow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestCarolyn Mooney: ⁠https://www.linkedin.com/in/carolyn-m-mooney/Nextmv: https://www.nextmv.io/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]⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson engage with Karthik Gada, a CFO and author, to explore the accelerating rate of change in technology and its implications for businesses, economies, and individuals. Gada discusses how technological advancements are interconnected and how they impact various sectors, including healthcare and education. He emphasizes the importance of adapting to rapid changes, fostering an entrepreneurial mindset, and leveraging individual strengths to navigate the complexities of the modern world. The conversation also touches on the challenges posed by institutional rigidity and the need for effective leadership in an era of constant disruption.Chapters0:00 - Preview0:40 - Meet Kartik Gada1:40 - Understanding Technological Disruption & Accelerating Pace of Change4:05 - The Impact of Cost Reduction on Technology7:40 - Bottlenecks in Healthcare and Education12:30 - How Individuals Respond to Change 15:48 - Embracing Change and Entrepreneurial Mindset20:50 - Education and Career Pathways in a Changing World24:15 - Managing Economic Complexity with AI28:20 - Leadership in an Era of Rapid Change31:50 - Closing Thoughts Follow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestKartik Gada: https://www.linkedin.com/in/kartikgada/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-07600a1⁠/About 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]⁠

In this episode of the Decision Intelligence Laboratory podcast, Borja Menéndez, Lead Operations Research Engineer at Trucksters who also earned his PhD in OR, joins us to discuss his journey into the field of operations research, the impact of his work at Trucksters, and the importance of integrating data science with traditional optimization methods. He shares insights on the relay optimization problem, the challenges of implementing algorithms in logistics, and the significance of user-centric design in developing effective solutions. Borja also reflects on the transition of his blog, Feasible, to a paid model and the future of operations research in the context of Gen.AI.Chapters00:00 Introduction to Operations Research and Borja Menendez01:14 The Journey into Operations Research04:39 Real-World Applications of Operations Research07:03 Understanding Trucksters and Relay Optimization12:48 Challenges in Relay Optimization16:21 Non-Technical Aspects of Implementing OR20:15 Testing and Quality Assurance in Optimization Models25:22 User-Centric Design in Optimization Solutions27:47 Transitioning to a Paid Model for Feasible29:32 Integrating Data Science with Operations Research35:52 The Future of Operations Research and Gen.AIFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Check out Borja Menéndez's Newsletter: https://www.feasible.club/Connect with guestBorja Menéndez: ⁠https://www.linkedin.com/in/borjamenendezmoreno/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]⁠

In this episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson engage with Ben Alamar, a PhD economist and sports analytics expert. They explore the evolution of sports analytics, the impact of motion tracking technology, and the challenges of integrating data into decision-making processes in sports. Ben shares personal anecdotes about his experiences with teams, the importance of understanding risk, and the role of leadership in fostering a data-driven culture. Ben offers advice for aspiring sports analysts and concludes with recommendations for further reading.Chapters00:00 - Preview1:30 - Meet Ben Alamar3:21 - The Evolution of Sports Analytics7:04 - Motion Tracking and Its Impact10:03 - Translating Sports Analytics to Business12:00 - Behavior Change and Mindset Shifts14:40 - Risk Perception in Decision Making19:16 - Fan Engagement and Business Objectives20:20 - Draft Decisions: Case Studies28:00 - Fostering Evidence-Based Culture in Sports33:43 - Advice for Aspiring Sports Analysts36:46 - Recommendations for Further Reading Book Recommendations: - "Basketball Beyond Paper" by Dean Oliver- "Ballistic" by Henry AbbottCheck out P3 Applied Sports Science: https://www.p3.md/Follow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestBenjamin Alamar: ⁠https://www.linkedin.com/in/benalamar/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]⁠

In this episode, Mike & Vijay sit down with Peter Cacioppi, Principal Operations Research Scientist at Decision Spot to dive into the intersection of model building and production software development. Peter shares insights on the tools he's developed, the role of Python in the Gurobi ecosystem, and the transformative impact of LLMs on software development workflows, particularly in real-time optimization. Tune in to explore the challenges and innovations in bringing production models to life.Chapters0:00 - Preview 1:20 - Meet Peter Caccioppi1:47 - Integrating Computer Science in OR7:16 - Tidy, Tested, and Safe16:55 - The History & Utitlity of Ticdat 31:21 - GenAI's Role in OR and Software DevelopmentFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestPeter Cacioppi: ⁠https://www.linkedin.com/in/peter-cacioppi-86519210/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]⁠

