# Where to Start an AI Project: 5 Steps from Meeting to Production > The five steps that take AI from pilot to production in your company: discovery meetings, an opportunity map, a prototype, integration and measurement. - URL: https://twinlikee.com/en/blog/where-to-start-an-ai-project/ - Published: 2026-10-19 - Category: AI - Tags: artificial intelligence, automation, digital transformation, process - Publisher: Twinlike (https://twinlikee.com) AI is on every meeting agenda. Yet in many companies the picture is the same: a few tools have been tried, one or two pilots started, but nothing has really changed in the teams' daily work. The problem is rarely the technology — it is usually **where you start**. In this article we explain where to begin with AI and how to take an idea from pilot to production in five steps. ## 1. Start with a meeting, not a tool The most common mistake is to pick a tool first and then look for work for it. The right order is the opposite: understand the work first. That is why every AI project we run starts with **discovery meetings with the teams**. Sales, finance, operations or customer service — we ask every team: - Which task takes up most of your day? - Which tasks do you repeat the same way every week? - Where is the risk of mistakes highest? - Where do you lose the most time looking for information? The answers show exactly where AI will make a real difference. ## 2. Build an opportunity map After the meetings you will have a long list. Instead of starting everything at once, score each opportunity on two axes: - **Impact:** How much time or cost is saved when this task gets faster? - **Feasibility:** Is the data accessible, is the process clear, can a working version be built quickly? Use cases that are both high-impact and quick to deliver are your first candidates. Preparing proposals, reporting, summarising documents, answering frequent questions and classifying e-mails fall into this box in most companies. ## 3. Build a small prototype with real data For the single use case you picked, build a small prototype that **works with your real data**. Experiments on demo data hide real-world problems. At the prototype stage the goal is not perfection but an answer to one question: *does this really speed up this team's work?* If yes, move on; if not, change the use case. You find the right direction without spending big budgets. ## 4. Integrate it into the tools your team already uses AI projects that stay in pilot have one thing in common: they live outside the tools the team uses. Solutions that require a separate screen, a separate login and a separate habit are quickly forgotten. For lasting value, embed the solution in the existing flow: - Connect it to your e-mail, CRM or ERP, - Design user permissions and data access on the principle of least privilege, - Mask sensitive data and design data-protection requirements in from the start. This step also shows why [software development](https://twinlikee.com/en/services/software-development/) and [cybersecurity](https://twinlikee.com/en/services/cybersecurity/) expertise matter so much in AI projects. ## 5. Measure, improve, roll out For every solution that goes live, define a success metric up front: time spent, error rate, response time or the number of requests handled. Measure after a few weeks, collect feedback from the team and improve. Once the first use case creates value, move to the next item on your opportunity map. AI transformation is not a one-off project — it is this loop, repeated. ## Conclusion: start small, grow in real work Successful AI projects do not start with big promises but with one well-chosen small use case. Listen to your teams, pick the highest-impact task, test with real data, integrate into existing tools and measure the result. At Twinlike we run our [AI solutions](https://twinlikee.com/en/services/ai-solutions/) service exactly this way: we sit down first, meet your teams and embed solutions that speed up your work in your daily flow. To take the first step together, [get in touch](https://twinlikee.com/en/contact/). ## FAQ ### Do we need a large dataset to start an AI project? Usually not. Your existing business data — documents, e-mails and forms — is enough to start with assistant and automation use cases. What matters is that the data is accessible and up to date. ### How soon is the first working version ready? It depends on scope. For one well-chosen use case, a prototype that works with your real data can usually be ready within a few weeks. ### How is our data kept secure? Access is designed on the principle of least privilege, sensitive data is masked and the solution is built with data-protection requirements (KVKK/GDPR) in mind.