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AI value realization consulting

Prove what the AI spend changed, in numbers your CFO accepts.

AI value realization is the measurement discipline that ties AI spend to results a CFO and a board will accept: a baseline before, measurement during, and a read after that finance signs off on. At Fulkerson Advisors it is built into every Build from week one and reported by the Standing Partner each quarter.

Most AI investments have no credible way to show what they returned. We build one, with the same rigor you would apply to any capital decision, for CFOs, transformation leads, and boards that need the AI line to read like an investment.

How the Build works

How it works

What this looks like in practice

A founding partner leads the work, and our people sit with your finance, transformation, and product teams rather than on a video call from a delivery center. We work with the controllers building the cost model, the analysts pulling the baseline, and the operators whose work the AI is supposed to change. The output is a measurement system that lives in your own reporting tools, a board narrative your CFO can defend, and a stop-or-scale decision reviewed each quarter. When the work needs a specialist in data engineering or sector benchmarks, we bring one in.

When to call

When you should talk to us

A few situations bring CFOs and transformation leads to us. The board has asked for an AI return figure for the next quarterly review, and the number the team prepared will not survive the audit committee. A pilot has shipped, usage looks fine, and no one can say whether it moved revenue or cost. The CFO suspects it did not. Or the CIO is asking for another large budget to scale, and the company has no defensible view of what the first budget bought.

How we do it

How we approach value realization

We treat AI initiatives the way a serious investment committee treats any investment: measure the baseline before deployment, measure during production, compare against a group that did not get the tool where possible, and re-check the case on a fixed schedule. The routine is dull, and it is what separates the programs that earn their next budget from the ones absorbed into IT overhead. We do not publish a return we cannot defend, and that is why the numbers we do publish carry weight inside the building.

  • Baselines measured in the live system rather than modeled in a spreadsheet
  • The full cost of each use: computing, review time, maintenance, and what else the money could have done
  • Stop criteria and a review schedule agreed at the start
  • Results compared with a group that did not get the tool, never against a vendor's estimate
  • Board narrative drafted with the CFO's office and tested against the questions the audit committee will ask

Proof

Case files

The work below shows what disciplined measurement looks like when it is built in from the start rather than retrofitted the quarter before a board review. Each engagement produced a number the CFO's office signed off on, and a routine the team kept running after we left.

What we don't do

We don't write return estimates before a baseline exists. The number is not real until it is measured against something. We don't accept a vendor's savings estimate as the source of truth. We don't ship measurement that depends on us to operate. If the controllers can't run it after the engagement, we built it wrong. And we don't take on programs where the sponsor is not prepared to act on a recommendation to stop.

Measurement only holds if someone owns it every quarter. In the fractional Chief AI Officer engagement, that is the partner's job.

Related case files.

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  2. Asset Management

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  3. Retail (Caribbean conglomerate)

    Demand forecasting Center of Excellence

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See all case files

Questions we hear most.

How do we baseline before we ship?

Before any model goes near production, we instrument the current state: cycle times, error rates, labor hours, decision latency, whatever the use case moves. We capture six to twelve weeks of pre-deployment data where the workflow allows, or a matched control where it doesn't. The baseline becomes the contract, and the post-deployment number is measured against it.

Which costs are real, which are theatrical?

Real costs are fully-loaded: inference, fine-tuning, evaluation infrastructure, the engineers maintaining it, the analysts reviewing outputs, and the opportunity cost of the team that built it. Theatrical costs are the ones in vendor decks that quietly exclude observability, human review, and rework. We build the all-in unit economics so the CFO sees the same number we do.

When do we double down vs. when do we kill an initiative?

We set kill criteria at the start of the engagement. If the system misses its baseline delta after a defined runway (typically two quarters of production use), we recommend sunset. If it clears the bar, we model what the next dollar of investment buys and stack-rank against other initiatives. The discipline is making the call with the same evidence either way.

How do we report AI ROI to the board without making things up?

We report three numbers a CFO can defend: incremental margin or cost out (measured against the baseline), all-in cost to operate with the build included, and the residual risk on the books (model drift, vendor concentration, compliance exposure). We help draft the board narrative so it survives questioning from the audit committee as well as the technology committee.

How long until a value realization framework starts producing usable numbers?

Six to ten weeks to baseline and instrument, and another quarter of production data before the numbers carry signal. We resist publishing ROI earlier than that. Premature claims are the fastest way to lose credibility with the CFO's office, and once lost it doesn't come back in the same fiscal year.

Do you measure adoption separately from value?

Yes. Adoption is a leading indicator. Value is the lagging one. We track both because a system can be heavily used and produce negligible margin impact, or lightly used and move the number that matters. Conflating them is how AI programs end up reporting activity instead of outcomes.

Bring us a question and we’ll tell you whether it’s worth pursuing.

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