Insights on AI adoption, decision intelligence, and digital transformation
Only 12% of organisations report sufficient data quality for AI. But data quality is the surface issue. The harder problem is that the same data means different things depending on who's using it and why.
42% of companies abandoned most AI initiatives in 2025, up from 17% in 2024. The helpers organisations bring in — consultants, vendors, integrators — all hit the same wall. The knowledge needed for good AI decisions is distributed across the workforce, and none of them can access it.
Only 21% of organisations have governance mature enough for AI agents, while agentic AI usage surges from 23% to 74% within two years. The knowledge you need is usually there — it's just scattered across parts of the organisation that don't talk to each other.
62% of organisations are stuck running AI pilots that never reach production. Only 7% have fully scaled. The gap between pilot and scale isn't technology; it's that each transition demands organisational elements that pilots never tested.
53% of CEOs say their teams can't align on their AI priorities. The root cause is structural: strategy, capability, and operationalization live in different parts of the organization, and in most companies, they never meet.
Harvard Business Review reports 80% of enterprise AI projects fail. Traditional IT had ~42% failure rates; add AI to the mix and they double. The real obstacles are organizational, not technical.