Enterprise AI Transformation That Scales Beyond Pilots
I help executive teams move from GenAI experimentation to measurable, enterprise-wide impact through enablement, operating model design, and adoption systems that work in the real world.
Most leaders I work with aren’t asking whether to invest in AI.
They’re asking why, after pilots and proofs of concept, progress feels uneven or slow.
I’ve seen AI initiatives stall not because the idea was wrong, but because enablement, measurement, and trust weren’t designed upfront. Tools were deployed before teams understood how to use them well. Governance showed up too late. And success was measured in activity, rather than impact.
That gap isn’t just technical.
It’s how technology, people, and decision-making come together in the real world.
Strategic AI Advisory
I partner with C-suite leaders, boards, and transformation teams to design AI strategies that actually work at scale.
This includes:
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Clarifying where AI should and should not be used
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Designing enablement that meets people where they are, not where we wish they were
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Ensuring the technical setup, governance, and security can support growth
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Defining how impact will be measured in ways leaders trust
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I don’t advise from the sidelines. I work hands-on inside organizations that are navigating real constraints, real risk, and real accountability.
This Work Is Not “People or Tech.” It’s Both.
There’s a popular narrative that AI transformations fail because of people or process, not technology.
That hasn’t been my experience.
Some AI initiatives fail because the technology isn’t ready for scale.
Others fail because people don’t trust it, don’t understand it, or don’t see how it helps them do better work.
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Successful transformations take all three seriously:
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Technology that is secure, usable, and fit for purpose
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Enablement that builds confidence across the enterprise
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Measurement that shows real business value, not vanity metrics
My role is to make sure none of those are treated as an afterthought.
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​To help executive teams see the full system, I use a practical lens I call SHIFT. It’s a quick way to pressure-test whether an AI strategy can actually scale inside a real organization.
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SHIFT looks at five interdependent forces that determine whether AI investments translate into business impact: strategy, humans, infrastructure, fluency, and trust. When one moves without the others, adoption slows, ROI erodes, and shadow AI fills the gaps.
Selected Enterprise AI Transformation Work
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Across financial services, insurance, and consumer goods industries, I’ve helped organizations move from early AI experimentation to sustained, measurable adoption.
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Below are representative examples of the kind of work I do and the outcomes leaders care about.
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Enterprise GenAI Enablement at a Financial Infrastructure Company
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Industry: Payments / Financial Technology
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Partnered with a financial infrastructure organization to design and scale an enterprise GenAI program from early pilot to sustained adoption.
Beyond workforce enablement, I reviewed the organization’s internal GenAI tool configuration, access model, and user workflows to identify technical and UX friction limiting adoption. This included feedback on prompt interfaces, permission structures, and use-case discoverability that were constraining value realization.
In four months, the number of users scaled 4X, achieved 90+ NPS, and supported another 7-figure investment decision. A quantitative measurement framework documented 30%+ productivity gains and projected millions in annual value at scale.
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What mattered most: aligning technical readiness, governance, and enablement so adoption could scale without creating downstream risk.
Scaling GenAI Adoption Across a Regulated Financial Services Enterprise
Industry: Insurance / Financial Services
Led an enterprise-wide GenAI adoption initiative reaching over 1,000 employees across corporate and field operations, including deployment of a proprietary internal AI tool alongside Microsoft Copilot.
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In addition to designing the enablement strategy, I worked with stakeholders to assess how tool design, governance constraints, and security considerations were shaping real-world usage. This ensured training aligned with how the technology actually functioned in a regulated environment.
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Delivered executive workshops, modular training assets, and office hours that balanced technical capability, risk awareness, and practical application. Iterative feedback loops informed both training design and implementation adjustments, driving measurable adoption gains without compromising compliance.
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What mattered most: integrating AI enablement with governance and technical realities so adoption didn’t stall after launch.