Value Driven Data Science

Dr Genevieve Hayes

Your stakeholders don’t want a better model. They want a better decision.

Value Driven Data Science is a masterclass for data professionals ready to become strategic experts, valued for their judgement, not just the technical outputs they produce.

Each week, Dr Genevieve Hayes speaks with leading thinkers and data practitioners who have already made that shift. You’ll leave every episode with an idea or framework you can start using right away.

If you’re tired of being treated as just another data scientist, Value Driven Data Science will transform you into the trusted advisor your stakeholders ask for by name.

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#127
Wednesday · 22 min
Episode 127: From Algorithms to Agents and What It Means for Data Science
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 Bio Dr 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. Links Connect with Imran on LinkedIn Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#126
October 7 · 21 min
Episode 126: Why AI Excels at Some Things and Sucks at Others
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 Bio Lauren 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. Links Connect with Lauren on LinkedIn Subscribe to The Daily CFO newsletter Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#125
September 30 · 26 min
Episode 125: The AI Chicken Nugget Problem
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 Bio Lauren 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. Links Connect with Lauren on LinkedIn Subscribe to The Daily CFO newsletter Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#124
September 23 · 11 min
Episode 124: [Value Boost] 8 Disciplines Every Decision System Needs
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 Bio Adam 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. Links Connect with Adam on LinkedIn Bit Bros website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#123
September 16 · 25 min
Episode 123: How to Build a Decision Factory Inside Your Organisation
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 Bio Adam 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. Links Connect with Adam on LinkedIn Bit Bros website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#122
September 9 · 11 min
Episode 122: [Value Boost] 4 Questions Every Data Scientist Should Ask About AI Strategy
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 Bio Victor 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. Links Connect with Victor on LinkedIn Victor's article on the four-layer framework Artefact website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#121
September 2 · 29 min
Episode 121: What Hybrid Agentic AI Organisations Mean for Data Science
The debate about whether AI will replace human workers has already been settled - not by academics or futurists, but by the organisations that fired their humans, discovered AI couldn't do what they needed, and quietly hired them back. The future isn't AI replacing humans. It's humans and AI working together in ways that neither could manage alone. In this episode, Victor Coimbra joins Dr Genevieve Hayes to share what hybrid agentic organisations actually look like in practice, and what data scientists need to do to position themselves at the centre of them. You'll discover: Why thinking of AI as a tool rather than a coworker is the mindset holding most organisations back [03:58] The four archetypes that determine which tasks belong to humans and which to agents [08:17] How AI is turning data scientists back into scientists [18:24] The two skills that will define an indispensable data scientist in a hybrid organisation [27:01] Guest Bio Victor 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. Links Connect with Victor on LinkedIn Artefact website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#120
August 26 · 12 min
Episode 120: [Value Boost] The AI Silent Correctness Problem
AI hallucinations get all the attention. But hallucinations are relatively easy to catch because the output is obviously wrong. The failure mode that should worry data scientists more is when the agent uses facts that are true to draw conclusions that are false, producing outputs that look perfectly fine. This is known as silent correctness. In this Value Boost episode, Jia Huang joins Dr Genevieve Hayes to explore why silent correctness is the most dangerous failure mode in agentic AI systems and what data scientists can do to catch it before it causes serious harm. You'll discover: Why silent correctness is harder to catch than a hallucination [04:17] Why sampling and auditing are non-negotiable in agentic systems [07:09] Four techniques data scientists can use to catch silent failures [09:28] The one safeguard every agentic AI system should have [11:10] Guest Bio Jia Huang is a lead research engineer at A*STAR, Singapore's Agency for Science, Technology and Research, and is the author of multiple books on AI engineering and agent design, including Designing AI Agents and RAG from First Principles. His work focuses on turning agentic AI from impressive demos into reliable, auditable, and value-producing engineering systems. Links Connect with Jia on LinkedIn Follow Jia on Substack Agent Design Pattern Society (ADPS) website Jia's AI agent design position paper Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#119
August 19 · 29 min
Episode 119: Rewiring Your Data Science Thinking for the Agentic AI Era
