TECH TALK
AI changes the data equation
AI brings the problem into focus, making this longstanding problem much harder to overlook. governance and audit exposure. Retrospectively resolving problems is generally more expensive and disruptive than establishing appropriate controls at the outset.
• 89 % of respondents to UKI SAP User Group research said data challenges would slow AI adoption.
• 87 % said high-quality data was essential to achieving return on AI investment.
The findings demonstrate why data readiness is moving beyond traditional reporting and governance discussions. As AI becomes embedded across business processes, trusted information becomes fundamental to whether those investments deliver measurable value.
According to recent UKI SAP User Group research, 89 % of respondents said data challenges would slow AI adoption, while 87 % believed high-quality data was essential to achieving a return on AI investment.
The findings underline a fundamental reality: AI can only be as dependable as the information supporting it.
Historically, employees have compensated for poor data through experience, judgement and institutional knowledge. A person who encounters an obviously incorrect customer record, unusual financial figure or missing piece of information can investigate the problem, consult colleagues or apply their understanding of the business.
AI does not inherently possess those safeguards. It processes the information available to it, meaning inconsistencies, gaps, duplication and inadequate governance can rapidly influence its outputs.
As organisations attempt to automate more processes and deploy AI across the enterprise, problems that were previously regarded as inconvenient can therefore become significant barriers to progress.
AI is not necessarily creating new data problems. Instead, it is exposing weaknesses that organisations have often tolerated for years.
Where the hidden costs emerge
The financial consequences of poor data readiness rarely carry a convenient label. Instead, they appear across several familiar areas of business activity.
Rework and manual intervention are among the most obvious. Employees spend time correcting, validating and reconciling information because standards, ownership or controls are unclear. Once these activities become embedded in everyday operations, organisations can struggle to identify how much employee time they consume.
Transformation delays represent another substantial cost. Major transformation programmes frequently concentrate on applications, platforms and infrastructure while underestimating the work required to prepare underlying data. Problems discovered during migration or implementation can result in extensive remediation, additional consulting costs and delays to expected benefits.
Slower decision-making can have equally serious consequences. When executives lack confidence in the information available to them, decisions inevitably take longer. Teams request additional analysis, figures are checked repeatedly and opportunities can disappear while organisations establish which version of the truth is accurate.
Compliance and risk provide another dimension. Inconsistent definitions, unclear ownership and inadequate controls can increase regulatory,
Then there is the question of AI returns. Organisations are committing substantial resources to AI, but even sophisticated technology cannot compensate indefinitely for unreliable underlying information. Projects can struggle not because AI technology is incapable, but because the data foundations required to support it are inadequate.
Together, these pressures suggest organisations face something broader than a collection of individual quality problems. They point towards a capability gap that affects performance across the enterprise.
Business-ready data becomes a strategic capability
One reason these problems have persisted is that data has traditionally been treated primarily as an IT responsibility rather than an enterprise business capability.
Provided applications continue functioning and reports can be produced, organisations may give data less attention than areas such as finance, customer experience or operations.
That position is becoming increasingly difficult to maintain.
Data now supports almost every significant business activity, including regulatory compliance, forecasting, customer engagement, operational efficiency, Digital Transformation and AI deployment. Rather than viewing information simply as something stored within applications, organisations increasingly need to regard it as part of their core infrastructure.
Weak infrastructure creates consequences throughout the business.
Reliable data, by contrast, allows organisations to move faster because employees spend less time verifying information. It can improve confidence in decision-making, reduce friction during transformation and create stronger foundations for automation.
The difference becomes particularly important as AI moves from isolated experiments towards enterprise-scale deployments.
50
INTELLIGENT CIO EUROPE www. intelligentcio. com