Who I Help

Segment 01

Financial Services & Wealth Management

Asset managers, wealth platforms, banks, fintech, and capital markets organizations sitting on enormous data assets that aren't yet an advantage. Client intelligence is fragmented, advisor and analyst productivity depends on manual work, and AI adoption has to clear model risk, compliance, and board scrutiny before it clears anything else.

Leadership wants a fact base for data investment, but maturity and progress aren't measured
Analytics runs as a report factory rather than governed data products with owners and SLAs
Client and advisor intelligence is scattered across systems that don't talk to each other
Generative AI is in use, but governance, model risk, and human oversight haven't caught up
Reporting cycles consume analyst capacity that should be going to insight

Enterprise data management OKR program and a 15-driver maturity framework inside the data organization of a top-tier asset manager; investor reporting rebuilt on a generative AI workflow at a private equity fund, 35% faster with tripled analytical coverage; end-to-end AI governance standard for a capital markets platform.

Representative engagements

Vanguard
Federal Reserve Bank of Kansas City
Federal Reserve Board of Governors
iConnections
+ confidential PE, fintech & capital markets engagements

What I deliver

Enterprise data strategy & maturity scoring
Governed data products & semantic layers
Client & advisor intelligence analytics
KPI & OKR measurement programs
AI governance & model risk frameworks

Segment 02

Healthcare Payers & Providers

Health plans and provider organizations operating under strict compliance requirements, legacy data infrastructure, and board-level AI scrutiny. You have real AI ambitions but real constraints: regulatory risk, model governance requirements, fragmented data, and leadership teams that don't yet agree on what AI should actually do.

AI pilots are running but not scaling, or scaling without governance
The board or regulators are asking questions about AI risk and oversight
Data infrastructure is fragmented across legacy systems and cloud platforms
You've hired AI talent but don't have the operating model to deploy them effectively
AI spend is growing but ROI accountability is weak or nonexistent

$53M transformation and $10M+ savings in 12 months as CDO and Chief Analytics and AI Officer at CareFirst BlueCross BlueShield, serving 3.2 million members; 30%+ cost savings at Radiant Healthcare; RCM and billing productivity gains as interim CDAO at Envision Healthcare.

Representative clients

CareFirst BlueCross BlueShield
Radiant Healthcare
Envision Healthcare
ZS Associates
AMN Healthcare
Thomas Jefferson University
Pfizer
Capgemini
+ additional Blues & health plans

Segment 03

Boards, C-Suite & PE Sponsors

You're a CEO, CFO, or board member navigating AI strategy with an information asymmetry problem. Your technology team is telling you AI is progressing. Your instincts say something's off: the metrics aren't clear, the governance isn't real, or the strategy hasn't been tested against the business model. You need an independent perspective from someone who has sat in the chair.

You want an independent data and AI audit not produced by the people running it
The board needs AI literacy: operational clarity rather than hype or fear
You're evaluating a CTO, CDO, or CAIO hire and want a credible reference point
An acquisition target's AI and data claims need pressure-testing before close
The value creation plan depends on data and AI leverage that hasn't materialized

Technical and data diligence on a $2B+ private equity fintech transaction, surfacing $40M+ in exposure used in the investment committee's valuation. Board of Advisors at A3.AI; Board of Directors at Humanim.

Advisory formats

Quarterly board AI briefings
Independent data & AI strategy audit
Buy-side & sell-side technical diligence
CAIO / CDO hire evaluation support
Executive AI literacy workshops

Typical commitment

One to two days per month. Project-based engagements also available for discrete reviews and diligence work. Scope and structure discussed on discovery call.

Honest disclaimer

This isn't for everyone

Being direct about fit saves everyone's time. Here's where I'm genuinely not the right match.

Early-stage startups without data infrastructure

My expertise is in scaling AI in complex environments with existing data assets, compliance requirements, and operational workflows. If you're pre-product or pre-data, the engagement won't deliver what you need.

Pure product-engineering AI roles

I'm an enterprise operator, not a product builder. If the mandate is to ship AI features into a consumer product, there are better-suited people. My edge is business value, governance, and operating model, not product roadmaps.

Organizations not ready to act on findings

I don't do advisory theater. If the goal is to have a credible-sounding AI strategy for internal optics, without the commitment to act on what we find, we'll both be disappointed.

Generic AI consulting without business accountability

Engagements where success is defined by deliverables rather than outcomes aren't where I do my best work. I prefer mandates where we can agree upfront on what measurable improvement looks like.

Financial Services & Wealth Management

Healthcare Payers & Providers

Boards, C-Suite & PE Sponsors

Representative organizations

This isn't for everyone

Let's find out if
there's a fit.

Who I help

Financial Services & Wealth Management

Healthcare Payers & Providers

Boards, C-Suite & PE Sponsors

Representative organizations

This isn't for everyone

Let's find out ifthere's a fit.

Let's find out if
there's a fit.