Episode 131: Building a Causal Mindset in a Predictive World

A machine learning model can tell you which of your customers are likely to cancel. It can't tell you what would happen if you intervened to change that. Prediction and causation are two completely different problems, and the lessons that make a data scientist good at solving one can backfire if applied to the other.In this episode, Dr Quentin Gallea joins Dr Genevieve Hayes to explore what a causal mindset involves and how causal AI brings causal inference and machine learning together to support better decisions than either can alone.You'll discover:The question to ask whenever you hear "because" [05:20]Where predictive habits go wrong when applied to causal work [06:47]What causal AI actually is, and what it isn't [17:39]How causal AI changes the conversation with executives [23:11]Guest BioDr Quentin Gallea is the founder of The Causal Mindset and the author of The Causal Mindset Handbook. He has taught causal thinking to thousands of practitioners worldwide, including from top tech companies, and advises C-suite and data leaders on causal inference and AI impact.LinksConnect with Quentin on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Building a digital twin without a clear objective is like showing up at a construction site without a blueprint. You might produce something, but it's unlikely to be structurally sound, and it will certainly cost more than it should.In this Value Boost episode, Dr Greg Biegel joins Dr Genevieve Hayes to share a practical framework for mapping a physical system to its digital representation - one of the first and most critical steps in building a digital twin.You'll discover:The three things to write down before you build anything [01:45]How to decide which parts of a physical system to model and which to ignore [03:27]The four types of models that make up a digital twin [04:00]How to work backwards from a decision to the data and models you actually need [05:25]Guest BioDr Greg Biegel is the author of Digital Twins in Action and has been working as a researcher, software developer, architect and product manager across academia, startups and large enterprises for the past 26 years. He currently works as a Solution Architect with Amazon Web Services.LinksConnect with Greg on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

When the cost of failure is low, experimentation is often the best way to make decisions. But when a wrong call could cost millions or endanger lives, you need an entirely different approach. This is where digital twins come in. And recent technological advances have made them accessible to far more organisations than ever before.In this episode, Dr Greg Biegel joins Dr Genevieve Hayes to explore what digital twins actually are, how they enable better executive decision-making, and what data scientists need to know to start working with them.You'll discover:What a digital twin actually is and what distinguishes it from a traditional simulation [02:35]The five capability levels of digital twins explained through Google Maps [09:12] How digital twins transform raw operational data into executive decisions [14:07]Why you should always start from a decision and not a technology [25:20]Guest BioDr Greg Biegel is the author of Digital Twins in Action and has been working as a researcher, software developer, architect and product manager across academia, startups and large enterprises for the past 26 years. He currently works as a Solution Architect with Amazon Web Services.LinksConnect with Greg on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

One of the most frustrating things about working with AI is when it seems to suddenly forget everything it knew about you and your work. This is annoying when using AI chatbots. When building AI systems, it can be catastrophic.In this Value Boost episode, Dr Imran Ahmad joins Dr Genevieve Hayes to explain what memory actually means in AI agents and what data scientists need to understand to build systems that actually remember what matters.You'll discover:The three types of memory every AI agent needs [01:54]Why context and memory are not the same thing [05:37]What context rotting is and why it matters for how you design AI systems [09:15]How to make sure LLMs pay attention to what actually matters [10:39]Guest BioDr Imran Ahmad is a data scientist and president of data consultancy Cloudanum. He is also the author of 30 Agents Every AI Engineer Must Build and 50 Algorithms Every Programmer Should Know.LinksConnect with Imran on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

