How long does an AI implementation take? Realistic timelines for SMEs

The realistic timeline, phase by phase

The honest answer to "how long" is: the first useful results in weeks, the full return over years. Those two horizons confuse most buyers, so it helps to separate them. Below is what each phase actually involves and what governs its length.

Audit and scoping — roughly 2 to 4 weeks. We map your processes, data and systems, then pick one bounded use case where the value is clear and the risk is contained. What drives the length here is not our speed but access: how quickly you can hand over sample data, name the process owner, and get us into the relevant systems. A company with a clean dataset and one decision-maker finishes in two weeks; one that has to gather data from four departments takes four.

Proof of concept — roughly 4 to 8 weeks. We build a working version against your real data, not a demo. The variable is data readiness. If the data is already structured and reachable through an API, we are building in week one. If it lives in a legacy system, in spreadsheets, or behind a vendor who charges for export, half the time goes into plumbing before any AI work starts.

Production build — roughly 2 to 4 months. This is where the tool gets hardened: security, logging, error handling, monitoring, and integration into the systems people already use every day. The spread between two and four months is decided by how many systems it must touch and how much your team can test alongside us.

Iteration — ongoing. Once live, the tool improves with real usage. This never fully "ends," but it stops being a project and becomes maintenance within the first month or two.

What makes it faster or slower

Four factors explain almost every difference in timeline between two otherwise similar projects:

• Data readiness. Clean, labelled, reachable data is the single biggest accelerator. Missing or messy data is the single biggest delay — and it is usually invisible until you look.
• System access. Modern systems with APIs are quick to connect. Legacy software without one forces workarounds that add weeks.
• Decision speed. The clock does not stop while a proposal waits for sign-off. One empowered owner beats a committee every time.
• Scope discipline. "While we're at it, can it also…" is the most expensive sentence in any project. Every added feature pushes the launch date.

This is exactly why we insist on starting small. A narrow first use case reaches production faster, proves the value, and earns the internal trust that makes the second project easier. Our fixed-price AI-scan exists to find that first bounded case before anyone commits to a build.

Time-to-value versus full ROI

Here is the distinction that changes how you should plan. Time-savings appear in weeks; full financial return takes years. Knowledge workers using AI in production recover a median of about 6.4 hours per week (Anthropic Economic Index) — that shows up almost immediately once a tool is live. But full ROI on a typical AI use case often takes two to four years, longer than the seven to twelve months you would expect from ordinary business software. The upfront cost is real and the payback curve is slower, which is why chasing a giant first project is a mistake.

The headroom is large. McKinsey estimates 60 to 70% of work hours are partly automatable, yet only about 1% of companies describe themselves as "AI-mature." Most of the value is still on the table — the question is sequencing, not whether it exists. For how the cost side works, see what an AI project costs.

The Dutch context that adds time

Timelines in the Netherlands carry obligations that lighter markets do not. Plan for them from day one rather than discovering them at launch:

• AVG (GDPR). If personal data is involved, a data-processing assessment is part of scoping, not an afterthought. Doing it early costs days; doing it late costs a rebuild.
• Works council (OR). Staff-facing tools that affect how people work often need OR consultation or sign-off. That approval runs on the council's calendar, not yours — start the conversation in the scoping phase.
• EU AI Act. Low-risk uses proceed normally. Higher-risk classifications (for example anything touching hiring, credit or access decisions) add documentation, risk assessment and human-oversight requirements — real weeks of work. Classify the use case before you scope it.

None of this should scare you off; it is manageable when it is planned. It is also why 74.6% of Dutch non-adopters cite lack of experience as their reason for holding back (CBS), even as 29.8% of Dutch SMEs already use AI. The gap is know-how, not technology.

A week-by-week example

Take one bounded case — automatically drafting replies to routine customer emails:

• Weeks 1–2: scoping, AVG check, pull a sample of past emails, agree what "good" looks like.
• Weeks 3–6: proof of concept on real historical mail; measure draft quality against human replies.
• Weeks 7–14: production build — connect the mailbox, add human-in-the-loop review, logging and monitoring.
• Week 15 onward: live with oversight; iterate on the cases it handles poorly.

Projects run late for predictable reasons: data arrives later than promised, sign-off stalls, scope quietly grows, or a legacy system has no clean way in. Name those risks at the start and the timeline above holds. If you want a second opinion on your own case, our AI consultants will tell you honestly whether it is a two-month job or a two-quarter one.