Why AI investments failAI EconomicsOur offeringsPerspectiveTalk to usWhy AI investments failAI EconomicsOur offeringsPerspectiveTalk to usWhy AI investments failAI EconomicsOur offeringsPerspectiveTalk to usWhy AI investments failAI EconomicsOur offeringsPerspectiveTalk to usWhy AI investments failAI EconomicsOur offeringsPerspectiveTalk to us
The AI Practice

One study.One design.One accountable layer,until the numbers move.

Most AI engagements start with a tool and end with a report. Ours start with a question that has a number attached, and end when the number has moved. Three sprints. Enter at any one. None of them begin with technology.

We do not build AI. We do not sell it. We take no fees from anyone who does.

$684bnspent on AI in 2025
21%of companies can point to a measurable benefitMorgan Stanley
7%have it fully integrated, though nearly all now use it
How we think
  1. 01

    Everything AI sells is a prediction. So we price it like one.

    Most business AI is prediction: data you have becoming information you do not. Priced that way, the decision stops being about technology and becomes a number. Then the question nobody writes down: what does a wrong answer cost?

  2. 02

    AI is brilliant at one task and confidently wrong at its twin.

    The error arrives beautifully written, so “does this look right” waves it through. We map the frontier task by task, automate inside it, and put friction exactly where a reviewer would otherwise nod.

  3. 03

    The value was never in the tool. It is in the workflow.

    A licence changes nothing. Redesign the work around it and every run produces operational data no competitor holds and no vendor can sell you. That compounds.

  4. 04

    The real threat is a competitor who prices AI first.

    Their cost curve bends, and revenue growth will not close the gap. We size it early, while there is room to answer deliberately. The answer is rarely a smaller payroll. It is the same people, moved onto work that was never getting done.

Does AI change this business?Where?By how much?Build, buy, partner, or waitHonest noes. Specific yeses.Until the numbers move.Does AI change this business?Where?By how much?Build, buy, partner, or waitHonest noes. Specific yeses.Until the numbers move.
The three sprints

Enter at any sprint. Leave when the numbers move.

Sprint 1
4–8weeks · onsite for 2
01
4 to 8 weeks. Onsite for 2.

AI Readiness and Financial Feasibility

Can your business carry AI, where does it pay, and is the move fundable?

  • The readiness of your data systems, at the point a decision uses them
  • Every critical workflow, marked task by task for what AI can carry
  • The financial case: where AI pays, what it costs, and whether it is fundable
See how Sprint 1 works
Sprint 2
6–8weeks · onsite for 2
02
6 to 8 weeks. Onsite for 2.

Workflow AI Strategy and Design

For the workflows that pay: how they get designed, built or bought, and what they return.

  • Every step allocated: run it, assist it, or leave it to a person
  • Requirements written technology-agnostic. The design is yours
  • A costed P&L per use case, and a 90-day proof
See how Sprint 2 works
Sprint 3
12–36weeks
03
12 to 36 weeks.

Transformation and P&L Accountability

The same principals who ran the study, now accountable for the outcome.

  • The human side: role redesign, upskilling, redeployment
  • Actual against projected, in the P&L, not a status deck
  • Every quarter: continue, redirect, or kill
See how Sprint 3 works
Prequate Plug-ins

The engagement, productised.

Eight decision engines, grouped by the lever they move. Not one of them is a report.

Growth3 engines
  • Demand forecast readiness

    Which SKUs have the data for AI forecasting, and which it will get wrong

  • Pricing intelligence

    The decision engine that closes the pricing leak humans still govern

  • Market entry simulation

    Which expansion research AI can carry, and which needs in-market judgment

Efficiency2 engines
  • Costing data fabric

    Truth in costing before any AI touches pricing

  • Cost-to-serve signals

    Which customers, channels or orders quietly lose money

Transactions2 engines
  • AI due diligence

    Buy-side: data moat, cost-structure risk and technology debt, priced into the deal model

  • AI readiness for capital

    Sell-side: the AI chapter of the raise that survives the second investor meeting

Portfolio Value1 engine
  • Portfolio AI screen

    Every portfolio company on one comparable scale: exposure, opportunity, urgency

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One relationship becomes several. Everything connects. This wasn’t where we started. It took us seven months, and we are still at it.

The short version

Where most chase AI, Prequate prices it.

Tell us the decision in front of you. We will tell you, honestly, whether AI is the investment that pays. And what it takes to get there.

One business, one honest study, one set of numbers that moved, at a time.

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© 2026 Prequate Advisory. All rights reserved.Where most chase AI, Prequate prices it.