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:

1. The three types of memory every AI agent needs [01:54]
2. Why context and memory are not the same thing [05:37]
3. What context rotting is and why it matters for how you design AI systems [09:15]
4. How to make sure LLMs pay attention to what actually matters [10:39]

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

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:

1. How the data science toolkit has fundamentally shifted since the start of the AI era [02:09]
2. The difference between broad AI and deep AI [10:18]
3. The problems that still demand deep algorithmic solutions [14:10]
4. 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

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

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:

1. What the AI chicken nugget problem is and why it matters [02:31]
2. Why vibe coding is not a rare skill and never will be [05:12]
3. Why obsessing over something rare and valuable is your best career bet [17:17]
4. 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

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:

1. The eight disciplines that make up the modern decision stack [01:24]
2. The most overlooked layer in most decision systems [04:47]
3. Why optimization textbooks are teaching data scientists the wrong things [06:17]
4. 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

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:

1. What separates a decision factory from a machine learning pipeline [02:11]
2. Why uncertainty is an input into good decision making [10:51]
3. How to test a decision policy before it costs you real money [12:11]
4. 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

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:

1. The four layers of an agentic AI strategy and why most organisations only think about one [01:53]
2. The three symptoms that signal an organisation's AI strategy is breaking down [05:52]
3. The four questions that reveal what an organisation's AI strategy is actually missing [08:21]
4. 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

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:

1. Why thinking of AI as a tool rather than a coworker is the mindset holding most organisations back [03:58]
2. The four archetypes that determine which tasks belong to humans and which to agents [08:17]
3. How AI is turning data scientists back into scientists [18:24]
4. 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