How to leverage RPA and AI to achieve intelligent automation

RPA and AI/ML development have run on separate tracks in most enterprises for years. RPA moved data and clicked buttons on schedule. AI sat in a handful of Data Science projects that rarely touched production workflows. That separation is closing fast. McKinsey's latest state-of-AI survey found that 62% of organizations are experimenting with or piloting AI agents, yet no more than 10% are scaling them in any given business function. The tools are proven. The path from pilot to production is not.

N-iX has run this exact transition inside client engineering and operations teams for years, first as an RPA implementation partner, then as those same clients asked us to bring AI into the same bots. Every major RPA vendor has since rebuilt its platform around this pairing, and the vendor landscape looked meaningfully different a year ago than it does today. This guide covers what changed. It walks through what each technology now does for the other, what the current generation of platforms actually ships, and how to measure whether any of it is working. It also covers how to structure a rollout so an unproven approach is identified early, before it becomes an expensive one.

Key takeaways

• RPA still owns structured, rules-based tasks. AI now owns the judgment calls, exception handling, and unstructured data that used to stop a bot cold.
• Combining RPA and AI is what actually produces intelligent automation. Neither technology alone covers the full range of a real business process.
• Every major RPA vendor has rebuilt its platform around an orchestration layer in the past year, coordinating AI agents, bots, and human reviewers in one governed workflow rather than shipping them as separate tools.
• Vendors are also relabeling existing RPA and chatbot products as "agentic" without adding real autonomy. Businesses need to test for genuine reasoning, not just a new name on the same automation.
• A baseline measured before deployment is what separates a provable ROI claim from a guess, whether you are running RPA, a coding assistant, or a full agentic workflow.
• N-iX runs AI adoption using the APEX framework across all our engineering engagements, scaling only what the numbers justify.

How RPA and AI are combined

Although RPA excels at automating rules-based tasks, it is limited in its ability to recognize patterns or understand unstructured data. AI succeeds in exactly these areas, bringing pattern recognition, data analysis, and decision-making capability into the automation process.

Combining both technologies is what creates intelligent automation: RPA handles structured processes, and AI manages unstructured data and complex decisions. Deloitte describes this convergence as agentic process automation, and draws a clear line between the two: RPA automates well-defined systems and tasks, while AI agents can reason across dynamic, unstructured workflows that traditional automation was never built to handle. That is also why the two technologies need each other rather than one replacing the other. An AI agent with no RPA layer underneath it still needs a way to reach into legacy systems, log every action, and execute the routine parts of a workflow without spending a frontier model's reasoning budget on a form field.

Here's how these technologies enhance each other's capabilities.

RPA enhancing AI capabilities

• Data acquisition: RPA bots can quickly gather, clean, normalize, and label data from multiple systems, providing high-quality training data for AI models and accelerating AI development.
• Legacy system integration: Some legacy systems lack the connectors or APIs needed to interact with AI directly. RPA bridges that gap, enabling smooth operation across different technology generations.
• Understanding AI reasoning: RPA can track the steps an AI model takes, offering transparency and helping explain how a specific conclusion was reached. This overview supports review of AI decision-making processes.
• Human-in-the-loop (HITL): Acting as a safety net, RPA can flag potentially questionable AI outputs for human review. If AI approves a loan for a customer with a poor credit history, for instance, RPA ensures compliance by flagging it for further human assessment.
• AI supervision: RPA continuously monitors AI systems for errors, bias, and performance drift, flagging data anomalies to maintain AI accuracy and reliability over time.

AI enhancing RPA capabilities

• Exception handling: AI identifies and resolves exceptions that might otherwise halt an RPA process, such as unexpected data formats or errors, keeping automation running and reducing downtime.
• Cognitive automation: Through pattern recognition and data analysis, AI mimics human judgment, enabling more accurate, insightful outcomes and expanding RPA's functionality beyond simple rule-based automation.
• Self-optimizing processes: AI can analyze past workflow performance and suggest improvements to RPA processes, driving continuous optimization rather than waiting for a scheduled redesign.
• Predictive capabilities: Using historical data, AI predicts potential process issues and recommends preventative action, improving overall efficiency and reducing the likelihood of future problems.
• Advanced data processing: AI's ability to analyze and interpret unstructured data, including images, text, and speech, allows RPA to execute more complex tasks, broadening the range of automatable processes.

