Trust in fully autonomous AI agents has plummeted, with only 27% of organizations now confident in their abilities. This sharp decline comes as companies like AWS unveil tools to automate multi-step business processes and Google introduces Opal, a platform for building custom AI workflows. Despite the hype, most organizations remain unprepared: 80% lack mature AI infrastructure, and fewer than 20% report strong data readiness.

Human-AI collaboration is emerging as the key to rebuilding trust. Over 60% of companies plan to form human-agent teams within the next year, where AI handles routine tasks while humans focus on creativity and high-value work. These partnerships could boost employee engagement by 65% and creativity by 53%, according to Capgemini’s research. However, success requires transparency – explaining how AI makes decisions – and ethical safeguards to address risks like bias.

The EU’s AI Act, which took effect in 2024, mandates human oversight for high-risk systems, reflecting a global shift toward augmented AI that keeps humans in control. This approach aligns with findings that AI delivers the most value when humans remain actively involved, even in automated workflows. For example, Google’s Big Sleep uses AI to proactively block cyberattacks by monitoring domain behavior, demonstrating how human oversight can enhance security.

While the potential economic value of agentic AI could reach $450 billion by 2028, organizations face significant hurdles. Many current ‘agents’ are simply workflows with AI components, not true autonomous systems. To bridge this gap, companies must adopt an AI-first mindset, reimagining processes around AI capabilities rather than retrofitting old systems. As Franck Greverie of Capgemini notes, success requires baking ethics and safety into AI development from the start.

In healthcare, agentic AI could revolutionize revenue cycle management by automating appeals processes, but implementation demands careful planning to avoid job displacement and maintain trust. Similarly, Claude Code’s sub-agents show how specialized AI tools can handle repetitive coding tasks, freeing developers for more complex work. These examples highlight the need for change management – retraining employees and redesigning workflows – to fully realize AI’s benefits.