The cost of abandoned genAI projects? Garbage code, orphan apps, and security issues

"By 2030, 50% of enterprises are expected to be dealing with delayed AI deployments or higher maintenance costs from delayed or abandoned projects, Gartner said in a survey released last month. "The punitively high cost of maintaining, fixing or replacing AI-generated artifacts such as code, content and design, can erode genAI's promised return on investments," Arun Chandrasekaran, a distinguished vice president analyst at Gartner, said in a statement."

"GenAI is developing fast, with new features seemingly arriving every few weeks or months, making it hard for IT leaders to keep up. As a result, poorly architected AI upgrades could create what the industry calls "technical debt," in which the cost of maintenance rises over time. Short-term fixes could leave tools and code with limited reuse value, increasing the maintenance costs."

"Studies from Omdia, McKinsey, MIT, and Forrester put projected failure rates for genAI projects as high as 95%. And while the new tools could reduce costs and increase productivity, they also can pile on new technical debt, HFS Research said in a study last month. The study was done in conjunction with software firm Unqork. About 43% of the participants surveyed by HFS expect AI to create new technical debt, while more than 80% expect cost reductions and productivity gain"

Rapid experimentation with generative AI is leaving behind garbage code, abandoned applications, and security issues that increase long-term maintenance burdens. Many genAI projects fail or are abandoned, creating technical debt that raises costs over time. Frequent platform changes and short-term integrations with legacy systems further exacerbate maintenance complexity and reduce reuse value. Surveys and studies report very high projected failure rates for genAI initiatives, and a substantial share of practitioners expect new technical debt alongside anticipated productivity gains. By 2030, half of enterprises may face delayed AI deployments or higher maintenance costs from abandoned or delayed projects.

Read at Computerworld