Artificial intelligence is becoming less of an experiment in enterprise IT and more of a standard component of transformation programs. A 2026 study from Natuvion and NTT DATA Business Solutions, surveying more than 1,100 executives and IT specialists across 15 countries, found that 76% have already used AI in IT transformation projects. Yet the research points to a familiar constraint beneath the enthusiasm: poor data quality remains one of the biggest obstacles to turning AI capabilities into measurable business value.
For years, enterprise IT transformation was largely a modernization exercise: replace aging systems, migrate data, reduce costs and rebuild infrastructure around newer platforms.
AI is changing the equation.
According to the fifth annual International IT Transformation Study from Natuvion and NTT DATA Business Solutions, 76% of surveyed executives and IT specialists have already incorporated AI into transformation projects. Another 34% expect their use of AI to increase in future initiatives.
The numbers suggest that AI has moved beyond the pilot stage for many organizations. It is increasingly becoming part of the machinery used to plan, execute and validate large-scale technology transformations.
But the study also highlights an important limitation. AI can accelerate transformation, but it cannot compensate indefinitely for unreliable enterprise data.
AI is moving upstream in transformation projects
One of the more significant findings is where organizations are using AI.
AI is not being reserved for customer-facing applications or post-transformation analytics. Companies are deploying it throughout the transformation lifecycle.
Some 63% of respondents use AI to analyze their existing IT landscape before a transformation begins. Nearly 60% use AI for data-quality assessment and migration activities, while 56% apply it to strategic planning.
That changes the role of AI in enterprise technology.
Instead of simply becoming another application layer, AI is increasingly acting as an analytical and automation layer around the transformation itself.
For an organization consolidating legacy applications, for example, AI can help identify dependencies and inconsistencies before migration. During data conversion, it can assist with classification and quality checks. In testing, AI can potentially accelerate the identification of anomalies and repetitive validation tasks.
The underlying attraction is speed.
Large transformation programs can involve thousands of applications, enormous datasets and complicated dependencies. AI can process and correlate information at a scale that would be difficult for human teams to reproduce manually.
Leadership is pushing AI deeper into the enterprise
The shift is also being driven from the top.
The study found that 55% of top management respondents view AI as an important innovation driver. That matters because enterprise transformation historically requires sustained executive sponsorship.
When AI is treated as a strategic capability rather than an isolated technology project, it becomes easier to integrate it into architecture decisions, migration programs and operating models.
The largest organizations appear to be moving particularly quickly.
Among companies generating more than €1 billion in annual revenue, 55% use AI for quality assurance and testing. That compares with 39% among smaller organizations.
The disparity is not necessarily surprising. Large enterprises typically have bigger technology estates, larger transformation budgets and more data to process. They also face greater pressure to standardize complex environments.
But scale creates another problem: complexity.
The more applications, databases and business processes an organization operates, the more difficult it becomes to maintain consistent and trustworthy data across them.
The data problem isn’t going away
That is where the study’s most important finding may lie.
Despite the rapid adoption of AI, data quality remains one of the industry’s persistent transformation challenges.
The researchers say data quality has appeared as a major barrier in every edition of the study over the past five years.
That consistency is significant.
AI systems can identify patterns, automate tasks and generate recommendations, but their output remains dependent on the information available to them. Inaccurate customer records, duplicated information, incomplete metadata or inconsistent data structures can undermine an otherwise sophisticated AI deployment.
The problem becomes particularly acute during migrations.
Moving data from legacy systems into a new enterprise platform is not simply a matter of transferring records. Organizations need to understand what the data means, whether it is complete, whether duplicate records exist and whether different systems use incompatible definitions.
AI can help identify those problems, but it does not make them disappear.
This is one reason integrated transformation platforms are attracting attention. Tools such as Natuvion’s Data Conversion Suite (DCS) are designed to combine data conversion, system transformation and AI capabilities within a broader workflow.
The strategic idea is straightforward: transformation teams can get more value from AI when intelligence is integrated into the processes where data is already being assessed, migrated and validated.
The next phase is about operationalization
The research also reflects a broader shift in enterprise AI.
The industry has spent the past several years debating foundation models, generative AI assistants and copilots. Enterprise IT leaders are increasingly asking a more practical question: where can AI produce measurable improvements inside existing technology programs?
Transformation may be one of the most obvious answers.
Unlike speculative AI projects, IT transformation already has defined workflows and measurable milestones. Organizations know what applications need to be migrated, which systems are being retired and which data needs to be validated.
That creates natural opportunities for AI automation.
Microsoft, Google, AWS, SAP, Salesforce and other enterprise technology providers are increasingly embedding AI into their platforms, reinforcing the expectation that intelligent automation will become part of standard enterprise infrastructure rather than a separate category.
For CIOs, however, adoption should not be confused with value.
The study’s findings suggest that successful AI-enabled transformation depends on three connected capabilities: reliable data, appropriate automation and strong governance.
Companies that focus only on deploying AI tools risk automating flawed processes faster.
The organizations likely to gain the most may instead be those that treat data quality as foundational infrastructure and AI as an operating capability built on top of it.
That represents a meaningful change from the transformation priorities of 2022, when cost reduction, legacy-system replacement and restructuring dominated the agenda.
Today, AI and innovation are increasingly part of the business case.
But the old problem remains underneath the new technology.
Better AI does not eliminate the need for better data.
Market Landscape
Enterprise IT transformation is evolving from application modernization toward AI-enabled transformation.
The emerging stack combines cloud infrastructure, enterprise SaaS, data platforms, automation and AI services. Major vendors including Microsoft, AWS, Google, SAP and Salesforce are integrating AI directly into enterprise workflows, while specialist providers are targeting specific transformation bottlenecks such as migration and data conversion.
The Natuvion study indicates that adoption is already broad, but maturity remains uneven. Larger enterprises are more likely to use AI for testing and quality assurance, while organizations of all sizes continue to struggle with data quality.
For CIOs, the implication is that AI strategy and data modernization can no longer be treated as separate initiatives. AI-ready transformation increasingly begins with trustworthy data, clear governance and processes capable of incorporating automation.
Top Insights
- 76% of surveyed IT professionals have used AI in transformation projects, signaling that artificial intelligence is becoming standard infrastructure for enterprise modernization.
- AI is being deployed before migration begins, with companies using it for IT landscape analysis, data-quality assessment, strategic planning and transformation execution.
- Large enterprises are leading AI-enabled transformation, with 55% of companies above €1 billion revenue using AI for quality assurance and testing.
- Data quality remains a persistent transformation barrier, underscoring why AI deployment depends on accurate, consistent and governed enterprise information.
- CIOs increasingly need integrated AI, data and automation strategies rather than isolated AI pilots as transformation programs become more intelligent and continuous.
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