Two datasets about enterprise AI agents point in opposite directions, and the tension between them is the most useful thing a leader can read this week. The Capgemini Research Institute's survey of 1,500 executives at billion-dollar organizations across 14 countries (fielded April 2025) found that confidence in fully autonomous AI agents fell from 43% to 27% in a single year. Snyk's Volume II State of Agentic AI Adoption (August 2026), built from anonymized scans of more than 3,000 enterprise accounts spanning about 1.39 million code repositories, found agentic architecture adoption climbing from 28% to 33% of organizations in six months - and among adopters, the share running the full stack (agent frameworks plus MCP servers) jumping from 36% to 50%. Executives trust agents less. Companies deploy them more. Both are true at once.

What the survey says

Capgemini's numbers describe a trust problem with a twist. Only 2% of organizations had fully scaled agent deployment, and nearly two in five executives believed the risks of AI agents outweighed the benefits. But trust is not simply collapsing - it is being earned unevenly. Among organizations in the implementation phase, 47% report above-average trust, versus 37% of those still exploring. Trust follows contact, not contemplation. Meanwhile 93% of business leaders believed scaling agents within 12 months would bring competitive advantage, while nearly half of organizations lacked a strategy for doing it. Capgemini projects up to $450 billion in economic value from agentic AI by 2028 - treat that as the vendor's projection, not a forecast - with organizations at scaled implementation projected to realize about $382 million on average over three years, against $76 million for the rest.

What the code says

Snyk's data is behavioral, drawn from what organizations actually commit to their repositories, and it is blunter. The full AI footprint - agent frameworks, MCP servers, retrieval systems, vector databases, datasets - is roughly three times what a model inventory shows, and that ratio held across every region measured. Only 51% of organizations with at least one deployed model declare any dataset in their repositories: half cannot trace, at code level, what data their production models touch. The model mix is also shifting fast: Anthropic's share of enterprise model occurrences rose from 4% to 11% while OpenAI's fell from 44% to 35%. Both findings come with a caveat: Snyk's sample is organizations that use Snyk, and Snyk sells the platform that closes the gaps it measures.

The caveats

The Capgemini survey was fielded in April 2025 - its trust numbers describe mid-2025 sentiment, and attitudes have likely moved since. Snyk's repository scans show what is deployed, not what is used or valuable. Neither dataset tells you whether agents are working; together they tell you how organizations are behaving.

My read

Read together, the two datasets describe an industry deploying faster than it can see. Three implications. First, visibility precedes autonomy: if your AI inventory is a list of models, you are governing a third of your surface - the agents, tools and data pipelines are the majority. Second, the trust numbers are not a reason to slow down; Capgemini's own data says trust grows with implementation. They are a reason to deploy in ways that earn trust - narrow scopes, human oversight, measured expansion - rather than pilots designed to impress a board. Third, the data-lineage gap is the sleeper issue: half of model-deploying organizations cannot link a production model to its data. When the first serious agent incident hits your industry, "we cannot say what it touched" is the answer that turns an incident into a scandal. The companies that scale agents successfully will be the ones that built the map before they needed it.

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