🗒 Summary
Lou Fraser joined us to share what she's seeing in the market - and what Instant Impact are actively doing - as AI reshapes how organisations hire, develop, and structure their workforce. Bottom Line: most companies are still focused on individual productivity gains rather than actually redesigning how work happens. The window to get ahead of this is now.
📡 What's Changing Out There
👉 The entry-level pipeline is thinning The Big Four cut graduate intakes by up to 30% this year. Two-fifths of companies have already cut or plan to cut entry-level and junior roles due to AI (CIPD). The risk: a longer term capability gap if this isn't managed intentionally. Counter-trend: some organisations are turning to more intensive apprenticeships and paid work trials to capture early career capability in a way that feels lower risk.
👉 Skills are expiring faster than ever Skills in AI-exposed roles are evolving over 60% faster than in roles that aren't AI enabled. Job roles are continuously broadening as a result. This isn't a one off training problem, it requires continuous development, communities of practice, and a different approach to L&D altogether.
👉 Org structures are flattening Gartner predicts 20% of organisations will flatten by end of 2026. The manager's role is shifting from supervising work to supervising direction. Roles are broader and less specific, which makes traditional career ladders feel increasingly hard to justify.
👉 Horizontal progression is the new reality - and it's hard to sell As hierarchies flatten, organisations are starting to frame progression as learning new skills or moving along a re-skilling pathway rather than moving upwards. Lou's view: this is going to be genuinely difficult to navigate, because most people are wired to think about progression hierarchically. The narrative around it matters a lot.
👉 Internal mobility is becoming a strategic lever Organisations are realising their fastest, lowest risk talent pipeline is often already inside the building. Clear skill profiles, internal talent marketplaces, project rotations, and internal gigs are all on the rise. Microsoft Copilot has quietly released a people skills inference tool that can map your organisation's skill taxonomy automatically from the work people are actually doing.
👉 Hiring is shifting toward real - work assessment Skills-based hiring is mainstream. Soft skills assessments are rising. The more interesting shift: organisations are moving toward work simulations, paid trials, and more intensive processes to genuinely test how people work - not just what their CV says. Employer brand and candidate experience are becoming competitive advantages in a world where AI generated CVs all look the same.
👉 AI fluency is becoming a baseline hire screen Not "can you prompt", but: can you define the work, use AI responsibly, validate outputs, and exercise good judgement? Many organisations are building AI scenarios into assessments, including policy awareness, data handling, and human in the loop checks.
⚡ What Instant Impact Have Done
- Merged People and Transformation into one function. The conversations about technology and the conversations about people were the same conversation. Keeping them separate was slowing things down.
- Started with the work, not the org chart. Broke roles down into outcomes and workflows first, then decided what's best done by humans, what by AI, and what by automation.
- Redesigned roles when productivity improved - not just absorbed the time saving. The risk with giving someone a better tool is they fill the freed up time with low impact work. Instant Impact's approach: redefine the role and the goals alongside the tool, so the expectation shifts too. Their data team now prompts Claude Code to build their data warehouse. Their role has shifted, and so have their goals.
- Started mapping the future state. Recruitment operations will likely become largely automated within a couple of years. So Instant Impact are building toward a differentiator that sits outside of that: strategic thinking, workforce design capability, and quality of human judgement. That's already shaping how they're setting up roles and teams now.
✅ What You Should Do: A Practical Step Plan
1. Start with the work, not the org chart Pick one team or function. Break down what they actually do into outcomes and workflows. Then ask: what's best done by a human, what by AI, what by automation? Don't redesign the structure until you've done this.
2. Redesign the role when you introduce a tool - not after If you give someone a tool that saves them 3 hours a week, those 3 hours will disappear into low impact work unless you deliberately redefine what that role is now for. Set new goals and expectations at the same time as you roll out the tool.
3. Get close to the organisation's big bets Understand the longer term strategy and future state of the business, even if it'll change. If you know where the business is heading, you can work backwards and start shaping workforce decisions from there rather than reacting when it's already happened.
4. Build the foundations before you scale Document how work actually happens. Fix broken handoffs. Get basic data discipline in place. Without that, AI just accelerates the chaos that already exists.
5. Treat internal mobility as seriously as external hiring Map your internal skill profiles. Look at where project rotations, mentorship, or internal gigs could fill capability gaps faster and cheaper than hiring externally. Use it as a development tool, not just a retention one.
6. Define what AI fluency means at every level in your organisation Not just "uses AI tools" - but can they define the work, validate outputs, and make good judgements? Bake that definition into hiring, onboarding, and performance conversations.
7. Put deliberate thinking back into assessment and development As AI takes over routine tasks, critical thinking risks quietly atrophying. Build in AI-free moments - live problem solving, written reasoning, white-boarding - so that skill stays sharp across your team.