By Srikant Sowmyanarayan, Head of Solutions and Presales – Generative AI Business Services, Happiest Minds Technologies
By 2025, AI has matured into an agent of tasks. Enterprises have moved from systems that created content, to systems that take action, based on the goals of your business, as well as design and execute workflows. Imagine being able to ask your system to “scan supply chain metrics, order low stock, schedule delivery, and notify impacted stakeholders” – and have it all happen. This is the destiny of Agentic AI, and is already changing the way organizations manage automation and productivity.
The proliferation of AI emerging technology has been rapid. Generative AI is focused on creating anything new that is text, code, visuals, audio, etc. based on prompts and collaboration has been one of the leading contributors to the global private investment of $33.9 billion in Gen AI in 2025 and has been widely adopted in all sectors. This technology has improved speed in creativity, research and content production, enabling organizations to accelerate their thinking.
Agentic AI advances automated functions by taking action. These systems are autonomous, goal-driven agents that can plan, make independent decisions, and execute a series of multi-step actions across systems and connected systems. Rather than responding to a prompt, to accomplish a task, the Agentic AI actually accomplishes that task, enabling deeper automation and dynamic and adaptive decision-making.
What are Generative AI and Agentic AI?
Generative AI and Agentic AI are two separate – but often interrelated – paradigms. Generative AI excels in authoring or creating content from prompts, while Agentic AI involves taking autonomous actions to achieve objectives in complex workflows that involve multiple steps.
Real-World Use Cases: Where Each Shines
Generative AI: Creation & Synthesis
Generative AI has completely transformed how organizations innovate and create. By running collections of information through large datasets, Generative AI can generate new text, designs, simulations, and even complete code bases, acting like a creative copilot to assist teams in moving faster.
In pharmaceuticals, Generative AI is accelerating drug discovery by simulating generative molecular design. Scientists now use AI to design exciting new molecular structures with properties such as higher efficacy or lower toxicity–things that required years of lab time and preparation. Things that took months or years of trial and error can be simulated, at the very least, things that took months and years of trial and error can now be simulated in days, accelerating research and development timelines to record lows.
In media and entertainment, Generative AI enables marketers to do hyper personalized marketing programs at scale. Global companies–like Coca Cola–have already demonstrated how AI can create localized artwork, ad copy, and visual assets for thousands of local micro campaigns all at once. Rather than produce a single, universally accepted ad campaign, now, marketers can personalization marketing assets by instantly customizing text and creative visuals to appeal to regional dialect, age, gender and even time of day to maximize consumer engagement and conversion through personalization.
Further, in the realm of software development, GenAI has been a significant multiplier to productivity. Tools like GitHub Copilot and Gemini code assist engineers in producing code, documentation, and even translation between programming languages. The result: increased development speed, consistent quality of code, and reduced grunt work.
Agentic AI: Self-Executive Action & Goal-Oriented Performance
Agentic AI is the next step to advances in data science – from construction to self-execution. They act as intelligent digital workers capable of managing a vast array of complex multi-step workflows. In banking and financial services, Agentic AI enables autonomous function for trading and portfolio management. Given a strategic objective like “maximize return within an acceptable risk parameter,” it can perform autonomously by monitoring market signals, executing traders’ decisions by rebalancing assets and adjusting portfolios, all in real-time. It can operate across exchanges and data sources and within milliseconds, constantly defining, prioritizing, and executing trading decisions. The result is timely decisions, fast execution, and a broadly consistent portfolio.
In logistics and supply chain, Agentic AI enables autonomous orchestrating of operations. When an anomaly occurs – like a port closure or a weather event – specialized agents can immediately identify the anomaly, collaborate to reroute shipments, and update downstream inventory systems. This real-time capability reduces a company’s downtime at numerous junctures, it improves customer commitments, and is designed to remove manual effort during crisis management workflows.
In the areas of customer service and sales, Agentic AI is changing the SDR workflow as we know it. They can now scan the CRM data, identify warm leads, write an outreach email (sometimes under the guidance of GenAI), track responses, and even schedule meetings for the reps. Over time, these agents learn which outreach methods initiate the most business and become better at their jobs – improving efficiency, closure rates, and all-around business success.
