Abstract
Agentic AI isn’t just a new tool. It’s a quiet shift in how work gets done. In this article, Shyam Ravindranathan draws from firsthand experience building and deploying these systems to explain what agentic AI really is, how it differs from traditional automation, and where it’s already delivering results across industries. With practical insights and a grounded perspective, this piece offers a clear-eyed look at the opportunities, challenges, and next steps for teams exploring this new model of enterprise intelligence.
After years of working with automation, I thought I understood AI.
Then I saw an example of an agentic system analyze a requirement, rewrite a business process, and implement the update on its own. No alerts. No approvals. It just happened.
That was the moment I realized this wasn’t just another step forward in enterprise software. It was a shift in how work actually happens.
In this article, I’ll explain what agentic AI is, how it’s already being used across industries, and what I’ve learned from building and working with these systems. I’ll also share practical insights for teams looking to get started, and why the companies exploring this now are positioning themselves to lead what's coming next.
What Agentic AI Really Means
Let’s keep it simple. Agentic AI is a type of artificial intelligence that can take a goal, make decisions, and take action on its own. It doesn’t wait for someone to tell it what to do, step by step. You give it direction, and it figures out how to get there.
The best way I explain it is this. Traditional automation is like a GPS that you feed instructions into. Agentic AI is like a skilled driver who understands the destination, watches for traffic, notices roadblocks, and adapts in real time.
That shift, from following instructions to acting with intent, is what sets agentic AI apart.
And business leaders are starting to notice. According to an IBM study, 75% of CEOs see AI as a future competitive advantage, and 50% say it’s already being integrated into their operations.
What Makes Agentic AI Different
There are five qualities that I consistently see in effective agentic AI systems. These are the traits that make them valuable in real-world situations.
1. It Acts Independently
Once I have set a business objective, the system runs with it. I have experienced setups where agentic AI has optimized procurement schedules, adjusted budget allocations, and coordinated vendor responses without requiring anyone to babysit the process.
2. It Adapts in Real Time
Things change. A shipment gets delayed. A team misses a handoff. Agentic AI doesn’t freeze or wait for someone to intervene. It recalculates and moves forward, keeping work on track.
3. It Works Across Teams
Most AI tools stay locked inside a department. But agentic systems move across finance, HR, logistics, and compliance. They coordinate between systems and workflows, which reduces delays and enables faster decision-making.
4. It Spots Problems Early
This is one of my favorite features. These systems pick up signals that something’s off before the impact is obvious. I’ve seen agentic AI catch compliance issues, operational slowdowns, and even potential churn long before a human team would have flagged them.
5. It Tailors Its Actions
Agentic AI isn’t one-size-fits-all. In specific uses, it may use real-time and historical data to make decisions that fit the specific context. That might mean adjusting onboarding tasks for a new hire or offering targeted product recommendations to a returning customer.
You might think of it this way:
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If you're dealing with... |
Agentic AI helps by... |
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Constantly changing rules or regulations |
Interpreting new requirements and updating processes |
|
Repetitive decision-making across large volumes |
Making consistent, data-driven decisions at scale |
|
Projects involving multiple departments or systems |
Coordinating workflows and handoffs without delays |
|
Sudden disruptions in operations or supply chains |
Responding instantly and keeping work on track |
|
The need to tailor experiences for users or teams |
Adapting actions to real-time behavior and context |
A 2023 Deloitte analysis underscored this shift, emphasizing that agentic AI has the potential to automate not just discrete tasks, but entire workflows, especially in knowledge work.
Where Agentic AI Makes a Real Difference
I have been fortunate to work on AI initiatives for multiple enterprise use cases. Here’s where I have seen agentic AI not just work, but deliver real value.
Manufacturing
Factories are using AI to monitor machines, adjust production schedules, and predict failures. One setup I worked with reduced downtime just by letting the system schedule maintenance based on patterns it picked up automatically.
Healthcare
Hospitals are using agentic systems to manage bed allocation, schedule staff shifts, and reduce patient wait times. The AI helps them keep care moving, especially when human capacity is stretched thin.
