Generative artificial intelligence is proving to be a game-changer that is complementing other technologies to give enterprises faster and more optimal results, according to Scott Likens, global AI and innovation technology leader at PricewaterhouseCoopers United States.

“We’ve talked about this convergence,” said Likens (pictured). “We study something called the Essential Eight, the emerging technologies we think are essential, but the convergence of them is where the true power comes in. Gen AI is going to help us generate much more in the simulated world. You think about the multimodal concepts of generating 3D images, generative AI is going to advance the Metaverse in a way we haven’t seen. Thinking about how we use this to accelerate the time-to-market, I think that’s just a no-brainer.”

Likens spoke with theCUBE industry analyst Dave Vellante at Supercloud 4, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how gen AI is enhancing other technologies and what the future holds for this cutting-edge technology.

Gen AI is a good transformational vehicle

With gen AI setting the technological innovation ball rolling, the acceptance rate is remarkable based on the transformations presented. As a result, it should be made as practical as possible in business, according to Likens.

“Gen AI has given us a really good vehicle to bring that to executives to say, ‘The world’s changing, let’s accept it, let’s invest in it,’” he pointed out. “To be at a firm like PWC, we have such a broad spectrum of industries we support. That’s really what makes it exciting for me is how do we then apply that into the practical.”

Based on the constrained nature of the regulated industry, decisions might not move as fast as expected. Nevertheless, gen AI can take this pain point away because of the presence of foundational models, according to Likens.

“I love pharma life sciences, being able to get to market critical drugs and help to people that need it … I think we’re constrained,” he said. “I think about every industry we’re working with, banking and insurance, massive amounts of information that they have to deal with. To me, it’s all about that speed, and generative AI has shown us a way to do this differently.”

For gen AI to exit the experimentation phase and enter the enablement stage, it has to operate in a secure environment, according to Likens. Brainstorming the specific issues and use cases that gen AI ought to handle is also of the essence.

“First, get a secure environment,” he said. “We’ve moved from the educational into the experimentation, but now I think we have to move into the enablement … then thinking about the patterns of things that gen AI solves well. I’ll give you an example, summarization of documents. That’s a huge pattern. We work obviously in finance, but across all of our clients, there’s summarization of documents.”

The need for AI agents

Since automation propels value proposition, AI agents will come in handy as more emphasis will be laid on skillsets rather than the processes. As a result, generative models that are human-led will become the norm, according to Likens.

“Large language models on their own are not going to give us the automation,” he explained. “We now have to think about that next wave and that next pattern. That’s where you’re seeing things like AI agents that are going to help string together tasks and kind of break up process into smaller pieces. We want the humans to lead through, but then using the AI or the automation, the agents will actually accelerate some of the processes and add quality along the way.”

With data being the backbone of enterprises, it is also at the epicenter of the gen AI transformation. On the other hand, gen AI and AI in general have the capability of taking the interaction between humans and data to unprecedented levels, Likens pointed out.

“I think we’re in an era where we’re going to see a different interaction model with our data as humans, being able to talk to our data,” he said. “You’re starting to see this emerge now. These generative models or AI in general can look at both structured and unstructured data in a different way. Having this AI actually walk us through the data as a human, I kind of feel like structuring data is for us, not for the machine.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Supercloud 4:

Photo: SiliconANGLE