Agentic AI Readiness: A Strategic Guide to Preparing Your Enterprise Processes

Introduction

Business leaders are under continuing pressure to do more with less. Demands for efficiency, speed, and innovation are intensifying, even as budgets and headcount remain constrained. Meanwhile, enterprise operations remain complex, with processes that involve variants, exceptions, and real-time decisions. Traditional automation tools weren’t built for this kind of dynamic work. They’re rule-based, inflexible, and fall short when tasks require judgment or adaptation.

Agentic AI is emerging as a response to this pressure. These systems go beyond answering questions or generating content — they can take initiative, make decisions, and autonomously execute tasks across fragmented systems. In doing so, they offer a path toward scalable, adaptive automation that complements human expertise rather than merely replacing manual steps.

Deloitte predicts that 25% of companies using generative AI (GenAI) will pilot agent-based automation this year, with adoption expected to reach 50% by 2027. Yet despite growing interest, enterprise adoption remains limited. Most current efforts focus on narrow use cases — like summarizing content or writing code — rather than rethinking how core business processes could be orchestrated end-to-end by AI agents.

The barrier isn’t technological — it’s organizational. Most enterprise processes weren’t designed with autonomous agents in mind. They're fragmented, inconsistent, and lack the visibility and structure required for agents to act with confidence and context.

This guide provides a strategic roadmap to prepare your enterprise processes for agentic AI, including:

• Where agentic AI delivers value — and where it encounters challenges
• High-impact enterprise use cases
• How task mining and process intelligence uncover process opportunities
• A five-step framework to prioritize and redesign processes for AI readiness

By preparing your workflows first, you reduce implementation risk, accelerate adoption, and position your organization to realize meaningful business value from agentic AI.

What is agentic AI?

Agentic AI refers to artificial intelligence systems that independently complete tasks and achieve objectives with minimal human input. Unlike traditional AI, which follows predefined 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. 

Three ways agentic AI drives enterprise efficiency and cost reduction

AI agents can automate complex, decision-heavy processes in ways that traditional tools can’t, allowing organizations to streamline operations, reduce costs, and respond more effectively to change. Here’s how:

Takes in unstructured data

With advanced image recognition and natural language processing (NLP) capabilities, AI agents are well-equipped to handle unstructured data. They can extract insights from emails, reports, and contracts, as well as interpret visual content like diagrams or scanned documents, enabling them to take action even when data is messy, ambiguous, or incomplete. By reducing the need for manual review and data entry, this ability drives both efficiency gains and cost savings.

Automates decision making

Agentic AI analyzes data, identifies patterns, and determines the best course of action—without requiring constant human input. By processing high volumes of information in real time, agents can weigh options, predict outcomes, and make fast, consistent decisions. This not only accelerates processes but also reduces human error and resource overhead, leading to lower operational costs and faster turnaround times.

Adapts to dynamic environments 

Unlike traditional robotic process automation (RPA) systems that can break when a user interface (UI) changes, AI agents are designed to adapt. By continuously monitoring its context and adjusting to changes—such as a modified form or a new approval step—it keeps processes running smoothly without interruption. This ability to adapt reduces process friction, minimizes downtime, and enables more resilient, cost-efficient operations across the enterprise.

Why enterprises struggle to adopt agentic AI

While out-of-the-box AI agents show promise, they aren't trained on the unique processes, systems, and proprietary knowledge that define most enterprises — making effective adoption a challenge without significant preparation.

Enterprise processes are unique

Compare the claims handling processes at two different Fortune 100 insurance companies, and you’ll find distinct structures, systems, and standards. Processes also vary across regions, departments, and teams. Just as enterprises lack a one-size-fits-all process, no single generic AI agent can seamlessly integrate into these varied environments. 

Enterprise processes are non-public

You can ask ChatGPT how to book a flight, but you can’t ask how an insurance company handles a claim and expect a complete answer because most enterprise processes happen behind closed systems. Claims are also evaluated using proprietary factors like policy coverage and adjuster assessments — information that AI agents typically lack access to, limiting their effectiveness.

In this year’s Gartner® Market Guide for Task Mining Tools, Gartner highlights how task mining has evolved from a back-office tool into a strategic enabler of enterprise AI — particularly in complex, dynamic environments where traditional automation falls short. The report reinforces what we’ve seen firsthand: enterprises need accurate, real-world visibility into how work gets done before they can confidently deploy AI agents. Without this foundation, efforts to scale agentic AI often stall or fail to deliver expected ROI.

