Nice expectations for generative AI
The notion that generative AI could revolutionize enterprise patterns and product decisions stems from its capability to tap into vast, previously inaccessible data stores. According to Baris Gultekin, head of AI at Snowflake, an estimated 80 to 90 percent of global knowledge remains unstructured. “What’s particularly exciting is the prospect of AI unlocking new avenues for organizations to derive meaningful insights from this vast repository of knowledge, previously inaccessible.”
A global survey conducted by MIT Technology Review Insights asked executives what value they expected to derive from generative AI in their organizations. Experts’ potential is seen as a key factor in driving efficiency and productivity, with 72% of respondents citing this as a primary goal. Additionally, 55% aim to leverage expertise to gain a competitive edge in the market, while 47% seek to develop higher-value services and products. While some perceive the expertise’s primary impact as driving higher earnings (30%) or reduced costs (24%), this perspective hints at executives’ more aspirational goals. Respondents’ high aspirations for generative AI seem to harmonize seamlessly. More than half of companies identify achieving market competitiveness as one of their top three strategic objectives, with two primary avenues to pursue: enhancing operational efficiency and delivering superior products or services.
As companies embark on deploying generative AI, these choices will not necessarily stand apart as separate entities. The entrepreneur perceives a slender margin between efficacy and innovation in the current endeavour. As companies start leveraging generative AI brokers internally, they’re unlocking valuable time that can be redirected to higher-value tasks, such as enhanced customer service or innovative initiatives. Gultekin agrees. “We’re witnessing a surge in innovative prospects developing internal generative AI products that unlock vast value,” he remarks. “These new facilities are designed to optimize productivity and streamline operations.”
For instance, Chakraborty references pioneering advertising campaigns that showcase the transformative potential of generative AI in redefining the entire supply chain of innovative entrepreneurship. This will create new ranges of effectiveness, but simultaneously likely generate innovations in how you bring new product concepts to market. A global technology conglomerate and Snowflake customer has leveraged AI to make 700,000 pages of research available to its team, allowing them to ask questions and accelerate their own innovation tempo.
The impact of generative AI on chatbots, as aptly described by Gultekin as “the bread and butter of the latest AI cycle,” is arguably its most compelling demonstration to date. The rapid proliferation of chatbots leveraging AI blurs the line between the evolution of existing tools and the emergence of a novel innovation. As expected, nearly half of those surveyed believe that increased customer satisfaction will demonstrate the value of generative AI.
Our examination of survey results reveals a striking correlation between the augmentation of productivity and the development of innovative products or services. Approximately 30% of respondents placed increased productiveness and innovation among their top three goals for achieving value through generative AI, alongside other valuable outcomes. The primary route often serves as the main path to the other side.
While effectiveness features may not be the sole pathway to services or product innovation. Certain major corporations are making substantial investments in wholesale innovation driven by generative AI. He specifically highlights pharmaceutical companies as instances of his point. They query about harnessing generative AI’s potential to innovate therapy approaches and transform their clinical trial processes: “Can I leverage generative AI to pioneer novel treatment paths or rethink the trajectory of my medical trials?” Can we accelerate the drug development timeline from a decade-long cycle to a five-year window or even shorter?
