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Enterprises are betting big on machine learning (ML). According to IDC, 85% of the world’s largest organizations will be using artificial intelligence (AI) — including machine learning (ML), natural language processing (NLP) and pattern recognition — by 2026.
And a survey conducted by ESG found, “62% of organizations plan to increase their year-over-year spend on AI, including investments in people, process, and technology.”
But despite all the money flowing into ML projects, most organizations are struggling to get their ML models and applications working on production systems.
The market researchers at Gartner say that “Only half of AI projects make it from pilot into production, and those that do take an average of nine months to do so.”
IDC’s numbers look even worse, with only 31% of enterprises surveyed saying that they have AI functioning in production. In addition, “Of the 31% with AI in production, only one third claim to have reached a mature state of adoption wherein the entire organization benefits from an enterprise-wide AI strategy.”
And another recent survey has the worst numbers of all, finding that 90% of ML models are not deployed to production.
So what’s the problem? Why are...