Futurum Research estimates that agentic AI will create up to USD 6 trillion in economic value by 2028. Even today, agents are being deployed into enterprises with platforms like Salesforce Agentforce, Microsoft Copilot Agents, and IBM watsonx Agents.
Future Considerations and Strategic Outlooks
The difference between Generative AI and Agentic AI is starting to fade. We are heading toward a future version of generative models being the “thinking engine” of agentic systems. It will not be Generative AI versus Agentic AI. Intelligent systems will reason, create and act across business ecosystems.
For this to happen, there will be a need for interoperable systems and common standards. There are frameworks such as the Model Context Protocol (MCP) and metadata standards like AgentFacts already laying the groundwork for a transparent and plug-and-play agent ecosystems to provide trust, transparency, and safe collaboration for agents between platforms.
These changes will also bring changes to organizations. Jobs or roles such as AgentOps Engineer, AI Auditor, and Chief AI are likely to be commonplace. Governance, risk frameworks, and skills will all change to support these adaptive continuously learning systems.
We are transitioning from “help me write this” to “go do this for me.” From assistive to autonomous.
For AI executives, the process will be easy –
> Start small, then scale safely. Test Agency AI across any low-to-medium risk applications.
> Establish early governance. Safeguards, approvals, and auditable logs – these are not optional.
> Invest in reusable architecture. Think of modular interoperable frameworks vs silo prototypes.
> Measure for what matters. Consider business impact and reliability vs vanity metrics.
> Monitor rapidly. Standards and responsible AI implications.
The future of AI in business will be an active journey vs passive. The organizations that can move from the bare-bones generation of content, to the autonomous orchestration of everything will not only be exceptional but will step into the next chapter of enterprise transformation.
How to Get Started with Agentic AI
Adopting Agentic AI can seem daunting, but a structured approach helps organizations unlock value quickly while managing risk. Here’s a step-by-step guide to launching your Agentic AI journey:
1. Define Your Vision and Objectives
Clarify the “why”: Identify the business goals Agentic AI should support (e.g., automating supply chain, enhancing customer service, accelerating R&D).
Align stakeholders: Ensure leadership, IT, and business teams share a common vision for agentic transformation.
2. Assess Readiness and Identify Gaps
Evaluate current infrastructure: Determine if your data, systems, and workflows can support autonomous agents.
Spot gaps: Traditional, siloed, or batch-based systems may need upgrades for real-time, cross-functional orchestration.
3. Start with High-Impact, Low-Risk Use Cases
Pilot in targeted domains: Begin with processes that are repetitive, rules-based, and have clear APIs (e.g., IT ticket triage, customer support, financial reconciliation).
Demonstrate quick wins: Early success builds momentum and trust across the organization.
4. Build the Right Team and Skills
Cross-functional collaboration: Bring together business owners, IT, data scientists, and process experts.
Upskill your workforce: Invest in training for prompt engineering, agent design, and human-AI collaboration.
5. Establish Governance and Guardrails Early
Embed responsible AI practices: Set up policies for transparency, explainability, and human oversight.
Monitor and iterate: Use feedback loops to refine agent behavior and ensure alignment with business goals.
6. Scale and Integrate
Expand successful pilots: Gradually roll out agentic solutions to more complex or mission-critical workflows.
Leverage modular architectures: Use reusable frameworks and APIs to accelerate adoption across business units.
7. Measure Impact and Continuously Improve
Track business outcomes: Focus on metrics like efficiency gains, cost savings, and customer satisfaction.
Foster a culture of innovation: Encourage experimentation and learning as agentic AI matures.
One of the most effective ways an organization will change its approach toward agentic AI is by instituting an “Agentic AI Center of Excellence” (CoE). A CoE allows organizations to form a strategic hub pooling cross-functional expertise from IT, business, data science, and operations. The CoE will enable centralization of knowledge, best practices, and governance that accelerates the journey from adoption, duplication of effort, and alignment with organizational goals.
The Center of Excellence will steer the development of re-usable frameworks, set the standards in mentoring with responsible AI principles, and interface with training and support as an agentic solution is being tested on the small scale and upscaled. As an innovation trigger, it will prepare teams to safely experiment, share lessons learned, and improve processes on an ongoing basis. In effect, an Agentic AI CoE promotes organizations to go beyond pilot projects by enabling a culture of collaboration and continuous learning, an essential ingredient for sustained transformation that occurs at an enterprise level.