Gartner Finds Half of Generative AI Projects Are Abandoned
While Gartner reports that half of generative AI projects are abandoned after the proof-of-concept stage, some enterprises are successfully securing returns through targeted agentic tools.
A 2026 report from Gartner reveals that approximately half of generative AI initiatives are abandoned after their proof-of-concept phase. According to Gartner analyst Arun Chandrasekaran, these failures stem from poor data quality, escalating costs, or unclear business value. The financial risks are real; for instance, a Pizza Hut franchisee sued the franchise in May, claiming an AI delivery platform cost millions rather than delivering savings. While many enterprises struggle to connect productivity gains to tangible metrics, Chandrasekaran advises that 80% of enterprise use cases should achieve a return on investment within a single year.
Despite these hurdles, some organizations are proving that returns are achievable. Alight Solutions, a benefits administrator, successfully deployed a beta recruiting agent from Phenom in September 2025 to detect candidate fraud. The tool quickly flagged a candidate applying twice under different names, demonstrating immediate value through time savings. Meanwhile, OBI Creative, an advertising agency with fewer than 50 employees, built custom agentic tools using models like Gemma, Qwen, OpenAI GPT, and Anthropic Claude. One of OBI's tools, which aligns creative campaigns with client goals, became a licensed product, helping the agency project at least 20% year-over-year growth.
Other institutions are redefining how they measure success. Cornell University provides its faculty with access to frontier models via Microsoft Azure, Microsoft Copilot, and Claude Desktop. Ayham Boucher, Cornell's head of AI innovations, explains that the university rejects tokenmaxxing, which is the practice of measuring employee performance by token consumption, and instead aligns AI value with academic discoveries and student services. For practitioners, these diverse outcomes show that AI success requires moving past simple cost-cutting metrics. Instead of focusing solely on lines of code or tokens burned, businesses must establish clear, operational value metrics before launching any AI pilot.
This is our own summary of reporting by AI Business
