Dr Jim Kennedy

If you have spent decades becoming good at something, your advantage is not simply the information you have accumulated. It is the judgment underneath it: the patterns you recognise, the warning signs you notice, the trade-offs you understand, and the standard you refuse to compromise.

Much of that judgment has become automatic. It may be difficult to explain, package, or pass to someone else. AI changes that. It can help make your experience visible and reusable — then reduce the cost of turning it into something useful.

AI can make execution cheaper. Experience decides what is worth doing, what good looks like, and whether the result can be trusted.

Forty years ago, I was part of a skunkworks team working on something many people still regarded as a toy: the personal computer. We helped take it to the mass market. I have been fascinated by the gap between a technology's promise and its practical consequences ever since.

My own path has crossed science, technology and organisational leadership. I have a PhD in Chemistry and spent five years in medical research. I later became a worldwide marketing manager for a Fortune 500 company, served on an international board, and led across the Asia Pacific region as a vice president responsible for more than 10,000 people.

I have spent much of my working life in rooms where technology promises meet organisational reality — where the decision still has to work after the presentation ends. That taught me to look beyond the demonstration and ask what changes, who benefits, what can fail, and who remains responsible.

I began taking AI seriously in 2015. By 2020 it had become central to my work. Since then I have used it, tested it and built with it — from research and operating systems to applications, voice tools and data infrastructure. The important lessons have not come from collecting more tools. They have come from discovering where context, judgment and a clear definition of good change the result.

Too much AI content treats experienced people as if they are late: late to technology, late to coding, late to the newest tool, late to a future designed by somebody younger. I think that gets the relationship backwards.

When execution becomes widely available, knowing what to execute becomes more important. So do intent, context, standards, trust and accountability. These are precisely the things that real experience can provide.

The opportunity is not to imitate a twenty-five-year-old internet entrepreneur or become an expert in every AI product. It is to capitalise on the knowledge you have already earned — perhaps as income, a useful system, a small business, an asset, meaningful work, greater freedom, renewed usefulness, or a combination that suits your life.

Some pieces will explain a difficult idea. Some will decode a report or an insider document. Some will show a real build, including what failed. Others will follow the personal question underneath all of this: what can decades of experience become now that execution is getting cheaper?