A key challenge lies in legacy IT systems and scattered information repositories that impede the centralisation and cleansing of data. According to a recent Techment report, poor data quality costs enterprises an average of $12.9 million annually and represents a major barrier to AI success. Similarly, Precisely’s 2025 survey found that 64% of organisations identify data quality as their top integrity challenge, with two-thirds lacking full trust in their data.
This “garbage in, garbage out” (GIGO) problem is particularly acute in AI. Machine learning systems heavily rely on the quality and representativeness of training data. Without accurate, unbiased, and real-time data, AI models produce unreliable, even misleading results.
Moreover, media hype often exaggerates how extensively AI is actually deployed. McKinsey’s 2025 global survey reveals that while 71% of companies claim to use generative AI, most remain at early pilot stages or implement only superficial applications. This leaves many initiatives vulnerable to disappointment, wasted resources, and eroded trust, especially when expectations run ahead of technical and organisational readiness.
Experts argue that true competitive advantage comes not from blindly chasing the latest AI fad but from a patient, strategic approach rooted in building disciplined data infrastructure and choosing practical AI use cases. Organisations must first invest in centralised data governance, clear ownership, and automated quality checks to prepare high-quality data pipelines.
As one analyst put it, “Data is the ultimate weapon in the AI era. But AI without well-curated, governed data is no better than an expensive illusion. Organisations must treat data integrity as a mission-critical priority.”
The path to AI success is clear: it depends on rigorous data management, realistic expectations, and continuous improvement. Only by looking beyond buzzwords and marketing fanfare can businesses unlock AI’s transformative potential for real impact.
