AI Consulting & Workflow Audits
Choose a first AI project with a clear owner, scope, and success measure.
The problem
Different departments have proposed AI projects, but nobody knows which can be delivered with available data or how success will be measured.
We observe one recurring task, map its handoffs, and compare candidate projects by time spent, data readiness, and consequences of errors.
What we handle for you.
Workflow audit
Review actual work samples and interview the people responsible for the task.
Pilot planning
Agree on a baseline, acceptance criteria, and the point at which to stop or expand.
What you walk away with.
- A prioritized list of feasible projects
- A pilot brief with ownership and measurement criteria
Available with this service after launch.
This is not a separate service. It is the ongoing support option for keeping AI Consulting & Workflow Audits useful, maintained, and improving as your business changes.
In practice
Choose a pilot you can measure
Compare two candidate workflows using real volumes, time spent, data availability, and the cost of a wrong answer. The best first project has a clear owner and a testable outcome.
Bring two tasks competing for your team's time.
Discuss a workflow audit →Frequently asked questions
What do we need for the audit?
A process owner, a typical work sample, and a description of the systems involved are enough to start the discussion.
What decision does the audit support?
You receive a pilot scope, baseline, and acceptance criteria so you can decide whether to build, change the scope, or defer.
How do you compare competing AI ideas?
Compare task volume, time per task, cost of errors, and available data. Prefer a pilot whose result can be checked against a baseline.
Can you review a project we have already started?
Yes. Bring the intended outcome, current workflow, sample outputs, and examples that failed. These help separate data issues from implementation gaps.
How much staff involvement does discovery need?
Include someone who performs the task and someone who owns the outcome. They often see different exceptions and constraints.
Does the audit always lead to an AI build?
No. If a form change, clearer ownership, or a simple rule solves the problem, the audit should make that option visible.