Enhance Intelligent Automation and RPA with Agentic Process Intelligence 

Intelligent automation platforms like UiPath, Automation Anywhere, Blue Prism and Pega are built to execute work, and KYP.ai’s agentic process intelligence enhances them by showing what to automate, why it is worth it, and grounding the AI agents that act, so your automation rests on how work actually gets done rather than on guesswork. 

Key takeaways: 

• Intelligent automation and RPA platforms are strong at executing automations, but they are weak at deciding what to automate. Their built-in process mining sees the system, not the people. 

• Legacy process mining and task mining modules miss the frontend: the desktop work, application switching, and manual steps between transactions where most automation opportunity actually sits. 

• Agentic process intelligence captures that frontend ground truth, quantifies the ROI of each opportunity, and feeds AI agents the context they need to act reliably. 

• KYP.ai is the agentic process intelligence layer for intelligent automation. It is platform agnostic by design, so it enhances UiPath, Automation Anywhere, Blue Prism, Pega and the rest of your stack rather than replacing them. 

Most enterprises have already bought their automation platform. The bottleneck is no longer execution. It is knowing what to automate, proving it is worth doing, and giving AI agents enough context to act without breaking. This guide covers the intelligent automation landscape, the insights gap that legacy process and task mining leave open, and why agentic process intelligence is becoming the new best option for AI-driven automation. 

The intelligent automation and RPA landscape in 2026

Intelligent automation combines robotic process automation, AI and orchestration to handle work that once needed a person. The market is mature and consolidated, and a handful of platforms lead it. 

UiPath 

The largest RPA ecosystem, ranked at the top of Gartner’s RPA Magic Quadrant for several years running. UiPath pairs its automation suite with a native Process Mining module that turns system data into dashboards of bottlenecks and root causes, positioning itself as a broad automation platform with discovery built in. 

Automation Anywhere 

A cloud-native, AI-first automation platform. It added process mining through its acquisition of FortressIQ, giving it a way to analyse and map processes and surface automation candidates inside its own ecosystem. 

Blue Prism (SS&C) 

An enterprise-grade, governance-heavy RPA platform popular in regulated industries. Blue Prism does not offer a native process mining product, and instead relies on partnerships with specialist process mining vendors to feed its discovery. 

Pega 

Less a pure RPA tool than a process orchestration and case management platform with RPA built in, Pega suits complex, long-running workflows that span many systems and decisions. 

Each of these is strong at the execution half of automation. Where they are consistently weaker is the decision half: working out which processes are worth automating, in what order, and with what expected return. That weakness traces back to how their discovery actually works. 

The insights gap legacy process mining and task mining leave open 

Most automation platforms answer the “what should we automate” question with process mining, and some bolt on task mining. Both have the same structural limit. 

Process mining software reconstructs a process from system event logs. It is strong for backend, transaction-heavy flows that live inside an ERP or CRM, but it can only analyse what the system recorded. A large share of knowledge work never reaches an event log: the emails, the spreadsheets, the lookups in a second application, the copy-and-paste between screens, the manual decisions and workarounds between transactions. Industry estimates put the portion of knowledge work invisible to event-log-based mining at roughly 70%, and that invisible portion is exactly where a lot of automation opportunity hides.  

Legacy task mining tools tries to see the desktop, but the common implementations break where enterprises need them most. Screenshot-based capture creates privacy exposure, is non-deterministic, and depends on manual start-stop activation that leaves the data incomplete. It rarely scales past a pilot, and it almost never connects what it sees to a dollar value. 

So the gap is twofold. The discovery is partial, because it cannot see most of the human work. And it is unquantified, because it tells you what you could automate without telling you what you should, ranked by return. For rule-based bots that was tolerable. For AI-driven, agentic automation it is not, because an agent dropped into a process it does not understand does not just underdeliver. It acts wrongly, with confidence. 

What agentic process intelligence adds 

KYP.ai’s agentic process intelligence closes both halves of that gap. It captures the frontend ground truth: a continuous, observed record of how work actually gets done across every application and desktop, the part legacy mining cannot see. Then it does two things that turn observation into action. 

First, it quantifies. Every inefficiency and automation opportunity arrives with the ROI attached, so you can separate what you can automate from what you should automate. This is the distinction that decides whether an automation programme pays back. RPA platforms are built to answer can. Agentic process intelligence answers should. 

Second, it grounds agents. AI agents are only as good as the context they can reach, and the context they need is how the work really runs, including the exceptions and the manual steps. Agentic process intelligence supplies that ground truth, and turns it into production-ready agent code rather than a dashboard a human still has to interpret. The result is automation that understands the process it is acting on. 

Why KYP.ai is the agentic process intelligence layer for your automation stack 

KYP.ai provides desktop-centric process intelligence: it captures how work actually gets done, quantifies the ROI of every opportunity, and generates the context and agent code that AI-driven automation needs. For an organisation that has already invested in an automation platform, three things make it a fit rather than a rip-and-replace. 

• It is platform agnostic by design. The agent code KYP.ai generates is deployable on whatever you already run: UiPath, Automation Anywhere, Blue Prism, Pega, Power Automate, SAP Joule, n8n, Camunda, ServiceNow, or CrewAI. You enhance your stack, you do not lock yourself into a new one. 

• It answers should, not just can. KYP.ai quantifies the business case for each opportunity, so automation budget goes to the processes with the highest return instead of the ones that happened to be easy to see. Every initiative arrives with a number, not a gut feeling. 

• It is privacy-by-design, which enterprise automation requires. Sensitive data is anonymised at source, on the workstation, before it ever leaves the device, with granular configuration over what is and is not captured. No sensitive information is processed or transferred externally. GDPR, SOC2 Type II, and ISO27001 follow from that architecture. 

Where conventional process discovery shows a fragment of the process and stops at a chart, KYP.ai captures the whole picture, attaches a value to it, and hands your automation platform something it can act on. That is the difference between automating what is visible and automating what matters. 

What this looks like in practice 

Atos ran KYP.ai as its own Client Zero. The team had governance but not enough visibility to underwrite ROI on its automation pipeline. Using KYP.ai, they identified 56% automation potential and more than 400 use cases across business functions, and recorded a 25% FTE productivity improvement in Purchasing, working through a See, Shape, Scale framework. As Pete Evans put it: “Visibility made ROI defensible. ROI gave us investment discipline.” 

The lesson for any team running an automation platform is that the platform was never the constraint. Knowing what to point it at, and being able to defend that choice with numbers, was. 

The bottom line 

UiPath, Automation Anywhere, Blue Prism and Pega are capable platforms for executing automation. What none of them fully solves is the decision that comes first: what to automate, in what order, and with what return, grounded in how work actually gets done. Their built-in process mining sees the system. Their task mining, where they have it, struggles to scale and rarely attaches a value. 

Agentic process intelligence fills that gap, and KYP.ai is the layer built for it. It captures the frontend ground truth legacy mining misses, quantifies the ROI of each opportunity, and grounds the AI agents that act, all while remaining platform agnostic so it strengthens the automation stack you already own. As enterprises move from rule-based bots to AI-driven automation, that grounding stops being optional. 

Setup takes minutes. Deployment takes days. Most environments see statistically relevant insight within three weeks. Book a demo to see how KYP.ai enhances your intelligent automation platform with agentic process intelligence. 

Frequently asked questions about intelligent automation