As per the Capgemini Research report, trust is collapsing in the age of autonomous AI and that’s the wake-up call enterprises didn’t expect.
Agentic AI systems designed to plan, decide, and act with minimal human input are rapidly moving from experimentation to enterprise deployment. Yet just as ambition peaks, trust is slipping. Only 27% of organizations say they trust fully autonomous AI agents, down from 43% a year ago, according to Capgemini Research Institute’s 2025 study on agentic AI, Rise of agentic AI; How trust is the key to human-AI collaboration.
The drop reflects a sobering recalibration. Early optimism is giving way to a more grounded understanding of what true autonomy requires in live business environments—robust data, resilient systems, and accountable governance.
Adoption Is Rising; Autonomy Is Not
Despite declining trust, adoption continues to surge. Nearly four in ten organizations are piloting or deploying AI agents, and a large majority believe early movers will gain a competitive advantage. The economic upside is significant: agentic AI is projected to unlock hundreds of billions of dollars in productivity gains and revenue uplift over the next three years. AI-Agents_Final_210725.
Yet most implementations remain at low to intermediate levels of autonomy, with humans firmly in the loop. Only a small share of enterprise processes are expected to become fully autonomous in the near term. This caution is revealing: organizations are discovering that reliability, safety, and governance maturity not model capability alone determine how far autonomy can responsibly scale.
Why Trust Is Breaking Down
Three structural gaps are driving the trust deficit.
First, capability blind spots persist. Only about half of leaders say they understand what AI agents can and cannot do, and fewer can pinpoint where agents meaningfully outperform traditional automation.
Second, weak foundations undermine confidence. Fewer than one in five organizations report high data readiness, and more than 80% lack mature AI infrastructure; making autonomous systems fragile at scale. AI-Agents_Final_210725.
Third, governance lags ambition. Concerns about data privacy, bias, and opaque decision-making remain widespread, but concrete mitigation practices lag behind executive rhetoric.
The Human Factor: Anxiety at the Edge of Autonomy
Trust is not only a technical issue; it is human. Employee anxiety is rising, with many fearing displacement as agentic systems expand. Yet reskilling and redesigning the operating model are not keeping pace. The report underscores a critical shift: AI agents must be treated as team members, not tools, with defined scopes of authority, escalation paths, and human oversight embedded into workflows. Organizations that begin with human-supervised or “read-only” modes tend to build confidence faster, as consistent performance and transparent guardrails demonstrate reliability over time.
How Leaders Rebuild Trust Before Scaling Autonomy
The most credible path forward is pragmatic. Winning enterprises are redesigning processes end-to-end, investing in data quality and platforms, and formalizing AI governance before increasing autonomy. They are also adopting hybrid build–buy strategies combining off-the-shelf agents with custom development for critical workflows and setting explicit success criteria for scale. In practice, autonomy is an earned privilege, not a default setting.
From Hype to Hardening: The Trust Reset
The trust slump is not a failure of AI; it is a correction to inflated expectations. As hype fades, the discipline required to make autonomous systems safe, explainable, and resilient is becoming clear. Organizations that rebuild trust through governance, readiness, and human–AI collaboration will turn agentic AI from a risky experiment into a durable operating advantage.