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.
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.
Workflow discovery
Map the actual workflow as it runs today: inputs, outputs, handoffs, owners, exceptions.
Bottleneck mapping
Identify where delay, rework, and errors actually accumulate in the current process.
Use-case prioritization
Rank AI candidates by readiness, impact, and validation feasibility. Disqualify low-readiness items.
Tool and data assessment
Evaluate what tools and data sources are available, accurate, and accessible today.
Prototype design
Design a constrained prototype with explicit inputs, outputs, limits, and a human approval point.
Output validation
Compare AI outputs against 20 known-good cases. Define acceptance criteria. Document approval points.
Staff training
Train the team on what the system does and does not do, and what triggers a human override.
Measurement
Track at least one quantitative metric: time saved, response time, error rate, or revenue impact.
Scale or stop decision
If the metric improves and validation holds, expand. If not, stop or redefine. No obligation to scale.
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.
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.