Abhi Maheshwari is the CEO of Aisera Inc.
Businesses have been quick to adopt AI (especially generative AI) to create content, images and much more. But when it comes to real, day-to-day challenges for an enterprise, generative AI can understand but not execute. Large organizations need AI that can work across domains, handle tasks end-to-end and make smart decisions on its own.
That’s where agentic AI brings a huge promise—designed to take action without human input. This could be the next big step for companies optimizing complex operations.
Realizing AI Has Its Limits
Generative AI has done a lot to boost creativity and handle small content-creation tasks. But in larger organizations, things aren’t so simple. Most companies rely on dozens, sometimes hundreds, of interconnected systems and generative AI isn’t meant to manage it. In my conversations with leaders, I’ve often heard that AI needs to do more than create content—it needs to streamline processes to execute end-to-end tasks.
This could be part of the reason Gartner predicts that 15% of all day-to-day work decisions will be made by agentic AI systems as soon as 2028.
As companies ramp up their AI investments, I’ve noticed many now look for more integrated solutions like agentic AI. Some even have multi-year roadmaps, planning for systems that link everything across departments. But it’s a gradual shift—especially for companies that are still getting comfortable with how much autonomy they want their AI to have.
From Content Creation To Active Partner
In many companies, repetitive tasks can be draining and lead to high turnover. Generative AI can help in some capacities but still requires humans to confirm decisions. Agentic AI can make a difference by handling these routines autonomously, freeing up employees to focus on more rewarding work. Moving from passive support to a task-focused approach also improves workplace morale—employees aren’t bogged down by tedious, manual tasks.
This shift turns AI from a simple tool into a real partner. Agentic AI can complete tasks without needing constant oversight, which makes it a great fit for today’s busy work environments. But it’s not a magic fix. I’ve found that certain tasks, especially complex ones, still need human attention and fine-tuning.
Moving Past One-Off Wins
For AI to be truly useful, companies need to see clear value. Over half of the leaders in a recent Gartner survey struggle to prove the ROI of generative AI. Agentic AI on the other hand, is task-oriented. It is capable of automating whole workflows across different parts of the business, bringing value across the entire organization. Unlike traditional workflows that depend on manual, rules-based programming or predefined paths, users can generate workflows through natural language leveraging large action models, eliminating the need for workflow chart design and maintenance. I’ve seen that companies can find more value by connecting departments through agentic AI, which helps them unlock larger optimizations across the organization. This could be why 92% of executives in a recent IBM poll agreed that their organizational workflows will be digitized to leverage AI-enabled automation in 2025.
Agentic AI often relies on smaller, specialized models, which are more affordable to train and deploy than larger generative ones. These smaller models add a layer of domain-specific intelligence that can help with “hallucinations” or errors. Agentic AI is evolving; in my experience, choosing the best use cases early on can bring consistent results more quickly than exploring and testing a wide range of uses.
Breaking Down Silos Across Departments
In many companies, each department often operates on its own, creating barriers to efficiency. Agentic AI helps by connecting different departments, bringing everything together into a single, streamlined system. This is especially valuable in industries like healthcare, manufacturing and finance, where multiple teams need to work together closely.
Gartner research suggests that 80% of large enterprises will embrace this kind of enterprise-wide AI to cut down on inefficiencies. From what I’ve seen, this approach is becoming essential, not optional. But readiness is key; some companies need time to adjust their systems, and many take a phased approach to fully integrate AI across departments.
Making Automation Easy For Everyone
One of the best things about agentic AI is that it’s accessible. Many companies are short on developers, so having a low-code, natural language interface is a big help despite its criticisms. I’ve seen how this makes it easier for non-technical teams to set up workflows and get started with automation quickly. Teams in HR and customer support can set up AI-driven workflows independently, reducing strain on IT teams and speeding up adoption.
For most enterprises, security, cost and scalability are major concerns when it comes to AI. Agentic AI, which often relies on smaller, domain-specific models, usually costs less than larger systems while still meeting security standards. This lets companies handle high volumes of tasks without driving up costs. And for industries that need to follow strict rules, like finance or healthcare, agentic AI can be configured to stay compliant.
As security and compliance standards change, companies also need to keep an eye on how their AI is performing. In my experience, staying compliant is an ongoing process, especially for companies that rely on precise data management.
A Final Word
Agentic AI is more than a trend—it’s a path for companies to bring AI into their everyday operations. While generative AI can handle certain tasks, agentic AI integrates directly with a business’s systems, improving productivity, freeing up employees and connecting departments. For companies ready to make AI a part of daily work rather than a novelty, agentic AI offers a solid way forward. Full integration can take time, but the results are worth it.
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