Agentic AI refers to autonomous agents that can reason, act, and make decisions on behalf of teams promise faster execution, lower costs, and entirely new ways of working. Unlike traditional AI, which follows pre-defined rules and requires oversight, agentic AI has the ability to learn, adapt, and make decisions on its own – giving it true "agency" in how it operates.

But while the promise is compelling, most enterprises are struggling to move from experimentation to real impact. Many organizations are still unclear on what agentic AI actually is, where the technology stands today, and how to prepare for implementation. As a result, early deployments are often more expensive than expected, produce inconsistent results, and struggle to demonstrate measurable business impact or proper governance.

This challenge is widespread. Gartner predicts that more than 40% of agentic AI projects will be cancelled by 2027, and Fortune reports that 95% of generative AI pilots fail. 

The issue isn’t lack of interest – it’s lack of readiness. At Mimica, we believe successful agentic AI adoption starts with process intelligence. Before enterprises deploy autonomous agents, they need a clear, organization-specific understanding of how work actually happens day-to-day.

Below, we outline a practical five-step framework to help enterprises prepare their processes and their teams for agentic AI that delivers real results.

Step 1: Analyze and document enterprise processes as they exist today

Agentic AI cannot operate effectively without a clear understanding of real-world workflows.

The first step is gaining objective visibility into how work is performed across teams and systems – not based on documentation or assumptions, but on actual user behavior. By leveraging task mining, a technology that captures the user-level activity that powers business processes, enterprises can quickly gain an end-to-end understanding of enterprise operations. 

This includes capturing user actions across desktops, categorizing tasks into named processes, and measuring time spent, frequency, and variation at each step.

With this level of insight, organizations can:

• See end-to-end processes as they truly exist today
• Identify bottlenecks, rework, and hidden complexity
• Establish a factual baseline for improvement and automation

This foundation is critical. Without it, agentic AI decisions are built on incomplete, inaccurate, and non-organization-specific representations of work.

Step 2: Eliminate, simplify, and standardize processes before automating them

It’s common for transformation teams to want to move quickly into automation. But in practice, many processes aren’t ready to be automated yet.

Before introducing any form of intelligent automation, it’s important to assess whether steps can be streamlined, standardized, or eliminated altogether. This helps avoid automating a broken process and scaling inefficiency instead of value.

Once processes are visible through task mining, the next step is to improve them before introducing any forms of intelligent automation. That means:

• Removing unnecessary or redundant steps
• Simplifying overly complex workflows
• Standardizing execution across individuals and teams

Enterprise processes often vary widely from person to person. Agentic AI performs best when processes are stable and repeatable, not when agents must learn from inconsistent steps and edge cases. Standardization reduces risk, lowers costs, and creates the conditions AI needs to operate reliably at scale.

Step 3: Identify which tasks can be handled by agentic AI vs. other forms of automation 

Not every task should be handled by an agent. With standardized processes in place, enterprises can evaluate which steps are best suited for:

• Agentic AI
• Generative AI
• Robotic Process Automation (RPA or rules-based automation)
• Intelligent Document Processing (IDP)

Task mining helps distinguish between tasks that are structured and repeatable versus those that require judgment, emotional intelligence, or ethical oversight. Mimica analyzes the level of structure within each process – including the number of structured and semi-structured steps, decision points, and decision paths – to determine how automatable a process truly is. 

Based on this analysis, Mimica assesses how easily automation can be deployed, recommends the most effective automation technology to use, and quantifies the potential time savings to help teams understand expected ROI.

This ensures agentic AI is applied with intention where it delivers value, rather than broadly or blindly.

Step 4: Turn “happy path” workflows into training data for agentic AI

Once AI-appropriate tasks are identified, the next challenge is training agents effectively.

Rather than learning in production, agents can be pretrained using “happy path” workflows, the most common and efficient version of a process across all variants discovered during task mining. 

Mimica creates detailed process definition documents (PDDs) that can be exported to give AI developers the inputs needed to build reliable automation models. These PDDs include all:

• Actions
• Inputs
• Decision points
• Exceptions
• Variants
• Business rules

This approach reduces variability, improves decision accuracy, and results in agents that are more reliable from day one.

Step 5: Measure conformance and ROI of process improvements with continuous monitoring

No automation, including agentic AI, is truly set-and-forget forever.

Continuous monitoring allows enterprises to track whether processes and agents are performing as expected. Common metrics to keep track of include:

• Cycle time reduction
• Error and exception rates
• Process conformance across teams
• Operational cost savings

This visibility also supports governance and oversight, ensuring agents remain aligned with business objectives and compliance requirements as processes evolve.

Agentic AI success is built on process intelligence

The excitement around agentic AI is understandable. But technology alone isn’t enough.

Enterprises that succeed will be the ones that invest in understanding and improving their processes first. Process intelligence provides the organization-specific knowledge that generic AI agents lack, turning AI from a black box into a measurable, governable capability.

This five-step framework helps bridge the gap between promise and reality, laying the groundwork for agentic AI that delivers consistent business impact.

Ready to prepare your processes for agentic AI?

If your organization is exploring agentic AI but struggling to move beyond pilots, process intelligence is the missing foundation. Learn how Mimica helps enterprises understand real workflows, identify AI-ready tasks, and deploy agentic AI with confidence by requesting a free demo.