Most AI projects fail because they start with the tool, not the work. This discovery starts with your organization: where the benefit is real, how to govern it, how to secure it, how to train your people, and how you'll know it worked. Everything sovereign and your data never leaves the building.
Buying AI is simple. The hard questions are the ones vendors skip: which work should it touch first, who's accountable when it's wrong, what data must never reach it, whether your people will actually use it, and how you'll prove it returned anything. This discovery answers all five before you commit to a build.
Where would AI help most?
Every candidate workflow scored on volume, repetitiveness, document-dependence, and cost of error. Your top three opportunities ranked, each with task-level labor math.
Includes a "why not the others" section, what we ruled out, and why.
Who's in charge of it?
Who approves agent output, who can change its behavior, what stays human forever, and the escalation tree when it errs. Plus a written AI acceptable-use policy and a review cadence.
Turns "we deployed AI" into "we operate AI" ...the part boards and auditors ask about.
What must never leak?
A data-classification line, a map of where sensitive data flows today (including the shadow AI nobody documented), and the access, network, and audit-logging requirements for a sovereign deployment.
Regulated frameworks flagged where they apply; interpretation routed to your counsel.
Will people actually use it?
A role-by-role map of who needs which level of training, leadership, managers, frontline on a schedule tied to the rollout.
Untrained staff is the most common reason adoption stalls. This is the fix, planned in.
How will you know it worked?
Baseline metrics captured before anything deploys, four agreed measures (hours returned, error rate, cycle time, adoption), and a one-page monthly report template.
A plain section on what won't be measurable in 90 days so no one invents ROI later.
Are you ready to build?
A go / no-go checklist: hardware and network prerequisites, data hygiene, the named internal champion, and IT availability followed by an honest "not yet" if you're not there.
Cheaper to find a gap here than three weeks into a deployment.
The assessment is yours even if you never deploy a thing.
The Value Map, governance, security, training, and measurement plans are useful no matter who builds your AI. We'd rather earn the deployment with an honest report than win it with a rigged one. No ROI guarantees, no invented case studies, no demo with your real data.
A Discovery Session scopes the assessment for your organization. No deck, no demo with your data, no pressure to go further than the facts support.
