What happens when there are dysfunctional teams?

Most enterprises can get a generative AI pilot off the ground. Far fewer can turn it into something that runs across the business and pays for itself. A 2024 Deloitte study found that among enterprises that had invested in GenAI, 70% had pushed 30% or fewer of their pilots or proof-of-concept projects into production — let alone scaled them widely enough to show measurable ROI. That gap between experimentation and enterprise-wide impact is the subject of this report.

Drawing on established industry research — including Deloitte, McKinsey, PwC and the World Economic Forum — alongside real-world AI deployment case studies, this whitepaper examines why GenAI initiatives stall after the pilot stage and what separates organisations that scale successfully from those that don't. It looks at how use cases are chosen, how data and workflow silos limit AI's reach, and why workforce readiness and governance become harder to ignore as adoption grows.

For leaders under pressure to show returns on AI investment, understanding why the 70% gets stuck — and what the other 30% are doing differently — is the more useful question to answer first.

Download the full report to see the roadmap enterprises are using to close that gap.