This is also where the two technologies start to blur into a single system rather than two tools bolted together, which is exactly the shift the next section covers.

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From RPA to agentic process automation

The newest layer on top of this pairing is agentic process automation (APA), where AI agents do not just execute a fixed script but reason about which action to take next and coordinate multiple tools and systems to get there. Gartner projects that at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from effectively none in 2024. Moreover, 33% of enterprise software applications will include agentic AI by the same year, up from under 1% today.

That momentum comes with a clear warning attached. Gartner also predicts that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. It also estimates that only about 130 of the thousands of vendors marketing "agentic AI" products actually deliver meaningful autonomy. The rest are relabeling existing RPA bots and chatbots, a pattern the firm calls agent washing.

The real payoff comes from applying agentic AI across the enterprise as a whole, not to isolated individual tasks. Agents make sense at the points where a genuine decision has to be made, routine steps are better left to standard automation, and simple lookups belong to assistants rather than agents.

That framing matters for anyone deciding where to spend the next automation budget: not every workflow that could take an AI agent should get one. A well-tuned RPA bot with a narrow AI assist is often the cheaper, more reliable answer, and reasoning capability should be reserved for the steps that actually require it.

Deloitte's own 2025 Tech Value Survey of roughly 550 cross-industry leaders found a similar confidence gap: 80% of respondents believe their organization has mature capabilities in basic automation. However, only 28% say the same about automation paired with AI agents. On expected payback, 45% expect basic automation to return its investment within three years, against just 12% who expect the same from automation plus agents in that timeframe. The gap is not a question of whether the underlying technology works. It reflects how much longer the maturity curve is than most budgets assume, and it is exactly what shows up in how differently the major platforms have been rebuilt over the past twelve months.

What AI functionalities do AI-powered RPA platforms have?

AI-powered RPA platforms use several advanced technologies to extract information and act on it across structured, semi-structured, and unstructured processes.

Intelligent data extraction

AI-powered RPA platforms parse letters, invoices, receipts, and other structured documents, ensuring comprehensive data extraction across different formats. This capability extends to parsing and understanding semi-structured data from forms and reports, and unstructured data from emails and PDFs. For visual data, computer vision reads and interprets text on images and handwritten notes, converting them into structured data to prepare high-quality input for AI models. Document understanding capabilities significantly reduce manual effort and minimize errors in data extraction.

Task mining

Task mining uses AI to analyze and understand employees' actual work, pinpointing repetitive and rule-based processes that will benefit from automation. Task mining tools monitor user activity and gather data to identify the best candidates for RPA implementation, automating the identification process and ensuring the most suitable tasks are chosen. It also helps continuously refine and improve workflows based on real-time data. In a finance department, for example, task mining can analyze employee interactions with financial software to identify repetitive tasks such as invoice processing and data entry.

Generative AI

Generative AI models learn and adapt over time, improving performance based on new data and feedback. This self-learning capability makes RPA bots more efficient and accurate, reducing the need for constant human intervention and updates. Generative AI also automates content creation, freeing human resources for strategic tasks while keeping communications consistent in quality and tone. A customer service department, for instance, can use generative AI to draft responses to customer inquiries automatically, providing quick, accurate, and personalized support.

AI model integration

Integrating AI models into RPA workflows enables advanced analytics: models that analyze large datasets to predict trends, detect patterns, identify anomalies, and suggest optimal actions. This lets RPA bots perform more complex tasks and make informed decisions, adapting workflows to changing data and conditions in real time. In supply chain management, for instance, AI model integration can predict demand trends and optimize inventory levels, automating the reordering process and reducing excess stock.

Agent orchestration

This is the capability that did not exist in most platforms a few years ago, and it is the one every major vendor has invested in most heavily over the past year. Rather than one bot performing one task, an orchestration layer coordinates multiple AI agents and RPA robots across a long-running, cross-system process, with a human retaining oversight and authority at defined checkpoints. It is what turns a set of individually useful bots and models into a single, governed workflow, and it is the capability the platform comparison below is really built around.

