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On AI - Pragmatic AI strategy, not AI hype.

I help companies make sound decisions about where AI fits, where it doesn't, and what to do about it based on operating experience, not slideware.

What I believe about AI.

I'm an experienced Leader and Architect, not an AI consultant. I have been shipping software long enough to recognize when a new technology is genuinely useful and when it's mostly noise. AI is both, often at the same company, sometimes in the same meeting.

Some AI work is paying off right now. Code assistance, search, summarization, structured extraction from messy inputs. Those are real wins when applied to the right problem, with the right engineering guidance. For engineers and organizations with the right training, the vision to plan, and the testing and operations discipline to keep things working in production, the payoff can be huge. Without the right footing, it's easy to burn calendar quarters on demos that never become products.

The hard part is telling the difference. That takes someone who has built and operated systems before, who can read a roadmap, talk to engineers, and ask the boring questions about data, latency, cost, and what happens when the model is wrong.

My bias is toward outcomes. If a simpler tool solves the problem, I will say so. If the right answer is to wait six months until the tooling settles, I will say that too. That's the value I bring, not a pitch deck.

What I help with - Practical decisions about AI.

Most of the work is judgment, not implementation. Here is where a second set of eyes from someone who has shipped real systems pays off.

  • Evaluating an AI initiative. An honest read on whether a proposed project is likely to ship, deliver value, and survive contact with production. No cheerleading, no doom.
  • Prioritizing AI vs. alternatives. Sometimes the best move is a database index, a workflow change, or a boring integration. I help compare the AI option against the alternatives on cost, risk, and time to value.
  • Build vs. buy decisions. Wrap an API, fine-tune a model, or buy a vendor product. Each path has different long-term costs and lock-in. I help you pick with eyes open.
  • Knowing when 'wait' is right. For some problems the right answer right now is don't build yet. I will tell you when that's the case and what to watch for so you know when the calculus changes.

What I don't do - A few things you won't get from me.

Being clear about the work I don't take on is part of being useful. If any of these are what you need, I am not the right fit and I will tell you so.

  • Implementing AI hype. I won't bolt a chatbot onto a product because the board asked for an AI story. If the use case isn't real, the project won't be either.
  • Turn it over to vibes. The research is in. AI tools are powerful multipliers, but you're only going to get the best from them if they're being driven by experienced engineers who can fully specify the problems, vet the output, and verify that your deployments are clean.
  • Generic AI-consultant credentials. I won't claim deep expertise in every model, framework, or research paper that came out last week. What I bring is operating experience and judgment, applied to your situation.

Want a clear-eyed read on your AI work?

A Technical Diagnostic is a focused engagement to assess where you are, what is worth pursuing, and what to skip. No deck, no upsell.

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