Logistics
Shipping agents make real-time adjustments to delivery routes based on traffic, weather, or warehouse conditions. These decisions occur in seconds, not hours, and that makes a significant difference in both cost and customer satisfaction.
Retail
I’ve seen agentic AI match marketing promotions to live inventory, adjust prices mid-campaign, and personalize offers across regions. It helps retailers stay responsive without having to manually recalculate every variable.
Finance
Banks and fintech platforms are using these systems to detect fraud, generate reports, and ensure compliance. One system I worked with saved days of manual audit prep each month by handling documentation and exceptions ahead of time.
Customer Support
According to Gartner, by 2028, a third of enterprise software will have built-in agentic AI features. This includes the ability to handle up to 15% of routine decisions without involving human agents.
What Companies Need to Watch Out For
I’m excited about the possibilities, but I’m also clear-eyed about the challenges.
Accountability
When an AI system makes the wrong call, who’s responsible? Companies need to be intentional about oversight, especially in regulated environments.
Trust
I’ve seen firsthand that people hesitate to rely on systems they don’t understand. If teams can’t follow how a decision was made, they’re less likely to trust or adopt the solution.
Compatibility
Many companies still run on complex, patchwork systems. For agentic AI to work well, you need a solid data foundation and systems that can talk to each other. Integration matters.
According to McKinsey’s 2023 report, 38% of companies expect to reskill over 20% of their workforce in the next three years due to AI adoption.
How To Get Started
You don’t need to overhaul everything to try agentic AI. In fact, I often advise teams to start small.
Pick one process that’s complex, repetitive, and often gets delayed. Compliance, internal onboarding, ticket routing — those are all good candidates.
Make sure your data is organized and your systems are connected. Then give the AI enough context and rules to act, but also keep some oversight in place. Let the team see how it works, ask questions, and build confidence.
It’s not just about tech. It’s about helping your team trust the system enough to let it do what it’s good at.
Why Now and What’s Coming Next
So why is this moment different? Because the pieces have finally come together.
We’ve advanced large language models and information retrieval models, such as RAG, and the infrastructure to power these AI models. We’ve got platforms that can act across applications. And most importantly, we’ve got more data than ever to learn from.
Looking ahead, I expect we’ll see:
- Multi-agent networks where different AI systems work together like teams
- Better tooling to monitor and adjust agent behavior in real time
- New governance models that keep AI aligned with human goals and legal requirements
- Expanded use beyond operations into sales, HR, procurement, and customer experience
We’re only scratching the surface of what this can do.
What I’ve Learned So Far
Agentic AI is not just an upgrade to what we already had. It’s a new way of thinking about how work gets done.
It gives us systems that don’t just execute tasks. They solve problems. They support teams. They make businesses more resilient without adding more complexity.
And the organizations exploring this now? They’re not just trying to keep up. They’re building an operating model that’s ready for what’s next.
About the Author
Shyam Ravindranathan is Senior Director of Product Management at SAP, where he leads AI-driven enterprise solutions focused on compliance and automation. He has worked across industrial IoT, edge computing, and generative AI, holds multiple patents, and co-authored a BookAuthority-recognized title on IoT development. His work centers on building practical, human-focused AI products that deliver real business value.
References
- IBM (2023). Three of Four CEOs See AI as Competitive Advantage. IBM via Investopedia.
https://www.investopedia.com/three-of-four-ceos-see-ai-as-competitive-advantage-7554248 - Deloitte (2023). Autonomous Generative AI Agents Are Still Under Development—But Could Transform Workflows. Deloitte Insights.
https://www2.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/autonomous-generative-ai-agents-still-under-development.html - Gartner (2024). What Is an Intelligent Agent in AI? Gartner.
https://www.gartner.com/en/articles/intelligent-agent-in-ai - McKinsey & Company (2023). The State of AI in 2023: Generative AI’s Breakout Year. McKinsey.
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-AIs-breakout-year
