I get asked three questions quite consistently about AI, and they all focus on one theme — “Will AI replace us?” The three questions are: 1. Which LLM is the best for the type of work I’m doing? 2. What AI course do you recommend? 3. Which jobs will be replaced by AI in the next 12 months? That last question is the one where I feel the most anxiety and tension emanating from the person asking it, and I understand why. Since ChatGPT launched in November 2022, entry-level job p

This is the eleventh post in a series I write for People Leaders who want clarity, not hype. My aim is always the same: to take the parts of the AI conversation that feel technical, abstract, or simply overwhelming, and translate them into something a people leader can actually use. This post is about a decision that has almost certainly already been made in your organisation, either with you or without you. It is about what that decision really commits your people to — and w

There is a growing narrative that “building AI agents” is the next step for progressive People teams, and that if you are not experimenting with agents you are somehow lagging behind. I’m hearing this in leadership conversations, it’s all over LinkedIn and it’s certainly filling vendors sales pipelines. I call this the Agent Illusion . The Agent Illusion is the belief that deploying AI agents will unlock productivity on its own, without redesigning workflows, cleaning up data

Why HR doesn’t have an AI tools problem, it has a judgement problem it cannot yet see. In a recent session with a Global Leadership team, I opened with a simple line: HR does not have an AI tools problem. HR has a capability visibility problem. The room went quiet, and not because it was controversial, but because it was uncomfortably accurate. Across enterprise and scaling People functions, AI adoption is now measurable. Dashboards exist, licences are activated, prompts are

Most organisations are having the same conversation about AI, just with different tools on the agenda. Which platform should we roll out? Which workflows should we automate? Which teams need training first? Those questions are not wrong, they are just incomplete. The harder question, and the one most People Leaders are quietly avoiding, is this: What human capabilities need to be strengthened so people can  work with  AI without surrendering judgement to it? That is where  br

Most organisations now accept that AI training is necessary.What far fewer are clear on is how to tell whether that training actually worked. Completion rates are high, internal feedback is positive and there is a perception that collective confidence has gone up.And yet, weeks later, decision quality looks unchanged. That gap is not accidental. It is structural. Why AI competence is so hard to measure There is no globally recognised scale for AI competence, and that is not a

Employee training hasn’t failed because of AI. It’s failed because AI has exposed how fragile most training models already were. For years, employee training has followed a familiar pattern: Set a company-wide goal Design learning for all roles and functions Prioritise virtual delivery for scale Motivate participation through campaigns and incentives Brief managers so they can support the rollout Encourage employees to apply learning through projects In principle, this sounds