In an exclusive conversation with Financial Express, Deepak Pargaonkar, Vice President of Solution Engineering at Salesforce India, highlighted how enterprises are evolving into 'agentic' organisations where humans and AI agents work together to drive business outcomes.
The enterprise landscape is undergoing a structural transformation, moving from human-led operations to a model where humans and AI agents work alongside each other. As explained by Deepak Pargaonkar of Salesforce, the concept of an ‘agentic enterprise’ is already taking shape across industries. Traditionally, customer engagement relied heavily on human interaction through branches, call centres, or physical touchpoints. Today, AI-powered agents are increasingly capable of handling customer queries, managing service requests, and supporting internal workflows continuously.
Gartner has projected that by 2027, chatbots will become the primary customer service channel for roughly 25% of organisations. IBM has reported that AI can handle up to 80% of routine customer queries in service environments. McKinsey & Company estimates that AI could automate 60–70% of employee time across tasks, many of which include customer interactions.
Despite growing investments in AI, many organisations remain confined to pilot stages. The core issue lies in the inability to translate experimentation into measurable outcomes. Pargaonkar highlighted that successful AI deployment must deliver tangible returns, not just technological advancement.
Salesforce’s internal experience provides insight into this transition. The company has reported approximately $100 million in annualised support cost savings, along with a similar increase in its sales pipeline through AI-driven lead qualification. This aligns with broader findings, where organisations that scale AI effectively report productivity improvements of 20–30%.
The larger takeaway is that AI success depends less on deployment and more on its measurable contribution to business performance.
"Organisations should be able to attribute top line, bottom line, productivity and experience to their AI initiatives". ~ Deepak Pargaonkar
Customer-facing functions have emerged as the primary beneficiaries of AI adoption. Areas such as customer service, sales, marketing, and digital commerce are seeing early and meaningful integration of AI agents.
These agents enable round-the-clock engagement, addressing a key limitation of traditional operations. This capability is increasingly important as customer expectations evolve. Recent data indicates that around 75% of customers expect immediate responses from businesses, making real-time engagement a critical factor in customer satisfaction and retention.
The effectiveness of AI systems is directly linked to the quality of the underlying data. According to Pargaonkar, organisations must ensure that their data is accurate, contextual, and governed within clear safeguards. Without these elements, AI systems risk producing unreliable outcomes.
This perspective is supported by industry research, which shows that over 80% of AI project failures are linked to poor data quality or inadequate data governance. As a result, building strong data frameworks is becoming central to any AI strategy.
The growing presence of AI is reshaping workforce requirements rather than eliminating jobs. The focus is increasingly on developing AI literacy across functions and equipping employees with skills related to data, prompt engineering, and compliance.
Global trends reinforce this shift. Estimates suggest that 44% of workers’ skills will need to be updated by 2027 due to technological advancements. In this context, the ability to effectively collaborate with AI systems is becoming a critical workplace capability.
For organisations, the success of AI initiatives will be measured across multiple dimensions, including revenue growth, cost efficiency, productivity, and overall experience. Enterprises that clearly define these parameters and align their AI strategies accordingly are more likely to achieve sustained impact.
"The success of an organisation entirely depends on the type of data available for its AI efforts."~ Deepak Pargaonkar
This also requires a structured approach, identifying the right use cases, implementing governance frameworks, and continuously measuring outcomes against business objectives.
While AI systems can generate human-like outputs, they do not fully replicate creativity, empathy, or relationship-building. These remain inherently human strengths. Leaders, including Marc Benioff, have suggested that future organisations will be defined by their ability to manage both human employees and AI agents effectively.
“When humans and agents are performing business activities together to drive outcomes for customers and organisations, that is what we call an agentic enterprise.” ~ Deepak Pargaonkar
The emergence of the agentic enterprise signals a broader shift in how businesses operate. The focus is moving beyond adopting AI as a standalone tool towards integrating it into core processes in a meaningful and measurable way. Organisations that prioritise data quality, define clear outcomes, and prepare their workforce for this transition are better positioned to realise the potential of AI while maintaining the human edge.
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