In this episode of the Decision Intelligence Lab podcast, host Vijay Mehrotra and co-host Michael Watson engage with Zahir Balaporia, an expert in analytics and decision-making. They explore the concepts of the 'last mile' and 'extra mile', and the politics of analytics. Zahir also shares real-world examples of how organizational dynamics can impact the success of analytics initiatives. The conversation highlights the significance of beliefs in decision-making, the necessity of building trust, and the challenges of change management in the face of evolving technologies like AI. Chapters0:00 - Preview1:00 - Meet Zahir Balaporia2:00 - The Last Mile & Extra Mile Problems in Supply Chain 7:24 - Real-World Examples of Analytics Failures11:54 - Building Trust and Relationships in Analytics21:25 - Cultural Dynamics in Technology Adoption29:15 - The Future of Work with AI and Change ManagementFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestZahir Balaporia: ⁠https://www.linkedin.com/in/zahirbalaporia/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]⁠

In this episode of the Decision Intelligence Lab podcast, Ganesh Ramakrishna joins our hosts Vijay & Mike to share his journey from co-founding Opex Analytics to launching Lyric. He discusses the challenges of building a software company, the importance of fundraising with scaled ambitions, and the critical role of product-market fit. Ganesh emphasizes the need for an AI-first mindset in supply chain intelligence and the responsibilities that come with venture capital. He also highlights Lyric's innovative approach to solving supply chain problems through a comprehensive platform that integrates data, algorithms, and user experience.Chapters00:00 Introduction to Ganesh Ramakrishna03:05 The Birth of Lyric: Vision and Challenges06:05 Navigating the Startup Landscape: Fundraising Insights08:59 Building a Software Company: Early Stages and Funding12:07 Achieving Product-Market Fit: Lessons Learned14:58 The Role of Venture Capital: Control and Responsibility17:58 Supply Chain Challenges: Lyric's Approach21:02 AI-First Mindset: The Future of Supply Chain IntelligenceFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestGanesh Ramakrishna: ⁠https://www.linkedin.com/in/ganeshatalk/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]⁠

In this podcast episode, Michael Watson & Vijay Mehrotra interviews Harley Davis, Founder and CEO of Athena Decision Systems. They discuss the rise of generative AI and its integration with business rules, emphasizing the importance of reliable decision-making in mission-critical applications. Harley explains the limitations of LLMs, referring to them as 'stochastic parrots,' and highlights the challenges of scaling these technologies while ensuring privacy and compliance. The conversation also covers the role of RAG in enhancing LLM performance and shares real-world use cases where AI can improve administrative processes.Chapters0:00 - Introduction & Preview 1:19 - Meet Harley Davis & Athena Decision Systems4:30 - Business Rules in Mission-Critical Applications9:23 - Understanding LLMs and Their Limitations12:05 - Integrating LLMs with Business Rules14:12 - Challenges in Scaling LLMs and Privacy Concerns17:34 - The Role of RAG in Enhancing LLM Performance20:15 - Deploying LLMs in Real-World Applications24:24 - Improving Administrative Processes with AI27:10 - Final Thoughts and RecommendationsFollow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestHarley Davis: ⁠https://www.linkedin.com/in/harleydavis/Athena Decision Systems: https://athenadecisions.com/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]⁠

We live in a world filled where our lives and livelihoods increasingly depend on complex systems.  In this episode, Professor Laura Albert from the University of Wisconsin discusses a recent paper on the “Grand Challenges in Industrial and Systems Engineering” and offers some suggestions on how educational programs must evolve in the age of Artificial Intelligence.Timestamps0:00 - Preview & Introduction3:20 - Definition & Importance of Resiliency 5:38 - The Need for Big Thinking in the Field6:45 - Significance of case studies in bridging academia and industry needs9:00 - Rethinking educational approaches in light of GenAI advancements11:23 - The Threat of Irrelevance for Industrial & Systems Engineering12:55 - Challenges of Teaching with GenAI13:55 - Lessons from COVID-19: Supply Chain Resiliency16:32 - AI in Decision-Making and Governance18:15 - Identifying and managing trade-offs21:24 - Barriers to Impact in Data Science and OR Fields23:45 - Cybersecurity as an Opportunity for Industrial and Systems Engineering & Operational Research26:10 - The 'Equifax Breach Story' 29:10 - Laura Albert’s blogFollow the showApple: ⁠⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠⁠Spotify: ⁠⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠⁠Connect with guestLaura Albert: https://www.linkedin.com/in/lauraalbertphdLaura's Blog: https://punkrockor.com/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]: Industrial Engineering, Grand Challenges, Resiliency, Education, AI, Cybersecurity, Supply Chain, Governance, Decision-Making, Operations Research, Industrial and Systems Engineering, Operations Research, Resiliency, Complex Systems, Cybersecurity, Supply Chain, Education, Case Studies, Artificial Intelligence, Generative AI (GenAI), AI Governance, Engineering Curriculum, AI Ethics, Human-AI Teaming, Systems Thinking, Optimality, AI Audits, Workforce Training, Trade-offs, Data Science Projects, Impact Measurement, Public Outreach, Software Supply Chains, Power Systems, Digital Transformation, Decision-Making, Risk Management, Academic Blogging

