Governance and silos hinder AI in businesses: only 16% have fully integrated data. The analysis by Cloudera
Italian managers (and not only) look ahead and know well that they must invest in AI to remain competitive. The problem is that limits persist which prevent companies from fully capturing the potential of this disruptive technology. In practice, there is awareness, but governance needs improvement: enhancing processes, adapting risk management to the new reality, and, above all, breaking down informational silos.
This summarizes Cloudera's view on how European businesses are facing the technological revolution of artificial intelligence. This view does not arise from the feelings of the company's executives but derives from the study "The Data Readiness Index: Understanding the Foundations for Successful AI" commissioned by the company.
From Proof of Concept to Production: Why Do So Few Enterprise AI Projects Scale?
As we have repeatedly pointed out on Edge9, there is a problem regarding AI adoption: the vast majority of companies are integrating it into the business, developing pilot projects and testing it in various scenarios. However, when these experiments need to go into production, something gets stuck, and scaling is challenging, if possible at all.
It is a global problem, objectively, which is more evident in Italy, at least compared to other EU countries, as highlighted by Cloudera's research. In practice, many companies are operating "with the handbrake on," explains Fabio Pascali, Regional Vice President Italy, Greece, and Cyprus of Cloudera during a meeting with the press. "The governance gap across the EMEA area, and particularly in Italy, is not a technological problem, but an execution issue. Senior leaders understand the requirements of an AI-ready infrastructure. What organizations have not yet built are the operational structures to implement it: named ownership, governance applied consistently across all environments, and architectures that bring governance to the data wherever it resides."
That governance is an issue is also highlighted by the fact that many companies are lagging behind the upcoming AI Act deadline, which is in August 2026 – practically around the corner.
The main problem is certainly that of silos: to this day, many companies think in watertight compartments and treat data in the same way. This is reflected in AI usage: valid pilot programs are experimented on, but once they enter production, a common data lake is missing that allows these tools to truly bring value to the business.
The research highlights that only 16% of the sample (about 300 large companies, 55 of which are Italian) report full data integration. More than 50% say they have "fairly integrated" data, and only 26% actually have full integration with immediate access to data. If we then talk about self-service data access, without having to involve a corporate function, it drops further to 18%.
According to Pascali, there are three main issues to address: it is necessary to break down informational silos, as already mentioned. But also to improve data quality, which is often poor and prevents AI systems from functioning effectively. Finally, there is the issue of costs: companies often use cloud solutions and do not focus on private AI. Consequently, the costs of tokens explode rapidly, so much so that in some cases they limit the number of tokens available.
How Cloudera Supports Companies in the Journey Towards AI Adoption
Cloudera, with its platform, positions itself as a partner to support companies in their digital transformation. By providing tools to break down silos and adequately organize data, it allows AI systems to always have up-to-date, non-contradictory, and well-structured information. It also equips companies with the tools for total visibility on data (Cloudera Data Catalog) and for securely managing it end-to-end, even from an authorization perspective (Cloudera Data Lineage).
Not only that: the company is also a partner of NVIDIA, and can support companies in shifting towards the model of private AI. This is crucial for two reasons: on one side because it works with large clients operating in regulated markets (utilities, finance, public administration, healthcare), and which must therefore meet very stringent criteria in handling sensitive information. The second reason is that a private AI improves ROI, return on investment. This is because adopting a token consumption model can lead to rapidly unsustainable prices. Conversely, by renting or purchasing GPUs, the cost for equivalent token consumption is significantly lower.
"With the Private AI from Cloudera, it is possible to develop the PoC in the cloud and then move it to the private infrastructure, using GPUs and not relying on tokens," says Yari Franzini, Group Vice President Southern Europe of Cloudera. He adds, "the real difficulty for European organizations is not in adopting AI, but in their ability to operationalize it beyond the experimental phase. AI is only as effective as the data that feeds it. Without coherent governance across all environments, organizations face limitations in accuracy, trust, and the business value that AI can generate. It is not possible to build reliable AI on data that cannot be fully governed."