Poor-quality data rarely appears as a single line on the balance sheet, yet its impact can be felt across productivity, transformation, decision-making and AI investment. Will Hiley, Head of Solutioning, Syniti, part of Capgemini, says as organisations accelerate their adoption of AI, the cost of unreliable information is becoming increasingly difficult to ignore.
Most business leaders understand that data matters. Reliable information supports better decisions, improves operational efficiency and provides the foundation for successful transformation programmes. Yet while organisations increasingly recognise the strategic value of data, far less attention is paid to the financial and operational cost of information that is not truly fit for purpose.
Poor data is expensive, but its cost is difficult to measure because it rarely appears as a distinct item on a management report. Instead, the impact is distributed throughout the organisation.
Employees spend hours checking information before using it. Finance teams reconcile conflicting figures. Executives delay decisions while numbers are verified. Sales teams maintain separate spreadsheets because they do not completely trust corporate systems. Operational teams develop manual processes to compensate for missing, duplicated or inconsistent information.
Individually, these activities can appear relatively insignificant. Across a large organisation, however, they represent a substantial hidden cost.
The gap between usable and business-ready data
Many organisations operate with data that is considered acceptable rather than genuinely reliable. Reports can still be produced, applications remain operational and employees have learned how to compensate for inconsistencies.
This creates an important distinction between data that is technically usable and information that is genuinely business-ready.
A finance department might reconcile figures manually before presenting them to senior management. Sales representatives may maintain personal spreadsheets alongside the CRM platform. Operations employees can find themselves validating information before acting on it because previous experience has taught them not to assume that the system is correct.
Over time, these behaviours become normalised. They are absorbed into everyday working practices and eventually become almost invisible.
Yet every manual check, correction and reconciliation consumes resources that could otherwise be directed towards customers, innovation or growth. Multiply a seemingly insignificant task across hundreds or thousands of employees and the cumulative impact can become substantial.
The problem extends beyond direct labour costs. Constantly questioning information introduces friction into processes, reduces confidence in systems and makes organisations slower to respond.
A business may have invested heavily in modern applications but still fail to capture their full value because employees do not trust the information flowing through them.
AI brings the problem into focus, making this longstanding problem much harder to overlook.
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, governance and audit exposure. Retrospectively resolving problems is generally more expensive and disruptive than establishing appropriate controls at the outset.
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.
Building stronger foundations
Organisations making meaningful progress are moving beyond fixing individual problems whenever they appear. Instead, they are adopting a more deliberate Data-First approach to designing and delivering change.
This means establishing clear ownership of critical information and ensuring accountability extends beyond IT teams. Standards need to be defined and applied consistently across business functions rather than recreated independently within individual departments.
Governance also needs to become part of everyday operations rather than an exercise conducted primarily for audits or major technology programmes.
Most importantly, organisations need to recognise that data readiness is not a one-off project with a fixed completion date. Information continually changes as businesses acquire customers, introduce products, enter markets, deploy applications and redesign processes.
Maintaining business-ready data is therefore an ongoing organisational capability.
The benefits extend considerably beyond reporting. Stronger data foundations can enable faster decisions, smoother transformations and greater confidence in automation. They can also reduce the amount of employee time consumed by activities that exist purely because information cannot be trusted.
A strategic question for leaders
The question for executives is no longer whether their organisation has data problems. Virtually every business does. The more important question is what those problems are costing.
Once organisations start considering the combined impact of rework, delayed transformation, slower decisions, compliance exposure and missed opportunities, the conversation changes.
Data readiness stops being viewed primarily as a technical concern and becomes a business performance issue.
That shift matters because the next generation of enterprise technology will place greater demands on information than the systems it replaces. AI agents, automation and increasingly autonomous processes require organisations to trust not only the technology executing decisions but also the information informing those decisions.
Poor data that once required an employee to correct a spreadsheet could potentially affect thousands of automated actions.
The organisations best positioned for the coming decade will therefore be those capable of combining ambitious technology strategies with equally strong data foundations.
Reliable, well-governed and business-ready information may attract less attention than the latest AI breakthrough, but it remains one of the most important foundations of sustainable business performance.
The value of good data may remain difficult to isolate on a balance sheet. Its absence, however, can be measured in wasted hours, delayed decisions, additional project costs, increased risk and technology investments that fail to deliver their expected returns.
As AI accelerates the pace at which organisations operate, those hidden costs will become increasingly difficult to absorb – and impossible to ignore.
