Great expectations for generative AI
The expectation that generative AI could fundamentally upend business models and product offerings is driven by the technology’s power to unlock vast amounts of knowledge that were previously inaccessible. “Eighty to 90% of the world’s data is unstructured,” says Baris Gultekin, head of AI at AI data cloud company Snowflake. “But what’s exciting is that AI is opening the door for organizations to achieve insights from this data that they simply couldn’t before.”
In a poll conducted by MIT Technology Review Insights, global executives were asked concerning the value they hoped to derive from generative AI. Many say they’re prioritizing the technology’s ability to extend efficiency and productivity (72%), increase market competitiveness (55%), and drive higher services and products (47%). Few see the technology primarily as a driver of increased revenue (30%) or reduced costs (24%), which is suggestive of executives’ loftier ambitions. Respondents’ top ambitions for generative AI appear to work hand in hand. Greater than half of corporations say latest routes toward market competitiveness are one among their top three goals, and the 2 likely paths they could take to attain this are increased efficiency and higher services or products.
For corporations rolling out generative AI, these should not necessarily distinct decisions. Chakraborty sees a “thin line between efficiency and innovation” in current activity. “We’re beginning to notice corporations applying generative AI agents for workers, and the use case is internal,” he says, however the time saved on mundane tasks allows personnel to concentrate on customer support or more creative activities. Gultekin agrees. “We’re seeing innovation with customers constructing internal generative AI products that unlock loads of value,” he says. “They’re being built for productivity gains and efficiencies.”
Chakraborty cites marketing campaigns for instance: “The entire supply chain of creative input is getting re-imagined using the ability of generative AI. That is clearly going to create latest levels of efficiency, but at the identical time probably create innovation in the best way you bring latest product ideas into the market.” Similarly, Gultekin reports that a world technology conglomerate and Snowflake customer has used AI to make “700,000 pages of research available to their team in order that they will ask questions after which increase the pace of their very own innovation.”
The impact of generative AI on chatbots—in Gultekin’s words, “the bread and butter of the recent AI cycle”—would be the best example. The rapid expansion in chatbot capabilities using AI borders between the advance of an existing tool and creation of a brand new one. It’s unsurprising, then, that 44% of respondents see improved customer satisfaction as a way that generative AI will bring value.
A more in-depth take a look at our survey results reflects this overlap between productivity enhancement and services or products innovation. Nearly one-third of respondents (30%) included each increased productivity and innovation in the highest three varieties of value they hope to attain with generative AI. The primary, in lots of cases, will serve because the fundamental path to the opposite.
But efficiency gains should not the one path to services or products innovation. Some corporations, Chakraborty says, are “making big bets” on wholesale innovation with generative AI. He cites pharmaceutical corporations for instance. They, he says, are asking fundamental questions on the technology’s power: “How can I exploit generative AI to create latest treatment pathways or to reimagine my clinical trials process? Can I speed up the drug discovery time-frame from 10 years to 5 years to 1?”
