AI and Workforce Strategy
There’s a lot of noise about AI, but for most organisations the real issue isn’t the technology. It’s the way work is designed. AI won’t magically create productivity. Without careful design and change, it will simply speed up the mess.
That’s why healthy AI adoption starts with a much simpler question than “which tools should we buy?” It starts with “how do we create value around here?”
If people are already overloaded, AI can quickly feel like one more thing to learn, one more system to navigate, one more expectation piled on top of an already full day. That’s when adoption becomes unhealthy. People feel pressure to move faster, produce more, and prove their value, while the underlying friction in the work remains untouched.
A better approach is to treat AI as a tool to improve ways of working.
Take meetings, for example, as Tom Hovey talks about in our recorded conversation. Many teams spend hours in update meetings, only to leave with slightly different understandings of what was agreed. That’s not a technology problem. It’s a clarity problem. In that situation, AI might help by summarising written updates, pulling out actions, or translating technical information into plain language. But the real gain comes from redesigning the process: fewer meetings, clearer written communication, and using meeting time for decisions rather than information-sharing. AI supports the shift, but it doesn’t create it on its own.
The same ideas apply more broadly. Healthy adoption doesn’t require a grand, organisation-wide AI strategy on day one. It needs to be much closer-in than that, where small experiments create the value you’re looking for. Pick one team or one frustrating process. Test how AI can help remove some friction. Learn from it, adjust and repeat. That approach builds confidence, surfaces practical lessons, and avoids the common trap of talking about AI in abstract terms while daily work carries on exactly as before.
It also helps organisations avoid one of the biggest risks in this space: using AI to push a “do more with less” agenda. People can usually sense when that is the real story. If AI is introduced in a way that sounds like “we expect more output from fewer people,” it will create anxiety, secrecy and resistance. People will want to look like they’re complying, but they can’t bring their best thinking to the change when their fear is in the driving seat.
A much healthier message is:
“We want to use AI to improve the value of work and give people back some time and energy.”
That’s where the idea of a shorter work week becomes so powerful. If productivity gains from AI and better work design are real, employees can benefit from them too. That could mean a nine-day fortnight, protected focus time, fewer meetings, or more flexibility in how work gets done. Sharing the benefits of AI creates trust, and that reciprocity is what makes people willing to experiment, contribute ideas and engage openly with change.
Leadership matters here as well. AI does not reward leaders who like to have all the answers. It rewards leaders who can create the conditions for learning. That means being honest about uncertainty, inviting experimentation, setting sensible guardrails, and learning alongside the team rather than standing above it. In an AI environment, the strongest leaders are not the experts, they are the ones building confidence, curiosity and a culture where people can test, learn and improve.
If AI is going to become part of everyday work, then work itself needs to become more thoughtful, more sustainable and more human. Better workflows, collaboration, healthier boundaries, shorter work weeks, and learning cultures are not separate from AI adoption. They are what make its value possible.
If you’d like help developing new ways of working that align with AI adoption, get in touch and let’s talk: gillian@gillianbrookes.co.nz