The Augmented Human’s Playbook: An Innovation Leader’s Guide to Using AI More Intentionally

I described myself as a “semi-professional conference attendee” last week.

I was speaking at the latest of the half dozen conferences I’ve found myself attending this year. It’s a practice I love, something we at C Space call “outdoor catting,” getting out, meeting people, hearing about what’s going on in their world(s) and collecting inspiration that can inform and sharpen our own work.

One thing you can be sure of about conferences in 2026? AI is going to be on the agenda. And I’m not afraid to say the quiet part out loud: I’m kind of over it. It’s a lot of the same people, talking about the same things, and some of it has stopped feeling inspiring.

And it’s not just me. Clients tell me they’re finding it boring, tiring and overwhelming, with presentations that are becoming indistinguishable from one another. When someone asked an AI moderated insight platform what the difference is between them and another competitor, their answer was: “We’ve raised more money.”

There’s got to be something more, right?

So, when Max Angelov, chief rocket launcher at the Global Innovation Forum (GIFLondon), invited us to attend Augmented Humans, I can’t say I was that jazzed at first. Another AI conference? “Do I have to go?” I asked our marketing team. But it was clear from my first skim of the website that this wasn’t just another AI conference. Its mission was curious: to explore how AI can amplify human creativity, leadership and innovation.

What I heard at Augmented Humans reaffirmed my faith in smart people using smart technologies to do better work. Not faster work, not more work, but better work. That is the central challenge for innovation leaders today, how to use AI to improve performance without weakening the human expertise, judgment and creativity that make good work possible. The rallying cry was this: we are not working in service of, or at the behest of, AI. It’s working for and with us. We are, or we should be, on a path to become augmented humans, people whose skillsets and capabilities are enhanced and augmented by the power and speed of AI, all this while remaining firmly in human hands.

Here, I’ll share the four principles that’ll help you, too, become an augmented human.

A ticking time bomb and the erosion of human expertise

One of the day’s sharpest anxieties came in the acknowledgment, by Lloyds Banking Group and within London city government, that AI is quietly eroding the foundational skills that make expertise meaningful.

AI has given social workers six hours a week back, as they use it to prep and produce case notes. But, for junior social workers who’ve never written a case note from scratch, how are they to navigate the legal, psychological, safeguarding elements of this very important document? How do they know what “good” looks like? Replace “social worker” with “lawyer,” “engineer,” “insight grad” or anything else and you’ll start to see a problem whose urgency is quietly building. By saving time now, organizations risk hollowing out their next generation of senior talent.

And the unfortunate paradox is that we’re trying to upskill everyone as fast as possible in AI tool adoption, while the very act of using AI is deskilling us. And it’s not limited to juniors, I’ve seen it in myself and in others with 10, 15 years’ experience in the industry.

Earlier this year, Microsoft’s own research with frontier professionals, users working with the most advanced models to do the most cutting-edge AI work, showed 43% of these users deliberately do some work without AI to keep their skills sharp, compared with 30% of non-frontier professionals. The more sophisticated your AI usage, the more likely you are to understand the dangers of over-reliance on AI and the more likely you are to more consciously protect your human practice.

So, how do you become an augmented human?

You wouldn’t hire a personal trainer and then sit around, expecting results, while they do the squats for you. The same applies here. Do the reps, do the work yourself, protect your intellectual muscle wherever and whenever you can, even when AI can do it faster.

The most advanced AI users understand that efficiency is not the same as expertise. Protecting the human work that builds judgment is essential to developing tomorrow’s leaders.

Friction is a feature, not a bug at work

Another highlight came in a workshop on “friction-maxxing” from the co-leads of Visa’s internal design studio. Friction-maxxing is the deliberate act of introducing human effort into high-value workflows where you might otherwise default to AI.

They tied it to what they called the “effort paradox”: the things that build capability, meaning and identity at work are hard, and that difficulty is what makes them meaningful. Their presentation was packed with fascinating studies and anecdotes, one drawing on a medical study where professionals delegated tasks to AI. They reported two compounding problems: first, a drop in their capability; and secondly, an identity crisis, if I’m not doing the thing I trained to do, who am I?

What does intentional AI use look like in practice?

What might intentional AI expertise use look like in practice, and what does it mean for insight and innovation folks?

• If idea generation is your human edge, try doing Crazy Eights by hand before going anywhere near AI.
• If writing is your craft, carve out deep work and thinking time. Draft your argument yourself before asking AI to develop it.
• If research synthesis is your value-add, start a page of notes with key quotes and the connections only you can make. Form your own hypothesis before asking AI to summarize.

The point isn’t to avoid AI. It’s to go in with something human already on the table.

