Navigating the Real-World Adoption of Agentic AI in Enterprises

The 2021 collapse of ‘Zillow Offers’ serves as a stark reminder of what can go wrong when AI is deployed without the right safeguards. Built to automate home buying, Zillow’s algorithms frequently misjudged market conditions – causing the company to overpay for thousands of homes. With unsold inventory piling up, Zillow took nearly a billion dollars in losses, laid off 2,000 employees, and eventually shut down the program.

While this played out in the consumer space, similar risks are surfacing inside enterprise AI pilots across banking, pharma, and manufacturing where agentic models are being rushed into production without the governance. However, real-world adoption of Agentic AI presents a complex landscape. What looks promising in a lab can fall apart in production. The gap between PoC and scalable success remains wider than most leaders realize.

The AI Adoption Journey

According to Gartner’s 2024 survey, a striking 80% of Generative AI proof of concepts (PoCs) fail to make it to full-scale production. Most enterprises begin their AI journey by collaborating with existing technology vendors and engaging strategy consulting firms to craft a tailored AI roadmap. This typically leads to the identification of a few proof-of-concept (PoC) initiatives aimed at testing AI’s viability within specific business processes.

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