What we built, and what it returned.

Leadership teams move from AI curiosity to a working build in the room.

• Life Sciences

  Medical affairs leadership ships its first working agent in one day

  Situation

  A 40-person medical affairs group had run three AI pilots in eighteen months and shipped none. Every idea died in the gap between the science team and IT.

  Build

  One-day executive session with the eight function heads. Each pair built a live agent against real (de-identified) source material; one — a literature triage assistant for medical information requests — went home with an owner and a budget line.

  Result

  • →First response time on medical information requests: 9 days to 2 days
  • →62% of inbound requests resolved without a scientist touching them
  • →3 of 8 prototypes moved to funded build within 30 days
  Owns it now: VP, Medical Affairs — internal, no vendor dependency
• Wealth Management

  Wealth advisors stop losing the first hour of every client meeting

  Situation

  Advisors at a $6B RIA spent 60–90 minutes preparing each review meeting, pulling from four systems by hand. Growth was capped by prep time, not demand.

  Build

  Two half-day sessions with 12 advisors. Built a meeting-prep assistant that assembles a household brief — positions, drift, life events, last-meeting commitments — from existing exports.

  Result

  • →Prep time per review: 74 minutes to 11 minutes
  • →Reviews per advisor per month: 18 to 27
  • →Adoption at 90 days: 11 of 12 advisors still using it weekly
  Owns it now: Director of Advisory Practice

An IMPACT® readiness score turned into a sequenced plan a board will sign.

• FinTech

  A FinTech board kills four of six AI initiatives — on purpose

  Situation

  A payments platform had six AI workstreams running across three business units with $4.1M committed and no shared definition of done.

  Build

  IMPACT® diagnostic across data, controls, talent, and adoption. Scored each initiative on value-at-stake versus readiness, then sequenced the survivors into a 90-day plan.

  Result

  • →Readiness score: 41 to 68 in two quarters
  • →$2.6M reallocated from stalled workstreams to two funded ones
  • →Time from idea to production decision: 5 months to 6 weeks
  Owns it now: COO, presented to the board as a standing quarterly review
• Life Sciences

  A biotech finds its bottleneck is data access, not models

  Situation

  A clinical-stage biotech assumed it needed a model strategy. Six weeks of stalled proofs suggested otherwise.

  Build

  Diagnostic across seven data domains. Found 71% of candidate use cases blocked on a single unresolved access-control question, not on model capability. Rewrote the roadmap around unblocking it first.

  Result

  • →Blocked use cases: 71% to 12% after one governance decision
  • →Two proofs revived and shipped within the same quarter
  • →Avoided a projected $900K platform purchase that would not have fixed it
  Owns it now: Chief Data Officer

AI that shortens the first 100 days instead of adding to them.

• Wealth Management

  Diligence on a wealth roll-up compresses from nine weeks to four

  Situation

  A sponsor evaluating a four-firm RIA roll-up faced 14,000 client agreements across incompatible custodians and no consistent fee schedule.

  Build

  Document extraction pipeline over the agreement set, surfacing fee terms, termination clauses, and assignment consents into a single comparable table with human review on every exception.

  Result

  • →Diligence cycle: 9 weeks to 4 weeks
  • →$3.2M in previously unmodeled fee leakage found pre-close
  • →Outside counsel review hours down 38%
  Owns it now: Deal team, handed to the platform CFO at close
• FinTech

  Two FinTech integrations, one 100-day plan that actually held

  Situation

  A lender acquired two originators in the same quarter. Integration teams were duplicating discovery work across both.

  Build

  A shared integration workspace: policy and process documents from all three entities indexed into an assistant the workstream leads query directly, with a weekly variance report to the IMO.

  Result

  • →Synergy capture at day 100: 82% of plan versus a 55% prior-deal baseline
  • →Duplicate discovery meetings cut by roughly 120 hours
  • →Day-1 policy conflicts identified pre-close instead of post-close: 47
  Owns it now: Integration Management Office lead

A six-week cohort that ships something monetizable, not a slide.

• Life Sciences

  A specialty pharma team ships an internal tool it now licenses out

  Situation

  A commercial operations group wanted an AI capability but had no build path that did not route through a two-year IT queue.

  Build

  Six-week cohort, five people, one build: a payer-coverage change monitor that reads policy bulletins and alerts field teams to formulary movement in their territory.

  Result

  • →Time to first production use: 6 weeks
  • →Field response time to a coverage change: 11 days to under 48 hours
  • →Now licensed to two non-competing manufacturers — the tool pays for the lab
  Owns it now: Senior Director, Commercial Operations
• FinTech

  A FinTech ops team builds the reconciliation agent it kept asking vendors for

  Situation

  Month-end reconciliation consumed 400+ analyst hours. Three vendor quotes came back between $340K and $610K annually.

  Build

  Cohort build of an exception-handling agent that clears matched items and drafts explanations for the rest, with a full audit trail for the controls team.

  Result

  • →Analyst hours per close: 410 to 96
  • →Exceptions auto-cleared: 78%
  • →Build cost under 8% of the lowest vendor quote, year one
  Owns it now: Controller, with sign-off from Internal Audit

One operator, one recurring bottleneck, one agent that removes it.

• Wealth Management

  A managing partner gets back a day a week

  Situation

  A firm principal was personally reviewing every proposal and IPS document — the single slowest step in new-client onboarding.

  Build

  Monthly advisory cadence. Built a first-pass reviewer that checks proposals against the firm's own standards and flags only genuine deviations for the principal.

  Result

  • →Proposals requiring principal review: 100% to 23%
  • →Onboarding cycle: 19 days to 8 days
  • →Roughly 7 hours a week returned to business development
  Owns it now: Managing Partner
• FinTech

  A Chief Risk Officer stops being the regulatory bottleneck

  Situation

  Every product change queued behind one person's read on whether it created a compliance obligation.

  Build

  A regulatory-question triage assistant trained on the firm's own prior determinations, so product teams get a cited precedent first and escalate only the genuinely novel questions.

  Result

  • →Questions escalated to the CRO: 60 a month to 14
  • →Median answer time for product teams: 6 days to same-day
  • →Zero determinations reversed on later review in the first two quarters
  Owns it now: Chief Risk Officer

A defensible AI policy written before the first regulator question, not after.

• Wealth Management

  A wealth manager passes an SEC exam question it had not been asked yet

  Situation

  Advisors were already using consumer AI tools on client material. There was no policy, no inventory, and no record of who was using what.

  Build

  Use inventory, a tiered acceptable-use policy, an approved-tool list, and a model register with named owners and review dates — drafted with counsel and adopted in six weeks.

  Result

  • →Shadow AI tools in use: 23 identified, 19 retired
  • →Policy adopted and staff attested: 100% within 30 days
  • →Exam request for AI documentation answered in 2 days with no findings
  Owns it now: Chief Compliance Officer
• Life Sciences

  A life sciences sponsor makes its AI evidence audit-ready

  Situation

  AI-assisted analyses were appearing in regulatory submissions with no consistent record of prompts, versions, or human review.

  Build

  A validation framework matched to GxP expectations: documented intended use, version pinning, reviewer sign-off, and retained inputs and outputs for every submission-bound analysis.

  Result

  • →Submission-bound analyses with a complete audit trail: 34% to 100%
  • →Internal QA findings on AI usage in the following audit: 0
  • →Review overhead added per analysis: under 20 minutes
  Owns it now: Head of Quality, co-owned with Regulatory Affairs