Companies adopting artificial intelligence should look beyond efficiency gains and assess whether the capacity created by the technology is being channelled into innovation and growth, Irina Ghose, managing director of India at Anthropic, said.
Organisations that are ahead in AI adoption are focusing on two outcomes: the efficiency created in existing operations and how the resulting capacity can be used to pursue new areas of growth, Ghose told NDTV Profit in an interaction on the sidelines of Global Fintech Fest In Mumbai.
“Efficiency is bringing in headroom for capacity for innovation,” Ghose said. Companies should also identify “those big rocks or the large moonshots” they want to pursue and track progress against milestones that can drive growth, she said.
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Beyond Efficiency
Ghose said businesses are at different stages of AI adoption. Some have committed to the technology and are moving ahead, while others remain cautious about the timing and approach. For companies that have adopted AI, efficiency remains a key measure. They are assessing how the technology can improve existing systems and create efficiencies in resources, she said.
But Ghose said efficiency should not be the only measure. “Now, are you really measuring the innovation?” she said. The innovation created from AI-driven capacity could provide the larger levers for growth, she said.
Companies should therefore measure both the efficiency AI creates across teams and departments and their progress on larger projects aimed at driving innovation and growth, Ghose said.
Hidden AI
Ghose said AI is also becoming more embedded in business processes, reducing the need for employees to interact directly with the technology.
“As long as you think of AI as something which is doing outside of you, there’s still a little level of a gap and a friction,” she said.
As AI becomes embedded in workflows, users may increasingly focus on the result rather than the technology producing it. Ghose gave financial reconciliation as an example. A person reviewing a month’s reconciliation could see only the completed outcome, while the underlying work and required logs are generated as part of the process.
A data analyst could similarly focus on the insights produced rather than how the work moves from a presentation deck to the final outcome, she said.
“Only the outcome matters,” Ghose said.
Building Advantage
Ghose said the AI model is only one part of the equation. The ability of people and organisations to build with the model will also determine how businesses benefit from the technology.
“The model is a game changer, but the capability of the builder who’s building things with the model is what defines and actually defines the entire change in the market,” she said.
She cited examples of AI use across Indian businesses, including Cognizant, which she said has put the technology in the hands of 350,000 employees. Access Bank is testing its use in engineering, while IndusInd Bank is examining how it can redefine knowledge work, she said.
Ghose said AI can be particularly useful in areas involving complex legacy applications and interoperability.
India’s Scale
India’s population scale, diversity and multilingual environment create problems that AI can help address, Ghose said.
She said efforts are under way to improve AI proficiency across more than 10 of India’s largest spoken languages and to build greater capability into training systems.
The technology can also be applied to sectors including agriculture, education, health care and skilling, she said.
“The minute you kind of try to solve population-scale problems by making it available to the last mile, that’s where the real difference lies,” Ghose said.
Ghose also pointed to work around where data is held and where AI inference takes place. She said making these capabilities available closer to Indian users could open up new opportunities for the country’s wider ecosystem.
Leadership Matters
For companies that remain cautious about AI adoption, Ghose said leadership involvement could help accelerate the process.
She said leaders should use the technology themselves and begin with the outcome they want to achieve rather than simply asking what AI can do.
“The biggest problem, which they are not asking what can AI do, but says what good outcome would look like,” Ghose said.
Defining the desired outcome gives companies a clearer problem to solve, after which they can apply AI and work with it iteratively, she said.
“I think that’s the way the entire culture will change,” Ghose said.
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