Services / AI Implementation Audit

Service

AI Implementation Audit

When an AI project sounds impressive but the economics are still fuzzy.

Don’t fall for the hype. Most AI proposals hide edge cases, review cost, and ownership problems. We audit the workflow first, then tell you whether the project deserves a pilot, a redesign, or a stop sign.

Audit an AI opportunity Calculate ROI
  • Build-or-no-build verdict grounded in workflow economics, not demo energy
  • Review of hidden supervision load, exception handling, and ownership gaps
  • Decision memo you can use internally instead of more AI fog
  • No-build

    is a real outcome when the proposal does not survive contact with the numbers

  • Review cost

    usually shows up where the AI pitch is most optimistic

  • Decision

    instead of another strategy deck that avoids the hard part

When an AI implementation audit is useful

This service is for decision pressure. Use it when an AI initiative sounds plausible, but nobody has yet forced the numbers, controls, and ownership into the same room.

  • Vendor proposal review. Pressure-test promised savings, claimed capability, and the hidden operational work behind the pitch.
  • Pilot rescue. Review an AI pilot that looks impressive in demos but fails on exceptions, oversight, or production fit.
  • Workflow triage. Evaluate multiple candidate workflows and identify which one has the cleanest path to a high-confidence pilot.

What you get

The output is not inspiration. It is a decision package you can use to proceed, redesign, or stop.

  • Workflow audit with baseline labor, failure modes, and current operating constraints
  • Economic case showing where the AI promise holds and where review cost eats it alive
  • Risk register for quality, control, monitoring, rollback, and exception ownership
  • Recommendation memo for build, redesign, or stop, with a defendable reason
  • Pilot scope and success criteria only if the case survives the audit

How we pressure-test an AI build

  1. 1. Inspect the proposal or pilot claims

    We look at what is being promised, what is being omitted, and which assumptions are doing the real work.

  2. 2. Force unit economics and control requirements into the model

    We quantify review burden, exception cost, quality risk, and operational ownership before anyone gets to say “scale.”

  3. 3. Issue the verdict and the next move

    The result is simple: proceed to pilot, redesign the approach, or stop before more budget and credibility disappear.

Fit

Strong fit

  • Leaders evaluating vendor pitches and trying to avoid expensive post-rationalization
  • Teams with a pilot or proposal that sounds good but still has fuzzy economics
  • Organizations that care more about control and operating reality than AI theater

Poor fit

  • Teams already politically committed to a vendor and only shopping for validation
  • Projects with no workflow owner, no baseline, and no real operating data
  • Buyers who want prompt tricks instead of a yes-or-no implementation decision

Need someone to say whether this AI idea survives contact with reality?

Start with the free first conversation. If the case is serious, we scope the paid assessment and give you a hard answer.

Audit an AI opportunity