Current agents are a black box

Unlike RPA, which rigidly follows predefined steps, AI agents have more freedom — but that flexibility can be a drawback when it’s unclear how or why decisions are being made. With today’s black-box AI agents, users can't easily explain how the model arrived at a specific decision or prediction. This lack of transparency can pose challenges in trust, accountability, and debugging — especially in enterprise settings where decision-making and regulatory compliance are critical. As a result, many enterprises are hesitant to give existing agents full freedom.  

While the core capabilities of agentic AI are promising, their real value emerges when agents are trained on the specific processes, systems, and context unique to the enterprise. Below are a few high-impact use cases where AI agents —correctly applied— can drive measurable improvements across enterprise operations.

Three high-impact use cases for agentic AI

AI agents understand both spoken and written questions, anticipate needs, make recommendations based on past interactions, and resolve complex issues by integrating with multiple systems. Here are three high-impact use cases for AI agents:

Improve handle time in customer service, IT support, and operations

Agentic AI speeds up the resolution of help desk and contact center requests by eliminating repetitive tasks, manual data entry, and time spent troubleshooting. This allows human agents to focus on higher-priority interactions. 

AI agents can:

• Automate workflows — automatically trigger workflows, such as issuing refunds, resetting passwords, or updating account details. For example, a virtual IT help desk can manage common IT requests like password resets, system configurations, or VPN troubleshooting.
• Recommend next best actions — dynamically suggest the best response or action based on historical resolution patterns. A virtual banking agent, for instance, could detect surplus funds in a savings account and recommend transferring the balance to pay off a credit card.
• Automate data entry and form filling — extract relevant details (e.g., order numbers or account information) from customer interactions and automatically populate customer relationship management (CRM) fields. In an insurance scenario, AI can assess claim eligibility, process documentation, and provide real-time claim status updates, reducing human error and manual effort.

Streamline financial close and other Shared Services processes

Organizations consolidate and centralize business functions like finance, HR, IT, and procurement in Shared Services to enhance efficiency, reduce costs, and standardize operations. Agentic AI can optimize Shared Services processes by automating repetitive, rules-based tasks, resulting in faster operations and improved outcomes. 

AI agents can:

• Automate data collection and validation — pull data from multiple enterprise resource planning (ERP) systems, sub-ledgers, and external sources without manual intervention. They can then make context-aware decisions, manage exceptions, and autonomously resolve issues. For example, AI can extract and validate data, flag missing information, detect anomalies, make approvals within defined thresholds, and recommend corrective actions.
• Perform smart matching and intelligent reconciliation — use machine learning to match transactions, reducing the manual effort and errors typical of traditional reconciliation processes. AI agents can identify anomalies, investigate issues, and suggest resolutions. In intercompany reconciliations, AI can flag mismatched journal entries and recommend adjustments.
• Orchestrate processes and cross-functional integration — act as process orchestrators, coordinating and assigning tasks across departments and systems, tracking progress, and ensuring deadlines are met. In a procure-to-pay (P2P) process, AI can ensure approvals are completed, monitor order statuses, manage vendor communications, and autonomously handle low-value procurement requests to reduce cycle times and costs.

Accelerate deployment of ERP, CRM, and other system migrations

System migrations — whether transitioning to a new system, upgrading platforms, or consolidating systems — are complex and resource-intensive, often involving large volumes of data, cross-functional collaboration, and the need to minimize downtime. Agentic AI can reduce manual effort and enhance accuracy, ensuring smoother and more efficient system migrations. 

AI agents can:

• Automate data extraction and cleansing — extract, categorize, and clean legacy system data before migration. AI agents can identify duplicates, inconsistencies, and incomplete records for correction, reducing manual data wrangling and accelerating extract, transform, load processes.
• Validate and reconcile data in real time — continuously validate migrated data against source records, instantly identifying missing, duplicated, or misclassified data. This reduces post-migration cleanup efforts and improves data integrity.
• Perform testing and user acceptance testing — automate test case creation and execution, simulating real-world business scenarios to validate system functionality. AI agents can detect anomalies before full deployment, minimizing rollback risks and ensuring a smoother transition.

These examples highlight the potential of agentic AI to transform enterprise operations — but potential alone isn’t enough. To deliver consistent, scalable impact, AI agents need to operate within well-defined, structured workflows. That’s why prioritizing and redesigning processes is a critical step before deployment.

