Great solutions require integration of traditional AI and generative AI

One helps with structure and accuracy, the other boosts creativity
We asked AI engineers whether there has been a shift in the focus of their work from traditional AI to its generative variant. Their answers were unequivocal: The transition from traditional AI to generative AI is not just a technological shift; it’s a fundamental reimagining of what’s possible in the realm of machine intelligence.

“It’s not about completely shifting away from one to the other,” says Ravindra Guntur, senior staff machine learning engineer at ServiceNow India. “It’s about integrating them both to create more comprehensive solutions.”
Traditional AI, with its focus on structured tasks and predictive analytics, has long been the backbone of intelligent systems. It excels at categorisation, optimisation, and learning from historical data. But as Sailesh Raghavan, VP of technology at Publicis Sapient, points out, “Generative AI shifts the focus toward creativity, empowering machines to generate new content, ideas, and solutions that were previ ously unimaginable.”

This leap from prediction to creation marks a significant milestone in the field of AI. Poojitha Ramachandra, lead engineer for GenAI in programming at Bosch Global Software Technologies, says that unlike traditional AI, which requires costly curated data and significant computational power, Gen AI drives creativity and adaptability at scale, reducing costs and effort.

The impact on the engineering workflow has been profound. At Microsoft India Development Centre, engineers are witnessing a democratisation of AI capabilities. Sundar Srinivasan, VP of AI & search at the centre, says previously ML engineers needed to work with models in code – now anyone can talk to the models in English. This accessibility has opened new avenues for innovation, allowing engineers to infuse AI into products faster and more efficiently.

This evolution is reshaping entire industries. In the realm of software development, AIenabled code completion tools are advancing from assisting with code writing to helping with unit testing and bug fixing. “This allows engineers to focus on building and designing for users rather than dealing with the nuts and bolts,” Sundar explains.
The transition is not about abandoning traditional AI but about finding the right balance. Ravindra emphasises the complementary nature of the two approaches: “By combining both, I can tackle a wider range of problems, using traditional AI for structure and accuracy, and GenAI for more flexible, creative solutions.”

As the field evolves, so too must the skills of AI engineers. The consensus among industry leaders is clear: Versatility is key. “Master the fundamentals of statistics and traditional AI, while also exploring the creative potential of generative AI,” advises Sowmya T K, data scientist at Siemens Healthineers.


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