Why Agentic AI Projects Fail—and How to Set Yours Up for Success
Why Agentic AI Projects Fail—and How to Set Yours Up for Success
Build agents that create ROI, not chaos.
Agentic AI can pursue goals, take actions, and adapt—but many projects flop due to bad use-case selection, weak guardrails, and “agent washing.” This article in The Harvard Business Review by Anushree Verma lays out where teams go wrong and how to design agents that are safe, measurable, and useful.
Key takeaways
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Start with the right job: Pick multi-step, cross-system workflows where autonomy adds real value.
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Define success up front: One verifiable outcome, tied to an existing KPI.
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Guardrail early: Human-in-the-loop checkpoints, scoped permissions, and full logging.
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Prove, then scale: Pilot narrowly, measure continuously, and graduate on evidence—not demos.
For
Owners/CEOs, CIOs/CTOs, product/ops leaders, and risk/compliance teams who need results without hype.
Try this now (quick start)
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Write the AI agent’s job description: goal, boundaries, systems it can touch, and escalation rules.
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Instrument day one: log prompts, actions, overrides, and incidents.
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Set a graduation bar: the metric and threshold that moves the agent from pilot to production.
Bottom line: Choose a focused workflow, add minimum viable guardrails, and earn autonomy through measurable wins.
