What is robotic process automation (RPA)?

RPA increases enterprise speed without compromising security, compliance, or control

RPA automates high-volume, repetitive tasks with precision, enabling manual tasks that can take hours to be executed at near-instant speeds and high volumes. Since RPA is inherently rules-based, automations maintain strong security, compliance, and control measures by following policies and protocols exactly while generating a complete audit trail of actions.

Security is also bolstered via RPA by eliminating opportunities for human error or exposure of sensitive data. And, technology silos are overcome with RPA’s seamless integrations and speedy data transfers across software tools regardless of technology, function, or department.

RPA’s efficiency lowers operating costs and improves the worker experience, but its value grows immensely as RPA, human workers, and agentic AI are orchestrated across high-volume business processes.

RPA improves process and decision accuracy for faster innovation

RPA executes tasks without human intervention, eliminating the potential for human error. APA adds intelligent orchestration to employ AI agents to make decisions and handle exceptions with high accuracy, only calling on human workers for critical exceptions, validations, or decisions that require HITL oversight. The result is near-perfect process accuracy and data extraction accuracy, at scale, to improve downstream insights, decisions, and outcomes.

RPA automates mundane, repetitive, and error-prone work and processes, elevating human workers into strategic, higher-value decisions and activities that spark ideas and drive innovations. APA then orchestrates RPA with agentic AI to improve those decisions, ideas, and innovations by eliminating technology silos and seamlessly connecting software tools across the enterprise. This provides deeper, more accurate, and more useful information and insights to human workers.

RPA frees human workers to focus on rewarding, fulfilling, and valuable work like solving problems, building relationships, and innovating at all levels.

RPA accelerates enterprise scalability with effective governance

Effective governance ensures compliance, data privacy and security, and, ultimately, customer satisfaction. RPA automates tasks while adhering to strict, rules-based compliance standards, performing actions exactly as prescribed every time — and generating a complete, detailed audit trail of every action for end-to-end governance. Enterprises can then quickly scale automations confidently, knowing RPA executes tasks reliably and compliantly.

For complex and end-to-end processes, APA relies on RPA’s dependable, trusted execution to ensure strict adherence to policies and procedures, enabling APA to orchestrate agentic AI to accomplish broader, more complex goals without compromising trust. RPA remains a critical element of overall governance, and can even automate governance tasks such as tagging and classifying documents, extracting information from documents and systems, and transferring documents and information to locations specified by governance policies.

RPA enables repeatable, trusted, compliant execution of enterprise tasks, providing a governed automation foundation APA builds on to quickly scale automations enterprise-wide.

The advent of generative AI builds on RPA and other AI technologies to enable even faster, easier automation development and interaction, such as natural-language automation assistance, automated generation of functional automations, and resilient, self-healing automations. AI now enables RPA to automatically recover from changes to underlying applications, reducing execution failures by over 50%.

Generative AI is also the engine of AI agents, a significant technology advancement harnessing the cognitive capabilities of large language models (LLMs) to make decisions, learn from data, interact with humans through natural language, and ultimately take action to achieve goals. Enabling action requires connecting AI agents with applications and systems where they can get work done, such as sending an email or saving a report. At the process level, agentic workflows can use RPA to execute tasks quickly and accurately.

New APA platforms use RPA and agentic AI to build, deploy, and manage AI agents easily. Architected for enterprise privacy, security, and compliance, APA platforms address the governance needs and complexities of enterprise deployments, including orchestrating thousands of automations with central management and oversight.

APA enables agentic workflows — RPA, AI agents, and humans working collaboratively and connecting with other AI agents to perform end-to-end processes as part of multi-agent systems that are forever changing the face of enterprise automation.

Put human workers’ needs at the center of planning, bring in HR early, and involve a broad cross-section of stakeholders, leaders, and automation champions who can influence change. Diverse input will help shape a robust, inclusive strategy that boosts adoption, so ask lots of questions — both internally and externally. Reach out to trusted partners, vendors, and other organizations already on the path to becoming an autonomous enterprise.

Start strong by creating an organization-wide center of excellence (CoE). The CoE should focus on effectiveness and governance and will be responsible for developing standards to ensure compliance, security, and continuous improvement across the automation life cycle.

Design the CoE as a hub of expertise, established to help the entire organization get up and running with consistent, high-quality automations by propagating best practices, tools, and templates.