The agentic AI era hasn't made algorithms irrelevant. It has just changed where data scientists need to focus their attention. And the data scientists who understand that distinction, and can explain it to stakeholders who don't, are the ones who will define what the profession looks like next.In this episode, Dr Imran Ahmad joins Dr Genevieve Hayes to explore the shift from algorithms to agents and what it means for how data scientists should be thinking about their role.You'll discover:How the data science toolkit has fundamentally shifted since the start of the AI era [02:09]The difference between broad AI and deep AI [10:18]The problems that still demand deep algorithmic solutions [14:10]How to push back when stakeholders insist on an LLM solution [19:21]Guest BioDr Imran Ahmad is a data scientist and president of data consultancy Cloudanum. He is also the author of 30 Agents Every AI Engineer Must Build and 50 Algorithms Every Programmer Should Know.LinksConnect with Imran on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Large language models excel at some tasks while struggling badly with other. At a time where it seems everyone is currently trying to make sense of AI, understanding and being able to explain exactly why this is the case, has become one of the most useful things data scientists can offer their stakeholders.In this special reverse interview episode, Lauren Pearl switches roles with Dr Genevieve Hayes to explore why LLMs work the way they do and what that means for anyone using AI in their work.You'll discover:1. What your training data tells you about what AI can and cannot do [05:05]2. Why high context problems are particularly hard for LLMs [06:28]3. How AI gives you the right answer for the wrong reason [08:08]4. Why some problems will always be beyond what LLMs can solve [14:22]Guest BioLauren Pearl is a business strategist, three-time founder and CFO advisor who helps start-up teams build data-driven businesses that thrive. She is also the resident start-up finance expert at NYU Stern’s Berkley Center for Entrepreneurship, where she teaches financial modelling to founders, and the co-host of The Growth-Minded CFO podcast.LinksConnect with Lauren on LinkedInSubscribe to The Daily CFO newsletterConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Building something valuable with AI doesn't automatically make you valuable. If the value sits in your creation and your employer owns your creation, you're the one who's dispensable, not the thing you built. This is the AI chicken nugget problem and many data scientists who are impacted by it haven't even realised it yet.In this episode, Lauren Pearl joins Dr Genevieve Hayes to explore where real expertise still lives in the age of AI and what data scientists should actually be focusing on to build a sustainable career.You'll discover:What the AI chicken nugget problem is and why it matters [02:31]Why vibe coding is not a rare skill and never will be [05:12]Why obsessing over something rare and valuable is your best career bet [17:17]The skills worth investing in for a sustainable career [22:18]Guest BioLauren Pearl is a business strategist, three-time founder and CFO advisor who helps start-up teams build data-driven businesses that thrive. She is also the resident start-up finance expert at NYU Stern’s Berkley Center for Entrepreneurship, where she teaches financial modelling to founders, and the co-host of The Growth-Minded CFO podcast.LinksConnect with Lauren on LinkedInSubscribe to The Daily CFO newsletterConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Machine learning is what most data scientists know best. But when it comes to building systems that enable better decision-making, machine learning is just one piece of the puzzle. And leaning on it too much can leave your entire decision system exposed.In this Value Boost episode, Adam DeJans Jr joins Dr Genevieve Hayes to walk through the eight disciplines that make up the modern decision stack and where data scientists should focus to close the gaps in their own skill set.You'll discover:The eight disciplines that make up the modern decision stack [01:24]The most overlooked layer in most decision systems [04:47]Why optimization textbooks are teaching data scientists the wrong things [06:17]What to learn next if you want to build better decision systems [09:45]Guest BioAdam DeJans Jr. is a decision scientist and optimisation expert who has led high-stakes decision systems in complex, uncertain environments, at companies including Amazon, Toyota and Ford. He is the co-founder of AI and decision intelligence consultancy Bit Bros and co-author of The Decision Factory: A Novel About Decisions Under Uncertainty.LinksConnect with Adam on LinkedInBit Bros websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Data scientists are trained to build better models. But stakeholders don't wake up hoping for a better model. They wake up hoping to make better decisions. And the gap between those two things is where most data science value quietly disappears.In this episode, Adam DeJans Jr joins Dr Genevieve Hayes to share what it actually looks like when an organisation stops optimising for better predictions and starts building systems that enable better decisions - that is, decision factories.You'll discover:What separates a decision factory from a machine learning pipeline [02:11]Why uncertainty is an input into good decision making [10:51]How to test a decision policy before it costs you real money [12:11]Why giving stakeholders options beats giving them the optimal solution [18:48]Guest BioAdam DeJans Jr. is a decision scientist and optimisation expert who has led high-stakes decision systems in complex, uncertain environments, at companies including Amazon, Toyota and Ford. He is the co-founder of AI and decision intelligence consultancy Bit Bros and co-author of The Decision Factory: A Novel About Decisions Under Uncertainty.LinksConnect with Adam on LinkedInBit Bros websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Choosing which AI tools to buy is the easy part of an AI strategy. The hard part - and the part most organisations are ignoring - is everything that sits beneath the tools, from platform and inference decisions to hardware and sovereignty.In this Value Boost episode, Victor Coimbra joins Dr Genevieve Hayes to share his framework for cutting through the noise of AI tool selection to the strategic decisions that will actually determine whether an organisation's AI future succeeds or fails.You'll discover:The four layers of an agentic AI strategy and why most organisations only think about one [01:53]The three symptoms that signal an organisation's AI strategy is breaking down [05:52]The four questions that reveal what an organisation's AI strategy is actually missing [08:21]The one question data scientists should lead with when advising stakeholders on AI [10:43]Guest BioVictor Coimbra is a Partner and CTO at Artefact, the world’s largest pure-play AI consulting firm and co-founded the firm’s Latin American operations. In 2024, he was recognised in the Forbes 30 Under 30 Brazil list for his outstanding contributions to AI innovation.LinksConnect with Victor on LinkedInVictor's article on the four-layer frameworkArtefact websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE