Skip to content
Fulkerson Advisors
Practice · No. 01

Decide where AI belongs in your business; and where it doesn't.

We work with Fortune 500 executives who need a defensible answer to a board that has stopped accepting enthusiasm as a strategy. The work is sequencing: which initiatives ladder into production capability, which are wishful thinking, and which deserve to be killed before they consume another quarter.

Discuss your AI roadmap

Engagement model

What this looks like in practice

Every strategy engagement is led personally by one of our managing partners; not a partner-and-bait setup where a senior name opens the door and an analyst pool runs the work. Christian Adib leads this practice directly, embedded with your executive team and a forward-deployed pod of senior practitioners. We sit alongside your operators, not across the table from them. When a domain question exceeds our bench, we pull from an expert network of 20+ subject-matter specialists rather than improvising. The deliverable is never a slideware-only artifact: by the end of a strategy engagement you have an executive-aligned roadmap, the financial model behind it, and the first instrumentation needed to defend the spend.

Buying triggers

When you should talk to us

Most executives find us in one of four moments. The board has issued an AI mandate and the CEO needs a position by the next quarterly review. A first wave of pilots ran for twelve months and produced demos but no P&L impact, and the CFO is asking pointed questions. A peer or competitor has made a visible AI commitment and the strategy team is being asked whether to match it. Or an internal champion has proposed a build-versus-buy decision the executive committee is not yet equipped to adjudicate. These are not problems that resolve with another vendor pitch; they resolve with a defensible point of view, written down, with numbers attached.

Methodology

How we approach AI strategy

We've refined this sequence across Fortune 500 engagements. The discipline is in moving in order: discovery before plan, plan before commitment, commitment before scale. We start by mapping where AI can plausibly create value in your business, then pressure-test each candidate against feasibility, data readiness, organizational appetite, and competitive necessity. The output is a ranked portfolio: a small number of initiatives worth funding now, a watchlist worth revisiting in six months, and an explicit list of bets we recommend against. The roadmap is sequenced so each initiative earns the right to the next, rather than a parallel sprawl of pilots competing for the same scarce talent.

  • Value mapping across functions, weighted by addressable spend and decision frequency
  • Feasibility scoring against data readiness, model maturity, and integration debt
  • Build, buy, or partner framework applied initiative by initiative; not as a blanket posture
  • Sequenced roadmap with explicit gating criteria between pilot, scale, and embed phases
  • Board-ready financial model tying each initiative to measurable business outcomes

Proof

Case files

Strategy work is only credible if you've sat in the seat afterward and watched the recommendations survive. The files below are engagements where the strategy phase fed directly into production systems we then helped build, instrument, and defend to the board. Each one started with the same question: of everything we could do with AI, what's actually worth doing first?

Anti-positioning

What we don't do

We don't write AI strategies for organizations that have not yet decided whether they want one; that's a different conversation and we'll tell you so. We don't produce hundred-page decks designed to justify a predetermined vendor selection. We don't run innovation theater: hackathons, ideation workshops, or capability showcases disconnected from a P&L. And we don't take engagements where the executive sponsor will not commit to making a funding decision at the end of the work. Strategy without a decision attached is an expensive form of procrastination.

Who you’ll work with

Christian Adib

Christian Adib

Founder & Managing Partner

Christian leads this practice on the back of forward-deployed BCG data science work and an MIT Leaders for Global Operations grounding; he has sat on both sides of the strategy-to-production handoff, including running a $2B quant book where the roadmap had to clear the market every morning.

Practice background

  • Christian Adib began his career in management consulting at Booz Allen Hamilton and the Boston Consulting Group, where, as a senior data scientist, he led forward-deployed teams — embedding engineers alongside client operators rather than handing over slideware. He then joined a hedge fund, where, as part of a small team managing a $2B portfolio, he drove quantitative research and built products from scratch.
  • An engineer by training, Christian went on to complete MIT's Leaders for Global Operations program, earning an MBA alongside a master's in engineering. He founded Fulkerson Advisors to close the gap he kept seeing between AI's promise and what survives contact with production — building the firm around one conviction: that the people who scope an initiative should be the ones who ship it.

Case files

  1. Asset Management

    Equity index backtesting for a $55B US pension fund

    Strategy work that identified, scoped, and validated an AI-enabled index strategy now running against a multi-billion-dollar portfolio.

  2. Enterprise Software

    GenAI strategy for an enterprise software startup

    Build-versus-buy roadmap that killed two proposed pilots and accelerated funding for the one initiative tied to renewal economics.

  3. Retail (Caribbean conglomerate)

    Demand forecasting Center of Excellence

    Sequenced strategy from single-category pilot to enterprise-wide forecasting capability, with the operating model and governance to sustain it.

See all case files
Frequently asked

Questions we hear most.

Where do we start? What's actually worth funding?
Start with a value map weighted by addressable spend and decision frequency, not by technical excitement. The candidates worth funding share three traits: a measurable economic outcome, data you already control, and an executive willing to own the operational change. Everything else belongs on the watchlist or in the do-not-fund pile. Our first deliverable is usually that ranked list, with the financial logic attached.
Should we build or buy?
It depends on whether the capability is a source of competitive differentiation or table stakes. Buy commodity capabilities (transcription, generic copilots, off-the-shelf agents); build the layer that encodes your proprietary data, workflows, or institutional judgment. The mistake is applying a blanket posture across the portfolio. We make the call initiative by initiative and write down the reasoning so the next executive can audit it.
How do we sequence pilots so they ladder into production capability?
Each pilot should earn the right to the next by clearing explicit gating criteria: business impact validated, integration path mapped, change-management owner identified. Pilots that cannot articulate their next stage at kickoff almost never reach it. We design the sequence so the second initiative reuses the data infrastructure, governance, and operating model built for the first, compounding rather than restarting.
How do we get the board comfortable with AI investment?
Boards get comfortable with numbers, not narratives. The strategy needs to land with an explicit funding ask, a ranked portfolio showing what gets funded and what doesn't, a financial model tying spend to outcomes, and a governance structure for killing initiatives that miss their gates. We build the materials for that conversation and, when useful, sit with the executive sponsor through the read-out.
How long does a strategy engagement take?
Six to twelve weeks for a focused executive-aligned roadmap; longer if the scope spans multiple business units or includes a build-versus-buy decision against a complex vendor landscape. We scope tightly at the start and prefer to ship a defensible answer to a narrower question than a sprawling answer to everything.
Do you stay on through implementation?
Often, but not always. Roughly two-thirds of our strategy engagements continue into the implementation practice; the other third hand off to internal teams or a chosen vendor with our roadmap as the contract of work. We don't gate the strategy work on a downstream commitment, and we'll tell you honestly when an initiative no longer needs us.

Bring us a question, and we’ll bring you an honest read.

Discuss your AI roadmapRead the case files