What the first week of an AI project actually looks like

One of the things that makes hiring an AI development team nerve-wracking is not knowing what you're getting into. What will the first week look like? Who will we be talking to? What decisions will we be making? What do you need from us?

We've found that transparency about process does more to set clients at ease than any amount of case study content. So here's an honest description of how we typically structure the first week of a new engagement.

Before day one: the setup we do ourselves

Before the first meeting, we do background work that we don't bill for and don't ask the client to participate in.

We read everything publicly available about the client's product, company, and market. We map the competitive landscape well enough to understand context. If the engagement was preceded by an AI Automation Audit, we review the audit findings and identify which recommendations we're scoping.

We also set up the collaboration infrastructure: shared document space, communication channel, a staging environment if applicable, and a working log where we'll document decisions as they're made. We'd rather start the first meeting already oriented than use half of it on logistics.

Day one: getting oriented in the actual environment

The first meeting is usually two to three hours. It covers a lot of ground.

We walk through the process or system we're building for — not just the description of it, but the actual thing. If it's a document workflow, we look at real documents. If it's a support queue, we look at real tickets. If it's a data pipeline, we trace a real record through it.

This matters because the description of a process and the actual process are almost always different. The description is the clean version. The actual process includes the exceptions, the informal workarounds, the edge cases that didn't make it into any documentation, and the one person who handles the unusual cases differently from everyone else.

We also ask about previous attempts. Has anyone tried to automate this before? What happened? What do people inside the organization think will be hard about this? These questions often surface constraints and context that save us from repeating mistakes.

By the end of day one, we should have enough to start forming a technical opinion.

Days two and three: understanding the data

The second and third days are usually the most technically intensive. We're looking at the actual data that the system will need to work with.

For most AI projects, this means: a sample of production inputs that's random and representative (not curated), a look at the edge cases and how they're currently handled, any existing labels or annotations if the task involves classification, and an understanding of how the data gets created and what might change upstream.

We're also doing preliminary evaluation at this stage. What does a naive approach get right? Where does it fail? This is where you find out whether the problem is actually tractable and at what level of difficulty.

The questions we're trying to answer by end of day three:

• Is the data good enough to build on, or is there pre-work needed?
• What's the realistic accuracy ceiling for this task?
• Are there edge cases that need special handling outside the main system?
• What does "good enough" actually mean for this workflow?

These questions shape everything that comes after.

Day four: architecture and approach

By day four, we usually have a point of view on the technical approach. We document it clearly — not a forty-page spec, but a clear description of what we're building, why we're making the choices we're making, and what we're explicitly not doing.

We present this to the client and work through it together. The goal is shared understanding, not sign-off on a document. We want the client to be able to explain to a colleague what we're building and why, without needing to reference the spec.

This is also where we identify dependencies that are on the client's side — data access, system credentials, stakeholders who need to be looped in, decisions that need to be made about edge case handling. These often become blockers later if they're not identified and owned early.

Day five: alignment on what comes next

The last day of the first week is for calibration. We review what we found, confirm the approach, agree on the definition of success for the first phase, and set expectations for the weeks ahead.

Specifically:

• What will we build, and what's out of scope for this phase?
• What does success look like at the end of the build, and how will we measure it?
• What are the known risks, and how are we planning to handle them?
• What does the client need to have ready for us next week?

If we've done the first week well, both sides should leave with a clear understanding of the work ahead, confidence in the approach, and a realistic picture of what's going to be hard.

What we're really doing in week one

Reading between the lines: the first week isn't really about technical discovery. The technical questions are important, but they're mostly answerable by looking at the data and the system.

What we're actually doing is building the working relationship that will make the rest of the project go well. Finding out how the client communicates and how quickly they make decisions. Understanding who the real stakeholders are and what they actually care about. Calibrating our mutual understanding of what "done" means.

Most of the AI projects we've seen fail didn't fail because of technology. They failed because of misaligned expectations, unclear ownership, or communication patterns that didn't surface problems early enough to fix them. The first week is where we try to prevent all of that.

If you're trying to decide whether to move forward with an AI project and want to understand what it would actually look like to work with us, book a discovery call. We'll walk you through our process and give you a clear picture before you commit to anything.