AI Transformation Services

Identify AI use cases that are actually worth building.

The board wants an AI roadmap by Q2. We need to understand how 500 people actually work. We can't shadow 500 people.

When to use this

You can't shadow 500 people

The board wants an AI roadmap but you don't know how 500 people actually work. Workshops give you opinions, not reality.

Big commitment, thin evidence

You're about to commit $5M to an AI platform but you haven't benchmarked where you actually stand. No scored baseline means no way to justify the investment or measure what it changes.

Pilots keep failing

Your AI pilots keep failing because you picked workflows that looked automatable from the outside but weren't in practice.

How it works
1

Define what you need to learn

Map the processes, workflows, and pain points you need to understand before committing to a direction.

2

Deploy across functions

Capture how work actually happens today from every stakeholder group - not just the loudest voices in a workshop.

3

Benchmark and find the real opportunities

Platform scores readiness across every function and identifies where time goes, where friction is, and what's realistically automatable - giving you a benchmark you can act on and track.

4

Surface what matters

Evidence-backed findings: which use cases have real potential, where adoption will stick, and where it won't - grounded in how people actually work.

Impact
Benchmarked, not guessed
Scored AI readiness across every function - a quantified starting point, not a workshop opinion
Right use cases
AI priorities based on real workflows and real pain, not assumed ones
Validated before committed
Use cases pressure-tested before budget is spent on platforms
Measurable over time
Repeat the benchmark post-initiative to prove what changed and where gaps remain