Executive advisory

AI is easy to start. Harder to make useful, governed, and scalable.

The problem is rarely a lack of AI ideas. It is deciding which bets matter, who owns them, what can be claimed, and how AI becomes repeatable business value. That is the operating model.

Who this is for

I advise healthcare, healthtech, medtech, diagnostics, life sciences, and tech-enabled services leaders who are moving AI from activity to capability. This work is most useful when AI is cross-functional, strategically important, and hard to contain inside one function.

What this work helps you avoid

Without an operating model, AI creates motion without capability.

Common failure modes show up in predictable places.

Pilots that never scale
Vendor tools that do not add up to strategy
Claims that get ahead of evidence
Governance that is too vague or too heavy
Slow decisions because teams are not aligned
Commercial stories that cannot survive scrutiny

The cost is not just inefficiency. It is wasted AI spend, slower adoption, avoidable risk, weaker board confidence, and months spent on use cases that should never have made the roadmap.

What I help leaders decide

Which AI bets are worth making?

Not every use case deserves investment. I help leaders evaluate opportunities on workflow value, buyer urgency, operational gain, data readiness, regulatory exposure, evidence burden, commercial potential, and organizational feasibility.

Build, buy, partner, or avoid?

AI strategy breaks when every opportunity becomes a build decision. I help teams decide where AI should become product capability, internal tooling, workflow automation, services support, vendor partnership, or a future option.

What can the company responsibly claim?

Claims can create commercial value or adoption friction. I pressure-test product, workflow, clinical, operational, economic, and AI claims against evidence, buyer expectations, regulatory posture, and enterprise review.

How should AI decisions get made?

AI cuts across functions not designed to decide together. I help define decision rights, review cadence, governance forums, escalation paths, intake criteria, and executive oversight so the model works in practice.

What will buyers, boards, and investors believe?

AI value has to survive scrutiny. I help identify the proof, controls, narrative, and operating discipline needed for buyers, partners, investors, and boards to believe the strategy.

Ways to work together

Four engagements, from a first read to ongoing senior judgment. Most teams start with discovery.

The goal is to understand the business, product, data, regulatory, commercial, and operating context well enough to identify where AI is likely to create value, where it is likely to create risk, and where the current operating model is not yet ready. This is a focused executive engagement designed to produce useful observations, decision clarity, and a better path forward.

Typical activities

  • Interview key leaders and functional stakeholders
  • Review current AI initiatives, roadmap items, vendor tools, or proposals
  • Understand the product, workflow, data, commercial, and operating context
  • Find where ownership, governance, claims, evidence, or decision rights are unclear
  • Assess whether the organization can execute the strategy it is considering

Typical outputs

  • Executive readout
  • AI opportunity and risk map
  • Operating model gap assessment
  • Decision-rights observations
  • Claims, evidence, and governance considerations
  • Recommended next steps
Best for: leaders who know AI matters but are not sure where to start; companies with multiple AI ideas but no clear prioritization; boards or investors who need a clearer view before committing more time or capital.

For companies that already have meaningful AI activity or urgency and need to define how AI decisions get made, reviewed, governed, productized, commercialized, monitored, and reported. The sprint creates practical operating structure, not a strategy deck that sits unused.

Typical activities

  • Map current and proposed AI use cases
  • Define AI intake and prioritization criteria
  • Clarify decision rights across product, data, engineering, commercial, clinical, regulatory, legal, privacy, security, quality, and executive leadership
  • Pressure-test claims, evidence, and buyer-facing narratives
  • Place governance where it needs to be lighter, stronger, or better positioned
  • Define how AI-enabled products, workflows, or services move from idea to approval to launch to monitoring

Typical outputs

  • AI operating model blueprint
  • Use-case intake and prioritization model
  • Build / buy / partner decision framework
  • Governance and decision-rights map
  • Product, claims, and evidence review process
  • Risk and escalation model
  • Executive or board reporting structure
  • 90-day execution roadmap
Best for: companies with scattered AI activity and unclear ownership; organizations adding AI to products, services, workflows, or data assets; teams preparing for enterprise adoption, partnerships, fundraising, or diligence.