Why streamlining and standardizing workflows is essential for agentic AI success

Enterprise processes are often complex and varied, involving numerous stakeholders, systems, and dependencies. That complexity is why it’s essential to streamline and standardize workflows before implementing agentic AI. Without a clear understanding of how processes function, introducing AI can lead to inefficiencies rather than improvements. Traditional methods of process discovery, such as manual documentation or basic automation analysis, often fail to capture the full picture, leaving gaps that can undermine AI performance.

Structured is best

AI agents perform best in structured, well-defined environments where decision pathways and data flows are predictable. Not every process is a good fit for AI — focusing on areas where it can deliver the most value is crucial. For example, highly repetitive, data-intensive tasks are typically strong candidates for automation, while processes that involve complex judgment or human empathy may not benefit as much. Attempting to apply AI to sub-optimal or poorly understood processes can lead to disappointing results, with inefficiencies and errors compounding rather than resolving.

Additionally, it’s important to recognize that AI isn’t perfect. Even with sophisticated machine learning models, exceptions and unexpected situations will arise. Effective exception handling pathways must be designed to ensure smooth operations when AI encounters challenges. This often involves maintaining a clear handoff to human decision-makers or secondary systems.

Start small and scale‍

For AI to integrate successfully, workflows should be modular and flexible, allowing organizations to adopt AI incrementally. Starting with smaller, well-scoped implementations enables organizations to refine processes and scale AI solutions over time. Finally, establishing baseline metrics before implementation is essential for measuring AI’s impact. Clear performance indicators — such as cycle time reduction, error rate improvements, or cost savings — provide valuable insights into whether AI is delivering its intended benefits.

By carefully redesigning workflows, identifying the right processes for AI, and preparing for exceptions, organizations can maximize the value of agentic AI while minimizing disruption.

How task mining enables successful adoption of agentic AI

To implement agentic AI, organizations need a clear understanding of how work is actually being performed — not just how it’s documented. Task mining and the resulting process intelligence provide this visibility. By capturing every click, keystroke, and action performed by employees across all systems, task mining enables organizations to identify tasks that AI can handle autonomously — and determine where human oversight remains essential.

Gain complete visibility into how work gets done

Task mining captures and analyzes user actions across desktops, including checking emails, accessing spreadsheets, and navigating enterprise systems. It then categorizes tasks into named processes and measures time spent in each system, providing a clear overview of all work performed within a team or department.

Understand and map human decision-making patterns with precision

Mimica automatically generates end-to-end process, overview step, and click-level maps that include all decision points, actions, exceptions, and variants. By analyzing these patterns, business leaders can identify bottlenecks, escalation paths, and areas for streamlining. In the case of frequent exceptions and deviations, business leaders can also pinpoint where AI agents may require additional training or human intervention.

Eliminate, simplify, and standardize processes before automation

The insights from the as-is process maps to can be used to streamline and optimize workflows by identifying and eliminating unnecessary steps, simplifying complex processes, and removing redundancies that add little to no value. With a unified process in place, you can deploy a single AI-driven automation that scales effectively across the enterprise, reducing variability and maximizing impact. This optimization not only enables scalable AI-driven automation, but also enhances operational consistency and reduces inefficiencies.

Determine which tasks AI can autonomously handle

Process intelligence categorizes tasks and steps by employee time spent, automatability, and ease-of-automation. Organizations can use this data to prioritize the highest-ROI transformation and automation activities. Mimica’s process intelligence provides tailored technology recommendations, indicating where AI, RPA, IDP (Intelligent Document Processing), or OCR (Optical Character Recognition) will be most effective.

Define when AI should intervene and when humans should stay in the loop

With this holistic view of how a process is completed, organizations can identify tasks requiring human judgment, emotional intelligence, or ethical oversight — and establish clear boundaries for AI intervention. This ensures that AI handles repetitive and structured tasks while humans remain responsible for complex decisions.

Turn happy path insights into actionable AI training data

Task mining creates detailed process definition documents (PDDs) that map newly streamlined workflows, capturing both the “happy path” and key intervention points for AI. This structured view gives AI developers the precise inputs needed to build reliable automation models. By pretraining AI agents with a clear understanding of process steps, organizations can improve decision accuracy and continuously refine AI algorithms for better performance.

