The most compelling promise of generative AI is the implication that adoption brings monetary benefit for businesses — and ultimately the whole economy.
Productivity boosts from AI-powered developer tools and services could increase global GDP by more than $1.5 trillion by 2030, according to GitHub research published Tuesday. The open-source software provider and code platform, itself a maker of AI-based solutions such as GitHub Copilot, calculated the potential GDP boost by analyzing a sample of around 935,000 users and their individual experiences with related productivity boosts.
Because generative AI is in a nascent state, projected impacts on revenue, and therefore the economy, are difficult to gauge accurately. Tech leaders are leaning on what they know today, as they experiment with the technology and work to set expectations across the company.
At IT networking company Juniper Networks, CIO Sharon Mandell has worked on AI technologies throughout her career. She typically has seen AI as a way to get more mileage out of tech spend.
“If you can find tools that help shift costs, it can free up money in one place to start spending in new ones, which is something I think most CIOs relate to,” Mandell said. “I think our careers are very much about: 'How do I do all the things I'm doing today for less money than I did yesterday.'”
Regardless of the project, Mandell said ROI is almost always a factor and metric used to measure success, especially when the CFO is a part of the conversation. For generative AI implementation, the hard part is turning the value of time and productivity into an ROI measurement.
To get the full picture, Mandell said there needs to be a focus on measuring productivity, becoming more agile and accelerating products to market speed. “That ultimately turns into dollars,” Mandell said.
With macroeconomic indicators still showing mixed signals, the idea of turning tech into more efficiency is appealing to executives and board members.
Jennifer Piepszak, co-CEO of consumer and community banking at JPMorgan Chase, called it a “game of inches” at a conference earlier this month.
“Every day, we will just get a little bit better and leverage tools like AI and ML to be able to do that,” Piepszak said during the conference.
Companies could expect to see ROI from explainable AI in two to four years, according to Forrester data from October. Yet, 2 in 3 IT pros said their organization planned to increase spending on emerging technologies this year.
In the software development space, AI-based pair programming has been billed as a key accelerator of productivity. But businesses are also looking at other areas for AI implementation outside of IT.
“A lot of people are looking into sales and marketing to create more customized types of proposals and to create better, more personalized user experiences,” Bill Wong, principal research director of AI and data analytics at Info-Tech Research Group, said. “There’s a lot of focus in the sales, marketing and customer experience areas.”
ROI isn’t the only metric businesses are using to measure success of generative AI projects. Proof of concept metrics also include scalability, ease of use, quality of response, accuracy of response and explainability or total cost of ownership, Wong said.
