AI portfolios are expanding far faster than the ability to govern them across enterprises. Most organizations run a contested field of platforms, each claiming to be the “primary” AI layer; few could confidently detect a model drifting or failing in production; and the single most-cited barrier to control is the absence of any one owner accountable for AI across the stack. The result is a widening control gap — ambition and spend racing ahead of visibility, ownership, and cost control — with autonomous agents already producing real financial and operational failures.This wave of VentureBeat Pulse Research examines the enterprise AI control gap: how many platforms claim to be the primary AI layer, who actually governs AI behavior across them, whether organizations could detect a mod [...]
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 [...]
Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged [...]
Two-thirds of enterprises have hedged their AI model strategy, and the past few weeks of controversy around Anthropic’s Claude Fable 5 model showed why that posture has gone mainstream. On June 12, [...]
Perplexity, the AI-powered search company valued at $20 billion, announced on Wednesday at its inaugural Ask 2026 developer conference that its multi-model AI agent, Computer, is now available to ente [...]
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 [...]
In Q1 2026, VentureBeat's Pulse Research surfaced the “Governance Mirage”: the gap between the governance org charts enterprises had drawn and the control layers they had actually built. Fort [...]
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 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 157 enterprises, organizations are granting AI agents more autonomy while trusting the evaluations meant to gate that autonomy less. Half have already shipped an agent that passed their interna [...]