As the AI landscape evolves from Generative AI to Agentic AI, organizations are preparing for a shift that automates decision-making and streamlines processes. While GenAI focuses on creating content, Agentic AI takes autonomous actions to drive outcomes, requiring tighter integration with company systems.
As companies explore this next phase, they are expected to witness significant revenue growth and improved customer engagement.
Dikshant Dave, Founder, of Zigment AI, explained the difference between the two technologies, “GenAI is responsible for creating content like text-based articles, images, videos, etc, whereas Agentic AI takes it a step further by acting on behalf of humans to drive outcomes, autonomously managing processes with real-time decision-making.”
According to a PwC report titled “Agentic AI – the new frontier in GenAI”, its evolution can be broken down into three phases — the integration of Machine Learning (ML) in the 2000s, which allowed agents to learn from large datasets, improving decision-making and performance abilities. Advances in Natural Language Processing (NLP) enabled agents to understand and generate human language more effectively, making interactions more natural and intuitive.
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In the 2010s, multimodal agents that could process and integrate information from various sources, like an agent analysing a text description, recognising objects in an image, and understanding spoken commands, emerged. These agents could also interact with users more dynamically by providing visual aids in response to text queries or understanding context from a combination of spoken and visual inputs.
Currently, agents can operate independently, rationalise and set their own goals, develop paths to attain said goals and make independent decisions without constant human intervention by leveraging data from multiple sources or synthetic datasets. According to the report, these agents can also think fast, provide slow reasoning, and make optimal decisions in real time, which is crucial for applications like autonomous vehicles and real-time customer service.
Companies like Microsoft and Salesforce are also leveraging Agentic AI technology, with Microsoft deploying six AI-powered Security Copilot Agents for cybersecurity tasks and Salesforce introducing Agentforce to automate customer support, sales, and marketing functions at scale.
Kamal Kanth, VP of Sales, Salesforce India, shared that the usage of GenAI or agentic AI is dependent on the maturity of the organization and what it wants to achieve.
“GenAI hit us sometime back. Salesforce was working on predictive AI for a while, which was primarily around numbers. But now, with languages, text, photographs, and the whole gamut coming into play, the possibilities are multitude.”
Organizations that are nascent in their maturity lifecycle, and want to solve simple, repetitive tasks like drafting sales emails, and creating call summaries, are leveraging GenAI.
However, AI agents take it a step further. They tackle the critical challenges—things the system can learn and evolve from. In essence, it is shaping, nurturing, and training the system to respond in a way that closely mirrors human behavior.
He continued, “GenAI solves mundane things, but agentic AI can do everything; it can go wall to wall. One must prioritize the use cases, draw out the KPIs, skill the people, and move forward. This is a clear inflection point and will be massive.”
GenAI is still gaining widespread adoption, and companies are accustomed to its capabilities. While it is now widely used by organisations in the content creation process, Agentic AI represents a natural evolution, where AI takes proactive actions based on insights and real-time context.
According to the report, Agentic AI systems can give organizations a competitive advantage by automating complex workflows, cutting operational costs, and enhancing decision-making. These systems are built to adapt to shifting business landscapes, boosting productivity and helping companies stay ahead. For instance, Agentic AI can anticipate market trends and customer preferences, enabling businesses to refine their strategies proactively.
These systems can also handle large volumes of data and extract actionable insights, which can be used to optimise operations and enhance customer experiences. By automating routine tasks, these systems free up human resources to focus on more strategic initiatives, thereby increasing overall organisational agility and responsiveness.
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“While Agentic AI may have a higher upfront cost due to its complexity, its long-term value lies in automating entire workflows, providing efficiency, and reducing operational overhead, he explained. Since Agentic AI needs a tight integration with the company’s technology and process stack, it is slightly more resource-intensive than Gen AI which operates more like an independent tool,” Dave added.
Alongside, Agentic AI’s transformative potential may shine in industries with high volumes of leads, long sales cycles, and constant customer engagement. Sectors like real estate, healthcare, and finance will see massive benefits, as Agentic AI streamlines lead qualification, engagement, nurturing, and follow-up, ultimately driving higher conversion rates and improving customer satisfaction. In other verticals like e-commerce, and retail logistics, Agentic AI can be deployed for operational tasks, he said.
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Published on March 30, 2025
