Few innovations throughout history have progressed as fast as generative artificial intelligence (AI) has.
And with the news Tuesday (April 16) that Intel, working with several other industry partners, is collaborating on a Sandbox Project called the Open Platform for Enterprise AI (OPEA) that aims to accelerate secure, cost-effective GenAI deployments for businesses, putting AI into action is increasingly top of mind for enterprises.
Per the release announcing OPEA, Intel noted that AI is “currently in a state of kinetic innovation, a byproduct of which is fragmentation of techniques and tools. This fragmentation is a barrier to enterprise adoption of GenAI and the immense value it brings to a business. Developers tasked with realizing this value are faced with a dizzying number of choices when it comes to incorporating GenAI.”
“OPEA has the capability to take GenAI to the next level by providing a standardized platform for assessment, development, and deployment,” Intel added.
After all, innovations with a defined use case are more likely to gain acceptance in the market. When businesses can see — and measure — how a new product or technology like GenAI solves a problem or improves incumbent workflows, they’re more inclined to adopt it.
Without a defined use case, it’s challenging to determine whether the innovation is achieving its intended goals or delivering value.
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The inability of organizations to define their own use case for GenAI is in part why OpenAI CEO Sam Altman hosted meetings this month in San Francisco, New York and London, each of which was attended by more than 100 corporate executives as the AI company looks to position its AI systems as enterprise-grade solutions and sell them to businesses.
OpenAI’s enterprise-grade chatbot, ChatGPT Enterprise, has been positioned as a value-add to enterprise functions such as call center management, translation and other applications. And OpenAI is far from alone in seeking lucrative corporate contracts for its AI systems.
As covered here, Amazon has launched its own enterprise-focused AI platform, Amazon Q, that boasts corporate partners including Accenture, BMW Group, Gilead, Mission Cloud, Orbit Irrigation and Wunderkind; while Microsoft last month announced the launch of two new, AI-optimized devices designed exclusively for business users: the Surface Pro 10 for Business and the Surface Laptop 6 for Business.
Google has also been breaking ground on the B2B GenAI front, bringing several new AI features for businesses to its Google Workplace suite this month (April 9) as it looks to help firms transform legacy tasks into more intelligent, automated processes.
“The ChatGPT light bulb went off in everybody’s head, and it brought artificial intelligence and state-of-the-art deep learning into the public discourse,” Andy Hock, senior vice president of product and strategy at Cerebras, told PYMNTS.
“And from an enterprise standpoint, a light bulb went off in the heads of many Fortune 1000 CIOs and CTOs, too,” Hock added. “These generative models do things like simulate time series data. They can classify the languages and documents for applications, say, in finance and legal.”
PYMNTS Intelligence data revealed that 7 in 10 consumers believe AI can already replace at least some of their professional skill sets. Young consumers, those earning over $100,000 and those working in an office environment are most aware of this skill overlap.
And it isn’t just the Big Tech giants that are pushing GenAI systems for enterprise use — an emerging cohort of AI startups and smaller firms are also targeting the corporate market with innovative solutions.
Read also: AI’s Future Is Becoming Indistinguishable From the Future of Work
Still, there exists an ongoing uncertainty across the marketplace as to whether GenAI models are “worth it” for business applications due to their cost, unpredictability, and the resources necessary to deploy them effectively.
As Adrian Aoun, CEO at Forward, told PYMNTS, “things need to be built for a world of AI in order for that AI to work and scale.”
Developing and implementing innovations require resources such as time, money and talent. A defined use case helps allocate these resources more efficiently, ensuring that they’re invested in projects with a clear purpose and potential impact.
“In many cases, even though firms have the AI use case, and they have the opportunity to leverage AI in a meaningful way, they don’t have sufficient access to relevant talent that can help their business do that work,” Pecan CEO and Co-founder Zohar Bronfman told PYMNTS, explaining that access to skilled data scientists who can effectively implement AI solutions is both valuable and in short supply.
Eddie Zhou, head of AI at Glean, underscored to PYMNTS the challenges posed by the marketplace-wide silos of fragmented data landscapes, emphasizing the importance of clarifying use cases and understanding the value proposition of investing in enterprise AI before deployment.
“A lack of clarity around what firms want to solve for can slow down integration,” Zhou said. “Yes, AI can help, but what do you want it to do? It’s a moving target … we’re just starting to uncover where real value is added.”
For further reading, the PYMNTS Intelligence “Generative AI Tracker®,” a collaboration with AI-ID, sorts the myths from the realities of AI and explains how businesses can leverage the technology wisely.
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