Many companies pursuing “agentic” artificial intelligence — systems designed to act autonomously on behalf of users or businesses — are finding that their lofty goals aren’t being met in practice. Rather than delivering fully self-directed, reliable AI assistants that can carry out complex tasks independently, organisations are grappling with systems that are unpredictable, inconsistent, or fail to meet expectations. The gap between ambition and real-world results is leading many teams to reassess how agentic AI should be developed and deployed.
One of the key reasons cited for these shortcomings is trust. Both internal stakeholders and end users are hesitant to rely on autonomous systems when they lack transparency or clear assurance of safety and accountability. Without trust — in how decisions are made, how goals are aligned, and how risks are controlled — agentic AI struggles to gain the confidence needed for broader adoption. Companies are finding that users remain uncomfortable handing over control to systems that can act independently without clear oversight.
The article also highlights how companies are trying to pivot toward more practical uses of AI that augment human work rather than replace it. Instead of full autonomy, many teams are shifting focus to tools that assist with specific tasks within well-defined boundaries. This approach helps ensure that humans stay “in the loop” on decisions, reducing risks and improving acceptance among users who want clarity and control over AI behaviour.
Overall, the message from industry is clear: agentic AI has potential, but it isn’t ready for widespread deployment yet, and building trust through transparency, accountability, and predictable behaviour is essential. Until those foundations are solid, companies are expected to continue tempering their ambitions and prioritising human-centric AI implementations.
