AI Proof of Value · Build and Prove
Prove the value in real work.
An AI proof of concept can show that technology works. Become builds the smallest credible capability that can show whether the business will work differently—and whether the value is real.
From proof of concept to Minimum Viable Capability
A conventional proof of concept usually tests a technical question in controlled conditions. That is useful, but insufficient when the investment decision depends on more than model performance.
A Minimum Viable Capability brings together the smallest credible version of the AI or automation, the human judgement it supports or changes, the workflow in which it operates, the data and controls it depends on and the measures that connect it to value.
The goal is not to make a miniature production system. It is to create enough reality to test the assumptions that determine whether further investment is earned.
What Become builds and tests
A working capability
Enough of the product, process or agentic workflow to perform the consequential part of the job—not a slideware simulation.
Real operating conditions
Real users, representative work, realistic handoffs and the constraints that will exist outside a demonstration.
Explicit human judgement
Where people must decide, review, challenge or take accountability. Human-in-the-loop is designed as part of the capability, not added as a disclaimer.
A value baseline
The current cost, time, quality, capacity, revenue or risk against which change will be judged.
Evidence for a decision
A defined threshold for scaling, revising or stopping—and an honest account of what remains uncertain.
The Build and Prove cycle
01 — Design the test
Identify the assumptions that could invalidate the value case. Define the minimum capability, cohort, baseline, measures, controls and decision thresholds.
02 — Build the capability
Combine the required AI, workflow, data and user experience. Keep the scope narrow enough to learn quickly but real enough to be credible.
03 — Run it in the work
Observe what happens with real users and real cases. Measure performance, exceptions, adoption, effort and unintended effects.
04 — Strengthen the evidence
Iterate only where new evidence can change the decision. Do not polish features that are irrelevant to the value logic.
05 — Decide
Scale, revise, narrow, integrate, defer or stop.
What you receive
- A working Minimum Viable Capability
- A documented test design and baseline
- Measured performance in representative conditions
- User, workflow, data and control findings
- An updated value case using observed evidence
- A view of the operating and technical requirements for scale
- A clear scale, revise or stop recommendation
When this is useful
- A priority opportunity has been selected but key assumptions remain unproven
- A technical prototype exists but has not been tested in the workflow
- The business has pilots but cannot explain their financial value
- A supplier demonstration looks promising but independent operating evidence is needed
- Leadership needs a credible decision before committing production funding
The decision at the end
We have enough evidence to invest further; we know what must change before we do; or the opportunity has not earned the right to scale.