Navigate AI adoption with a framework built for real enterprise environments. From policy and risk governance to practical enablement and tooling selection, we help organizations harness AI productivity gains without compliance exposure.
AI is no longer a future consideration — it's already inside your organization, whether you've sanctioned it or not. Employees are using ChatGPT, Copilot, and dozens of other tools to get their work done faster. The question isn't whether to adopt AI; it's whether you're going to do it with a governance framework that protects your data, your clients, and your business — or without one.
My AI Governance & Enablement practice helps organizations move from reactive to intentional. That means building policies that are practical enough to be followed, selecting tools that are right for your specific environment and risk profile, and identifying the use cases where AI will deliver real, measurable productivity gains rather than theoretical ones.
The advisory is vendor-neutral by design. I don't have a financial relationship with any AI platform vendor, which means I can give you an honest assessment of where Microsoft Copilot, Google Gemini, purpose-built vertical AI tools, or open-source solutions make sense for your organization — and where they don't.
Evaluate your current AI exposure (what tools are already in use), data governance maturity, compliance requirements, and leadership readiness. We identify the gaps that need to be closed before you can deploy AI responsibly at scale.
Draft and socialize an AI use policy that is clear, enforceable, and appropriate for your industry and regulatory context. This includes acceptable use guidelines, data handling rules, and a framework for evaluating and approving new AI tools.
Identify and score the AI use cases that will deliver the highest ROI in your specific environment. We map use cases to business processes, estimate productivity impact, and sequence them by effort and organizational readiness.
Conduct a vendor-neutral evaluation of AI platforms and tools against your prioritized use cases. Develop an enablement plan — training, change management, and success metrics — so your team actually adopts what gets deployed.
Typically a 6–10 week engagement structured in three phases: assessment, policy, and roadmap. Engagements can be scoped to focus on a single phase or run end-to-end. Post-engagement retainer support is available as AI policies and tooling evolve.
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