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Why do agentic AI projects fail to deliver value?

Agentic AI initiatives often underwhelm because teams fixate on the agent technology itself, rather than the end-to-end business workflow it needs to improve. Success depends on redesigning the entire process, rigorously evaluating agent output to prevent "AI slop," and recognizing that complex AI agents are not always the right tool for the job.

Key Takeaways

1️⃣ Workflow > Agent: The most successful agentic AI efforts focus on fundamentally reimagining entire workflows—the combination of people, processes, and technology. The agent is just one part of a larger system that might also include RPA bots and data analytics to be effective. 

2️⃣ Onboard Agents Like Employees: Many users complain about "AI slop" or low-quality outputs, which quickly erodes trust. AI agent deployment is no different from hiring a new employee: with a clear job description, proper onboarding, and continuous feedback and evaluation to improve performance. 

3️⃣ Prioritize Reuse to Scale: Creating a unique agent for every single task leads to significant redundancy and waste. By developing a centralized library of reusable agent components and services, companies can eliminate 30-50% of nonessential development work and scale their efforts more effectively.

The new McKinsey report “One year of agentic AI: Six lessons from the people doing the work” perfectly captures the shift from agentic AI hype to the reality of implementation. After a year, the lesson is clear: technology is the easy part. The real challenge—and opportunity—lies in operational discipline. 

Framing under the right mental model is critical: onboarding agents is more like hiring a new employee versus deploying software. We wouldn't hire a person without a clear role, training, and performance reviews. We must apply that same rigor to our agentic systems if we expect them to perform reliably and earn user trust. 

While the promise of unmatched productivity remains, it's clear the path to get there is paved with good old-fashioned business process re-engineering, quality assurance, and thoughtful human-in-the-loop design. In particular, prior to any automation, simplifying, standardizing and streamlining the workflow to achieve greater effectiveness can improve the ROI. 

What's the biggest hurdle you're facing in moving your AI agents from impressive demos and Proof-of-Concepts to real-world value? 

#AgenticAI #AIStrategy #Automation #DigitalTransformation #GenerativeAI #ProcessAutomation #AIImplementation #AIAgents

Agentic Workforce October 29, 2025
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