Artificial intelligence adoption across Europe is accelerating at an unprecedented pace, but a growing number of organisations are discovering that ambitious AI strategies are only as strong as the data infrastructure supporting them.
A new Digital Maturity Index from enterprise content management provider Hyland paints a striking picture of Europe’s digital transformation. While organisations continue investing heavily in AI, cloud computing and automation, many are neglecting the underlying content and information systems that enable those technologies to deliver measurable business value.
The result is an emerging paradox: digital maturity is improving overall, yet the foundational systems required for scalable AI deployment are deteriorating.
According to the study, which surveyed 3,000 IT decision-makers across Europe, overall digital maturity increased from the previous year, reaching 69 out of 100. However, content services—the systems responsible for organising, governing and delivering enterprise information—recorded the only decline among all measured categories, dropping 13 points to 56 out of 100.
The findings suggest that many organisations are accelerating innovation while overlooking the data governance and content management capabilities that modern AI systems depend on.
AI Adoption Is Growing Faster Than Enterprise Readiness
Enterprise AI adoption has expanded dramatically over the past year.
The report found that 15% of European organisations have now deployed AI across all major business systems, representing a significant increase from just 2% a year earlier.
While that growth highlights increasing confidence in AI technologies, it also exposes a widening gap between AI ambitions and operational readiness.
More than half (54%) of IT leaders admitted their organisations’ information and documentation remain too fragmented or disorganised for AI systems to use reliably.
Modern AI models rely heavily on well-structured, accessible and governed enterprise data. When information is scattered across disconnected applications, legacy databases or paper records, even the most sophisticated AI tools struggle to produce reliable results.
Rather than eliminating inefficiencies, AI can often expose them more clearly.
Cloud Migration Alone Isn’t Solving the Problem
Cloud adoption continues to rise across Europe, with 42% of organisations now operating fully in the cloud or migrating as much of their infrastructure as possible, up from 32% last year.
However, moving systems to the cloud has not automatically resolved longstanding integration challenges.
Half of surveyed organisations reported that their business systems still fail to communicate effectively, limiting collaboration and slowing innovation.
Germany faces particularly significant challenges, with 64% of organisations reporting poor system connectivity.
The findings reinforce a growing industry consensus that cloud migration is only one component of digital transformation. Without integrated data architectures and consistent governance policies, cloud platforms can simply relocate existing data silos instead of eliminating them.
Content Silos Continue to Hold Businesses Back
The report identifies fragmented enterprise content as one of the biggest obstacles to successful AI deployment.
Among the key findings:
60% of organisations operate with significant content silos that restrict access to critical information.
57% say innovation initiatives frequently stall before reaching production.
Only 9% have implemented a fully federated enterprise content management system.
5% still rely primarily on paper records for business operations.
These statistics illustrate how many organisations continue to struggle with information management despite years of investment in digital transformation.
Content silos can create duplicate information, inconsistent records and governance challenges that reduce AI accuracy while increasing compliance risks.
For businesses deploying generative AI or intelligent automation, fragmented enterprise knowledge can quickly become a bottleneck.
The AI Pilot Problem Persists
One of the report’s most notable findings is the high percentage of AI initiatives that fail to progress beyond experimental stages.
Nearly 60% of organisations reported experiencing innovation projects that become trapped in pilot phases without broader deployment.
This challenge has become increasingly common across the enterprise AI market.
Many organisations successfully demonstrate AI capabilities in isolated environments but struggle to scale those solutions across departments because underlying data remains inconsistent, inaccessible or poorly governed.
Industry analysts have repeatedly identified data quality—not algorithm performance—as one of the primary reasons enterprise AI projects fail to achieve expected returns on investment.
Strong Data Governance Is Becoming a Competitive Advantage
The study suggests that organisations prioritising information management are better positioned to scale AI successfully.
The United Kingdom achieved the highest digital maturity score in Europe at 74 out of 100, indicating stronger investment in foundational digital infrastructure alongside emerging technologies.
In contrast, Nordic countries recorded an overall score of 67 out of 100. Only 6% reported having fully federated enterprise content management systems, while 7%—the highest proportion in Europe—continue relying primarily on paper-based records.
Although overall digital maturity varies across regions, the findings indicate that organisations investing early in governance, integration and enterprise content management are generally better prepared for large-scale AI adoption.
Why Enterprise Content Matters More Than Ever
As generative AI becomes embedded across enterprise software, access to accurate, trusted information is becoming a strategic necessity rather than a back-office IT concern.
Large language models, AI assistants and intelligent automation platforms all depend on high-quality enterprise data to deliver accurate recommendations and reliable business outcomes.
Without consistent governance, organisations risk generating inaccurate responses, introducing compliance issues and limiting employee confidence in AI-generated insights.
Increasingly, technology leaders are recognising that AI success depends less on deploying the newest models and more on ensuring those models can access reliable enterprise knowledge.
The Bigger Picture
Europe’s digital transformation continues to gain momentum, but the latest findings highlight a growing imbalance between innovation and infrastructure.
Businesses are rapidly adopting AI, automation and cloud technologies, yet many continue to overlook the foundational systems responsible for organising and governing enterprise information.
As AI moves from experimental deployments to mission-critical business operations, organisations will likely face increasing pressure to modernise not only their AI capabilities but also the content management and data governance frameworks that support them.
For many enterprises, the next stage of digital transformation may not be defined by adopting another AI model, but by finally addressing the fragmented data environments that have quietly limited innovation for years.
