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:

1. Why delegating to AI is always a trade-off [03:00]
2. The crucial difference between writing to communicate and writing to think [08:40]
3. Why you should orient yourself around the problems you solve [14:28]
4. 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

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

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:

1. Why AI is not a truth tool and what that means for how you use it [03:21]
2. The red flags that signal an AI output shouldn't be trusted [12:21]
3. A simple prompting habit you can develop to reduce AI mistakes [16:13]
4. 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

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:

1. Why a single observation reveals more than you think [01:58]
2. How Laplace's Rule of Succession lets you estimate probabilities from tiny samples [08:25]
3. The Rule of Five and what it reveals about small sample statistics [12:08]
4. 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

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:

1. What Applied Information Economics is and how it works in practice [03:17]
2. Why organisations are systematically measuring the wrong things [09:23]
3. How the Lens Model can make expert judgment more reliable than the expert themselves [13:44]
4. 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

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:

1. Why looking for problems to solve with AI is a warning sign [02:05]
2. What happens when you use AI before you have the expertise to direct it [05:51]
3. Why your AI interactions should be conversations rather than one-way requests [06:54]
4. 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

The question haunting every data scientist right now isn't whether AI will change their work, it's whether there will still be a place for them when it does. The answer, according to Tim Dietrich, isn't to compete with AI but to do something far more interesting with it - in his case, building a virtual team of over 100 AI specialists to dramatically expand what he is able to achieve.

In this episode, Tim joins Dr Genevieve Hayes to share the principles and practicalities behind building a virtual AI team, and what data scientists can learn from his experience.

In this episode, you'll discover:

1. How Tim went from being the "world's most negative person on AI" to building a virtual team of over 100 specialists [03:08]
2. What a virtual team of AI specialists can do that a human team can't [06:11]
3. How to build your first AI agent and what to delegate to it [14:19]
4. Why the human in the middle is still the most important person on the team [17:11]

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

These days, every organisation wants to describe themselves as "AI-first". But in the rush to find opportunities to use AI, it can be easy to forget that AI isn't always the right answer.

In this Value Boost episode, Santosh Kaveti joins Dr Genevieve Hayes to explore the situations where AI isn't the answer, how to recognise them, and how to have the conversation with stakeholders who are convinced it is.

In this episode, you'll discover:

1. The types of problems where AI consistently falls short [01:36]
2. How to recognise when AI is the wrong tool for the job [04:46]
3. Why most AI conversations eventually lead back to data, people and processes [06:25]
4. How to push back on an AI solution without losing stakeholder confidence [09:43]

Guest Bio

Santosh Kaveti is the CEO and Founder of ProArch, a technology consultancy that helps enterprises operationalise AI securely and at scale. His expertise spans critical infrastructure industries, including power generation, manufacturing and healthcare, where he has seen firsthand how AI can drive business transformation in complex regulatory environments.

Links

• Connect with Santosh on LinkedIn
• ProArch website
• Connect with Genevieve on LinkedIn
• Be among the first to hear about the release of each new podcast episode by signing up HERE