Common mistakes in AI implementation – and how to avoid them

As AI continues to grow as a hot topic, more businesses are talking about implementing the technology. Unfortunately, many fall into the most common pitfalls. Here, we outline three frequent mistakes companies make when adopting AI – and what you should do to succeed.

Three common mistakes in AI implementation

1. Not having a clear strategy

It’s easy to be drawn to AI’s potential, but without an overarching strategy, it’s difficult to create real value. AI is not a magic solution that instantly transforms a business; it requires a concrete plan that ties AI to organizational goals.

To even connect AI to your business strategy, the first step is to define your business objectives – what do you want to achieve? Then, ensure the strategy is anchored across the organization and includes clearly defined use cases that link AI initiatives to your business goals.

2. Failing to define concrete use cases

Many companies start implementing AI without first defining clear and specific use cases – that is, the specific problems the technology is meant to solve. A good use case is directly tied to organizational goals and relies on access to relevant data. Without this connection, AI projects risk becoming expensive experiments without practical business value.

Start by identifying the problem you want to solve and ensure there is enough high-quality data to support your use case. If data is lacking, explore whether a pre-trained AI model could be used.

Starting with clearly defined use cases helps you prioritize the right projects and create real value.

3. Forcing AI where it doesn’t fit

Another common mistake is applying AI just because it’s trendy, cool, and seems like the right thing to do – even in areas where it isn’t needed. This can lead to expensive projects that deliver no added value. Many companies realize too late that AI wasn’t even the right solution to their problem.

To avoid unnecessary investments, you should:

• Evaluate whether there is genuinely a problem that AI can solve better than traditional methods
• View AI as one tool among many, not the ultimate goal
• Prioritize areas where AI can deliver real business value

AI is a tool – not the goal

One key piece of advice is to never let AI itself become the goal. The goal should always be to achieve strategic business objectives, with AI being just one of many tools to help you get there. As our CEO Oskar puts it:

‍You don’t go to a carpenter and tell them to use a drill. You tell them to build a house.

By avoiding these common mistakes, your company will be better equipped to understand when and how AI can deliver real value – and when other solutions might be more effective.