I partner with leadership teams to bridge the gap between high-level AI research and production-ready implementation. This page outlines the methodology I use to navigate complexity and deliver measurable ROI for portfolios exceeding $100M in revenue.
The Strategic Framework: Three Horizons of Impact
We categorize AI initiatives by their scale of impact and architectural requirements to ensure every project aligns with long-term business goals.
Horizon 1: Personal (The Node)
Focus on individual mastery and personal ROI. This moves team members from using tools to reasoning with models.
Horizon 2: Team (The Network)
Focus on collaborative intelligence. We re-architect shared workflows and teammate models to recapture 20% or more of team capacity.
Horizon 3: Organization (The System)
Focus on systemic orchestration. We build a proactive coordination layer that unifies disparate vendor ecosystems into a single intelligence layer.
The Diagnostic: Maturity Evolution Audit
Before building, we audit the plumbing gap—the friction between your current state and your desired horizon. Every organization operates within this four-level maturity pattern:
| Level | Maturity State | Operational Reality |
|---|---|---|
| Level 1 | Applications | Individual AI tools with manual coordination and high friction. |
| Level 2 | Integrated Systems | Connected workflows leveraging vendor AI ecosystems like M365 and Salesforce. |
| Level 3 | Coordination Layer | Proactive AI management and multi-agent system optimization. |
| Level 4 | Operating System | Seamless, high-velocity human-AI collaboration as the natural operating mode. |
The 6-Phase Methodology
This is the delivery mechanism—taking the diagnostic data and turning it into production reality. Each phase maps to specific NIST AI RMF subcategories, making this methodology defensible for enterprise governance requirements.
Understand
What's the actual problem? Before touching technology, establish the business outcome, stakeholders, and driver hypotheses. Define context and intended use (NIST Map 1.1).
Output: Context defined; accountability gaps identifiedAssess
Where are we now? Map current workflows, quantify the problem in dollars, and assess process readiness. The Mess-O-Meter determines if you're ready for AI or need to standardize first.
Output: Prioritized Transformation RoadmapDesign
What do we build? Apply the Intent Filter, make build vs. buy decisions, and classify governance requirements. For GAI, address confabulation, information integrity, and content provenance.
Output: System-level governance gaps identifiedGovern
How do we do it right? Assess failure modes, red team adversarially, establish RACI accountability, and configure KRI monitoring. Governance intensity scales with the Intent Filter.
Output: Governance Attestation (implemented, not planned)Adopt
How do we get buy-in? Activate champions, address resistance, execute training on AI limitations. Target 10-30% override rate—0% is a red flag for automation bias.
Output: Adoption Scorecard (human readiness)Prove
Did it work? Validate actual vs. projected ROI, track trustworthiness KRIs, and make the Scale/Retool/Retire decision at 90 days.
Output: 90-Day ROI Validation; Scale/Retool/Retire decision