The first kind of AI speeds up the existing workflow.
The second kind reimagines what the workflow is.
Two kinds of AI. Two very different outcomes.
Before the CFO asks what the millions bought, there is a more basic question most enterprises have not answered. What kind of AI are you actually building? The first kind speeds up the existing workflow. The second kind reimagines what the workflow is. The first produces a portfolio of point solutions with positive task-level metrics and no visible change to the business. The second produces an operating system that compounds. The distinction sounds semantic. It is the single most consequential architectural decision an enterprise AI program will make.
Our SERVICES
Strategic Advisory
AI selection, operating-model, and board-decision work for CEOs, sponsors, and senior leaders.
Focused engagements with named decisions and artifacts. Typically two to eight weeks.
Fractional CTO / CIO
Senior technology leadership in the seat. AI-enabled operating model, governance, and execution discipline.
Part-time, embedded inside the leadership team. Typically six to twelve months.
PE and VC Advisory
Independent technology diligence, value-creation review, and portfolio advisory on AI-enabled businesses.
Diligence sprints and recurring portfolio engagements.
The Thesis
A publication on the work that determines whether enterprise AI actually pays off.
Most enterprise AI writing is about models. This isn't. Enterprise AI is not a technology problem. It's a strategy, operations, and value realization problem.
Three pieces argue the operating system most enterprises are missing:
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Selection. One lighthouse use case, not fifty pilots, with finance co-signing the KPIs.
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Sequence. The use case forces the governance, not the other way around.
Operating discipline. Value realization, not uptime, is what survives the next downturn.  The discipline includes Boundary Design, a methodology for naming where AI stops and human judgment owns the decision
WHO I WORK WITH
I work across the four parties whose decisions determine whether enterprise AI delivers.
The work compounds across these audiences. Knowing how enterprises actually struggle to put AI into production informs the advice I give software companies about how their products will be evaluated. The diligence I do for investors informs how I help services firms position their practices. The cycle is the point.
Enterprises
Running portfolios of pilots that are not converging on outcomes, and senior leaders who need a credible answer for the CFO before the next budget cycle.
Software Companies
Whose AI products are losing deals at the technology review rather than on functionality, or whose go-to-market is pitched a level below the buyer who actually signs.
Service Firms
Building AI practices, deciding what to commit to, and trying not to sell yesterday's managed services model into tomorrow's market.
PE & VC Investors
Doing technology diligence on AI-enabled targets, or sitting on portfolios where the AI thesis needs an independent stress test.
Background
Two decades inside Fortune 100 enterprises. Most recently Managing Director in Accenture Technology Strategy and Advisory, where I launched and scaled the North America Technology Advisory practice. Founding executive of Cognizant Digital Business, scaled from inception to $3B+ in annual revenue and 40,000+ professionals in under three years. Earlier leadership at frog (now Capgemini Invent) and Wipro.
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Track record includes $90M+ in verified value realization on a board-approved cloud transformation, an ML diagnostic that compressed rare-disease diagnosis from 10-15 years to 1-2 years, and a national pharmacy platform modernization sustained at 99.7% availability. Harvard Business Review contributor. Stanford University Machine Learning Specialization and MIT Sloan Cloud and Data Business Advisor Program.