If you want to bring artificial intelligence into your company, it seems natural to start with the tool: pick one, connect it, and the work should change after that. In practice, this rarely happens. Software only delivers value once the people who work with it actually understand it. For AI, this is especially true.
The most common reason isn’t technical
When an introduction fails, the cause is rarely technical. A widely noted study by the Massachusetts Institute of Technology from 2025 found that around 95 percent of the AI pilot projects it examined produced no measurable effect on business results. The authors point not to the models or the tools, but to an organizational gap: the projects are not embedded into existing workflows and are not carried by the people involved.
The reason for this is simple. AI is not a product you buy once and then own. It resembles a capability that a company builds over time. As long as a tool is present but no one on the team understands how it works, its value stays low. An integration has to be operated, maintained and developed further, and that requires the knowledge of your employees to grow along with the application.
Suitable tasks reveal themselves in everyday work
Which task is right for a first step can rarely be derived from a list of possible use cases. A look at your own processes is more telling. A few simple questions help here: Where do activities repeat? Which task does your team find particularly tedious? And where does a lot of time disappear over the course of a day without a matching value at the end?
In most cases, this observation leads to the obvious, unloved routine tasks. That is exactly where it makes sense to begin, not with the most technically demanding work and not with whatever is being discussed in public at the moment. AI can be applied very broadly, yet the sensible entry point is almost always the obvious one, done properly.
The first use case is a starting point
The value of a first use case lies not only in the time you save. That figure turns out to be more sober than many vendors promise anyway. An analysis by the Federal Reserve Bank of St. Louis puts the actual time savings of active AI users at an average of around two hours per week. That is considerably less than the double-digit hourly figures often used in advertising.
What matters more than the time saved is something else. With the first use case, a learning process begins. Once you have fully automated a task, you usually recognize the next one much more easily. Your understanding of what is possible grows with every application. That happens not because the technology gets smarter, but because the people in the company learn. The first use case is therefore less a result than a starting point. It makes possibilities visible that were not apparent before.
Automation remains an ongoing task
A workflow that runs reliably today is not a finished project, but an interim state. Workflows can be extended and improved over time, provided they are maintained. That this ongoing effort is regularly underestimated is also reflected in the analysts’ forecasts. Gartner expects that by the end of 2027, more than 40 percent of projects involving autonomous AI agents will be discontinued, among other reasons because of unclear costs and underestimated complexity.
For a workflow to last, it needs people who can follow what happens inside it. Otherwise you end up with an application that no one fully understands and that fails at the next change in its environment, without the error being easy to fix. Automation is therefore less a one-time undertaking than a lasting way of working.
Where a sensible starting point lies
When you begin with automation, start small: with an obvious, recurring task that you implement properly, rather than with many projects at once. Just as important is involving your team, so that the necessary knowledge can grow with it. The first use case is then not the goal, but the beginning.