AI expands what a business can do.
So have many of the technology innovations that came before it. AI may be more powerful and more broadly applicable, but the underlying business questions haven't changed. Most companies aren't selling AI. They're trying to grow, run more efficiently, make better decisions, and serve customers better.
In the early days of the internet, many companies treated having a website or an online business as a strategy in itself, with many high-profile failures. The companies that created lasting value learned to integrate the internet into the business they were actually running: how they sold, fulfilled, served customers, made decisions, and operated. The same lesson applies to AI. It creates value when it becomes part of how the business actually works.
New technology changes what is possible. It does not suspend the economics of the business.
Where AI belongs
The starting point is the business itself. Where could decisions be better informed? Where is work slow, manual, inconsistent, or expensive? Where are customers waiting, getting generic treatment, or struggling to get what they need? Where are existing processes or economics limiting growth?
AI becomes valuable when it materially improves one of those things. It might change how a decision gets made, automate part of a process, make expertise available at much greater scale, improve how information moves through the business, or create a customer experience that was not practical before. The technology matters because of the business capability it creates.
That is why AI belongs in the same conversations as strategy, operating model, process, data, and technology. When I define a target state or work backward to a roadmap, AI is one of the capabilities I consider in deciding what the business should be able to do and how to get there.
How I work →What this looks like in practice
These span data, systems, automation, and now AI. In each case the technology mattered because of what it let the business decide or do.
Recruiting Performance
The Army's recruiting program was framed as a marketing problem: generate more leads, move prospects through the funnel faster. Looking across the whole journey showed something broader: no one owned conversion from lead through enlistment. Existing data and quick MVPs made it possible to infer where a prospect was, give recruiters better context, and prove the changes before committing to larger integrations.
Read the story →Retail Expansion
A plan to accelerate store openings was built on an unvalidated list of target markets. Combining financial modeling with geographic sales forecasting, then automating the analysis across the full market universe, revealed that the real opportunity was far smaller than leadership assumed, and changed how the expansion had to be managed.
Read the story →Market Selection
Choosing which customers to pursue first usually comes down to whoever argues best. I have been building a system that evaluates the candidates instead, scores them blind against a defined rubric, and checks whether the evidence behind a recommendation is real. In one run, only 15 of 39 sources held up.
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What is possible now that wasn't before?
The opportunity is to look at the business with a new set of capabilities available. Where could AI materially improve growth, economics, decision-making, customer experience, or how the business operates? That is the starting point for deciding where it belongs.
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