Kieran Gilmurray on agentic AI, software labor, restructuring roles, and AI native intelligence businesses (AC Ep84)

“Let technology do the bits that technology is really good at. Offload to it. Then over-index and over-amplify the human skills we should have developed over the last 10, 15, or 20 years.”

– Kieran Gilmurray

About Kieran Gilmurray

Kieran Gilmurray is CEO of Kieran Gilmurray and Company and Chief AI Innovator of Technology Transformation Group. He works as a keynote speaker, fractional CTO and delivering transformation programs for global businesses. He is author of three books, most recently Agentic AI. He has been named as a top thought leader on generative AI, agentic AI, and many other domains.

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BOOK: Free chapters from Agentic AI by Kieran Gilmurray

Chapter 1 The Rise of Self-Driving AI 

Chapter 2: The Third Wave of AI 

Chapter 3 – Agentic AI Mapping the Road to Autonomy

Chapter 4- Effective AI Agents

What you will learn

• Understanding the leap from generative to agentic AI

• Redefining work with autonomous digital labor

• The disappearing need for traditional junior roles

• Augmenting human cognition, not replacing it

• Building emotionally intelligent, tech-savvy teams

• Rethinking leadership in AI-powered organizations

• Designing adaptive, intelligent businesses for the future

Episode Resources

People

• John Hagel

• Peter Senge

• Ethan Mollick

Technical & Industry Terms

• Agentic AI

• Generative AI

• Artificial intelligence

• Digital labor

• Robotic process automation (RPA)

• Large language models (LLMs)

• Autonomous systems

• Cognitive offload

• Human-in-the-loop

• Cognitive augmentation

• Digital transformation

• Emotional intelligence

• Recommendation engine

• AI-native

• Exponential technology

• Intelligent workflows

Transcript

Ross Dawson: Hey, it’s fantastic to have you on the show.

Kieran Gilmurray: Absolutely delighted, Ross. Brilliant to be here. And thank you so much for the invitation, by the way.

Ross: So agentic AI is hot, hot, hot, and it’s now sort of these new levels of how it is we — these are autonomous or semi-autonomous aspects of AI. So I want to really dig into — you’ve got a new book out on agentic AI, and particularly looking at the future of work. And particularly want to look at work, so amplifying cognition.

So I want to start off just by thinking about, first of all, what is different about agentic AI from generative AI, which we’ve had for the last two or three years, in terms of our ability to think better, to perform our work better, to make better decisions? So what is distinctive about this layer of agentic AI?

Kieran: I was going to say, Ross, comically, nothing if we don’t actually use it. Because it’s like all the technologies that have come over the last 10–15 years. We’ve had every technology we have ever needed to make more work, more efficient work, more creative work, more innovative, to get teams working together a lot more effectively.

But let’s be honest, technology’s dirty little secret is that we as humans very often resist. So I’m hoping that we don’t resist this technology like the others we have slowly resisted in the past, but they’ve all come around to make us work with them.

But this one is subtly different. So when you say, look, agentic AI is another artificial intelligence system. The difference in this one — if you take some of the recent, what I describe as digital workforce or digital labor, go back eight years to look at robotic process automation — which was very much about helping people perform what was meant to be end-to-end tasks.

So in other words, the robots took the bulky work, the horrible work, the repetitive work, the mundane work and so on — all vital stuff to do, but not where you really want to put your teams, not where you really want to spend your time. And usually, all of that mundaneness sucked creativity out of the room.

You ended up doing it most of the day, got bored, and then never did the innovative, interesting stuff.

Agentic is still digital labor sitting on top of large language models. And the difference here is, as described, is that this is meant to be able to act autonomously. In other words, you give it a goal and off it goes with minimal or no human intervention. You can design it as such, or both.

And the systems are meant to be more proactive than reactive. They plan, they adapt, they operate in more dynamic environments. They don’t really need human input. You give them a goal, they try and make some of the decisions.

And the interesting bit is, there is — or should be — human in the loop in this. A little bit of intervention.

But the piece here, unlike RPA — that was RPA 1, I should say, not the later versions because it’s changed — is its ability to adapt and to reshape itself and to relearn with every interaction.

Or if you take it at the most basic level — you look at a robot under the sea trying to navigate, to build pipelines. In the past, it would get stuck. A human intervention would need to happen. It would fix itself.

Now it’s starting to work itself out and determine what to do. If you take that into business, for example, you can now get a group of agentic agents, for example, to go out and do an analysis of your competitors.

You can go out and get it to do deep research — another agentic agent to do deep research, McKinsey, BCG or something else. You can get another agent to bring that information back, distill it, assemble it, get an agent to create it, turn that into an article. Get another agent to proofread it. Get another agent to pop it up onto your social media channels and distribute it.

And get another agent to basically SEO-optimize it, check and reply to any comments that anyone’s making. You’re sort of going, “Here, but that feels quite human.” Well, that’s the idea of this.

Now we’ve got generative AI, which creates. The problem with generative AI is that it didn’t do. In other words, after you created something, the next step was, well, what am I going to do with my creation?

Agentic AI is that layer on top where you’re now starting to go, “Okay, not only can I create — I can decide, I can do and act.” And I can now make up for some of the fragility that exists in existing processes where RPA would have broken.

Now I can sort of go from A to B to D to F to C, and if suddenly G appears, I’ll work out what G is. If I can’t work it out, I’ll come and ask a person. Now I understand G, and I’ll keep going forever and a day.

Why is this exciting — or interesting, I should say? Well-used, this can now make up for all the fragility of past automation systems where they always got stuck, and we needed lots of people and lots of teams to build them.

Whereas now we can let th