FinTech (B2B Payments) AI Strategy & Advisory AI Strategy Sprint · fixed-price

Two weeks. A board-ready AI roadmap.

A Series A fintech had board pressure to 'do AI' and three stalled pilots to show for it. We ran a 2-week strategy sprint that surfaced three projects worth running, two pilots worth killing, and a roadmap that survived the next board meeting.

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Opportunity Map: 8 evaluated, 3 recommended, 2 explicitly stopped

Series A. Sixty people. Board pressure to "do AI." Three stalled pilots to show for it.

The fintech was 18 months into a Series A, building B2B payment infrastructure for mid-market platforms. The board's last quarterly meeting had ended with a slide titled "AI Strategy" that was empty. The CTO had been quietly working the problem for six months.

Three pilots had been started and quietly shelved: a support chatbot abandoned after 40 days because it couldn't answer questions about the actual product; a fraud-detection prototype with promising model results but no path to production; a "sales AI" license used by 2 of 8 reps with no measurable impact. The CTO had three problems converging simultaneously.

Where to invest

Multiple ideas, no framework for choosing. Every team had an AI pitch. None had clear ROI or a build path.

How to communicate up

The board wanted "AI strategy" framed in terms they could repeat to investors. The CTO needed a narrative, not a list of experiments.

How to communicate down

The engineering team was skeptical of every new pilot after the first three failed. Trust in the process had eroded.

The CTO put it plainly: "I needed an outside read. Not from a vendor, not from McKinsey. From someone who'd shipped this kind of work and would tell me which of our ideas was real and which was a slide."

Discovery first. Synthesis second. No ongoing retainer.

The Sprint is a fixed-scope, fixed-price engagement. It runs for exactly two weeks and ends with a 90-minute readout: a complete strategic picture with clear go / no-go decisions on every candidate project.

The Sprint process: no sliding timelines, no scope creep

Three projects worth running. Two pilots worth killing.

The Opportunity Map gave the CTO something more valuable than a prioritised list: explicit go / no-go decisions backed by evidence, not opinion.

Run these

Productionize the fraud model

The data team's prototype was solid: the blocker was a missing event-stream layer, not the model. Estimated annual fraud-loss reduction: $1.8M.

Support copilot, not a chatbot

The failed chatbot tried to replace agents. The right shape was a copilot drafting responses for human review inside the existing helpdesk. Estimated time savings: −22% of support-team hours.

Sales call analysis pipeline

A Claude-based pipeline scoring every call, surfacing coaching moments, and feeding structured insights into the CRM: faster new-rep ramp, better deal-stage discipline.

Kill these

Cancel the "sales AI" license

2-of-8 rep adoption after 4 months isn't a slow start. It's a leading indicator. Don't renew.

Pause the agentic-ops pilot

Premature for a 60-person company. Recommend revisiting in 12 months once data infrastructure is solid.

Four documents. One board narrative. Every question answered.

The Sprint ends with a complete package, not a slide deck of recommendations, but production-ready documents the CTO could act on the next morning.

DELIVERABLE · 01

AI Readiness Assessment

16 pages covering current state, blockers, opportunities, and technical-debt landscape, the honest starting point every strategy needs.

DELIVERABLE · 02

Opportunity Map

1-page ranked view of 8 evaluated opportunities, scored on impact × effort × time-to-value, with explicit rationale for the 5 we recommended against.

DELIVERABLE · 03

90-Day Roadmap

3 pages: three project briefs each with scope, timeline, budget cap, dependencies, and a named owner. The board read it almost verbatim.

DELIVERABLE · 04

Stack & Data Audit

4 pages: full vendor list, consolidation recommendations, and the three data-infrastructure fixes required before any AI can ship.

Plus a one-page board talking-points write-up the CTO read at the next board meeting almost verbatim. The "AI Strategy" slide was no longer empty.

All three recommended projects are live.

Within 90 days of the readout: one follow-on Build Engagement commissioned, two projects scoped and run in-house, two pilots killed. Twelve months later, the numbers held.

$2.1M

Annualized fraud-loss reduction (live 12 months later)

−31%

Support median first-response time (copilot live)

+25%

Faster new-rep ramp (sales call analysis live)

$250K

Misdirected spend prevented (two pilots killed)

The Sprint cost less than one month of one senior engineer's salary. It saved us at least a quarter of misdirected work and got us three projects that actually shipped. I've recommended this exercise to four other CTOs.

Jahred A. CTO · B2B fintech

Less than one month of one senior engineer's salary.

The Sprint is intentionally priced to remove the "is this worth it?" calculation. You know the cost before day one. You get the full picture on day fourteen.

2 wks

Fixed sprint · Day 1 to Day-14 readout

$6K

Fixed-price · no surprise invoices

Senior team · strategist, AI specialist, data engineer

$81K

Follow-on Build · fraud productionization · 9 weeks

The money-back guarantee was offered and honored. It was not invoked. The follow-on Build Engagement for fraud-detection productionization was capped at $81K and delivered in 9 weeks, on the exact path laid out in the Sprint roadmap.

Two weeks. A real plan.