Agentic AI is the latest buzzword in enterprise technology, promising to streamline operations, cut costs, and drive agility. But despite the growing hype, there’s one major hurdle — there’s no clear consensus on what an AI agent actually is.
Prem Natarajan, Chief Scientist and Head of Enterprise AI at Capital One, recently likened discussions about agentic AI to the classic parable of the blind men describing an elephant: “Everybody’s touching a different part of the elephant. Their description of it is different,” he told The Wall Street Journal.
The comparison is fitting. From AI vendors to enterprise buyers, the industry is full of differing definitions, ambiguous use cases, and a lot of hype. From our vantage point, the hype is warranted — provided enterprises take the right steps to prepare. Agentic AI isn’t just another tech trend — it has the potential to unlock transformative value across functions.
Read on to learn how enterprises can prepare for agentic AI, but first, let’s clear up some of the most persistent misconceptions.
1. “Agentic AI is just a smarter chatbot.”
It might look like a chatbot. You might talk to it like a chatbot. But agentic AI is doing something fundamentally different.
In a recent webinar on the topic of AI agents, Gartner’s Tom Coshow highlighted what separates agents from chatbots: their ability to reason and act autonomously. If a system is just responding to direct user input, it’s not truly agentic. The difference is that agents make decisions for you — not just with you — based on context, goals, and learned behavior.
So while your AI assistant helping you order lunch might feel like a chatbot, if it’s selecting a restaurant based on your schedule, dietary preferences, and past behavior — then placing the order and updating your calendar — it’s acting as an agent.
2. “We’ll worry about this when the tech matures.”
The reality is, agentic technology is maturing faster than most enterprises are adapting. Gartner recently polled webinar participants on whether they had deployed agents. Only 6% said yes. Not because the technology isn’t ready — but because most organizations aren’t.
Agentic readiness requires process discovery and optimization, new governance models, and fresh thinking about how decisions are made and monitored. Waiting too long might mean missing the opportunity to reshape how work actually happens.
3. “Agentic AI will replace all human work.”
We’ve all heard the alarm bells — AI is coming for our jobs. But in most enterprise contexts, what we’re seeing today are assistive agents, not fully autonomous ones.
Speaking to The Wall Street Journal, Robert Blumofe, CTO at Akamai, describes most current use cases as assistive agents: tools that still require human input or oversight. That’s not a flaw — it’s a feature. Agentic AI provides the most value when it augments human work: resolving complexity, following up on outliers, and making micro-decisions across systems.
Agentic AI is not about replacement, it represents a meaningful shift in how work gets done — from static automation to adaptive systems that can reason, decide, and act. For enterprises, the potential benefits are significant: greater operational efficiency, faster cycle times, improved customer responsiveness, and a more empowered workforce freed from repetitive, manual tasks.
4. “We can simply plug agentic AI into our existing automation strategy.”
It’s easy to assume agentic AI can fit into existing automation strategies — the kind built around RPA bots, scripted workflows, and tightly defined process rules. But that approach has limits. Traditional automation is rule-based — it follows fixed steps with predictable inputs and outputs. It’s designed for stability and repeatability, not adaptability.
Agentic AI works differently — it’s goal-driven, capable of reasoning, and adapts in real time based on context. That difference matters. It challenges conventional approaches to orchestration, monitoring, exception handling, and compliance. Trying to plug agentic AI into a rigid, rule-based framework without rethinking the overall structure is likely to create more problems than it solves.
5. “Being ready for agentic AI is just about having the right tech.”
Yes, you need the right models, APIs, and infrastructure. But tech capability is only one piece of the puzzle. True agentic AI readiness also involves:
- Processes structured to support AI-driven decision making and autonomy
- Clear criteria for when agents should act vs. assist
- Cultural comfort with machines making decisions
- Governance frameworks that ensure transparency and control
At the core of all of that is process understanding. The problem? Most enterprises still rely on manual discovery methods — interviews, workshops, and documentation reviews — that only capture a partial view of how work actually happens. Without a complete picture, it’s nearly impossible to identify agent-ready workflows or determine where autonomy will create the most value.
This is where task mining and gathering process intelligence becomes essential.
How task mining prepares your enterprise for agentic AI
Momentum around agentic AI is growing — but only organizations that take an intentional, structured approach will be positioned to benefit. Before agents can act intelligently, enterprises need a clear, data-driven understanding of how work actually gets done.
Mimica’s task mining technology captures the real, nuanced behaviors of your teams — the tasks across disconnected systems, exceptions, and decision points that make up day-to-day work. With this visibility, organizations can:
- Understand and map human decision-making patterns with precision
- Eliminate, simplify, and standardize processes before automating
- Determine which tasks AI can autonomously handle
- Pinpoint where autonomy will drive the most value
- Turn happy path insights into actionable AI training data
- Measure process conformance and ROI through continuous monitoring
In short, Mimica lays the foundation for agentic AI by automatically mapping end-to-end processes and identifying where automation technologies — including AI, RPA, IDP, and OCR — will be most effective. If you’re exploring the shift from automation to autonomy, this process clarity is an essential starting point.
See how Mimica can help you build a strong foundation for agentic AI — request a demo today.
