Low impact, high effort: The AI projects you should say no to
When prioritising AI ideas, everyone talks about the quick wins. But the most important box may be the one where you should say no.
When we prioritise AI and automation opportunities together with our clients, we use a simple model with four boxes. One axis is impact: how much time, quality or money can you gain? The other is effort: how much does it take to build and put into use?
Most people talk about the top left box: high impact and low effort. Those are the quick wins, and of course you should take them. But in this article I want to talk about the opposite corner: low impact and high effort.
Why do so many projects end up there?
It should be easy to avoid projects that cost a lot and deliver little. Yet we see them again and again. There are usually three reasons:
- The technology is exciting. A chatbot or an advanced AI model can seem like the natural first step, even though the problem could be solved more simply.
- Someone has promised it. A project has been started because the board, a competitor or a supplier has talked about it, not because it solves your biggest problem.
- The effort is underestimated. It is rarely the solution itself that costs the most. It is the data that needs cleaning up, the systems that need connecting, and the employees who need to start using it.
How to recognise a project in the wrong corner
Ask yourselves three questions before you start an AI project:
- Can you say exactly how many hours or how much money it saves, and who benefits from it?
- Does it require your data to be cleaned up or gathered from many systems first?
- Would a simpler solution, for example a better workflow or a simple automation, deliver most of the gain?
If the answer to the first is no, and the answer to the other two is yes, you are probably in the low impact, high effort corner.
A no is also a result
We are sometimes asked whether it is strange for a software company to recommend that clients do not build something. We do not think so. A project that is not started saves you money, time and, not least, employees who lose faith in AI because the first project did not work.
The best prioritisation is not the one with the most ideas. It is the one where everyone understands why you start there.
When we do a Shadow Walk, we place all the opportunities we find in the four boxes. That way you can see what to start with, what can wait, and what you can safely leave alone. If you would like to hear more, get in touch.
Want to see what it could look like for you?
We are happy to have a no-obligation talk about your day-to-day work and where AI and software can make a difference.
