eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.
I spoke with Pandurang Kamat, CTO of Persistent Systems, about generative AI’s impact on customer service, navigating AI challenges, and responsible AI.
See below for a podcast and video version of the interview.
Some key quotes from the interview:
Generative AI and User Experience
“Despite the early days where there are going to be some clunkiness to the experience, we firmly believe that over the long term, this is essentially going to provide us superpowers – not just to the knowledge workers, but to anyone who’s interacting with any digital tools.
“We see generative AI as predominantly providing very intuitive interventions within the workflow. It’ll be very assistive in nature, not necessarily fully autonomous – perhaps in the very long term, but not in the near term. We expect generative AI to be personalized so that people actually get very contextually relevant help, not just to the task at hand, but for the person performing the task.”
Concerns with Generative AI
“The number one concern that we are hearing about AI is: what is the scenario with data privacy? Is my content safe? Is it likely to be used to train the model and therefore create a data leak channel?
“The other concern is around AI ‘hallucinations, as they’re known, where these models can start making up things not backed by facts – that becomes a big concern. But there are a slew of grounding tools that have emerged and continue to get better. Additionally, there are a number of techniques, a series of very scientifically-based techniques, that you can use to ground and control these hallucinations.
“The third challenge that we see is around consistency. By the very nature of these models, consistency by default is not a trait that they come with. So you have to build that in the layer that you’re building on top of it.
“For the end user, the number one concerns are: A), am I having to learn something new or is this going to be intuitive enough, and B) is this going to make my job redundant? And those are very legitimate concerns.”
Software Development and AI
“But the biggest change that we see with artificial intelligence is in how we build software itself. And that becomes a very important question for us because the bulk of our business comes from building software for other people.
“The nature of how we build software itself is being completely transformed dramatically by gen AI. And we are doing this in two ways. One is complete greenfield or brownfield, if you will, software development. It involves how gen AI can augment what the developer is doing by helping with code snippets or writing test cases or even creating automatic pull requests and things like that.
“But we also do a lot of business in modernizing application and that’s a whole different journey, right? Because your legacy tech before and after is very different. The architecture of the system is very different. So a lot of our energy and effort is being spent right now on figuring out: how do we completely transform the journey for legacy modernization for the engineering teams as well as for the end customers? And what does that look like, if powered by gen AI? And that’s one of the things I’m most excited about.”
Transformative Changes
“There are going to be foundation AI models that are very general and can be used for more complex planning tasks, but then there are going to be very, very fine tuned and scaled down models. So task–specific, domain-specific models will do one particular task exceedingly well, much better than the general model, and will cost much less to train as well. So that’s number one.
“Number two, in the next year or so, we are going to see a lot of enterprises move from experiments and POCs toward scaling to stable, resilient, predictable, consistent applications of AI. We are already hearing CXOs talk about that to us.
“The third thing will be that generative AI will be deeply integrated into enterprise workflows and companies, especially the hyperscalers. If you see the releases they’re putting out, they’re constantly aiming to make it easier and faster to integrate – even going down to low-code or no-code ways of integrating their AI into the enterprise workflows. And the core of the enterprise as a result is going to be much more intelligent and generative by design.”
Podcast and Video
Listen to the podcast:
Also available on Apple Podcasts
Watch the video: