Companies Struggle To Deploy AI Due To Costs, Legal Threats, Data

Among the biggest impediments to implementing generative AI are challenges in the way data is organized, making it inefficient and expensive to convert for AI use. 

In addition, the costs of finding and paying talent, the cost of increased energy use which AI demands and the potential legal threats its use presents are all causing corporations to move more slowly through the proof of concept stage than entrepreneurs and venture investors had anticipated. JL 

Ryan Heath reports in Axios:

Behind the hype of generative AI, large companies are struggling to deploy the new technology - hitting cost and data management hurdles leaving many of their generative AI projects in pilot phase. Achieving the technology's potential is taking longer and costing more than many expected. 31% of respondents are still in proof-of-concept stage, outnumbering those who reached enterprise scale with an AI project - 28%. Many companies are finding their data isn't organized for AI - saved in different formats, in disparate datasets, and sometimes still on paper - "forcing a rethink of how data is stored, managed and processed."

Behind the hype of generative AI, large companies are struggling to deploy the new technology — hitting cost and data management hurdles that are leaving many of their generative AI projects stuck in pilot phase.

Why it matters: Companies remain optimistic overall about the boost in productivity promised by generative AI — but achieving the technology's potential is taking longer and costing more than many initially expected.

Driving the news: Deloitte and NVIDIA announced Wednesday they will supplement an existing AI partnership by establishing an "Ambassador AI program" to help struggling companies move to full-scale deployment of AI.

• More than half of AI decision-makers in top companies are facing cost barriers to deploying the latest AI tools, according to S&P Global's 2023 Global Trends in AI report, which includes a survey of 1,500 AI decision-makers in companies with more than 250 employees.

What's happening: Nearly 70% of respondents to the S&P Global survey said they have at least one AI project in production.

• But about half of those companies (31% of respondents) are still in pilot or proof-of-concept stage, outnumbering those who have reached enterprise scale with an AI project (28% of total respondents).

Details: Many companies are finding their data isn't organized for the AI revolution — saved in different formats, in disparate datasets, and sometimes still on paper — "forcing a complete rethink of how data is stored, managed and processed," said Nick Patience, senior research analyst at S&P Global Market Intelligence.

• Data management (cited by 32%), security (26%) and accessing sufficient computing resources (20%) are the top challenges for respondents to the S&P Global survey.
• Around half of the surveyed IT leaders said their organizations aren't ready to implement AI — and suggested it may take 5 years or more to fully build AI into their company workflows.
• Other knock-on effects of greater AI use include its climate footprint: 68% of respondents said their internal targets for energy use are now under strain because of how much computing power AI requires.

What they're saying: "We believe data represents the strongest long-term competitive moat in the AI arms race," Fred Havemeyer, Macquarie senior Enterprise Software analyst, wrote in a July 20 client note, citing database software that supports AI workloads as his "picks for the AI gold rush."

• Outdated data infrastructure is having "a direct, negative impact" on the ability to "achieve enterprise-scale AI deployment and to use AI sustainably," said Liran Zvibel, cofounder and CEO at WEKA, which commissioned the S&P Global report.

The big picture: Leaders in many large companies still have reservations about AI, aside from the hurdles to implementing it.

• Jon Stross, co-founder of HR software provider Greenhouse, told Axios that while he's working to find ways to use AI, he is "super nervous" about any situation where AI could amplify bias in the hiring process, especially when AI models cannot explain how they arrive at a decision — a basic step in any hiring process.
• Lani Phillips, VP of Channel Sales at Microsoft said she believed AI can be a time-saver and generate useful customer insights for even the most senior salesperson, but "there is no replacement for human connections with your customers."