RPA AI capabilities by platform

UiPath

UiPath's core advantage is reading unstructured input, documents, images, audio, and screens, and turning it into something a bot can act on. It reads text, sees what's on a screen the way a person would, and can have one robot manage a whole team of other robots to keep large-scale automation running smoothly.

Since 2025, UiPath has added a coordination layer on top of that: it can now run its own bots alongside AI agents from other providers, such as Google or Microsoft, inside a single workflow that keeps a full record of who did what. In practical terms, that means you are no longer locked into using only UiPath's AI. You can mix in outside AI tools and still have one governed, auditable process, rather than several disconnected ones.

Automation Anywhere

Automation Anywhere's capability has traditionally been turning manual, repetitive work into bots and using AI to flag which processes are worth automating in the first place, based on how employees actually work.

More recently, that same intelligence has moved from flagging opportunities to making decisions inside a live process. Where the platform used to point a person to a candidate process and let them build the automation, it can now let an AI agent decide how to handle a specific case as it comes in. A central coordinator keeps multiple agents and bots working from the same plan, instead of stepping on each other.

Tungsten Automation (formerly Kofax)

Tungsten Automation, formerly known as Kofax, is built around reading and classifying documents accurately at high volume: invoices, forms, contracts, correspondence. That has long been its strongest use case, and it remains one of the more reliable options for document-heavy processes.

Its newest capability extends that from reading one document to understanding a whole case. Instead of only pulling fields out of a single file, an AI agent can now reason across a full set of related documents, say, every file tied to one insurance claim or one loan application. That lets it understand what the case actually means, not just what each individual page says.

Microsoft Power Automate

Power Automate's advantage is how deeply it sits inside tools most enterprises already use: Microsoft 365, Azure, and Power BI. It has long supported building chatbots and catching problems early, such as flagging a likely equipment failure before it happens.

Its newest update lets a single step inside an otherwise fixed, reliable workflow get handed off to an AI agent when that step needs judgment rather than a fixed rule, then hand control back once it's done. For an IT team, the practical benefit is that this runs under the same security and oversight tools they already use to manage the rest of their Microsoft environment, so adding an agent doesn't mean adding a separate thing to govern.

Across all four platforms, the direction is the same: reliable, rule-based automation as the foundation, AI added for the steps that need judgment, and a way to keep the whole thing auditable. What changed in the past year is that this stopped being a future promise for any of them. It's available now, and organizations are already running it.

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How to implement intelligent automation?

At N-iX, we deliver a comprehensive suite of RPA services designed to fit our clients' diverse needs. Our expertise in RPA and AI gives us two distinct approaches, so we have a fit whether your company is new to RPA or already experienced in automation strategy. Both approaches share one rule we hold to on every engagement: the criteria for stopping or scaling a project get written down before the project starts, not after the results come in.

Option 1: Pilot project for RPA and AI implementation

Our first approach is for businesses looking to explore the potential of RPA with a pilot project, allowing them to test and refine before scaling.

1. Building a business case for automation. We help you select a specific process or task that can benefit from automation. This process is typically repetitive and rule-based but has a clear, measurable impact. We also identify where AI can enhance the RPA solution, such as incorporating ML models to handle exceptions or improve data extraction accuracy, and we set the kill criteria for the pilot in writing before it starts.
2. Proof of Concept. Our engineers partner with you to integrate an RPA solution that fits your business case. We conduct a thorough process assessment and build a proof of concept to confirm feasibility and effectiveness, selecting the most suitable technology stack for your existing infrastructure and integrating the required AI functionality.
3. Solution implementation. N-iX deploys your AI-powered RPA solution in a live environment, ensuring bots are configured correctly and integrate seamlessly with your existing systems. AI components are trained and validated to deliver accurate, reliable results.
4. Performance evaluation and monitoring. We continuously monitor bot performance, tracking accuracy, efficiency, and cost savings against the baseline captured before deployment. AI models are monitored to ensure they function optimally and improve over time, and we gather feedback to assess the impact on your business operations.
5. Scaling. If the pilot demonstrates significant benefits against the criteria set in step one, N-iX helps you plan to scale the RPA and AI solution across other processes and departments, using AI insights to continuously optimize the automation strategy. If it does not, we say so, and stopping there costs a fraction of what scaling an unproven workflow would.

