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Businesses are taking a “walk, then run” approach to incorporating artificial intelligence into their business operations, with many executives putting their toes in the water before jumping in with both feet. Simply put, a number of indicators suggest that adoption of AI isn’t as widespread as media reports and technology executives would have you believe.
Indeed, a survey by MIT Technology Review Insights and the Australian telecommunications firm Telstra found that only 9% of business leaders were using AI to a “significant” degree.
“There is a misconception about how easy it is to run mature, enterprise-ready, generative AI,” said Stela Solar, Inaugural Director at Australia’s National Artificial Intelligence Center in the survey report. Successfully implementing AI solutions, she said, may require improved data quality and privacy measures, access to AI skills and organization-wide AI governance.
The number of companies saying they’ll launch some kind of AI solution during 2024 indicates “ambition and hubris” more than anything else, the report said. Only 5.4% of U.S. business are currently using AI to produce their products and services. And, according to Gartner, most companies that plan to adopt AI end up not using it.
“Everyone’s mentioning it in some way or another, but you can’t take that at face value,” CompTIA Spark CEO MJ Shoer said in an interview.
Learning From Other's Generative AI Mistakes
Many of the companies that have actually implemented AI are putting it to limited use in a testing or low-risk environment, wrote Richard Vanderford for The Wall Street Journal. “There’s some inherent risks with gen AI,” Reynolds American CIO Aaron Gwinner told Vanderford. “Before we run off and we just start doing AI projects, we need to get the foundations and the basics in place.”
Rather than get caught up in the whirlwind of AI’s promises, many companies believe current AI efforts are too complicated and moving too fast to them choose their best course. In addition, they don’t trust the technology to deliver meaningful and accurate data, wrote Dave Vellante in Silicon Angle, and they’re comfortable learning from others’ mistakes.
Some of these businesses’ biggest concerns regard people as much as technology. Nearly three-quarters of customers polled by Salesforce in August 2023 reported being concerned about AI being used unethically, for example. That’s one reason the number of business customers willing to use AI is tightening. The percentage of business buyers ready to leverage AI to create better experiences dropped from 82% to 73% between 2022 and 2023, according to Salesforce data. Consumer enthusiasm dropped as well, from 65% to 51%.
Related Article: It Could Be 5 Years Before We See Productivity Gains From Generative AI
Perception Matters
Other companies hesitate because they’re uncertain how users will react to interacting with AI instead of people, reported Vanderford. Here he cited the example of Koko, an online mental health service based in San Francisco, which ran into choppy waters when it began using AI to offer faster response times to user messages.
Although its ChatGPT-based tool was able to send out replies more quickly, users were unhappy with the idea of receiving “empathetic” messages from an AI instead of a person. The fact a person had reviewed and approved the messages didn’t matter. As Koko CEO Rob Morris observed, “Simulated empathy feels weird, empty.” For now, the company has stopped using AI until it can find a better use case.
Marketing buzz aside, AI’s value to business remains unclear, Daniel Colson, co-founder of the AI Policy Institute told Vanderford. “And I think that’s led to uncertainty about whether language models are actually that valuable for business applications.”
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Cost is another factor. Researchers from the MIT-IBM Watson AI Lab found that most reports examining AI’s impact on business look at the technology’s potential to affect an area, without considering the costs and technical challenges involved. In practice, the researchers said, the cost of developing and deploying an AI solution for computer vision is usually greater than the predicted savings.
For example, a bakery with five bakers on staff might save $14,000 a year by creating an AI to automate checking food quality. That is “far less than the cost of developing, deploying and maintaining” a AI solution, the researchers said, adding “we would conclude that it is not economical to substitute human labor with an AI system at this bakery.”
And of course there are security concerns. According to the Journal, 92% of the privacy and security professionals polled by Cisco Systems said generative AI is “fundamentally different from other technologies and required new techniques to manage data and risks.” Meanwhile, 48% of the IT professionals polled by Salesforce believe their security infrastructure isn’t able to keep up with the demands of generative AI.
Security experts also worry about … human behavior. “One of the biggest issues is letting people slip out intellectual property,” said CompTIA Spark’s Shoer. Such “inadvertent disclosure” can not only result in making private information public, it can embed that information into public large language models, where it will be used by solutions providers on an ongoing basis. “Things become part of the LLM,” said Shoer. “Public LLMs are the risk.”
