January 11, 2024 | Posted in News
Generative AI models have been a hot topic of discussion in 2023, but their real-world impact on each of the major IT disciplines is just beginning.
If 2023 was the year of generative AI, 2024 will be the year it goes mainstream, with pervasive impacts on IT professionals’ day-to-day work, according to DevSecOps experts.
Generative AI (GenAI) exploded onto the scene with the general availability of OpenAI’s ChatGPT in November 2022. By November 2023, Microsoft and GitHub had re-founded themselves on the Copilot generative AI tool they developed in partnership with OpenAI. AWS and Google quickly followed with generative AI additions to every part of their product lines, from cloud computing to DevOps and enterprise productivity tools. Although many of these updates have yet to reach general availability, and few enterprise IT organizations have deployed generative AI in production, experts said the technology has already passed a point of no return.
For developers, this means potentially major changes to the fundamental work of software engineering, from increased developer productivity to non-technical colleagues who can create their own applications. For security, the risks of AI — from data leakage to regulatory compliance — loom as major challenges. And for IT ops, the proliferation of AI-enabled SaaS tools will raise the spectre of shadow IT.
“For the citizen developer, in a new, truly ubiquitous way, the path between idea and compiled code has just shrunk by several orders of magnitude,” said Mike Bechtel, chief futurist at Deloitte Consulting, headquartered in New York. “IT departments cannot necessarily put that genie back in the bottle.”
While only 4% of 670 IT professionals surveyed in 2023 by TechTarget’s Enterprise Strategy Group (ESG) had deployed generative AI tools in production, the majority were in the early stages of implementing it or willing to consider it. Just 15% of respondents to the overall survey said they had no plans to adopt generative AI and were not planning to consider it. An even smaller group — 2% out of 324 respondents to questions about application development — said they had no plans to invest in generative AI.
“If you look at generative AI as … just the next page in the book of increasingly intelligent machines that we’ve been writing at least since 1956, it starts to feel less like an alien life form,” Bechtel said. “It’s really just a tool.”
The potential uses for large language models (LLMs) and products based on them have only just begun to be explored, said Andy Thurai, an analyst at Constellation Research.
“There are already many GenAI-related use cases in the advanced piloting stage waiting for board approval, budgeting, etc.,” he said. “A lot of enterprises/vendors have implemented enhancements to existing products that will help them search … which will include some form of natural language query. I expect their adoption to be much quicker than straight [generative AI model] adoption.”
Meanwhile, LLMs themselves are continuing to develop, Thurai said.
“We are still scratching the surface when it comes to accuracy, predictions, multi-modality, languages other than English and real-time decision making,” he said. “Innovation will continue. But use it with caution.”