Option 2: Strategic RPA and AI implementation

The second approach is designed for companies with clear automation objectives and KPIs, ready to implement a comprehensive RPA strategy from the outset.

1. Business objectives validation. While your company already has defined goals and KPIs, N-iX works with you to validate and refine these objectives so they align with your overall business strategy, whether that means reducing operational costs, improving accuracy, increasing productivity, or enhancing customer satisfaction.
2. Building automation strategy. We conduct a comprehensive analysis to identify multiple processes that align with your automation objectives, evaluating each process's complexity, volume, and potential ROI. Our engineers use AI tools to discover and prioritize processes based on data patterns and operational insights, and build a detailed plan covering steps, timelines, resources, technology, and the go/no-go criteria for each phase.
3. Deployment. We implement the RPA and AI solution according to the detailed plan, developing and testing the bots, training AI models, integrating them with your existing systems, and incorporating continuous AI learning to adapt to change.
4. Monitoring, optimization, and scaling. N-iX monitors the solution's performance against the predefined KPIs, conducts regular reviews and optimizations, and scales successful implementations across your organization, using AI-driven insights to fine-tune and expand the strategy.

Both paths run on the same principle we apply across every AI engagement at N-iX: measure before you change anything, fund the next phase only once the last one proves its case, and put governance into the architecture rather than treating it as a step to revisit later.

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Why choose N-iX for AI-powered RPA solution implementation?

As a partner of UiPath and Automation Anywhere and a services provider for Kofax and Pega, we offer a broad spectrum of RPA tools to fit your specific needs.

• N-iX has a team of more than 200 data engineers with deep expertise in AI/ML, Data Science, and data analytics.
• As a Pragmatic AI Software Engineering company, we ensure every AI initiative, including RPA and AI rollouts, is measured on your actual codebase and actual processes before it scales.
• Our APEX framework (Assess, Pilot, Expand, eXcel) embeds AI into every stage of delivery, RPA and AI included, with a measurable evidence gate at each phase. No workflow scales across your organization until the data justifies it.
• We work according to agile methodologies, suited to the iterative nature of RPA implementation and AI/ML model training, enabling flexible, responsive delivery.
• With 160 active clients and 23 years in the industry, N-iX has a proven track record of forming lasting partnerships across various sectors.
• As an enterprise-focused company, we serve Fortune 500 organizations and global industry leaders, including Lebara, Gogo, Currencycloud, and Fluke Corporation.
• N-iX maintains the highest industry standards, adhering to PCI DSS, ISO 9001, ISO 27001, and GDPR, keeping our solutions secure and compliant with global regulations.

FAQ

What is the difference between RPA and agentic process automation?

RPA automates well-defined, rules-based tasks using fixed scripts. Agentic process automation adds AI agents that reason about which action to take next, handle unstructured data, and coordinate across systems and tools, with human oversight built into the workflow rather than removed from it.

Is RPA still worth investing in now that AI agents exist?

Yes, for the same reason it always was: RPA remains the fastest, cheapest, and most auditable way to execute structured, repetitive tasks. AI agents extend what RPA can touch; they do not replace the value of a reliable rules-based bot for the parts of a workflow that do not need reasoning.

How do you know if an AI-powered RPA rollout is actually working?

Track outcome metrics against a pre-deployment baseline, not activity metrics. Cycle time, cost per transaction, error rate, and exception-handling rate tell you whether the workflow changed. License counts and bot execution volume tell you the tool is present, not that it is working.

How long does it take to see results from an RPA and AI pilot?

A well-scoped pilot on real production work should produce a directional signal within a few weeks to a couple of months. Meaningful, board-reportable outcomes, built on a documented baseline and a full process cycle of data, typically take longer, which is why we recommend measuring utilization first and ROI only once the workflow has had time to stabilize.

Which processes should be automated with RPA versus reserved for AI agents?

Reserve RPA for stable, high-volume, rules-based tasks where the steps do not change: data entry, reconciliations, structured reporting. Reserve AI agents for the parts of a workflow that require judgment: exception handling, unstructured document or message interpretation, and decisions that depend on weighing multiple, sometimes conflicting inputs.