AI agents, not chatbots, will define enterprise tech’s next decade: Salesforce India VP

The landscape of enterprise artificial intelligence is evolving swiftly, moving beyond mere chatbots to sophisticated 'agentic systems' capable of executing complex tasks with minimal human involvement. This paradigm shift is already influencing hiring practices, workflows, and decision-making processes within organizations. In a recent statement, Salesforce CEO Marc Benioff highlighted how AI-driven coding tools are enhancing productivity, leading to a reduced need for additional engineers. He noted, "I’m not hiring more engineers in FY26 because I was using coding agents, which provided me with the extra capacity I needed for the year." This reflects a broader trend where AI is not just assisting workers; it is taking on significant responsibilities itself. Salesforce refers to this transformation as Enterprise General Intelligence (EGI). Deepak Pargaonkar, Vice President of Solution Engineering at Salesforce India, explained that EGI is not a singular model but a comprehensive capability designed to integrate AI throughout business operations. "It requires an operating system that provides agents with governed data and context, encodes existing business logic, and offers visibility into agent activities," he stated. This strategy revolves around what Salesforce describes as an 'agentic enterprise' stack, which is organized into four key components: context, work, agency, and engagement. Pargaonkar elaborated that the aim is to turn raw intelligence into actionable outcomes and promote a collective effort where numerous agents and humans collaborate on intricate tasks across various sectors. As companies transition from experimentation to full-scale deployment of AI, the momentum is palpable. A recent Salesforce CIO study revealed a staggering 282% increase in AI implementation in 2025, indicating that businesses are increasingly integrating AI into their core functions rather than relegating it to the sidelines. Notably, India stands at the forefront of this shift, with 91% of Indian sales professionals recognizing AI agents as crucial for business success. Despite this progress, many organizations still face hurdles, particularly those stuck in pilot phases. Challenges around data readiness and governance remain significant barriers. Pargaonkar cautioned, "AI agents are only as capable as the unified, real-time information they access, and leaders who grasp this are advancing more rapidly." Adoption rates vary across industries, with financial services, healthcare, and manufacturing leading the charge towards agentic systems. Pargaonkar noted that the structured workflows in these sectors, combined with existing data infrastructures and regulatory frameworks, make them prime candidates for agent-driven automation. However, deploying enterprise AI at scale is not without its complexities. Customization continues to pose a challenge, which Salesforce is addressing through its Agentforce ecosystem—featuring prebuilt agents, templates, and industry-specific solutions. The AgentExchange marketplace already includes nearly 800 reusable agent assets from over 160 partners, aimed at accelerating deployment efforts. Despite these advancements, Pargaonkar emphasized that customization remains essential. He argued that organizations should invest time in equipping agents with the necessary business knowledge, configuring them for specific workflows, and ensuring data readiness, as these factors ultimately determine performance. As enterprises adopt agentic systems, the implications for the workforce become increasingly significant. Benioff's comments on hiring trends reflect a broader recalibration in the tech sector. Pargaonkar views this shift as a transformation rather than a reduction in jobs, stating, "The talent pyramid is evolving, creating more opportunities than it displaces." He pointed out that the real challenge lies in how organizations rethink work design, emphasizing the need for employees to acquire new skills to manage and collaborate with AI agents. This transition will enable human efforts to focus on higher-value tasks that AI cannot replicate, such as judgment, creativity, and orchestrating large-scale human-AI collaboration. As enterprise AI progresses from simple chat interfaces to autonomous operations, success may hinge on companies' ability to seamlessly integrate agents into their workflows.