This is particularly important in our world, where so much of what we do is fundamentally about noticing things, about developing a sensitivity to human behavior, cultural nuance, and the gap between what people say and what they mean. What they want and what they’ll actually pay money for.

That sensitivity is built through exposure, practice and reflection. If you’re always relying on AI-generated summaries, you stop developing the ability to read the raw material. If you’re always getting the AI-generated synthesis, you stop trusting your own pattern recognition.

Friction, in this context, is how you stay sharp enough to know when an AI tool is wrong, or when it’s missing the killer insight that matters most.

So, how do you become an augmented human?

Introduce friction on purpose. Before you reach for AI, ask whether doing this yourself first would make you better at your job. Build moments of deliberate human effort into some of your highest value workflows as a feature, not a workaround.

Rubbish in, rubbish out

Eddie Copeland, Director at the London Office of Technology and Innovation (LOTI), was one of the most dynamic speakers of the day.

Many of us don’t think about local government as leading the way on innovation, but Eddie’s spent his career doing just that. His sharpest line was “AI plus bad data equals faster bad decisions.” Feed AI bad, biased or incomplete data and you don’t just get bad outputs. You get bad outputs at speed and at scale, with a veneer of authority. In other words, AI can amplify the weaknesses already present in your data, assumptions and decision-making process.

But there’s a subtler version of the same problem.

Aman Singh, senior digital brand experiences & marketing transformation lead at Unilever, who I interviewed in a fireside chat on AI in marketing, talked about the brief as the foundation of any campaign. The brief is where the consumer enters the room, he said.

If it’s vague, underpowered or built on weak insight, AI can’t compensate. It just executes the brief faster. The quality of your framing, your hypothesis, your stimulus matters more now, not less, because AI will follow it wherever it leads.

He also talked about synthetic data and its limits. He recalled sitting in consumers’ homes in India, watching how they actually use products, and how much of his real marketing instinct came from that. Synthetic personas are useful for rounding out a hypothesis. They can’t replace the unexpected, relationship-built insight that comes from genuine human encounter. If your understanding of people is shallow or second-hand, AI will faithfully reproduce and amplify that shallowness. The better the human understanding behind the brief, the more useful and relevant the AI-generated output is likely to be.

So, how do you become an augmented human?

Get in the room whenever you can. A sharper brief, richer stimulus and a clearer hypothesis matter more now, not less. No synthetic persona captures what you learn from sitting in someone’s kitchen. Physical presence and genuine human encounter are a competitive advantage in insight.

The volume trap and the opportunity to anticipate customer needs

If you’re using your AI dividend, the time you’ve saved by using AI, to create more outputs rather than better thinking, you’re creating cheaper work at higher speed. Eddie Copeland made a version of this argument, but flipped it into an opportunity: what if instead of processing complaints faster, you got ahead of the problem before anyone complained at all?

Those two ideas, the trap and the opportunity, are two sides of the same coin.

The trap has a specific mechanism. AI removes the production constraint, so the natural organizational response is to produce more. More insight, more content, more assets, more reports.

But volume doesn’t equal value.

The anticipation opportunity is the more exciting flip side, and it’s underexplored. Eddie’s public sector examples reframed the entire purpose of data and technology: not to respond to what people tell you, but to understand what they need before they’ve found the words for it.

For insight and innovation leaders, this is a genuinely different way of thinking about what the function is for. Most insight work is still essentially reactive: a business asks a question, insight goes and finds an answer. The anticipation opportunity asks whether AI-enabled insight could shift that model, using connected data, behavioral signals and cultural listening to surface what consumers are moving toward before they’ve articulated it, before it shows up in surveys, before it becomes a trend report.

So, how do you become an augmented human?

Ask what problems you can solve and what’s worth making, not what you CAN make. In a world of infinite content and synthetic insight, curation and discrimination become the premium capability. The insight leaders who win won’t be the ones producing the most, they’ll be the ones anticipating what consumers need next.

A closing thought on using AI with intention

It’s easy to be led by the hype cycle. I’m not moralizing or telling anyone to slow down. And this isn’t about choosing between stagnation and AI-driven progress. But if YouTube execs won’t let their own kids watch short-form video, and the most sophisticated AI users in the world are the ones most likely to deliberately put the tool down sometimes, I feel okay about making the case for more intentional adoption.

My closing message: think.

Think about why you’re using AI. Think about the data you’re inputting and the questions you’re asking. Think about how you’re going to keep your specialized skillset, something you’ve been building for years if not decades, sharp over the next five to ten years. Think about the consequences of a lost generation who don’t know what good looks like and intervene now. And, above all, think, critically, about where you can uniquely add value.

As AI becomes more capable, human judgement, critical thinking and creative expertise become more important, not less.

That’s not something you could ever automate.