AI Adoption · Written and maintained by Haink’s AI adoption team · Updated July 2026 · 9 min read

How Much Does AI Adoption Cost?

AI adoption costs scale with the decision, not the hype — from a free readiness check, to a five-figure go/no-go, to a program priced to the portfolio, to design and build costs that depend entirely on what you’re building. The single principle that keeps the budget honest: never buy a bigger instrument than the decision in front of you justifies.

The confusion around AI cost comes from lumping four very different things into one number: the cost to decide, the cost to design, the cost to build, and the cost to run. They differ by orders of magnitude, and the cheap ones exist precisely to protect the expensive ones from being spent in the wrong place.

The cost of the decisions

Before a line of code, adoption moves through a ladder of decision instruments. Each is priced openly, because a visible price is a filter — what repels a buyer is not the number but an unjustified number.

Prices are Haink’s published starting points as of July 2026; the exact figure is fixed in a one-page proposal before work begins.

What’s behind the price

An open price is a filter: what repels a buyer is not the number but an unjustified one. Four things stand behind it.

• Senior experts only. The work is done by senior practitioners, not a junior bench working from a template. You are paying for judgment that is hard to hire, not hours that are easy to bill.
• Weeks of expert work across the whole surface. Business priorities, every readiness dimension, constraints and risks are assessed together — because the answer lives in the intersections, not in any single checklist.
• Board-grade output. The deliverables are written to be put in front of the CEO and the board and survive their questions — a defensible document, not a slide export.
• Priced against the alternative. The number is set against what a wrong start would cost, which is multiples of it — the case made in full below.

Who does the work — and how it runs

The work is carried out by a senior team that actually deploys AI in enterprise companies — infrastructure, data platforms and production systems — not a research desk that only advises. The people in your workshop are the people who write the verdict: assessed by people who deploy AI, not just talk about it. You meet the exact team on the scoping call, before committing.

How it runs, in practice: a short discovery workshop of 60–90 minute interviews across both business and IT, followed by an information-collection phase built on descriptions of your environment — not the data itself. No access to your corporate network, databases or source code is required; the descriptions are cross-checked in the interviews, so inconsistencies get probed rather than passed into the result. The program and the blueprint build on the same evidence base, widening to the portfolio being sequenced or designed.

How long it takes

Timelines scale with the same thing as the price — scope — and each is fixed in the one-page proposal before work starts.

What moves the price up or down

Scope, not a day rate. For an assessment, the driver is the size of the organizational perimeter — how many business processes, systems and interviews are in scope. For a program or a blueprint, it is the size of the portfolio: how many initiatives are being sequenced or designed. A larger, more complex organization pays more because there is more surface to cover, not because the clock ran longer — which is why the figure is fixed against a defined scope in a one-page proposal, never quoted by the hour.

Priced against the alternative

A five-figure diagnosis looks expensive only in isolation. Set it against what it prevents. A wrong AI start costs multiples of the assessment: six months of your best people, a failed pilot, and — most expensive of all — the credibility of the next attempt, which a burned organization is far slower to fund. The assessment exists so that a six- or seven-figure build is never committed blind. Read the full argument in the cost of a wrong AI start.

The cost to build and run

The decision ladder is a small fraction of the eventual bill. Building and running the solutions is where most of the money goes — and it is worth knowing where inside that number the cost actually sits.

• The model is the cheap part. Roughly 80% of build cost is data engineering, integration, governance and measurement — not the model. Budgets that assume the reverse overrun. See the detailed custom AI / LLM development cost guide.
• Infrastructure is a separate line. Compute can be rented in the cloud or owned on-premises, with very different economics at scale — see the AI infrastructure cost guide and on-premises vs cloud LLM deployment.
• Run cost is ongoing. Inference, monitoring, retraining and support are recurring, not one-off — a model in production is a system to operate, not a project to finish.

How to budget AI adoption

1. Sequence spend to the decision. Pay for the readiness check, then the go/no-go, then design — and don’t commit build budget before the design exists.
2. Right-size the instrument. If a $20,000 diagnosis is out of proportion to the decision, start with the free score. If it isn’t, the diagnosis is the cheapest insurance you’ll buy.
3. Budget for the 80%. Plan the data, integration and governance work as the main build line, not a rounding error.
4. Match infrastructure to measured throughput, not to a vendor’s slide — size it after the pilot tells you the real load.

The honest verdict: the cheapest line item is the project you didn’t build

The whole logic of the cost ladder runs backward from a single fact: the most expensive thing in AI is not an assessment, a program, or even infrastructure — it is a large build aimed at the wrong target. Every cheap decision upstream exists to make that expensive mistake less likely. Spending a little to learn you should wait is not a cost; it is the highest-return line in the whole budget.

Frequently asked questions

How much does AI adoption cost?
It scales with the decision: a readiness check is free; an expert assessment starts around $20,000; a program from about $30,000 (full track from $48,000); a design package from about $15,000. Build and infrastructure depend on what you build, and dwarf the advisory ladder.

What drives the cost of an assessment or program?
Scope — the organizational perimeter for an assessment, the portfolio size for a program or blueprint. It’s fixed in a one-page proposal before work starts.

Is an AI assessment worth the money?
It’s priced against the alternative. A wrong start costs multiples of it. If the decision is small, start with the free score instead.

How much does it cost to build an AI solution?
It depends on the use case, but the model is rarely the expensive part — ~80% is data, integration and governance, plus infrastructure that can be rented or owned.

Who carries out the assessment and program?
A senior team that deploys AI in enterprise companies, not a junior bench or advisory-only desk. The people in your workshop are the ones who write the verdict, and you meet them on the scoping call.

How long does AI adoption take?
The readiness check takes minutes; the expert assessment runs from about three weeks; the program from two weeks, scaled to the portfolio; and building an individual solution to production typically takes from several months once designed (first working results can come sooner). Timelines are fixed in the one-page proposal before work starts.

What’s the cheapest way to start?
A free readiness check — it prevents the costliest mistake, which is committing build budget in the wrong place.