Implementation Process

The process is the differentiator

A ten-step method built from safety-critical engineering practice. Step 00 is an AI Readiness Gate — five-domain scoring that decides whether the engagement proceeds, pauses for cleanup, or stops entirely. Each subsequent step has explicit entry and exit criteria so that nothing scales before it works.

00

AI Readiness Gate

Score readiness across five domains: business value, workflow, knowledge/data, technical/security, governance/monitoring. Decide whether to proceed, run a cleanup sprint, or pause.

Output: Readiness scorecard + recommended path
✓ Gate: Weakest domain ≥ 3 to advance to workflow discovery

01

Workflow discovery

Map the actual workflow as it runs today: inputs, outputs, handoffs, owners, exceptions.

Output: Workflow diagram + owner map
✓ Gate: Workflow can be drawn on a whiteboard in 15 minutes

02

Bottleneck mapping

Identify where delay, rework, and errors actually accumulate in the current process.

Output: Bottleneck analysis + delay measurement
✓ Gate: One quantifiable bottleneck identified

03

Use-case prioritization

Rank AI candidates by readiness, impact, and validation feasibility. Disqualify low-readiness items.

Output: Priority list with readiness scores
✓ Gate: At least one high-readiness candidate confirmed

04

Tool and data assessment

Evaluate what tools and data sources are available, accurate, and accessible today.

Output: Tool inventory + data readiness checklist
✓ Gate: Required data is available and trustworthy

05

Prototype design

Design a constrained prototype with explicit inputs, outputs, limits, and a human approval point.

Output: Prototype spec + prompt or pipeline design
✓ Gate: Prototype boundary is narrow enough to validate

06

Output validation

Compare AI outputs against 20 known-good cases. Define acceptance criteria. Document approval points.

Output: Validation report + acceptance criteria
✓ Gate: Accuracy meets defined threshold

07

Staff training

Train the team on what the system does and does not do, and what triggers a human override.

Output: Usage guide + override procedures
✓ Gate: Staff can operate and override the system

08

Measurement

Track at least one quantitative metric: time saved, response time, error rate, or revenue impact.

Output: Dashboard or weekly metric report
✓ Gate: Metric shows measurable improvement

09

Scale or stop decision

If the metric improves and validation holds, expand. If not, stop or redefine. No obligation to scale.

Output: Decision record + next scope definition
✓ Gate: Decision made with documented evidence

Decision rule

If the workflow cannot be defined, measured, or validated, it is not ready to automate.

That rule is not theoretical. It is the difference between an AI rollout that improves operations and one that quietly creates new failure modes the team cannot see.

Score readiness before scoping
Define the workflow first
Constrain AI to one bounded task
Validate before deploying
Measure from day one
Scale only after validation holds

Ready to implement one workflow the right way?

Start with the AI Workflow Review. We will assess the bottleneck and tell you which step you should start with, or whether this workflow is not yet ready.