Trends in Data and AI for 2026: less agitation and more action

By: Carlos Larrea, Business Development Manager at Innova-tsn

The difference between agitation and action is clear: the former lacks purpose and direction. Over the past three years, we have seen a great deal of agitation surrounding artificial intelligence: it was not unusual to hear ‘we need to do something about artificial intelligence’ in the management committees of organisations of all sizes. The cause of this agitation is, in colloquial terms, the fear of being “left behind,” “being out of the picture” or “missing the boat.” As a consequence of these orders from General Management, we have seen many initiatives which, in most cases, did not go beyond the pilot phase, confirming what MIT pointed out in a recent report: 95% of AI implementations fail.

95% of AI implementations fail

This fact might surprise us, but if we look back for two years, MIT itself said that 95% of product launches also fail. Coincidence? Sincerely, I do not think so: after all, when we implement an AI system, we are implementing a product that is more or less sophisticated, but a product, nonetheless. And the common problems with the failure of both launches (AI and new products) seem to be rooted in three aspects: ignoring the needs of our market, not being aware of the reality of our organisations, and lacking alignment with the company’s strategy.

There is no Big Data, Advanced Analytics or Artificial Intelligence without Data

The two first reasons are severely impacted, among others, by the lack of properly ordered and processed data available at the companies’ points of decision. There is no Big Data, Advanced Analytics or Artificial Intelligence without Data. We have said data are the new gold for decades, yet not all sectors or companies seem to appreciate the value of that gold, judging by how they manage it. In the report on Customer management in the insurance sector 2025 carried out by Innova-tsn on insurance groups representing more than half the market, 63% of respondents pointed to data quality and availability as one of the main challenges for AI implementation.

More Data governance in 2026

In this regard, it seems that in 2026, organisations will tend to make an effort to improve the management of their data, and we will see a proliferation of data governance projects. At the same time, the role of the CDO will continue to consolidate as a key player in companies, promoting data management models where responsibility for data is distributed throughout the company and where “data owners” make them available to other internal and external consumers as if they were a product in themselves. Thus, it is foreseeable that data management will tend to be more federated than centralised, with the CDO acting as the orchestrator and guarantor of the data.

The third possible reason for the failure of AI implementations, misalignment with corporate strategy, is determined by three stubborn realities that are sometimes overlooked:

· At the institutional level, there is a lack of awareness of the potential of AI. No proper literacy programme has been implemented in this area, nor has there been any systematic effort to identify needs and capabilities.

· In addition to the above, the use cases that have been researched and implemented often correspond to proposals from units or individuals with initiative and concern who act in isolation and without coordination with other areas.

· Finally, at the individual level, many employees are already using generative AI tools that are either free to use or licensed at their own expense for tasks such as document classification, text writing, and translation, thereby improving their efficiency. This beneficial effect for the organisation, without any planned or organised intervention, causes concern among managers due to the lack of control it entails.

The combination of these three aspects is causing considerable unease among company management and the feeling that AI is “getting out of hand”.

2026 will bring more orderly and less impulsive AI management

In 2026 we will see AI governance initiatives which set a clear roadmap for the whole organisation regarding AI implementation, establish return KPIs aligned with corporate strategy, define clear policies and procedures and identify responsibilities across companies to ensure a successful implementation of AI.