Salesforce's push into agentic artificial intelligence is increasingly reshaping its core CRM (Customer Relationship Management) business, with AI agents now being integrated across sales, service, marketing, commerce and analytics, Arundhati Bhattacharya, President and CEO (chief executive officer) of Salesforce India, said in an interview with NDTV Profit.
Bhattacharya said AI adoption could be particularly significant in India because of the country's scale. She cited an NBFC that reduced the time taken to process two-wheeler loans from two days to 30 minutes using agentic AI.
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Salesforce's India and ASEAN operations use more than 100 agentic tools across different activities, she added.
The firm's AI offering Agentforce enables clients to build and deploy their own agents for service, sales, marketing and other purposes.
Bhattacharya said Salesforce does not see Agentforce as a separate product, but as an agentic layer being added across its existing offerings.
“All of the jobs that we were doing, whether it be sales, service, marketing, commerce, analytics, integration, all of them are now infused with agentic,” she said.
The adoption comes amid speculation that the software industry will see generative AI disrupt the traditional SaaS model, a trend that has been dubbed the 'SaaSpocalypse'. A trend that Bhattacharya did not reportedly believe in.
The Salesforce CEO stated that India has long viewed technology as necessary to expand access to services.
Bhattacharya called AI "the logical next step,” adding that AI can help address the scale of demand that can't be handled by humans alone.
Salesforce is already using agents across its own operations. In sales, agents handle smaller-value inbound leads, conduct initial conversations and nurture opportunities before handing them to human sellers once they reach a more advanced stage, as per the executive.
The company has also moved its analytics offering towards conversational dashboards, where users can ask for specific insights and have the system generate the relevant graph instead of relying on static dashboards.
Bhattacharya said the shift does not solely involve the replacing of employees. Salesforce has redeployed engineers from its helpdesk operations into customer-facing roles that involve building AI agents for clients.
The company's help operation is now entirely on an agentic layer and has achieved more than 80% first-call resolution, she said. Employees are instead moving into roles requiring higher skills and greater customer interaction.
She noted that legacy companies face a longer transition because they cannot simply replace their existing technology stacks. Bhattacharya said these companies need to rethink processes, automate workflows and build a strong data foundation alongside adopting AI.
“This is a must. This is something that's a given,” she said about AI investment, adding that technology spending is already an ongoing requirement for companies with legacy systems.
She also said AI transformation requires a complete technology stack, including data, context, automated workflows and a trust layer to protect data and address issues such as bias, toxicity and hallucinations.
Cost is another focus for Salesforce as AI adoption expands. Bhattacharya said the company is examining “token economics” and deciding which tasks require large language models and which can be handled using smaller models or other systems.
Salesforce is also focused on improving its agentic AI capabilities, semantic layer and ontology while making its products faster and more economical, she said.
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Bhattacharya said human oversight will remain important, particularly in sectors such as healthcare and financial services. She said AI can handle repetitive tasks, allowing doctors and other professionals to spend more time on decisions and customer or patient interaction.
“I do not believe that the human can be taken out of the loop, especially where health is concerned and where money is concerned,” she said.
For Salesforce, the strategy seems to be to combine what Bhattacharya described as “deterministic CRM” with the probabilistic nature of AI, ensuring AI-generated outputs are aligned with the specific rules and outcomes required by businesses.
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