In this episode’ of the Decision Intelligence Lab podcast we have Doug Gray, Director of Data Science at Walmart Global Tech and co-author of Why Data Science Project Fail. Whether you are a business leader trying to capture value from your analytic investments, a practicing data scientist, or a student in a technical field aspiring to bring your skills to solve real world problems, this episode will provide you with valuable insights, examples, suggestions, and inspiration.Chapters0:00 - Preview, & Introductions2:30 - Birth of Why Data Science Projects Fail?4:36 - Top 5 People-Related Factors in Data Science Project Failure13:00 - Cost and Complexity Beyond the Model20:34 - Business User Resistance to “Black Box” Models26:17 - Doug’s Estimate of an Optimal Failure Rate31:20 - Organizational Maturity and Its Impact on Success36:50 - Doug's Recommendations Follow the showApple: ⁠https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064⁠Spotify: ⁠https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3b⁠Connect with guestDoug Gray: https://www.linkedin.com/in/doug-gray-06bb4a4/Books: 1. Why Data Science Projects Fail2:  The Art of Data Science: A Practitioner's GuideConnect 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-07600a1⁠Additional Notes:BOOK: All in on AIARTICLE:  "$100 of Implementation for $1 of modeling"About 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] failure, analytics, organizational maturity, change management, Doug Gray, decision science, business value, success rates, data science projects, AI implementation, analytics failure rates, organizational maturity, change management, communication challenges, project management, unrealistic expectations, technology focus, business value, key performance indicators (KPIs), stakeholder engagement, decision intelligence, model deployment, production systems, data pipelines, integration challenges, testing and validation, airline operations research, supply chain optimization, cost avoidance, AI adoption in business, center of excellence, subject matter experts, user interface design, interactive optimization, prompt engineering, business process alignment, success rates in analytics, learning from failure

In the first episode of the Decision Intelligence Lab podcast, hosts Vijay Mehrotra and Michael Watson interview Dr. Irv Lustig from Princeton Consultants about the Princeton 20, a framework for identifying and mitigating AI and Optimization project risks. This evaluation framework includes both business and technical factors, and is a valuable methodology for project discovery, effective selling, and building and maintaining trust with clients. Chapters00:00 - Preview, & Introductions02:20 - The Princeton Twenty Framework Overview3:24 - How the Princeton Twenty Is Used In Practice7:10 - Client Perspectives on Risk Management8:27 - Overselling in Optimization and AI Projects11:11 - Understanding the Decision Scope (Environmental Factor 5)16:43 - Exploring Value Proposition (Environmental Factor 7)21:45 - Process Maturity (Technical Factor 10) and Ongoing Maintenance25:00 - Last Mile Problems in Decision Making30:33 - Broader Impact & Risk Tolerance 35:35 - Irv's RecommendationsFollow the showApple: https://podcasts.apple.com/in/podcast/the-decision-intelligence-lab/id1811085064Spotify: https://open.spotify.com/show/0lFoAVKqJHTYSZNpeN61ou?si=0ae973aab0174b3bConnect with guestIrv Lustig: https://www.linkedin.com/in/irv-lustig/Princeton 20: https://princetonoptimization.com/ai-project-management/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] 20, AI deployment, optimization, risk management, decision making, data science, consulting, project success, value proposition, process maturity, decision science, project management, technical factors, sales tool, client engagement, cost benefit analysis, value proposition, decision scope, process maturity, machine learning, change management, agile methodology, human-in-the-loop optimization, black box optimization, business problem framing, analytics framework, certified analytics professional, project delivery, solution deployment, operational scheduling, strategic consulting, informs analytics.

The Decision Intelligence Lab podcast explores the practical challenges of implementing data science, analytics, and AI projects, shining a light on many diverse links in the information value chain that transform raw data into real business outcomes. Each episode features conversations with leaders from technology firms, consultancies, enterprise data science teams, and academia.Hosted by Professor Michael Watson (Northwestern University) and Professor Vijay Mehrotra (University of San Francisco) — both seasoned entrepreneurs, consultants, and researchers — this podcast delivers real-world insights for data professionals, business leaders, and anyone seeking to leverage data for smarter decision-making.Subscribe and follow to join us as we uncover how decision intelligence is reshaping the future of business and technology. You can connect with us at  [email protected]