Making your long experience matter

The last working decade

I am turning 60 and have spent most of my career in big tech corporations. I left almost 4 years ago and consider myself lucky to have done it on my own terms.

Honestly, it was not because I had a wonderful business idea or a passion for entrepreneurship. I had just reached the point where I felt I had become more of a passenger than a driver in my career, and I did not like that feeling. I thought I had maybe 10 more years to give and wanted to see if I could find a combination of more control with still some reasonable income.

My plan from the beginning was to see how far I could grow my business while minimizing dependencies, long-term commitments, and responsibilities. I was happy to trade growth opportunities for everyday freedom.

If you are around 55 to 60 and still working at a large corporation, you may be thinking about your final decade of work. Should I stay and hope I am successful in dodging the bullets of frequent layoffs and reorgs, or should I seek more control and freedom elsewhere? That is your call. I cannot answer that. But what I can say is that, in either case, learning to use AI tools properly can help create more options for you.

My jump to self-employment

My jump into self-employment coincided with the introduction of ChatGPT. For someone trying to build a one-person business from scratch, this tool proved incredibly useful. Content- and substance-wise, my business of leadership and team development had no connection to technologies like AI, but I still found it helpful in nearly every aspect of my operations: strategizing, planning, lead generation, pipeline management, client communication, financial planning and accounting, content creation, etc.

But the benefits of AI tools did not come for free, and I do not mean just the subscription costs, which can add up to a high figure. A much higher cost was the opportunity cost of time I spent on learning, building, testing, fixing, etc. These tools are not magic that suddenly get you productive and organized if your files and work habits are all over the place.

After decades of trying and failing with a number of productivity systems (Outlook, Apple Notes, Notion, pen and paper…), this was the first ever to stick. Building a logical files and folders structure with proper indexing on my computer and letting AI (Claude or ChatGPT) run it was groundbreaking for me. A few examples:

• Automated daily research on specific topics, turned into private podcasts I can listen to while driving or exercising, makes learning and staying current easy.
• A project assistant that captures details from discussions, tracks progress, and proactively follows up on actions and issues.
• A personal admin who can channel all my requests and can find me things I need to know about my operations in seconds. No document, customer invoice, receipt, old email, or proposal draft is hard to find anymore.

But as I already said, this takes focused effort to build, and it takes two things: taking time to learn what the tools can do, and then pushing yourself to think through your workflows thoroughly and keep your focus on what actually adds value to your business.

Over the last three years, my business focus has gradually shifted from general leadership and team development to AI consultancy and training. I think I can honestly claim that, by now, I have closely observed AI adoption across all kinds of operations, from solopreneurs to companies with tens of thousands of employees.

Better judgment based on life experience

Decades of management experience are likely to put you in a better position to use AI tools. Yes, that is right: better than individuals with less exposure to everything that work and life have taught you as a leader or manager over the decades.

Learning from mistakes builds that hard-to-describe capacity to discern what works and what does not. That capacity makes a huge difference in how useful AI can be for you. You already know what good looks like, and you can spot problems that are less than obvious.

When you look at the AI output, your gut tells you what deserves checking. Wrong angle; reasoning that sounds good but misses the point; mixed priorities; focus missing; nuances of the real context misunderstood. Because of your experience, you know it when you see it. And one can only correct problems one sees.

Getting quality outputs from AI requires essentially two things: knowledge and the capability to use those tools properly, and sound judgment. Anyone, including you, can learn the first. Sound judgment develops through experience, and you already have it.

So back to my main point here. If you are like me and think you still have something more to give and want to decide how your final working decade feels, then preparing and creating options for yourself is a way to go. Whether you stay or leave the corporate job, getting better at using AI can help you do useful work on your own terms.

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