Despite the hype, AI is making a difference
A little more than a year ago, generative AI was being touted by some as the most revolutionary development since the splitting of the atom. And ironically, major vendors are turning to technology that splits atoms to power AI, as seen in Microsoft’s intention to restart the Three Mile Island nuclear reactor.
But these days, generative AI is seen as being in a bubble that is on its way to deflating.
“While the bubble story smolders beneath the surface and the money continues to flow from the faucet, we are observing a recent inflection point,” said David, a postdoctoral researcher at Cornell Tech’s Digital Life Initiative. Gray Weider and associate professor Mar Hicks wrote: He described this in a paper published by Harvard University’s Ash Center on data science at the University of Virginia. “Our interlocutors are starting to sound the alarm that AI is overvalued. The recognition that AI is a bubble, not a gold rush, is permeating the broader debate with increasing frequency and intensity. The more industry leaders protest that this is not a bubble, the more people will look twice.
Weider and Hicks argued that the AI hype cycle is simply following the pattern of past tech bubbles. “Efforts to make AI culturally, technologically, and economically essential at scale have not lived up to its promise. In some ways, this is not surprising, since generative AI is the wave of the future. It does not represent the ebb and flow of past waves.
Is this a fair assessment? Industry leaders agree that AI, especially generative AI, is in a hype cycle, yet AI is already delivering on much of its promise. “While the hype is overdone, the reality is that 85% of G2000 countries are experimenting with next-generation AI solutions and are beginning to deploy AI at scale,” says ISG, a global technology research and advisory firm. said Stephen Hall, partner and president of . myself. “Thousands of use cases and pilots are underway.”
“While the AI hype is real, many organizations around the world are looking to improve productivity, deliver new customer and employee experiences, drive the development of new digital products and services, and drive meaningful business value.” “We are reaping the benefits of the AI generation,” said Matt Candy, global managing partner at IBM Consulting.
Beyond the bubble, it is unclear how AI will reshape the world. “Could there be an AI bubble? Yes,” said Gabriel Werner, field chief technology officer at Blue Yonder. “Is there any question that AI will have a lasting and profound impact on everyone? No. Do we all already know what that will be? No.”
The perception of what constitutes AI hype is at issue, Candy added. “The question is not whether AI meets expectations, but whether organizations can take an enterprise-wide approach to AI adoption, from AI as a complement to using open-source, multi-model It’s about being able to scale with an AI-first mindset. Take an approach based on people, trust, and governance.”
Werner believes that AI will be “a global game changer, just like the internet was in the late 90s.” Initially we had no idea what it would mean for our daily life. That’s the role of our AI. ”
“AI generated more than $10 billion in new revenue for global service integrators in the subsequent 12 months,” Hall noted. “This is an increase of more than 60% quarter-on-quarter, and most service integrators remained in positive territory even as managed services retreated. AI currently accounts for less than 2% of the outsourcing market, but It’s growing at a fast pace.”
Meanwhile, Candy noted that generative AI has skyrocketed AI ROI from 13% to 31% since 2022, with operating profit growth directly attributable to AI nearly doubling to 5% from 2022 to 2023. did. “Several of our clients are already seeing significant increases in productivity.”
To make the promise of AI a reality, AI advocates must overcome “uncertain business strategies, complex data challenges, risk and governance implications, skills shortages, infrastructure and cost considerations, and more.” Candy says there is. “Overcoming these challenges requires a coordinated effort across the organization.”
Industry leaders also offer the following steps:
Reframe ROI expectations. Look for return on AI, or “ROAI,” rather than simple ROI, Candy said. Make sure your employees and customers believe in these investments. “It is measured by adoption rates, engagement rates, and user satisfaction.” Trust can also extend to model accuracy, data transparency, fairness, and accountability. ”
While the ROI of AI applied to tasks such as software development, defect reduction, and testing is showing tangible results, “the ROI for revenue-generating activities is still in its infancy,” Hall said. . “It has only been a few quarters since the announcement of GPT 3.5, which democratized AI. In this short period of time, organizations have trained thousands of people on genAI and established guidelines for its ethical use. Many pilots have been launched in the past six months, so it’s too early to see any qualitative ROI.
Measure your results. “As with any successful project, measurable goals must be defined at the outset so the team can quantify the results after technical completion,” says Werner. “With predictive AI, you can measure the quality of your predictions. With generative AI, you can also measure adoption rates, and with agents, you can measure the quality of your predictions if your staff is not powered by AI. You can also look at your business KPIs and see if things improve.”
Reconsider data resources, data management, and data security. “Data and data governance are the biggest challenges facing enterprises today” in making AI success a reality. “While the use cases show promise, the large language model raises data security concerns for clients,” Hall said. “Enterprises are turning to solutions that integrate data with LLMs in a secure environment, such as OpenAI running on dedicated Azure instances. This creates integrity challenges.”
Be open, transparent, and adaptable. Start with a clear mission and a growth mindset that reimagines how you do work,” Candy said. “Companies must transform around talent and skills to ensure no one is left behind. They have transparency into the data used to train their models, they can control and manage these LLMs across the enterprise, and they prioritize AI governance and ethics above all else. Starting from the level AI We continue to ensure application reliability and compliance throughout the solution lifecycle.”
