The real AI challenge isn't the technology. It's everything around it.

Last month, we convened two intimate breakfast roundtables at Fortnum & Mason in London alongside our cohosts and partners Culture Consultancy and Brainfood Consulting. Around the table sat 15 senior HR and L&D leaders from some of the UK's most forward-thinking organisations for a no-holds-barred discussion on navigating your people strategy during disruption and uncertainty.

What emerged wasn't a playbook. It was something more useful: a frank, grounded view of where AI adoption in people strategy stands, and where the real work still lies.

Here's what we heard.

The Big Picture: It's Not the Tech. It's the System.

If there was one idea that surfaced in every conversation, it was this:

AI adoption isn't a technology problem. It's a systems and change problem.

Most organisations in the room had spent the last year running pilots, experimenting, building proof of concepts. Some had made genuine progress. Many had not seen that effort translate into lasting, measurable impact.

The organisations most likely to pull ahead won't be the fastest experimenters. They'll be the most disciplined implementers: the ones who move from 'what if?' to 'what now?' by putting structure, governance and clear expectations around their AI investments.

As one participant put it: AI is not a hackathon, it's a transformation programme. Read on for the key takeaways:

Takeaway 1: You Can't Layer AI on Top of Broken Workflows

A lot of organisations are adding AI tools on top of existing roles, without redesigning how workflows or what excellent output looks like.

The result is that AI amplifies whatever is already there, including the inconsistencies.

So what should People Leaders do next? Design for augmentation, not just adoption.

That means role clarity; workflow redesign; updated performance expectations. When done thoughtfully, that leads to leadership alignment and governance guardrails. This is organisational redesign dressed up as a software rollout, and the sooner leaders treat it that way, the better.

Takeaway 2: Clarity Is the New Competitive Edge

AI is only as good as the standards you feed it. Which means organisations that have clearly defined what 'good' looks like across roles, processes and outputs are at a genuine strategic advantage.

Without clarity, AI amplifies inconsistency.

With clarity, AI amplifies excellence.

Multiple leaders spoke about the importance of benchmarking current work before automating it. Not just to protect quality, but to understand what you're trying to improve.

Takeaway 3: Governance Needs to Enable, Not Just Protect

The room had a nuanced view of governance: we pushed back on the instinct to slow things down until every risk is mitigated.

The most effective organisations co-design governance models with IT and Legal, rather than treating compliance as a blocker. They create guardrails that allow safe experimentation. They treat risk as something to manage, not eliminate.

One theme came up repeatedly: the risk of moving too slowly often outweighs the risks currently blocking progress. This is a reward-versus-risk conversation. Not a risk-only one.

Takeaway 4: Senior Leadership Has to Do More Than Say Yes

Enthusiasm from innovation teams doesn't scale. AI transformation needs visible sponsorship at the top, from the leaders who resource it, model it, and connect it explicitly to strategy and performance.

Without that enthusiasm, momentum stalls.

Equally, organisations without cross-functional coordination (such as a centralised AI function or centre of excellence) will find their progress fragmented and inconsistent. AI touches technology, risk, operations, people strategy and learning. You can't run it from a single team's roadmap.

Takeaway 5: Sprint Thinking and Programme Thinking Both Matter

AI transformation runs at two speeds, and you need both.

Sprint thinking means quick wins, use case testing, rapid iteration, momentum. Programme thinking means a cultural shift.

The organisations making real progress have learnt to carefully consider both simultaneously, considering agility and structure alongside speed and sustainability.

What's Next for People Leaders?

The sharpest tension in both sessions sat squarely within HR and L&D. AI is reshaping capability frameworks, performance models, career paths and role design, and people professionals are right at the heart of it.

Tip 1: This Isn't Consultancy Work

One of the strongest and most consistent views in the room was that AI transformation cannot be handed to a big consultancy and solved. It's simply too contextual. External advisors can offer frameworks, but they can't redesign your roles, navigate your culture, understand your informal workflows, or embed lasting capability.

The sustainable advantage lives inside the organisation, so the investment has to start internally.

Tip 2: Up-Skill the People Who Actually Know the Business

Your people are your secret weapon when it comes to the business redesign. The people who understand the business model, know where the inefficiencies sit, and can see real operational risk can build AI capability better than buying in consultants with flashy AI credentials.

AI capability must be built, not bought.

Up-skilling internal talent builds stronger judgement, faster iteration, higher adoption, and capability that sticks. Sustainable advantage comes from inside, not from importing it. 

Tip 3: HR and L&D Can't Sit Adjacent to This

Senior people leaders need to be leading AI transformation, not supporting it from the sidelines. That means having the time to up-skill themselves, the authority to rethink role design, influence over how performance models evolve, and a genuine seat at strategic decision-making tables.

This is core people strategy work. The capability frameworks, career pathways, performance expectations and knowledge flows of the future are being shaped right now. HR and L&D have to be in the driving seat.

Tip 4: Voluntary Curiosity Isn't a Strategy for Literacy

Relying on enthusiastic early adopters to pull colleagues along only gets you so far. Foundational AI literacy often requires mandated training, structured development pathways, and defined competence levels by role.

AI competence needs to be role-specific, observable, measurable, and discussed in performance conversations. Adoption is as much a mindset shift as a skills acquisition programme.

Your Performance Model Might Be Incompatible With AI-Augmented Work

This was a question that landed with visible effect in both sessions: if AI is improving efficiency, increasing output, and shifting value from execution to judgement - is your performance model keeping up?

Forward-thinking organisations are embedding AI usage into KPIs, tracking efficiency and effectiveness gains, recognising experimentation and knowledge sharing, and rewarding contributors to collective intelligence.

If AI usage doubled in six months, would your talent systems support it?

Tip 5: Role Redesign Is Now Core Talent Work

Which roles become more valuable in an AI-augmented world? Which become more vulnerable? Where does human judgement still differentiate? Where does automation erode execution value?

These aren't hypothetical questions anymore. And you can't answer them 'on the side.' Leaders need to intentionally create time for role redesign, workflow mapping, experimentation and reflection. Without space that’s actively protected by senior leaders, it simply won't happen.

Tip 6: Knowledge Flow Is a Strategy, Not a Nice-to-Have

AI transformation accelerates when knowledge moves across the organisation. Reverse mentoring, cross-generational learning, communities of practice, and internal content creators build infrastructure.

But a consistent gap emerged during our conversations: a lot is being launched. Not enough is being tracked, put through a retrospective process, and iterated. Talent strategy needs to recognise contributors, amplify early wins, and build systems that sustain knowledge sharing over time.

AI transformation is a continuous capability-building cycle, not a one-off programme.

So, how do we get started?

Over two breakfasts surrounded by the kind of candid conversation that tends to happen when the agenda is thinking, not pitching, the outcomes were clear.

The organisations that will create enduring advantage from AI are not the ones with the most tools or the biggest budgets. They're the ones treating this as a business transformation, with people strategy at the centre of it.

The technology is ready. The question is whether the systems, the culture, and the leadership around it are too. 

We're grateful to every leader who joined us for their honesty, their challenge, and their willingness to think out loud. The conversation continues. 

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