The shift to agentic AI doesn't make data science skills obsolete. But it does require data scientists to rewire how they think about familiar concepts, such as uncertainty, model evaluation and accountability, in their work. In this episode, Jia Huang joins Dr Genevieve Hayes to explore what that rewiring actually looks like, and why data scientists are better placed than almost any other profession to make it. You'll discover: Why data scientists are better prepared for the agentic AI era than they might think [03:00] How the data scientist's role is shifting from analyst to system designer [06:37] The three types of uncertainty in agentic AI systems [11:58] Why context engineering is the new feature engineering [23:19] Guest Bio Jia Huang is a lead research engineer at A*STAR, Singapore's Agency for Science, Technology and Research, and is the author of multiple books on AI engineering and agent design, including Designing AI Agents and RAG from First Principles. His work focuses on turning agentic AI from impressive demos into reliable, auditable, and value-producing engineering systems. Links Connect with Jia on LinkedIn Follow Jia on Substack Agent Design Pattern Society (ADPS) website Jia's AI agent design position paper Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#118
August 12 · 11 min
Episode 118: [Value Boost] Compounding Your Data Science Authority Beyond Blog Posts
A well-written blog post gets you noticed. But for data scientists who want to build authority that compounds over time, it's just the beginning. Every piece of writing is a potential stepping stone to something bigger - a conference talk, a book deal, or an opportunity you couldn't have anticipated. In this Value Boost episode, Cynthia Dunlop joins Dr Genevieve Hayes to explore how data scientists can convert blog writing into bigger opportunities and what it actually takes to make the leap from blog post to book. You'll discover: How conference organisers actually find their speakers — and why blogging is the answer [02:35] How acquisitions editors scout for authors and why you don't need a huge following [03:39] The low risk way to find out if you're ready to write a book [06:57] How each new opportunity compounds the authority you've already built [08:10] Guest Bio Cynthia Dunlop is the co-author of Writing for Developers and Senior Director of Content Strategy at ScyllaDB. She has co-authored four books for software developers and tech leaders and authored hundreds of articles for publications including TechCrunch, IEEE Computer, and The New Stack. Links Connect with Cynthia on LinkedIn Follow Cynthia on Substack Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#117
August 5 · 25 min
Episode 117: Writing Your Way to Authority as a Data Scientist
For data scientists who want to build authority beyond their organisation, writing is one of the most powerful tools available. But in a world flooded with AI-generated content, simply publishing is no longer enough. The data scientists who stand out are the ones writing things no AI could have written. In this episode, Cynthia Dunlop joins Dr Genevieve Hayes to share practical frameworks for writing blog posts that stand out, build genuine authority and actually get read. You'll discover: Why AI-generated content has made personal experience more valuable than ever [05:10] The three Ps test for finding topics you can write about with genuine authority [08:56] The blog post patterns that work best for demonstrating expertise [11:03] How to use AI to improve your writing without letting it replace your voice [16:33] Guest Bio Cynthia Dunlop is the co-author of Writing for Developers and Senior Director of Content Strategy at ScyllaDB. She has co-authored four books for software developers and tech leaders and authored hundreds of articles for publications including TechCrunch, IEEE Computer, and The New Stack. Links Connect with Cynthia on LinkedIn Follow Cynthia on Substack Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#116
July 29 · 13 min
Episode 116: [Value Boost] What Data Scientists Need to Know Before the AI Free Ride Ends
Right now, AI is cheap. But the companies providing it are losing billions of dollars a year and are expected to go public within the next 12 months. When they do, the price of intelligence is going up - and any organisation that has built its AI future entirely on rented intelligence is going to face a very unpleasant surprise. In this Value Boost episode, Nicholas Kelly joins Dr Genevieve Hayes to explore why organisations shouldn't be building their AI future entirely on frontier models, and what data professionals need to understand to be the strategic voice in the AI infrastructure conversation. You'll discover: 1. Why relying entirely on frontier AI models is a strategic error [02:00] 2. How the economics of AI are about to change [03:34] 3. The case for owning at least some of your AI infrastructure [07:53] 4. What data professionals need to know to become the strategic voice in AI infrastructure decisions [11:02] Guest Bio Nicholas Kelly is the co-founder and Chief AI Architect of Delivering Data Analytics, a consultancy that helps organisations turn data, BI, analytics, and AI into confident decisions people actually act on. He is also the author of Delivering Data Analytics, How to Interpret Data and the recently released The AI-Driven Data Team. Links Nicholas's Website Connect with Nicholas on LinkedIn Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#115
July 22 · 27 min
Episode 115: Evolving Your Data Career for the AI Era
