Over 40% of agentic AI projects will be cancelled by end of 2027, according to Gartner. Many more remain stuck in experimentation. And even those that scale often fail to deliver the intended return on investment. The technology is not the problem. The mental models are.

In this first of two episodes based on a major new study by Johannes Habel and colleagues — validated through a survey of 167 technical experts — Kenza and Pascal unpack the four management illusions that undermine agentic AI at the use case development and deployment stages.

Illusion Domain 1 covers two illusions around developing use cases. The agent-first illusion is the belief that AI agents can and should eventually enter virtually any business process. In reality, only about 10% of automations in a large-scale transformation actually require LLM-based agents — the rest can be handled by simpler, more deterministic tools. The end-to-end illusion is the expectation that a single AI agent can reliably navigate complex multi-step processes. Today's agents require highly specialized scope and human orchestration; broader process improvement emerges by combining multiple agents with other technologies and humans in the loop.

Illusion Domain 2 covers the deployment stage. The testing illusion is managers' systematic underestimation of the effort required to evaluate and safeguard agent reliability. AI agents that perform well in demos regularly fail in unattended production — often silently — and the availability of domain experts, not technical experts, is usually the bottleneck. The scaling illusion is the assumption that a successful pilot can be replicated quickly and cost-effectively. In practice, processes that look similar often differ in inputs, IT systems, and data quality — and the human resource challenges of scaling AI agents are as significant as the technical ones.

Key Takeaways

• AI agents should be the last resort for automation, not the first. Only about 10% of identified automations in one large-scale transformation required LLM-based agents.
• The end-to-end illusion: today's AI agents cannot reliably navigate complex multi-step processes. The right approach is specialized agents for clearly defined subtasks, orchestrated alongside humans and other tools.
• AI agents that pass demos regularly fail in production — often silently. A voice agent in automotive called former customers to reactivate them; it performed significantly worse than human agents and caused many opt-outs.
• The testing burden falls on domain experts, not technical experts. Their availability — not the technology — is usually what limits deployment timelines.
• Scaling is not replication. A pilot designed to prove feasibility is rarely designed to scale. The second implementation almost always takes longer and costs more than expected.
• Scaling AI agents ultimately means scaling human agents too — reorganizing roles, building new skills, and securing employee buy-in.

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