AI Investment Is Rising, Outcomes Are Not
By 2026, artificial intelligence is firmly embedded in everyday business operations. From marketing automation and customer analytics to logistics optimisation and fraud detection, AI is widely seen as essential for competitiveness. Global spending on AI is forecast to reach hundreds of billions annually, yet the success rate of AI projects remains surprisingly low.
Multiple industry studies indicate that between 70% and 85% of AI projects fail to meet their original objectives. While technical complexity and skills shortages are often blamed, analysts increasingly point to a more fundamental issue. The quality of the data feeding AI systems is frequently not fit for purpose.
This concern is reflected in BARC’s Trend Monitor, which identifies data quality management as the number one data and analytics trend for 2026, ahead of new AI platforms and tools. The message from analysts is consistent. AI systems do not fail in isolation. They fail because the data they rely on is unreliable.
The AI Failure Pattern
Most organisations begin AI initiatives with strong ambition. Models are tested, pilots are launched, and early results often appear promising. Problems tend to emerge when AI moves into production.
Post implementation reviews commonly reveal the same issues. Customer records are duplicated. Addresses are outdated. Phone numbers and email addresses are invalid. Consent and preference data is incomplete. As a result, AI models generate insights that conflict with operational reality.
Gartner has estimated that poor data quality costs organisations an average of 15% of revenue per year. IBM has gone further, suggesting that the global cost of poor data quality runs into the trillions. For AI initiatives, these losses are amplified because inaccurate data directly affects model accuracy and trust.
As one widely cited analytics principle states, “AI learns patterns, not truth.” If the underlying data is flawed, AI will faithfully reproduce those flaws at scale.
Why AI Is Especially Sensitive to Data Quality
Traditional business intelligence tools allow for human judgement. Analysts can identify anomalies, question outputs and adjust for known data issues. AI systems do not operate this way. They assume that the data they ingest represents reality.
This makes AI especially sensitive to data quality issues such as duplicate records, outdated addresses, invalid contact details and inconsistent formatting. Each issue introduces noise that reduces model accuracy and reliability.
Data decay compounds the problem. Industry research shows that B2B contact data decays at up to 22.5 percent per year, as people move, change phone numbers or update preferences.
What “AI-Ready Data” Means in Practice
As AI adoption matures, organisations are moving away from focusing purely on data volume and towards data readiness. AI-ready data is defined by reliability rather than scale.
It is accurate, reflecting real people and organisations. It is complete, with essential fields populated. It is consistent across systems, allowing patterns to be recognised correctly. It is current, acknowledging the constant change in customer information. It is also governed, ensuring that consent and preference requirements are respected.
BARC notes that organisations investing in systematic data quality management report higher trust in analytics outputs and faster AI deployment.
Data Capture as a Root Cause of AI Failure
Many AI data quality issues originate at the point of entry. Online forms, call centres and third-party data sources frequently introduce errors. Once inaccurate data enters core systems, it is reused across platforms and eventually incorporated into AI training datasets.
Real-time validation at data capture helps reduce this risk by preventing known errors from being stored. While traditionally associated with operational efficiency, this approach is increasingly recognised as foundational for AI reliability.
As MIT Sloan Management Review notes, “Automation does not fix bad data. It accelerates the impact of it.”
Historical Data and Model Risk
Even organisations that improve data capture still face challenges with historical data. AI models are typically trained on years of legacy information, much of which contains outdated or duplicated records.
Training models on this data introduces bias and distortion. Customer value may be overstated, targeting accuracy reduced, and predictions skewed. As a result, data cleansing activities such as deduplication and record suppression are increasingly viewed as prerequisites for AI initiatives rather than routine maintenance.
Trust as the Limiting Factor
Trust remains one of the biggest barriers to AI adoption. Surveys consistently show that users are reluctant to rely on AI outputs when results conflict with their operational experience.
In many cases, this scepticism stems from familiar data quality issues rather than algorithmic complexity. When AI outputs align with accurate, up-to-date data, confidence grows. When they do not, adoption stalls.
Conclusion
AI projects rarely fail because the technology is inadequate. They fail because the data feeding those systems is inaccurate, outdated or poorly governed.
In 2026, organisations that succeed with AI will be those that treat data quality as a strategic discipline rather than a technical clean-up task. Accurate, validated and well-maintained data is not optional for AI. It is the foundation on which every successful model depends.
AI does not solve data problems. It exposes them.