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

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:

1. Why silent correctness is harder to catch than a hallucination [04:17]
2. Why sampling and auditing are non-negotiable in agentic systems [07:09]
3. Four techniques data scientists can use to catch silent failures [09:28]
4. 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

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:

1. Why data scientists are better prepared for the agentic AI era than they might think [03:00]
2. How the data scientist's role is shifting from analyst to system designer [06:37]
3. The three types of uncertainty in agentic AI systems [11:58]
4. 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

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:

1. How conference organisers actually find their speakers — and why blogging is the answer [02:35]
2. How acquisitions editors scout for authors and why you don't need a huge following [03:39]
3. The low risk way to find out if you're ready to write a book [06:57]
4. 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

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:

1. Why AI-generated content has made personal experience more valuable than ever [05:10]
2. The three Ps test for finding topics you can write about with genuine authority [08:56]
3. The blog post patterns that work best for demonstrating expertise [11:03]
4. 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

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

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

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:

1. Why reputation is the most powerful sales tool a data professional has [04:33]
2. How to stop behaving like a vendor when you're trying to sell expertise [05:09]
3. Why the value of your work resides in your stakeholder not in you [09:53]
4. 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