Generative AI is a developer's delight. Now, let's find some other use cases

Research suggests badly thought-out AI solutions can be damaging, so companies should think carefully about how to use emerging technology appropriately.
Written by Joe McKendrick, Contributing Writer
Hand touching screen with code on it
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Some people might think of generative artifical intelligence (AI) as a solution in search of a problem, but the technology is already proving its worth in one area: software development productivity. Close to half of technology professionals use generative AI to build applications. What's more, one third of IT staff use AI for data analytics. However, research suggests other business use cases are not quite ready.

survey of more than 2,800 technology professionals, released by O'Reilly, shows that 44% of respondents use AI in their programming work, and 34% are experimenting with it. Data analysis is also a major use case for generative AI, with 32% of IT professionals using it for analytics, and 38% experimenting with it. 

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"We aren't surprised that the most common application of generative AI is in programming, using tools like GitHub Copilot or ChatGPT," Mike Loukides, author of the O'Reilly report, writes. "However, we are surprised at the level of adoption." 

There is also evidence of a healthy tools ecosystem that has already sprung up around generative AI, the report indicates. "As was said about the California Gold Rush, if you want to see who's making money, don't look at the miners; look at the people selling shovels," Loukides says. 

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"Automating the process of building complex prompts has become common, with patterns like retrieval-augmented generation (RAG) and tools like LangChain. And there are tools for archiving and indexing prompts for reuse, vector databases for retrieving documents that an AI can use to answer a question, and much more. We're already moving into the second generation of tooling." The research shows that 16% of IT professionals report their companies are building on top of open-source models.

The report authors believe developers' adoption of AI tools will grow, regardless of whether their management tries to discourage it. "We expect that programmers will use AI even in organizations that prohibit its use," Loukides adds. 

"Programmers have always developed tools that would help them do their jobs, from test frameworks to source control to integrated development environments. Programmers will do what's necessary to get the job done, and managers will be blissfully unaware as long as their teams are more productive and goals are being met."

The report shows there's also rising demand for professionals with AI expertise, particularly AI programming (66%), data analysis (59%), and operations for AI/ML (54%). General AI literacy (52%) is also critical, as users have learned when encountering the hallucinations that generative AI tools sometimes exhibit. 

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Rising levels of adoption of generative AI for data analytics reflects "OpenAI's addition of Advanced Data Analysis (formerly Code Interpreter) to ChatGPT's repertoire of beta features," the report adds.

However, the research suggests many other business use cases for generative AI are still works in progress -- and carry some risks. The most common direct business use case is applications that interact with customers, including customer support, the O'Reilly survey shows. Close to two-thirds (65%) of respondents report that their companies are experimenting with (43%) or using AI (22%) for customer-support applications. 

Yet the report warns that "customer-facing interactions are very risky" when used with AI. The authors suggest that: "Incorrect answers, bigoted or sexist behavior, and many other well-documented problems with generative AI quickly lead to damage that is hard to undo."

The difficulty in finding appropriate business use cases is cited by IT professionals as the most pronounced roadblock to generative AI adoption -- 31% for non-users, 22% for users. Blame a "move fast and break things" culture, Loukides writes. "Badly thought-out and poorly implemented AI solutions can be damaging, so most companies should think carefully about how to use AI appropriately."

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Another reason business use cases take time to formulate is that AI cuts deep into organizational processes. "We also have to recognize that many of these use cases will challenge traditional ways of thinking about businesses. Recognizing use cases for AI and understanding how AI allows you to reimagine the business itself will go hand in hand."

Finally, it's worth remembering that AI is still new. Overall, 38% of IT professionals report that their companies have been working with AI for less than a year. "Even with cloud-based foundation models like GPT-4, which eliminate the need to develop your own model or provide your own infrastructure, fine-tuning a model for any particular use case is still a major undertaking."

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