The ones that work change what's actually being paid for — hours, or outcomes. Done properly that's a growth program, not a cost program.
Each of them is being asked the same question by their own board, and most of them are answering it with a technology roadmap when it is a pricing question.
You have already bought the capability. Licenses are issued, pilots have landed, and the work still gets done the old way — so the cost line does not move and the board starts asking what the investment bought.
Behind it the growth agenda waits: products to launch across active, passive and multi-asset; AUM to grow from institutional and retail allocators; mandates to win; credit to underwrite faster; portfolios to run more efficiently.
Every client is now asking the same question: why is headcount still the unit of billing? Answer it badly and you spend the next three years defending a shrinking book on price.
Answer it well and services-as-software becomes a revenue line that no longer scales with people — which is a different company, valued differently.
You underwrote services and software growth on headcount, and the market has stopped paying for it. The threat is a re-rating you did not model. The opportunity is top-line growth as the value-creation lever rather than the cost line.
On the other side of the same trade: the AI-native challengers you have funded, where the question is not whether the product works but whether first customers become repeatable revenue.
Where your AI investment stops short of the P&L — the billing unit for vendors, the adoption gap for FS firms — what the numbers look like on the other side of fixing it, and the sequence to get there. Output: a written assessment and one working session with your leadership team. No implementation commitment.
Fractional CRO for AI-native challengers moving from first customers to repeatable revenue. Transformation lead for incumbents rebuilding a commercial model around outcome pricing. The same work from opposite ends: packaging, pricing architecture, and the go-to-market motion that makes new AI revenue repeatable.
Technology alone does not close this. Programs stall for one of two reasons: nobody uses what was built, or it works and burns more in tokens than the people it replaced. Both are failures at the seam between the AI team, the business, and the CFO. Expanding agentic workflows across financial services — cost-effectively — needs all three views at once: how the workflow actually runs, what the models can and cannot do, and what any of it is worth on the P&L. That is the seat I take.
For private equity sponsors: commercial diligence, revenue-per-FTE re-underwriting, and post-merger integration of sales and product organizations. For firms preparing to sell: rebuilding the equity story so revenue is visibly de-linked from headcount — the difference between a services multiple and a software-adjacent one. For firms acquiring AI capability: whether the target's technology survives contact with your delivery model, and whether your commercial model can carry it.
A segment-by-segment map of AI disruption across financial services — who is being disrupted, who is doing the disrupting, and what the incumbents, the challengers and the investors behind both should each do about it. Published on LinkedIn.
I ask five questions, you tell me where you actually sit, and we work out whether I am useful to you. If the answer is that you are not, I will say so.
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