I reviewed an AI vendor proposal last month that promised a “fully deployed, production-ready solution in 8 weeks.” The client — a mid-market manufacturer with 2,000 employees — was excited. I had to be the one to explain that the 8-week timeline assumed perfect data, no integration issues, immediate stakeholder buy-in, and a compliance team that doesn’t exist.

This is happening everywhere. There’s a systemic disconnect between what AI vendors promise and what enterprise deployments actually require. And it’s causing real damage — not just to project budgets, but to organisational trust in AI initiatives.

The anatomy of an unrealistic timeline

AI vendor timelines typically cover three things: model configuration or fine-tuning, basic integration with your existing systems, and user acceptance testing. What they almost never account for is the 60-70% of work that has nothing to do with the AI itself.

Here’s what actually eats your timeline:

Data preparation. Vendors assume your data is clean, structured, and accessible. It never is. I’ve seen data preparation consume 30-40% of total project effort in every single enterprise AI deployment I’ve been involved with. Your CRM has duplicate records. Your ERP data has inconsistent formats across business units. The data you need lives in three different systems that don’t talk to each other.

Stakeholder alignment. The business sponsor wants the AI to do one thing. The IT team thinks it should do another. Legal has concerns. The workers who’ll actually use it haven’t been consulted. Getting genuine alignment typically takes 4-6 weeks of meetings, workshops, and political navigation.

Integration complexity. Connecting an AI model to your production systems isn’t a plug-and-play exercise. Authentication, data pipelines, error handling, failover procedures, monitoring — this is infrastructure work that requires your existing engineering team’s involvement. And they’re already busy.

Change management. The best AI system in the world fails if people don’t use it. Training, communication, process redesign, feedback loops — this is a parallel workstream that runs alongside the technical build and often takes longer.

Compliance and governance. Depending on your industry, AI deployments may require privacy impact assessments, algorithmic audits, board approvals, or regulatory notifications. These have their own timelines that don’t compress because a vendor wants to close a deal.

A realistic timeline framework

After working on dozens of enterprise AI projects, I use a framework with four phases. It’s not glamorous, but it’s honest.

Phase 1: Discovery and scoping (4-8 weeks)

Before any technical work begins, you need to understand the problem properly. What specific business outcome are you trying to achieve? What data exists? What’s the current process? Who are the stakeholders? What are the constraints?

This phase produces a detailed project plan with realistic estimates. It also produces something equally important: a shared understanding among all stakeholders about what the project will and won’t deliver.

I know this feels slow. Clients often push back — “we already know what we want, let’s just build it.” But I’ve never seen a project that skipped proper discovery and didn’t pay for it later with scope changes, rework, or outright failure.

Phase 2: Data and infrastructure (6-12 weeks)

Get your data in order. Build the pipelines. Set up the environments. This is the unglamorous work that determines whether your AI actually works in production.

During this phase, Team400 helped one of my clients discover that the “clean dataset” their vendor had assessed during the sales process was actually missing 40% of the records needed for the model to perform accurately. That discovery, made early, saved them from a failed deployment. Made late, it would have been a six-figure write-off.

Phase 3: Build, test, and iterate (8-16 weeks)

This is where the AI model gets configured, trained, tested, and refined. It’s also where you learn that the real world is messier than the proof of concept suggested.

Build in time for at least three iteration cycles. The first version won’t perform well enough. The second will surface edge cases you didn’t anticipate. The third usually gets you to a viable production system.

Run your testing with real users doing real work, not synthetic scenarios designed to make the AI look good.

Phase 4: Deployment and stabilisation (4-8 weeks)

Phased rollout, monitoring, performance tuning, user support, and process adjustments. Don’t try to go live across the entire organisation simultaneously. Start with one team or one business unit. Learn from their experience. Then expand.

Doing the maths

Add those phases up and you get 22-44 weeks for a substantive enterprise AI deployment. That’s 5-10 months. Compare that to the “8 weeks” vendors promise.

Now, not every project is at the high end. Simpler deployments — adding AI to an existing workflow with clean data and limited integration — can be faster. But anything that touches core business processes, requires significant data work, or spans multiple systems is going to take time.

A study from McKinsey’s 2025 State of AI report found that organisations scaling AI successfully typically allocate 2-3x more time than initially estimated. The ones that stick to aggressive timelines report higher failure rates and lower ROI.

How to protect yourself

Five practical steps:

Demand a detailed breakdown. When a vendor gives you a timeline, ask them to itemise every phase, every dependency, and every assumption. If they can’t, they haven’t thought it through.

Add a data assessment phase. Before committing to a timeline, invest 2-3 weeks in a thorough data assessment. This single step prevents more timeline blowouts than anything else.

Build in buffer. My rule of thumb: take the vendor’s estimate, add 50% for data work, 30% for integration, and 20% for change management. Then add another 15% contingency.

Set milestone-based payments. Don’t pay on a fixed timeline. Pay on demonstrated milestones — data pipeline complete, model accuracy threshold met, successful pilot with real users. This aligns vendor incentives with realistic delivery.

Define “done” upfront. What performance metrics matter? What adoption rates constitute success? What’s the minimum viable deployment? Get this in writing before the project starts.

The real cost of unrealistic timelines

When AI projects miss their timelines — and most do — the damage goes beyond budget overruns. Leadership loses confidence in AI investments. Project sponsors lose credibility. Teams become cynical about the next initiative.

Setting realistic expectations from the start isn’t pessimism. It’s the foundation for sustainable AI adoption. The organisations with the most successful AI programmes aren’t the ones that deployed fastest. They’re the ones that deployed with clear eyes about what it would actually take.

Your AI project will take longer than the vendor says. Plan for that, and you’ll be one of the organisations that actually gets to production.