A structured, repeatable methodology for identifying, prioritizing, and planning AI initiatives — developed through years of enterprise transformation experience.
Most AI initiatives fail not because of technology, but because of poor problem selection, unclear ROI, and lack of structured planning. This framework addresses all three by forcing rigorous analysis across 8 interconnected dimensions before any implementation begins.
Each dimension builds on the previous one — you can't create a credible roadmap without first identifying and prioritizing opportunities, and you can't prioritize without understanding impact, feasibility, and risk. The result is a strategy that leadership trusts and teams can execute.
Systematic discovery of 4–6 AI, RPA, IPA, and ML use cases tailored to each business unit. Every opportunity is classified by technology type to ensure the right tool is matched to the right problem.
Approach: Stakeholder interviews, process observation, pain-point mapping, and technology-fit analysis.
Each opportunity is scored across two axes — business impact (revenue, cost, CX) and implementation feasibility (data readiness, tech maturity, change complexity). This creates a clear prioritization matrix.
Approach: Weighted scoring model with stakeholder-validated criteria and data readiness checks.
Top 2–3 use cases are elevated with ROI justification, risk-adjusted timelines, and executive-ready business cases. Prioritization is not just about impact — it accounts for organizational readiness.
Approach: ROI modeling, dependency mapping, and organizational change readiness assessment.
A 3-phase roadmap moves from quick wins (0–3 months) through medium-term scaling (3–6 months) to advanced AI capabilities (6–12 months). Each phase builds stakeholder trust before increasing complexity.
Approach: Agile planning with milestone gates, resource allocation, and dependency sequencing.
Visual mapping of how processes evolve from current state to AI-enhanced state. This makes the transformation tangible for operational teams and leadership alike.
Approach: Process flow analysis, value stream mapping, and future-state design workshops.
Measurable outcomes across four categories: time reduction, cost savings, accuracy improvement, and productivity gains. Each KPI has a baseline, target, and measurement timeframe.
Approach: Baseline measurement, target setting with industry benchmarks, and tracking cadence design.
Proactive identification of risks across data quality, model accuracy, compliance, change management, technical debt, and resource constraints — each with a concrete mitigation strategy.
Approach: Risk register development, probability-impact scoring, and mitigation ownership assignment.
Both positive and negative implementation scenarios are mapped, alongside detailed resource requirements (roles, headcount, duration, cost estimates) to ensure realistic planning.
Approach: Best/worst case modeling, resource capacity planning, and budget scenario analysis.