Implementing artificial intelligence in a company does not yet mean success. Often projects that promised to be promising end up in a drawer after a few months. Why does this happen? The answer does not always lie in the technology itself. Far more often it is a lack of organisational maturity, inappropriate project selection and structural deficiencies in data management.
As a recent survey of more than 400 companies from different countries shows, only 20% of organisations with low AI maturity maintain their initiatives for at least three years. This compares to 45% among mature companies. This is a big difference – and a clear signal that the problem is not in the algorithms, but in the approach.
The main reasons for failure? Data, security and lack of purpose
The study reveals that companies at different levels of AI maturity face different challenges. For companies with low levels of maturity, the biggest issue is data – lack of data or poor quality. As many as 34% of leaders in this group cite this as one of the key barriers to implementing AI. In mature companies, this percentage drops to 29%, suggesting that more advanced organisations are investing earlier in data quality and infrastructure.
Highly mature companies, on the other hand, are more likely to report security concerns, with 48% seeing this as one of their top three obstacles. This is logical: the more projects and data, the greater the risk and the need for threat management.
In companies with low maturity, an equally significant barrier is… the lack of meaningful use cases. 37% of them admit that they cannot find projects that actually have business value. AI implemented ‘because it falls out’, without an understanding of where it can benefit, is unlikely to survive.
Error No. 1: Wrong choice of projects
Organisations with low levels of maturity are rarely guided by business value and technical feasibility when selecting AI projects. The result? They implement solutions that are not embedded in strategy, have no data to act upon or have no real impact on results.
Meanwhile, in advanced companies, the approach is different. Projects go through selection based on hard criteria. Leaders ask questions: do we have the right data? Will the end user really benefit? Can the engineering team sustain the solution for several years?
3 questions before the start of the AI project
- Is the data available, of good quality and consistent?
- Does the project have a real customer, cost or revenue impact?
- Do we know who will maintain it and measure its effects?
Trust is a currency that many companies lack
Another problem is AI adoption among employees. Only 14% of companies with low AI maturity believe that their business units are ready to use AI-based solutions. This compares to 57% among mature organisations.
This is no coincidence. Advanced companies are investing not only in technology, but also in communication, education and building trust in the results generated by AI. They understand that even the best model won’t work if users don’t trust it or know how to use it.
What are companies doing that AI really works for?
The differences in approach between mature companies and the rest can also be seen in the management structure. In organisations with advanced implementations, as many as 91% already have designated AI leaders – those responsible for consistency, quality, scalability and technology development.
Their priorities are clear:
- supporting technological innovation (65%),
- provision of appropriate infrastructure (56%),
- building teams and organisations around AI (50%),
- solution architecture design (48%).
Added to this is a methodical approach to measuring impact. 63% of leaders in mature companies regularly analyse ROI, assess the impact of projects on the customer and identify financial risks. In this way, they can decide which initiatives to develop and which to stop.
Centralisation as a way out of chaos
Finally – consistency. Almost 60% of companies with high AI maturity centralise their AI strategies, data, infrastructure and project management. This avoids fragmentation of initiatives, competency conflicts and duplication of projects.
Organisational maturity is not about AI working everywhere, but about knowing where, why and under what conditions it should work. Centralisation does not mean inflexibility, but a framework within which experiments can grow and deliver sustainable value.
AI is not a sprint. It’s a marathon with an organisation on your back
The sustainability of AI projects does not depend on the latest language model or low-code platform. It depends on a company’s ability to choose the right project, gather data, build a team and measure results regularly. Without this, AI becomes just another trendy implementation that disappears off the radar after a few months.
Maturity in the area of AI today is not only a technological advantage, but above all a management advantage. And this is good news – because it means that success does not have to be a question of budget size at all, but of approach.