The AI era presents a choice for data professionals: wait to see what happens to your role, or get ahead of it and decide what it becomes. Nicholas Kelly made that choice two years ago, when a client told him ChatGPT could do 50% of what he did. What followed is a masterclass in proactive career evolution. In this episode, Nick joins Dr Genevieve Hayes to explore how data professionals can evolve their skills for the AI era, what that evolution looks like in practice, and why their existing expertise puts them in a stronger position than they might think. You'll discover: 1. How Nick evolved from dashboard consultant to AI architect [02:35] 2. Why the data team's role is staying the same even as everything around it changes [06:43] 3. How AI is enabling data professionals to build software that used to require a development team [12:11] 4. The best first project to build if you want to develop your AI skills [25:08] Guest Bio Nicholas Kelly is the co-founder and Chief AI Architect of Delivering Data Analytics, a consultancy that helps organisations turn data, BI, analytics, and AI into confident decisions people actually act on. He is also the author of Delivering Data Analytics, How to Interpret Data and the recently released The AI-Driven Data Team. Links Nicholas's Website Connect with Nicholas on LinkedIn Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#114
July 15 · 13 min
Episode 114: [Value Boost] The Four Conversations Every Data Scientist Needs to Master
For data scientists, getting a project approved is a sale. It might not feel like one and money might not change hands, but the dynamics are exactly the same. And like any sale, it goes a lot better if you go in with a plan. In this Value Boost episode, Blair Enns joins Dr Genevieve Hayes to explore how data professionals can use the Four Conversations framework to sell their expertise more effectively, whether as independent consultants or as employees within organisations. You'll discover: Why reputation is the most powerful sales tool a data professional has [04:33] How to stop behaving like a vendor when you're trying to sell expertise [05:09] Why the value of your work resides in your stakeholder not in you [09:53] The single question that unlocks what your stakeholder truly values [12:13] Guest Bio Blair Enns is the founder of Win Without Pitching, the leading authority on selling and pricing for expert advisors and practitioners. He is also the author of The Win Without Pitching Manifesto and The Four Conversations: a New Model for Selling Expertise, and is the co-host, with David C. Baker, of the podcast 2Bobs: Conversations on the Art of Creative Entrepreneurship. Links Connect with Blair on LinkedIn Blair's website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#113
July 8 · 26 min
Episode 113: The Experts' AI Manifesto
Building genuine expertise takes years. And in the age of AI, losing it can happen gradually and almost invisibly, through small delegations that each seem reasonable in isolation but add up to something significant over time. In this episode, Blair Enns joins Dr Genevieve Hayes to explore how data professionals can use AI without compromising their hard-earned expertise and reputation. You'll discover: Why delegating to AI is always a trade-off [03:00] The crucial difference between writing to communicate and writing to think [08:40] Why you should orient yourself around the problems you solve [14:28] How to decide which skills are worth protecting and which to let go [17:49] Guest Bio Blair Enns is the founder of Win Without Pitching, the leading authority on selling and pricing for expert advisors and practitioners. He is also the author of The Win Without Pitching Manifesto and The Four Conversations: a New Model for Selling Expertise, and is the co-host, with David C. Baker, of the podcast 2Bobs: Conversations on the Art of Creative Entrepreneurship. Links The Experts' AI Manifesto Connect with Blair on LinkedIn Blair's website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#112
July 1 · 16 min
Episode 112: [Value Boost] Lies, Damned Lies and Stakeholders
AI misinformation is a new problem. Misleading data is not. Long before anyone had heard of a hallucination, organisations were making bad decisions based on cherry-picked statistics, misunderstood averages, and numbers that confirmed what decision-makers already wanted to believe. In this Value Boost episode, Derek Gibson joins Dr Genevieve Hayes to explore how data professionals can help their stakeholders become better data sceptics and avoid being duped by misleading data long before it ever reaches an AI. In this episode, you'll discover: 1. The timeless data traps that catch even experienced decision makers [01:56] 2. How to arm your stakeholders with the right questions to push back on data [07:57] 3. Why confirmation bias is the most dangerous data vulnerability in any organisation [09:20] 4. What it means when an analytics team is asked to confirm a decision rather than inform one [13:24] Guest Bio Derek Gibson is a decision scientist, analytics educator, and has recently wrapped up his long career in financial services at Wells Fargo. He serves on the Wake Forest University MS Business Analytics Advisory Board. He is also a co-author of Data Duped: How to Avoid Being Hoodwinked by Misinformation and author of the upcoming Data, AI, and the Noise: Searching for Truth in Information and Algorithms. Links Connect with Derek on LinkedIn Derek's website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#111
June 24 · 26 min
Episode 111: Building Your Defences Against AI Misinformation