Measure process conformance and ROI through continuous monitoring

Post-implementation, task mining continues to monitor AI-driven processes to measure their impact. By tracking metrics such as cycle time reduction, error rates, and operational cost savings, organizations gain valuable insights into AI performance. Continuous monitoring also supports adaptive learning, helping developers to improve bots and refine their decision-making capabilities over time.

Organizations can build a strong foundation for agentic AI by using task mining to map existing workflows and identify opportunities for improvement. This strategic approach accelerates AI adoption, monitors its impact, and drives enterprise-wide efficiency and innovation. Read on for a five-step plan to help you get started.

Your five-step plan to improve processes for AI agents

1. Map existing processes across all systems

Identify bottlenecks, inefficiencies, steps requiring excessive human intervention, and repetitive, high-volume tasks with structured inputs.

Key questions to ask:

• Where do delays or bottlenecks typically occur?
• What percentage of the process is rule-based vs. requiring complex judgment?
• How often do manual workarounds happen?
• Is the process better suited for RPA than AI?

2. Standardize and document processes

Define process boundaries, standardize inputs and outputs, and create process documentation (flowcharts, SOPs, decision matrices).

Key questions to ask:

• Are all process steps consistently followed across teams?
• Is there variability in how the process is executed?
• Do we have clear rules or guidelines AI can use for decision-making?

3. Create adaptable modules for AI

Break processes into discrete, automatable components, identify areas for human oversight, and ensure process flexibility for AI-driven adaptation.

Key questions to ask:

• Can this process be broken into modular, automatable components?
• Where should human judgment remain in the loop?
• How will AI adapt to changing business needs?

4. Define exception handling and escalation paths

Identify situations where AI may struggle, set rules for human intervention, and ensure AI decisions are auditable and explainable.

Key questions to ask:

• What are the edge cases AI may not handle well?
• How will humans be notified when AI needs assistance?
• Can AI’s decisions be audited and explained?
  

5. Establish baseline performance benchmarks

Define KPIs (cycle time, accuracy, efficiency gains, cost reduction, compliance adherence), conduct pre-implementation benchmarking, and plan for continuous monitoring of AI performance and conformance.

Key questions to ask:

• How will we measure AI’s impact on process efficiency?
• What historical data do we need for accurate benchmarking?
• How often should we reassess AI performance?

This step-by-step approach helps ensure your processes are ready before introducing AI agents. With the right structure and visibility in place, you can reduce risk, move faster, and get better results. Here are a few key takeaways to keep in mind as you plan your next steps.

Key takeaways for agentic AI readiness

1. Prepare your processes for agentic AI: successful adoption requires a thorough understanding of your enterprise processes. Task mining provides the necessary visibility to map, analyze, and optimize workflows for AI readiness.
2. Focus on high-impact use cases: prioritize processes that are repetitive, data-intensive, and rule-based for agentic AI implementation. Processes like customer service automation, financial close processes, and system migrations are good starting places to pilot an efficiency initiative.
3. Address AI’s limitations with human oversight: while AI is adept at decision-making and automation, human judgment remains essential in managing complex negotiations, creative problem-solving, and relationship management.
4. Redesign workflows for flexibility and scalability: modular, adaptable workflows allow for phased AI implementation, ensuring smoother integration and reducing disruption. Establish clear exception handling protocols to manage AI limitations effectively.
5. Leverage data for continuous improvement: establish performance benchmarks before implementation to measure AI’s impact. Continuous monitoring using task mining ensures AI-driven processes remain optimized and deliver intended outcomes.

Preparing for What’s Next

Agentic AI 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.

But unlocking these gains requires more than technology. It demands a rethinking of how processes are structured, where decision logic resides, and how work flows across systems. Enterprises that take the time to prepare — by gaining visibility into how work is actually performed, identifying opportunities for autonomous execution, and redesigning processes with agents in mind — will be best positioned to lead in this next era of intelligent automation.

Mimica accelerates this journey. By using AI to map and analyze real user behavior, Mimica helps organizations uncover hidden process complexity, identify high-impact opportunities for agentic automation, and build the foundations required for scalable deployment. Whether you're exploring your first agent use case or scaling adoption across business units, Mimica provides the intelligence and precision needed to move with confidence.

Agentic AI is here. Organizations that act now — and lay the right groundwork — will be better positioned to see meaningful results, faster ROI, and smoother adoption as these capabilities mature.

‍Ready to take the first step? Request your free 14-day Mimica proof of concept and discover how process intelligence can prepare your processes for agentic AI.