AI Enablement Program

What you'll walk away with

• AI strategy aligned to the business, not to vendor marketing
• AI governance framework (acceptable use, data classification, model allowlists, review cadence)
• One pilot use case delivered end-to-end — selected for real ROI, not demo value
• Internal team enabled to evaluate future use cases themselves

The problem this solves

AI is the fastest-moving technology in a decade, and pharma and biotech sit at an uncomfortable intersection. The potential is real — R&D, clinical ops, regulatory reporting, commercial automation — but so is the exposure. Patient data, IP, and GxP records don't belong in a consumer LLM, and regulators are watching. And let's be real, there is no ONE AI that will fit all your needs. Progressive organizations pick one AI for one job and different AI for another. The importance is to manage the process and exposure.

Most companies land in one of two bad positions: moving too fast, with sensitive data flowing into unmanaged tools, or paralyzed by the compliance question. I help companies find the middle path — strategy, a governance framework that survives audit, and one concrete pilot delivered end-to-end under your actual regulatory constraints.

What the engagement looks like

Four phases over eight to twelve weeks, delivered virtually. Example: The pilot runs on M365, Azure, and enterprise AI endpoints — not consumer tools — so sensitive data stays in your tenant.

Weeks 1–2: Discovery. I map where AI could matter in this business — R&D, regulatory submissions, clinical ops, commercial workflows. I benchmark against peers and surface existing shadow usage. In most companies, employees are already using consumer AI before any company program exists.

Weeks 3–4: Governance framework. I build the governance layer before touching any tooling: acceptable-use policy, data classification, model allowlists (enterprise Claude, Copilot, or approved internal tools), review cadence, and incident response.

Weeks 5–8: Pilot selection and delivery. I select ONE use case with genuine ROI — not a demo — and I push back hard on anything chosen for show. I build and deploy it on your M365 and Azure stack, then measure the result.

Weeks 9–12: Team enablement. I walk the team through evaluating the next use case without external help. I draft a working group charter if useful and train internal champions.

Who it's most useful for

• Leadership teams under board pressure to "have an AI strategy" but without one they'd defend in a room
• Companies whose employees use consumer AI personally with no company policy in place
• R&D or regulatory teams with recurring time sinks that feel AI-shaped
• Post-launch companies with enough operational surface to automate repeatable processes
• Companies concerned about patient data, IP, or GxP records flowing into unmanaged tools

What you'll walk away with

A real AI program — not a slide deck. Governance that survives an audit. One delivered pilot that proves the pattern inside your regulatory realities, with a result you can bring to the board. A strategy written for your business, not vendor marketing. And a team that can evaluate the next use case without calling me.

Common questions

Will you help us pick LLM vendors? Yes — vendor selection is part of the strategy and governance work. I assess enterprise options against your data residency requirements and M365 and Azure footprint. No vendor relationships shape that recommendation.

What if our data can't leave our tenant? That's the typical starting point for pharma and biotech, and the default I build around. Everything runs on enterprise AI endpoints inside your tenant. Consumer tools are out of scope.

How do we keep the pilot from becoming shelfware? Pilot selection is the most important decision I make in this engagement. I push back hard on use cases chosen for demo value. My criteria: real workflow, measurable result, sustainable after I leave, no data exposure risk.

Most effective alongside ongoing leadership

AI evolves monthly. Governance policies, vendor options, and regulatory guidance all shift. That steady review fits naturally inside a Fractional IT Leadership retainer, where I stay close enough to catch what's changing before it becomes a problem.

Deliverables

• AI strategy document
• AI governance policy and review cadence
• One delivered pilot use case (build + deployment + measurement)
• Internal AI working group charter (if applicable)