AI doesn't lie - at least, not intentionally. It just sounds completely confident while filling in the gaps with whatever seems most plausible. And in a world where AI outputs are increasingly being used to inform high-stakes decisions, the ability to spot what's wrong, before it reaches a stakeholder, is becoming one of the most important skills a data professional can have. In this episode, Derek Gibson joins Dr Genevieve Hayes to share practical strategies for identifying unreliable AI outputs and building the defences necessary to keep AI-generated misinformation from reaching your stakeholders. In this episode, you'll discover: Why AI is not a truth tool and what that means for how you use it [03:21] The red flags that signal an AI output shouldn't be trusted [12:21] A simple prompting habit you can develop to reduce AI mistakes [16:13] Why the skill of verifying AI outputs is one you need to build yourself [24:25] Guest Bio Derek Gibson is a decision scientist, analytics educator, and has recently wrapped up his long career in financial services at Wells Fargo. He serves on the Wake Forest University MS Business Analytics Advisory Board. He is also a co-author of Data Duped: How to Avoid Being Hoodwinked by Misinformation and author of the upcoming Data, AI, and the Noise: Searching for Truth in Information and Algorithms. Links Connect with Derek on LinkedIn Derek's website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#110
June 17 · 16 min
Episode 110: [Value Boost] Why You Need Less Data Than You Think
In high-stakes decision-making, waiting for more data is often not an option. Yet many data scientists assume that without a large dataset, meaningful analysis is impossible. The good news is that rigorous, quantitative analysis is possible with far less data than most data scientists realise - in some cases with just a single datapoint. In this Value Boost episode, Douglas Hubbard joins Dr Genevieve Hayes to share practical techniques from How to Measure Anything that data scientists can start using right now to support high-stakes decisions when observations are scarce and every data point counts. In this episode, you'll learn: Why a single observation reveals more than you think [01:58] How Laplace's Rule of Succession lets you estimate probabilities from tiny samples [08:25] The Rule of Five and what it reveals about small sample statistics [12:08] The simplest and most overlooked technique for reducing measurement uncertainty [14:07] Guest Bio Douglas Hubbard is the founder and president of Hubbard Decision Research and the creator of Applied Information Economics. He has over 35 years’ experience in management consulting focusing on the application of quantitative methods to decision making. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and The Failure of Risk Management: Why It’s Broken and How to Fix It. Links How to Measure Anything website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#109
June 10 · 29 min
Episode 109: How to Measure Anything and Make Better Decisions
Data scientists are trained to work with large datasets. But the decisions that truly make or break an organisation are rarely the ones with large datasets behind them. They are the high-stakes, one-off decisions made under significant uncertainty - and most data scientists have no framework for handling them. In this episode, Douglas Hubbard joins Dr Genevieve Hayes to share how combining techniques from statistics, economics and decision theory can help data scientists tackle the problems that matter most. In this episode, you'll discover: What Applied Information Economics is and how it works in practice [03:17] Why organisations are systematically measuring the wrong things [09:23] How the Lens Model can make expert judgment more reliable than the expert themselves [13:44] How AI can turbocharge the Applied Information Economics approach [21:10] Guest Bio Douglas Hubbard is the founder and president of Hubbard Decision Research and the creator of Applied Information Economics. He has over 35 years’ experience in management consulting focusing on the application of quantitative methods to decision making. He is also the author of How to Measure Anything: Finding the Value of Intangibles in Business and The Failure of Risk Management: Why It’s Broken and How to Fix It. Links How to Measure Anything website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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#108
June 3 · 10 min
Episode 108: [Value Boost] How to Use AI Without Losing Your Edge
AI has the potential to dramatically expand what data scientists can do. But used without care, it also has the potential to quietly erode the expertise that makes them valuable in the first place. In this Value Boost episode, Tim Dietrich joins Dr Genevieve Hayes to explore how to stay on the right side of that line and what mindful AI use actually looks like in practice. In this episode, you'll discover: Why looking for problems to solve with AI is a warning sign [02:05] What happens when you use AI before you have the expertise to direct it [05:51] Why your AI interactions should be conversations rather than one-way requests [06:54] How to use AI to become a better thinker not just a faster worker [08:40] Guest Bio Tim Dietrich is an independent software developer with over 25 years’ experience building business software for organisations ranging from startups to Fortune 50 companies, including Siemens and the Library of Congress. Recently, he has become known for building a virtual team of AI specialists that allows him to operate with the output and breadth of a small firm, while remaining a team of one. Links Connect with Tim on LinkedIn Tim's website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE
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