Scope and objective framing
Define the target workflow, business objective, and non-negotiable constraints (risk tolerance, governance requirements, timeline, stakeholder ownership).
Method
Six-step diagnostic sequence
This is a repeatable process, not a case study. The objective is to convert operational ambiguity into clear intervention points, measurable outcomes, and implementation readiness.
Current-state workflow mapping
Document the live process: handoffs, decision points, systems, data dependencies, and failure modes. Include hidden workarounds and manual overrides.
Friction and bottleneck quantification
Measure latency, rework frequency, exception volume, and decision delays to identify where operational drag is concentrated.
Capability intersection mapping
Map bottlenecks to AI capabilities (prediction, extraction, classification, generation, orchestration) and isolate which capabilities can resolve multiple issues at once.
Leverage scoring and sequencing
Rank interventions by impact, feasibility, implementation complexity, and governance load. Sequence into quick wins, foundational builds, and strategic bets.
Readiness and handoff
Produce implementation-ready outputs: target-state map, prioritized intervention backlog, data prerequisites, and decision rights by stakeholder.
Outputs
Artifacts produced by the process
Diagnostic checklist
Used in interviews and workflow observation to ensure consistency across teams and functions.
- Decision owner by step
- Input source reliability and lag
- Manual judgment vs rule-based work
- Current exception handling path
Impact matrix
Compares interventions across business impact, implementation effort, and model risk exposure.
- High-impact / low-complexity quick wins
- Medium-term foundational capabilities
- Long-term moat opportunities
Dependency register
Identifies technical and organizational prerequisites before development begins.
- Data quality and accessibility blockers
- System integration constraints
- Approval and compliance gates
Target-state workflow map
Shows future-state operating model with AI intervention points and human accountability boundaries.
- Human-in-the-loop checkpoints
- Control and audit layers
- Escalation and override logic
Engagement Cadence
How the process runs in practice
Typical timeline: 2-4 weeks
- Week 1: stakeholder interviews, workflow observation, baseline mapping
- Week 2: friction quantification and capability mapping
- Week 3: leverage scoring, dependency analysis, governance review
- Week 4: final synthesis, implementation handoff, roadmap sequencing
Output: a prioritized, execution-ready AI opportunity pipeline grounded in the real workflow, not abstract ideation.