Artificial intelligence is getting a lot of hype — and it’s easy to see why investors are excited. Goldman Sachs estimates that generative AI in particular will drive almost $7 trillion in global economic growth over the next decade, with a total addressable market (TAM) of $150 billion. Tech giants such as Microsoft , with its AI-powered Bing search engine , and Alphabet -owned Google, with its Bard platform, have been among the most obvious beneficiaries. Nvidia is another top pick on Wall Street, with the chipmaker seen as the “grand marshal” of the AI parade . But AI’s applications extend beyond just search engines and cloud computing. Bernstein, for one, said it believes the world is witnessing an “exciting inflection” of AI adoption in manufacturing. Stock picks In a note on March 28, Bernstein analyst Jay Huang named a raft of stock picks with “outperform” ratings to ride the AI opportunity in manufacturing, including Japanese electronics firm Keyence . And he’s not the only one bullish about Keyence. About 83% of analysts covering the stock rate it a “buy,” giving it average upside of 10.7%. Other stocks that made his list include U.S.-based Cognex , Estun Automation and Hangzhou Hikvision Digital Technology . A.I. in manufacturing: 3 big areas In a report last year, Bernstein analyst Jay Huang estimates the total addressable market for artificial intelligence in manufacturing will grow by 10 times in five years to reach $11 billion in 2025. Huang confirmed in an email to CNBC Pro on Wednesday that he is standing by his estimates. The bank identified three areas in which AI is utilized in in the manufacturing process: industrial machine vision, robot guiding and industrial software. AI-powered industrial machine vision significantly expands the scope of automated inspection — it can cut costs and inspection times by more than 90% over manual inspection, according to Huang. The payback period of the cost outlay is also short — typically less than a year. And AI robot guiding systems improve the speed of assembly in automotive production lines and result in a cost reduction of 60% to 70%, according to Huang’s estimates. He said he believes these manufacturing applications are gathering pace and are on track for rapid adoption. “The entire field is at the dawn of mass adoption, with some players moving faster. The bird’s-eye view is important: at such early stage, all players are collaborating rather than competing — collectively, they…
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