Arvita Tripati · Founder, Vahana Labs

AI is easy to pilot. Hard to make operational.

I help healthcare and regulated health leaders turn AI activity into business value: which bets to make, how to govern them, what they can claim, and the operating model that makes it real.

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Product and compliance executive with nearly two decades in regulated healthcare technology. 30+ regulated products shipped or scaled across AI, SaMD, diagnostics, clinical trials, privacy, security, and data governance.

Arvita Tripati
ATAdd arvita-hero.jpg
18+
years in regulated healthcare technology
30+
regulated products shipped or scaled

AI · SaMD · Diagnostics · Clinical trials · Privacy · Security · Data governance

The executive problem

Most companies are not short on AI ideas. They are short on operating clarity.

AI shows up everywhere at once, and every function sees a different version of it.

Product wants roadmap clarity.
Operations wants efficiency.
Data teams see opportunity.
Commercial wants a stronger story.
Legal, compliance, privacy, security, quality, clinical, regulatory see risk.
The board wants to know what it all adds up to.
Without a practical operating model, AI becomes pilots, vendor tools, roadmap debates, approval bottlenecks, unsupported claims, and unclear ownership.

The cost: wasted AI spend, slower adoption, weak board confidence, and teams spending months on use cases that should never have made the roadmap.

What I help leaders decide

Five questions that decide whether AI becomes capability or just activity.

Which AI bets are worth making?

Evaluate use cases on workflow value, data readiness, risk, evidence burden, commercial potential, and organizational feasibility.

Build, buy, partner, or avoid?

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?

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?

Define decision rights, review cadence, governance forums, escalation paths, and executive oversight.

What will buyers, boards, and investors believe?

Identify the proof, controls, narrative, and operating discipline needed for AI value to survive scrutiny.

Where most engagements start

A focused paid discovery engagement for leaders who need clarity before committing to a larger AI effort.

Primary entry point
AI Operating Model Discovery

I interview key leaders, review current AI initiatives and decision processes, identify the highest-friction gaps, and provide an executive readout with observations and recommended next steps.

Best for: CEOs, executive teams, investors, or boards that need a clear read before investing more time, capital, or political energy into AI.
Book a discovery call

Typical outputs

  • AI opportunity and risk map
  • Operating model gap assessment
  • Decision-rights observations
  • Claims, evidence, and governance considerations
  • Recommended next steps

Ways to work together

From a first read to ongoing senior judgment. See full advisory detail.

Diagnose
AI Operating Model Discovery
Find where AI strategy, data readiness, governance, and organizational capability are breaking down.
Design
90-Day Operating Model Sprint
Define how AI is selected, prioritized, reviewed, governed, productized, commercialized, monitored, and reported.
Advise
Fractional Executive Advisory
Ongoing senior judgment as AI moves from roadmap to market, workflow, or enterprise deployment.
Evaluate
Board, Investor & Diligence Advisory
Separate AI promise from AI operating reality.

Where this work applies

The product
What you sell
AI changes what the company sells, claims, validates, supports, or commercializes.
The workflow
How work gets done
AI changes how care, operations, documentation, billing, authorization, quality review, or service delivery gets done.
The data asset
What you hold
AI depends on product, clinical, workflow, operational, device, or customer data whose value and use rights are unclear.
The enterprise story
What you claim
AI is part of the buyer, investor, board, or partner narrative, and it needs to hold up under scrutiny.

Or join a CEO Circle

A confidential, curated peer room for CEOs deciding where AI should change the business. Limited to 6 to 8 peers per circle. How the circles work.

Healthcare Services AI CEO Circle

Healthcare services & tech-enabled care

For CEOs deciding where AI should change operations, margin, quality, capacity, and patient experience, without vendor sprawl or disconnected pilots.

Explore this circle

Healthtech AI CEO Circle

AI-enabled health products

For CEOs bringing AI-enabled products to market that have to survive buyer, investor, enterprise, and regulatory scrutiny, and convert pilots into contracts.

Explore this circle
Forthcoming · Fall 2026
Built to Survive
Building Trusted AI Products and Organizations

AI products and organizations do not scale on promise alone. Early teams need speed and product judgment. Growing companies need evidence, repeatability, culture, and operating discipline. Scaling organizations need governance, decision rights, investment choices, and trust infrastructure that can survive buyers, boards, regulators, investors, and partners. The book explores what companies need to build before growth exposes the cracks.

Co-authored with Kimberly Bloomston, CPO at 6sense.

Explore the book

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.

My lens

Nearly two decades across regulated healthcare technology, product strategy, compliance, privacy, security, data governance, AI-enabled products, SaMD, diagnostics, clinical trial technology, and enterprise healthcare. That background lets me translate across functions that often talk past each other.

Best fit

  • Healthcare companies adopting AI across products, services, or operations
  • Healthtech, medtech, diagnostics, life sciences, and tech-enabled services adding AI to existing offerings
  • Companies with valuable data but no clear AI product or commercialization strategy
  • AI-enabled companies preparing for enterprise adoption, fundraising, diligence, or partnerships
  • Boards and investors evaluating AI credibility, adoption risk, and value creation

Not a fit

  • Technical AI implementation or data engineering execution
  • EHR integration delivery
  • Generic AI training or policy templates
  • A consultant to rubber-stamp an existing AI story
  • Work that stays in one functional lane
The commercial practice

Need market-readiness advisory for a regulated healthtech product?

I lead Vahana Labs, an advisory practice that helps healthtech and medtech companies get to market in the right order. Vahana Labs focuses on buyer fit, claims, evidence, pilot-to-contract strategy, data readiness, and US healthcare-market fit.

Visit Vahana Labs

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.

Book a call See the advisory work