How much does an AI implementation cost?

How much does an AI implementation cost?

AI workflow automation usually costs $25,000 to $75,000. Operational AI, agent systems and custom AI applications generally cost $75,000 to $250,000. Enterprise AI platforms and multi-system implementations are scoped individually.

That is the build. Model usage is billed per request by the provider and is a running cost for as long as the feature exists, which is the part most AI quotes leave out.

Why the running cost deserves its own line

A traditional system costs what it costs to build and then costs very little to run. An AI feature costs what it costs to build and then costs money every time someone uses it.

That is not a reason to avoid it. It is a reason to know the number before you commit, because a feature that is cheap at ten uses a day and uncomfortable at ten thousand should be designed differently from the start.

A competent implementation partner should be able to explain the expected model and API usage for your workload and how it scales as volume grows. If nobody can give you that, the running cost has not been worked out yet, and that is worth resolving before the build rather than after it.

What makes an AI project cost more

• Where the data lives. A model reasoning over documents you already have in one place is straightforward. A model that first needs a pipeline built to reach data scattered across five systems is mostly a data project.
• Whether it acts, or only advises. Summarising is contained. Taking actions in live systems needs permissions, approval steps, audit logging and a way to undo, and that is the bulk of an agent build.
• How wrong it is allowed to be. A drafting assistant can be wrong and a person fixes it. Anything touching money, compliance or a customer promise needs review paths and evidence, and those cost more than the model call.
• Volume. Design changes above certain volumes: caching, batching, smaller models for the easy cases.

Where normal automation is the better answer

Most of the time, honestly.

If the rule can be written down, write it down. Calculating a due date, escalating an overdue item, sending a reminder, routing by postcode, applying a pricing rule: these are deterministic problems. Deterministic code handles them predictably and fast, it can be tested and audited, and it adds no model charge per decision.

AI earns its place where the input is genuinely unstructured and variable: reading documents that arrive in twenty formats, classifying free text, pulling fields out of a photographed form, drafting something a person will edit. If you can describe the rule in a sentence, you do not need a model.

We will say so in the plan when that is the case, including when it means a smaller project.

What a sensible first AI project looks like

One workflow, with a human still in the loop, where the current cost is measurable. Document intake and data extraction are the usual honest starting points, because the before state is someone retyping things and the after state is countable.

Start there, measure it, then decide whether the second one is worth it.

Likely ongoing costs

• Model and API usage, billed by the provider, itemised separately from the build
• A service plan covering monitoring and changes
• Hosting and any vector or search infrastructure the feature needs
• Review time from your own people, which is a real cost on anything consequential

What we will not claim

Goldstone has no standalone AI customer case study and claims no compliance certification. The architecture, the permissions model and the cost controls are things we will show you. Outcomes at another company are not evidence about yours.

What to do next

AI Solutions covers the six categories of work and how each is delivered. Describe your problem and the plan you get back will say where AI fits and where it does not.