Why your company is struggling to scale up generative AI – Crypto News

Dig deeper, though, and the situation is more nuanced. Generative ai appears to be one of those innovations, such as email or smartphones, whose the most eager early adopters are individuals. Companies are being far more tentative.

In the two years since OpenAi unveiled ChatGPT, generative AI has had a faster rate of adoption than PCs or the internet. Fully 39% of Americans now say they use it, according to a study by Alexander Bick of the Federal Reserve Bank of St Louis and co-authors; 28% say they use it for work, and 11% that they do so every day.

Many of them, though, seem to be secret cyborgs, using the technology at work even as their employers dawdle. Just 5% of American businesses say they are using the technology to produce goods or services, according to a survey by the us Census Bureau. Many companies seem to be suffering from an acute case of pilotitis, dilly-dallying with pilot projects without fully implementing the technology. In a recent survey conducted across 14 countries by Deloitte, a professional-services firm, only 8% of company leaders said their firms had deployed more than half of their generative-AI experiments (see chart).

As a consequence, revenue from selling AI services to companies remains limited. Although Mr Jassy said AWS now generates “multi-billion” dollars of revenue from AI, that is a smidgen of the $110bn of annual revenue for its cloud business as a whole. Accenture, a consulting giant that recently announced it would train 30,000 staff to help companies adopt generative AI, said in September that it had booked $3bn-worth of work related to the technology over the past 12 months, a ten-fold increase year on year. But compared with the company’s total sales of more than $81bn, that too is small beer.

Why are many bosses hesitating to adopt generative AI? One reason is that they worry about the downsides. Listen to the tech giants and they will tell you—as Sundar Pichai, the boss of Alphabet, said in July—that “the risk of under-investing is dramatically greater than the risk of over-investing”. Alphabet, Amazon, Microsoft and Meta are expected to pour at least $200bn into AI-related capital expenditures this year. Bosses in other industries are more circumspect. At a recent closed-door discussion, the head of a big American business group spoke of two types of fears chief executives have about generative AI. One was being left behind if they adopted it too slowly. The other was being embarrassed if they moved too quickly and damaged their firm’s reputation.

Legal and regulatory risks loom large. Lawsuits related to privacy, bias and copyright violations are making their way through the courts. In August the European Union’s AI Act came into force. AI bills have been introduced in at least 40 American states this year. Bosses in heavily regulated industries, such as health care and finance, are especially wary. Although they see the potential of generative AI to transform their businesses, say by speeding up drug discovery or fraud detection, they are keenly aware of the threats to privacy and security if their customers’ medical or financial data are compromised.

Another problem is that the benefits of adopting generative AI can be uncertain. Accessing large language models (LLMs) is expensive, whether via a company’s own servers (safer) or via cloud-service providers (simpler). Full-scale implementation of generative AI may increase revenues and reduce costs, but the payoff is not immediate, raising concerns about returns on investment. In its recent survey Deloitte found that the share of senior executives with a “high” or “very high” level of interest in generative AI had fallen to 63%, down from 74% in the first quarter of the year, suggesting that the “new-technology shine” may be wearing off. One executive sums up the scepticism by recounting the story of a chief information officer whose boss told him to stop promising 20% productivity improvements unless he was first prepared to cut his own department’s headcount by a fifth.

Even when companies are eager to amp up their use of generative AI, though, they may find it tricky. To reap the full rewards of the technology, businesses have to first get their data, systems and workforce into shape, says Lan Guan, Accenture’s head of AI. She reckons companies’ readiness for generative AI is much lower than it was for previous technology waves such as the internet or cloud computing.

One problem is messy data, scattered in different formats across various departments and software systems. Ms Guan gives the example of a telecoms firm that wanted to train a call-centre AI assistant by feeding it PDFs, manuals, call logs and more. The bot found that instead of one standard operating procedure—what she calls “a single source of truth”—the company had 37, accumulated over decades. A failure to organise data before using it to train a bot increases the risk of hallucinations and mistakes, she says.

Another problem is that IT systems are often creaky and old, a problem known as “technical debt”. That can make it difficult to plug in LLMs without causing trouble. Integrating semi-autonomous AI agents into systems built for humans might also create security vulnerabilities.

Then there is the problem of skills. Many companies are still struggling to get their hands on enough AI specialists. According to Lightcast, a research firm, AI-related job postings in America have surged by 122% so far this year, compared with an 18% rise in 2023. Elizabeth Crofoot, an economist at Lightcast, says that this increase is mostly explained by generative AI, with job descriptions mentioning ChatGPT, prompt engineering and large language modelling on the rise.

Companies also want workers in other roles who know how to use generative AI. A sales rep with AI skills can earn $45,000 a year more than one who lacks them, says Ms Crofoot. No wonder, then, that even as some bosses prevaricate about scaling up generative AI, their employees are all in.

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