Some companies do not need a full-time executive. They need an experienced operator in the room at the right moments: when priorities are set, claims are shaped, risk is reviewed, vendors are selected, or the board is asking whether AI activity is becoming business value.

Typical support

  • Executive decision support
  • AI use-case prioritization
  • Product and market strategy
  • Claims and evidence review
  • AI governance cadence
  • Workflow adoption and enterprise readiness

Also

  • Board or investor narrative
  • Commercial and buyer-facing positioning
  • Vendor and partner strategy
  • Implementation partner alignment
Best for: CEOs and leadership teams that need senior AI, product, and governance judgment without hiring a full-time executive, especially while preparing for board meetings, fundraising, diligence, enterprise adoption, or partnerships.

AI-enabled companies are easy to describe and harder to underwrite. The question is not only whether the company has AI. It is whether the AI strategy is credible, adoptable, governable, commercially meaningful, and supported by the right product, data, evidence, claims, and operating model.

Typical questions

  • Is this AI story credible?
  • Are the claims ahead of the evidence?
  • Does the company have the data rights and readiness required?
  • Will buyers believe the product or workflow value?
  • Can the company govern and monitor this as it scales?
  • Is the team organized to execute?
  • What needs to happen in the first 90 days post-close?

Typical outputs

  • AI adoption risk memo
  • Diligence readout
  • Product, claims, and evidence assessment
  • AI operating model gap analysis
  • Post-close 90-day value creation plan
  • Board-facing recommendations
Best for: investors evaluating AI-enabled healthcare companies; boards overseeing AI strategy or adoption; portfolio companies preparing for diligence; teams that need to pressure-test whether the AI story holds up.

Common advisory situations

“We have AI pilots everywhere. Which ones matter?”
I help leadership sort activity from strategy, identify the use cases worth pursuing, and define how future AI opportunities should be evaluated.
“We have valuable data. What should it become?”
I help determine whether product, clinical, workflow, operational, device, or customer data can become product value, workflow value, reimbursable value, buyer value, or investor value before the company invests in technical buildout.
“The board wants an AI strategy.”
I help translate AI activity into a coherent operating model: priorities, governance, decision rights, evidence, risks, execution roadmap, and board-level reporting.
“We are adding AI to a product or service.”
I help pressure-test the use case, claims, evidence, workflow fit, enterprise readiness, and operating model needed to bring the AI-enabled offering to market.
“We need to move faster without creating risk we cannot support.”
I help teams decide where governance needs to be stronger, where it needs to be lighter, and where unclear ownership is slowing progress.
“We need an implementation partner, but we do not know what to ask for.”
I help leadership define the problem, scope the work, identify the right internal owners, and create the brief that technical or implementation partners can execute against.

How I work

I do not come in with a 20-person team or a generic transformation playbook. I start by understanding the specific business, product, data, regulatory, commercial, and operating context. From there, I help leadership decide what needs to change, what can be handled internally, and where outside implementation support may be needed.

I am not the implementation team. I am the senior advisor who helps leadership decide what should be implemented, governed, claimed, and scaled. The goal is not to make the company dependent on an advisor. It is to help the team build the decision muscle, operating cadence, and cross-functional clarity to keep making better AI decisions after the engagement ends.

My lens

  • Nearly two decades across regulated healthcare technology
  • Product strategy, compliance, privacy, security, and data governance
  • AI-enabled products, SaMD, diagnostics, and clinical trial technology
  • Enterprise healthcare and regulated commercialization
  • Translation across product, regulatory, commercial, clinical, data, legal, and executive functions

What I do not do

  • Technical AI implementation or data engineering execution
  • EHR integration delivery
  • Generic AI training or policy templates without operating decisions
  • One-time innovation workshops with no follow-through
  • Rubber-stamping an existing AI story

Prefer a peer room to a 1:1 engagement?

The CEO Circles put you in a confidential, curated room with other healthcare or healthtech CEOs working through the same AI operating decisions. Some leaders start there, then move into direct advisory where it would help.

Explore the CEO Circles

AI does not become valuable because it exists.

It becomes valuable when the company knows how to operate it. If your AI work is moving faster than your operating model, I can help you find the gaps, make the decisions, and build the structure needed to scale.