Methodology
A four-phase engagement: discovery, interviews, design, rollout.
The process is intentionally concrete. It starts with business context, moves through department-level evidence, then ships a workflow that can be trained, measured, and improved.
Engagement phases
Phase 01
Discovery
Clarify the company structure, service lines, constraints, tools, approval paths, and the business outcomes the engagement has to move.
The output is a short engagement map: departments, candidate workflows, risk areas, and the first workflows worth interviewing in depth.
Phase 02
Department interviews
Sit with the people doing the work and document the real sequence of inputs, judgment calls, reviews, rework, and final delivery.
This prevents fake automation. The workflow is designed around the team's real language, edge cases, and quality bar.
Phase 03
Design
Build the AI workflow: prompts, retrieval inputs, approval gates, exception paths, human review moments, and the success metric.
The deliverable is a working system the team can use repeatedly, not a slide deck about what AI might do later.
Phase 04
Rollout
Train the team, pilot the workflow under real load, tune failure points, and hand over a documented operating rhythm.
Rollout ends when the team can run the workflow without the consultant in the room and leadership can see the productivity gain.
Why this order matters
Design comes after interviews because the workflow has to match reality.
AI workflow failures usually come from skipping the middle: teams buy a tool, ask people to experiment, and never encode the actual review standard. This methodology keeps the work grounded in department evidence before any prompt, agent, or automation path is shipped.
Next step
Ready to map your AI workflow?
The discovery call turns your current operating model into a practical AI workflow roadmap.