AI enablement · delivered by an agent

The AI use-case map your own people draw.

An agent interviews every employee — then hands you the ranked use cases that will actually pay off, the heatmap, and the roadmap. The McKinsey AI-readiness study, in days, not months.
Map your AI opportunity — free → See a sample report
Built for regulated DACH FS & insurance Doubles as EU AI Act Art. 4 evidence
Employees' answers flow through the Ansatz agent, which draws the company's AI opportunity map — every cell traceable to a person. your-org · opportunity map Claims U/W Service #1 68% Claim summary
your people → the agent → your map · open the full sample report →
GDPR & works-council native EU AI Act Art. 4 aware EIOPA / DORA context DACH-insurance taxonomy
01

Everyone bought the GenAI mandate.

almost no one knows where it pays off

Insurers are spending heavily on AI and stalling in pilot purgatory — because the prioritisation is a guess.

? ?
01 · Today
Everyone's guessing where AI actually pays off.
02 · With Ansatz
One agent interviews your whole company.
IN DAYS
03 · The outcome
A ranked map your people drew — not a deck.
65%
of insurers already use GenAI — but most are stuck at proof-of-concept, unable to prioritise at scale.
EIOPA · Feb 2026
4 weeks
and six figures for a Big-Four readiness study — that lands as a top-down deck, then stalls.
Big-Four opportunity-assessment norm
~95%
of enterprise AI pilots never reach production — the prioritisation was never grounded in real work.
Industry estimate · narrative anchor

"The people who know are the ones doing the work every day — and no top-down survey ever really asks them."

02

Bottom-up, in four stages.

one agent · every employee · days
1

Adaptive interview

A 10–20 min conversation per employee — it branches, probes the real time-sinks, and verifies AI-literacy live. Not a questionnaire.

→ work-fingerprint
2

Two-step synthesis

Each interview synthesised independently first, then clustered org-wide — counting how many people asked for each use case.

→ demand-counted
3

Value × Feasibility

The Big-Four 2×2 — filled automatically from real hours, real demand, real enthusiasm. Value 45% · feasibility 35% · demand 20%.

→ ranked priority
4

Three artifacts

Ranked list, opportunity heatmap, 3-wave roadmap — every cell drillable to the anonymised quote behind it.

→ list · heatmap · roadmap
03

Three artifacts no competitor outputs together.

every figure traceable to a quote
1Status & process emails30%
2Claim-history summary20%
3Suggested email replies10%
4Submission → CRM extract20%
5Rejection-letter drafting30%
Artifact 1

The ranked use-case list

Demand count, hours saveable, value × feasibility, enabling tooling, and the data & regulatory prerequisites — per use case.

Claims
U/W
Service
Actuary
Artifact 2

The opportunity heatmap

Function × task, coloured by AI-opportunity density. Drill any cell down to the real employee evidence underneath.

Quick · 0–90dStatus emailsClaim summary
Build · 3–9moTriage / routingWording Q&A
Transform · 9mo+Medical extract
Artifact 3

The 3-wave roadmap

Quick Wins, Build, Transform — each use case a card with value, demand, hours, owner and adoption likelihood.

04

Pick a few roles. Watch the map redraw.

try it live · 30 seconds

A 30-second taste of the engine. For the full version on your own functions and headcount, map your AI opportunity.

Who's in the room?

Choose the roles you'd interview first. Ansatz clusters their real time-sinks into demand-counted, ranked use cases.

↳ This teaser uses pre-captured sample fingerprints. The real product runs a live adaptive interview per person.
Opportunity heatmap · function × category0 roles
Top use cases · ranked by value × feasibility × demand
Value figures are planning estimates extrapolated from the sample — not guarantees. The full report shows every assumption.
05

Everyone else scores from the top. We ask from the bottom.

the white space is open

aBottom-up beats top-down

Top-down tools guess which tasks AI suits. We surface the use cases your employees already want — demand-counted, in their own words, tied to real hours.
top-down guessbottom-up demand

bA benchmark that compounds

Every assessed org deepens a cross-company benchmark — "teams like yours recovered X hours from use case Y." A data network effect no hours-based consultancy can clone.

cBuilt for regulated DACH FS

Pre-loaded insurance taxonomy, EIOPA/DORA/AI-Act awareness, works-council- & GDPR-native handling — aggregate-only, consent-managed by design.

dCompliance is the second door

The same assessment doubles as your EU AI Act Article 4 literacy evidence. One purchase, two budgets: transformation and compliance.

06

Wedge → Delivery → Data-moat.

the 3–5 year arc
Year 1 · land

Wedge

The diagnosis nobody else can run — sold into DACH insurers as a fast, fixed-fee replacement for the six-figure readiness study, doubling as Art. 4 evidence.

Years 2–3 · expand

Delivery

The roadmap stops being a PDF and becomes a living backlog the platform helps ship — re-run quarterly to track realised hours-saved.

Years 3–5 · defend

Data-moat

The cross-company benchmark becomes the product: "your claims team is in the 40th percentile vs DACH P&C peers." A network effect no consultancy can clone.

Credible because it doesn't require out-capitalising OpenAI or BCG — it wins one defensible vertical slice (DACH regulated FS) on depth, regulatory specificity and compounding data.
07

Land with a diagnostic. Expand into a subscription.

planning ranges · set per engagement
Step 1 · Land

Diagnostic

€20–60k fixed
Org-wide bottom-up assessment + the three artifacts, in days. The fast, fixed-fee replacement for the four-week readiness study.
↳ list · heatmap · roadmap
Step 2 · Scale

Per employee

per-seat metered
A per-employee-assessed price for large rollouts — meters cleanly with org size as you extend to the whole company.
↳ full-org coverage
Venture-grade
Step 3 · Expand

Living subscription

annual platform
Quarterly re-runs, realised-ROI tracking, benchmark access. The roadmap becomes a living backlog — the recurring line.
↳ quarterly re-run + benchmark
Step 4 · Premium

Outcome-based

shared upside
A share of verified hours-saved on shipped use cases — once the delivery layer exists. Price aligned to realised value.
↳ pay for shipped value
Anchor: a McKinsey / QuantumBlack opportunity assessment runs $500k–$5M+ over 4–9 months; specialist AI firms $50k–$500k over 4–12 weeks. Ansatz delivers the agent-led diagnostic in days. Typical ACV: ~€15–50k small · €50–150k mid · €150–500k+ large.
THE 72-HOUR DEMO

Don't watch a pitch.
Watch it run on your own org.

Org chart and a few job descriptions in — a partial heatmap, your top-10 use cases and a roadmap teaser out. On your people, your words. Then you decide.

Book the 72-hour demo → Map your AI opportunity first
Day 0 Ingest org chart + 3–5 JDs Day 1–2 Interview 10–20 employees (or role-play live) Day 2–3 Deliver partial heatmap + top-10 + roadmap teaser