Your AI Pilot Is Working. Can Your Champion Prove It?
How AI founders and sellers can help buyers defend purchase and expansion decisions
In my work with AI founders and sellers, I’m seeing a common challenge.
The companies buying from them are getting encouraging results from early AI use cases. Those buyers must now decide whether the evidence supports a larger purchase, renewal or enterprise standardization.
My clients need to help their internal champions defend that next investment when the seller isn’t in the room.
That changes the burden of proof.
A promising pilot may establish that the technology works in a limited setting. Standardization requires evidence that the improvement matters to the business, can be repeated across teams and can be sustained at an acceptable cost and level of risk.
The technology may work. Employees may use it. The deal can still stall when the internal champion lacks the evidence to defend renewal or expansion.
Ideally, the baseline, target and business outcome are agreed before the pilot begins. When they aren’t, establish them before asking the buyer to renew, expand or fund the next phase.
This is becoming a familiar enterprise challenge. In McKinsey’s 2025 global survey, 88 percent of respondents said their organizations regularly used AI in at least one business function, while 39 percent reported an enterprise-level EBIT impact. Those figures illustrate the gap between widespread use and reported enterprise-level financial impact.
For AI founders and sellers, the implication is straightforward: usage can open the door, but evidence supports the next investment.
Follow the value into the workflow
Attendance, licenses, logins, prompts and satisfaction scores are useful. They show whether people were introduced to the technology and whether they are trying it.
Repeat use inside an important workflow is a stronger signal. Continued investment usually requires evidence that the workflow improved and that the improvement matters to the business.
The measurement path is:
Adoption → workflow improvement → business outcome
For example:
| AI use | Workflow measure | Possible business outcome |
|---|---|---|
| Sellers use an AI research assistant | Less preparation time | More selling capacity |
| Estimators use AI during bid preparation | Faster completion or fewer missed requirements | More qualified submissions or less rework |
| Employees use an internal knowledge assistant | Faster access to reliable information | Shorter case or contract resolution time |
| Service teams use AI to prepare responses | Faster and more consistent responses | Lower cost to serve or better customer experience |
These outcomes are hypotheses until the buyer validates them with its own data.
Time saved also needs careful treatment. Released capacity becomes financial value when the company uses it to produce more profitable work, reduce spending, avoid planned hiring or reduce a meaningful risk. If none of those happens, the benefit may still matter, but it shouldn’t be presented as realized financial return.
Label each benefit clearly as measured, estimated or still to be validated.
Define success before the pilot
For each use case or phase, pair adoption with one workflow measure and one business outcome.
Workflow measures might include:
Cycle time
Throughput
Rework
Accuracy
Response time
Cost per transaction
Business outcomes might include:
Revenue capacity
Operating cost
Risk exposure
Time to market
Customer retention
The right measures depend on the workflow and the executive accountable for it. Ask how that executive is measured, what must improve and what evidence will be credible when the next funding decision arrives.
If the pilot is already underway, establish the baseline and target before the next phase. Imperfect evidence that the buyer understands is more useful than precise-looking math built on assumptions no one has accepted.
Establish evidence before scaling
Before recommending expansion, help the buyer answer six questions:
What was happening before the AI use case was introduced?
What improved, by how much and over what period?
How will the buyer determine how much of that improvement came from the AI use case?
Is the benefit revenue, lower spending, avoided hiring, reduced risk or released capacity?
What did the first phase cost to deliver, and what will it cost to sustain?
Did quality, compliance and operating risk remain within acceptable limits?
Include the full cost where it can be determined: software, integration, infrastructure, data preparation, human review, security, support and governance.
AI also rarely acts alone. Process changes, management attention and other initiatives may contribute to the result. Use a prior baseline, similar teams or a phased rollout where practical. Compare the AI approach with reasonable alternatives, including process changes or conventional automation.
The business owner should remain accountable for the outcome. Finance can help validate the assumptions, timing and level of evidence needed to support continued investment.
Turn early success into an enterprise standardization case
Enterprise standardization means moving from a promising use case to a repeatable way of working across more people, teams or business units. That decision requires more than proof that the first group succeeded.
Before asking the buyer to standardize, document:
What the first phase was intended to prove
The baseline and the change observed
Which results were measured, estimated or are still being validated
What the first phase cost
Which conditions made the result possible
What can reasonably be repeated at greater scale
Which assumptions and risks remain
What the next phase will cost and what it is intended to prove
Avoid assuming the pilot result will scale at the same rate. More users may require new integrations, support, governance, training and workflow changes. State what the current evidence supports and what the next phase still needs to validate.
This gives the buyer a staged investment decision. They can expand where the evidence is strongest, learn what changes at scale and limit exposure while the remaining assumptions are tested.
Help the champion defend the next decision
Your champion may believe in the technology. Their harder job is explaining why the company should keep investing when you aren’t in the room.
Equip them with a short, forwardable expansion case that an executive outside the pilot can understand:
What changed
Why it matters to the business
How confident the team is in the evidence
What the next phase will cost
What additional value or risk the next phase will test
What decision is needed now
The central question is simple:
Alongside adoption, what improved in the work, why does it matter to the business and what evidence supports expanding it?
Licenses, training and logins show that AI is being introduced.
Repeat use shows that it is entering the workflow.
A defensible expansion case gives the buyer a reason to standardize what is working and keep investing.
About Mark: Mark Phinick is a B2B Deal Coach who works directly with founders, sales leaders, and sellers inside live opportunities that aren’t moving. He helps teams uncover what is blocking the customer’s decision, strengthen the business case, equip champions to build internal support, and create a credible path to go-live.