Recent reports about AI project failure rates have raised uncomfortable questions for organizations investing heavily in AI. Much of the discussion has focused on technical factors like model accuracy and data quality, but after watching dozens of AI initiatives launch, I’ve noticed that the biggest opportunities for improvement are often cultural, not technical.Internal projects that struggle tend to share common issues. For example, engineering teams build models that product managers don’t know how to use. Data scientists build prototypes that operations teams struggle to maintain. And AI applications sit unused because the people they were built for weren't involved in deciding what “useful” really meant.In contrast, organizations that achieve meaningful value with AI have [...]
Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]
Across 108 enterprises, trust in automated agent evaluation rose sharply in July — and the failure rate it is supposed to predict did not move at all. The share of organizations that fully trust aut [...]
Across 116 enterprises, agents are in production and so are the incidents: A majority have already had a confirmed agent security event or a near-miss. Two-thirds of enterprises enforce scoped permiss [...]
Across 170 enterprises, AI infrastructure has moved decisively into production — two-thirds now run AI workloads live and three in 10 run them at scale — while the ability to account for what that [...]
Across 107 enterprises, agentic orchestration is not a choice of a single platform.The typical enterprise runs three orchestration platforms at once, and selects them for flexibility across models rat [...]
Visa's president of technology, Rajat Taneja, walked the VB Transform 2026 audience through aiming Anthropic's Mythos at Visa's own payment network. The model stitched minor weaknesses [...]
A company builds a governed context layer specifically to stop its AI agents from confidently giving wrong answers. Once that layer is live, the company is more than twice as likely to report the fail [...]
Across 107 enterprises, AI infrastructure spending is accelerating well ahead of the ability to see or steer its economics. Most organizations run their AI on a familiar base of hyperscalers and model [...]
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context sourc [...]