In a survey of 157 enterprises, many are giving AI agents increased autonomy without fully trusting the tests designed to ensure their reliability. Half of these companies have deployed AI features that passed internal evaluations but later failed in real-world customer interactions. Despite this, two-thirds allow AI agents to be deployed to production automatically, without human oversight. The biggest issue is the "evaluation gap"—a disconnect between the autonomy granted to AI agents and the trust in the evaluations meant to oversee them. Only 5% of organizations fully trust automated evaluations, citing poor alignment with real-world outcomes as the primary concern. Moreover, monitoring in production often checks only if the AI is functioning rather than verifying the correctness of its outputs. The market for evaluation tools remains fragmented, with many companies relying on provider-native tools or none at all, though investment in human review and observability is growing. While enterprises are moving towards greater automation, they continue to invest in oversight, highlighting a cautious approach amidst burgeoning autonomy. This points to a critical need for evaluations that better reflect actual performance to ensure AI agents can be trusted as their independence grows.

Across 101 enterprises, the challenge isn’t accessing AI context—it’s trusting it. Retrieval-augmented generation (RAG) dominates as the leading method to feed AI agents relevant business information, yet many organizations experience their AI confidently providing incorrect answers due to inconsistent or missing context. Despite the rise of provider-native retrieval tools like OpenAI’s file search and Google’s Vertex AI Search overtaking traditional vector databases, a significant trust gap remains. Most enterprises are actively building or piloting a governed semantic layer to address this gap, aiming for hybrid retrieval models that combine accuracy with governance. However, widespread adoption is still in progress, with many caught between the convenience of bundled provider tools and a desire to maintain best-of-breed solutions. This tension highlights that the core issue is not retrieval volume, but the quality, governance, and consistency of AI context—elements critical to AI’s reliable performance in business environments.

A study of 107 enterprises reveals significant gaps in AI agent security as organizations grant these agents system access without adequate controls. Over half (54%) have experienced security incidents or close calls involving AI agents. Despite this, only about a third of enterprises assign each agent a unique scoped identity, while most still share credentials, increasing risk. Additionally, just 30% isolate their highest-risk agents in sandboxes to limit damage if compromised. Security measures primarily rely on native provider tools from OpenAI, Google, and Microsoft rather than specialized agent security vendors. Satisfaction with these controls remains high, though spending on AI agent security is generally minimal, accounting for less than 10% of overall security budgets. Enterprises are divided on whether their AI defenses surpass attackers, with only a third confident they are ahead. A majority plan to update their security tools soon, indicating recognized vulnerabilities amid growing agent use.

A study across 107 enterprises reveals that AI infrastructure spending is growing rapidly, yet most organizations struggle to understand the actual costs involved. Many companies rely on familiar hyperscalers and model-provider APIs, but their next investments are focused on specialized AI compute resources rarely used today. Over half of these enterprises plan to switch or add providers within the next year, placing value on integration and total cost of ownership rather than on token price. However, GPU utilization remains low, with 83% running at 50% capacity or less, and only 44% of companies rigorously track their AI compute expenses. This mismatch between fast spending and poor cost visibility creates a "compute gap"— heavy investment without sufficient control. The report also notes high churn intentions among vendors and an emerging but under-recognized shift from GPU compute to memory bandwidth as inference demands grow. The findings suggest enterprises are eager to expand AI capabilities but need better tools to measure and manage their investments effectively.

Artificial intelligence is dominating conversations across organizations, influencing goals and workflows. However, many leaders overlook critical risks associated with AI adoption. From ensuring content relevance and data quality to preventing over-reliance on autonomous systems, these risks can undermine trust, creativity, and culture if ignored. Industry experts highlight issues such as diminished critical thinking, security vulnerabilities from unsecured AI use, the financial costs of AI deployment, and gaps in user adoption. Success with AI depends not just on technology but on human judgment, transparent governance, and aligning teams with clear goals.

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