Agentic AI · AI Agent · MIT Technology Review
Breathless claims about AGI and new capabilities fall apart pretty quickly under scrutiny
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Meet the new kids nipping at the heels of the AI giants.
Key facts
- The purpose of this report, which is based on a survey of 300 data, AI, and other technology executives, is threefold
- On average, only around a third (34%) of organizations’ agentic AI projects make it into production
- This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff
- Ten years after AlphaGo’s match against Go champion Lee Sedol, today’s AI still isn’t tapping into the machinery that made that win possible
Summary
For all the data that AI systems continually amass and analyze, enterprise AI agents often suffer from a curious shortcoming: a lack of knowledge. A lack of knowledge, their research finds, is a major reason agentic AI use cases never make it to production. The purpose of this report, which is based on a survey of 300 data, AI, and other technology executives, is threefold. Second, the report probes the challenges organizations face in improving access to knowledge and ultimately to getting more agent use cases into production. Data and knowledge weaknesses consistently stall AI agent progress.