Choose a vendor that aligns with your long-term automation and business goals. APA's success hinges on workforce adoption, which means selecting software that is both technically robust and user-friendly for all employees, not just IT specialists. Key considerations include:

• Pricing model: Transparent cost structures help you avoid unexpected expenses as your APA deployment scales. Look for flexibility in pricing, including options for unlimited bot usage.
• Innovation: APA and AI technologies are rapidly evolving. Look for vendors with proven experience and stability and that are committed to continuous innovation. Prioritize vendors with dedicated teams focused on evolving RPA and advancing APA capabilities, including migration tools, orchestration, and AI integration.
• Support: Robust customer support, comprehensive training programs, and active community engagement will enable a successful automation journey.
• Security: One of the core benefits of APA is strengthening security, which relies on the security architecture and certifications a solution adheres to. If relevant, consider vendors that offer vertical-specific solutions for industries with stringent data security requirements, such as government, financial services, and healthcare.
• Flexibility: Select solutions that enable RPA, agentic AI, and full APA orchestration for flexibility in task execution and complex, end-to-end process automation. APA should also integrate seamlessly with existing systems, including legacy applications and cloud services. Choose vendors that invest in AI-driven features to improve automation efficiency and adaptability.
• Accessibility: Consider APA platforms that offer low-code or no-code development options that enable technical and non-technical users to easily create and use RPA- and AI-driven automations. Look for training and tools that encourage adoption yet maintain IT oversight to ensure quality, security, and governance.
• Scale: Design automations with growth in mind. Even if beginning with small pilots, ensure that platform infrastructure, licenses, and governance frameworks can adapt as automation needs expand. Look for solutions that dynamically scale to accommodate increasing workloads and user demands without compromising performance.

Determine which processes will yield the greatest ROI through automation. Many enterprises choose to start small, automating tasks that are part of a larger process. RPA applied in this way is referred to as attended automation; it allows workers to become familiar with automations firsthand while providing a foundation for eventually using APA and AI agents to automate more complex tasks and processes.

Whether it’s RPA automating a simple task or APA orchestrating AI agents and human workers to automate an end-to-end, mission-critical process, track automation performance, uptime, and ROI. This data is crucial to optimizing processes and refining automation strategies. Ensure APA solutions include management features for monitoring, configuration, and governance of automated processes. Regularly gather user and stakeholder feedback to refine automations and address post-deployment opportunities and challenges.

RPA might be the automation entry point, but APA is the goal. Make sure any RPA is part of a complete agentic automation fabric that considers everything from APIs to agents to analytics. This ensures RPA remains instantly adaptable, infinitely scalable, and always up to date in the journey to becoming an autonomous enterprise.

Process discovery and optimization

Enterprises often struggle to accurately identify and map existing processes or face challenges when attempting to optimize them due to a lack of visibility into process nuances and bottlenecks. This can lead to missed automation opportunities — for RPA deployments and more modern APA initiatives.

Process challenges can easily be overcome by selecting an automation platform with integrated AI tools for discovering, documenting, and visualizing processes granularly. Automation Anywhere offers AI-powered process discovery insights that reveal how work really gets done, helping organizations identify, understand, capture, and optimize processes across the enterprise.

Data structuring

Without APA and AI-derived insights, RPA can only operate effectively with structured data. Unfortunately, most business data is in unstructured formats, like emails and documents. This limits RPA effectiveness and pushes more exceptions to human workers for evaluation and rework.

Agentic AI understands unstructured data, captures and organizes information, and injects relevant data into RPA and AI-driven automation workflows. Automation Anywhere’s Document Automation uses natural language processing (NLP), computer vision, generative AI, large language models (LLMs), and machine learning (ML) to accelerate document classification, data extraction, and validation across any document type.

Governance

A lack of governance models can lead to automation inconsistencies that undercut scalability, whether those automations are RPA- or AI-driven.

Effective governance ensures the reliability, compliance, and operational effectiveness of automation programs, supporting scalability and helping to protect organizations and their customers from risks like disruptions and data breaches. Automation Anywhere’s CoE Manager centralizes oversight from the earliest stages of process discovery through ongoing ROI, with guardrails, visibility, and clarity that ease governance and compliance.

Maintenance and resilience

Enterprises may not have enough skilled personnel to maintain RPA. That challenge is exacerbated as organizations rush to deploy APA and agentic AI automations, and as business applications advance and evolve in ways that disrupt rigid, rules-based RPA. Without the right skills, enterprises face automation downtime, costly reliance on external resources, and risky workarounds.

New tools use generative AI to recognize changes and update automations quickly. Automation Anywhere’s Automator AI lets organizations build self-repairing, reusable automations that adapt to application UI changes to reduce automation maintenance costs.