Half of companies now use AI across multiple business functions — but data management is becoming the bottleneck

By end of 2025, 50% of companies deployed AI in at least three business areas (finance, supply chain, HR, customer service), according to a recent survey. AI has shifted from experimental projects to everyday tools that directly affect how work gets done.

The problem: AI systems need clean, well-organized data to work reliably. Without a 'strong data fabric' (a unified system that connects and standardizes data across scattered databases), companies get inconsistent results — one department's AI gives different answers than another's, or outputs become unreliable over time.

If you work in any of these departments, this affects you now: Finance teams using AI for forecasting may get conflicting predictions if their data isn't synchronized; supply chain planners relying on AI recommendations could miss risks; HR systems automating hiring decisions may use outdated employee records. Companies that don't fix their data foundation will see AI tools fail or give wrong answers, forcing workers to waste time double-checking AI outputs instead of trusting them.