What to prepare before your first AI project

The gap between an AI demo and an AI system that people use every day is rarely the model. It is almost always the preparation: a vague goal, data nobody has looked at, or no agreed way to tell whether the result is good.

None of that requires technical knowledge to fix. Here is what to have ready before your first conversation with an AI development partner, and why each item matters.

1. One problem, described in plain words

Start with the business problem, not the technology. "We want a chatbot" is a solution. "Our analysts spend two days reading filings before every client meeting" is a problem, and it can be measured.

A good problem statement answers:

• Who has the problem today?
• What do they do now, step by step?
• What would change for them if it were solved?

If you have several ideas, pick the one where a win is easiest to see. A first project that clearly saves time for one team builds more trust than an ambitious one that half works for everyone.

2. A clear picture of the data

AI systems are built on data, so the most useful thing you can bring is an honest inventory:

• Where does the relevant information live? PDFs, a database, email, a CRM, spreadsheets?
• Roughly how much is there, and how often does it change?
• Is it clean and consistent, or scanned, handwritten or spread across formats?
• Who owns it, and are there privacy or compliance rules around it?

You do not need to clean anything in advance. You only need to know where it is and who can grant access. A small, representative sample, even ten typical documents, makes the first scoping conversation far more concrete.

3. A definition of "good enough"

This is the step most teams skip, and it is the one that decides whether a project ships.

Write down ten to twenty real examples of the task with the answer you would accept. For a document assistant, that means real questions and the correct answers. For a classifier, real items and the right labels. For a vision system, real images and what should be detected.

This small set becomes the test every version of the system is measured against. Without it, progress is a matter of opinion, and projects drift.

Also decide what an error costs. A wrong product recommendation is an inconvenience. A wrong figure in a financial summary is not. That difference shapes how much human review the system needs.

4. Where it has to live

An AI system is only useful where people already work. Think about:

• Who will use it, and how? A web app, a Slack bot, an internal tool, an API another system calls?
• Does it need to connect to existing tools, such as a CRM, a document store or a ticketing system?
• Are there constraints on where data can be processed, such as on-premises or in a specific cloud?

5. A realistic first milestone

Plan for a first version that does one thing well for one group of users, then grows. A useful first milestone has:

• A narrow scope that can be delivered in weeks, not quarters.
• A test set and a target, as described above.
• A small group of real users who will give feedback early.

Simpler systems such as document Q&A or a focused chatbot can often reach a first working version in a few weeks. Multi-step agent systems or custom computer vision usually take longer because there is more to evaluate.

6. One owner on your side

Finally, name one person who can answer questions, give access to data and make small decisions quickly. Projects with a clear owner move noticeably faster than projects where every question waits for a meeting.

Bringing it together

If you can describe the problem, point to the data, show a handful of examples of a good result and name an owner, you are better prepared than most teams starting an AI project.

That is exactly what our free consultation is for. Book a 30-minute call and we'll walk through your use case, assess feasibility and outline the next steps.