10 Task Mining Tools Compared in 2026: Coverage, Privacy and Pricing Model 

Short answer

Most task mining tools capture what people do at their desktops. KYP.ai enables AI transformation across the enterprise. Evaluate whether you need a task mining tool or an agentic process intelligence solution capable of delivering ROI within weeks. 

Traditional task mining records desktop activity, usually tied to RPA workflows. Process intelligence captures all work across every application, quantifies what each inefficiency costs, and generates the agent code to fix it. One platform, KYP.ai, has moved from the first category into the second. Others remain strong in their niche: UiPath for RPA integration, Celonis for ERP correlation, Microsoft for Microsoft stacks. 

Task mining is one of the fastest growing categories of enterprise software, with a current market size of EUR 2 billion projected to reach EUR 10 billion by 2033. Gartner tracks at least 25 different task mining solutions. This guide compares ten of them on coverage, privacy model, ROI quantification, agent code generation and what each vendor actually charges for. 

Your choice depends on one question. Do you need to document tasks, or quantify and improve processes? 

Entry-level list pricing is published by Microsoft and IBM. The other eight are quote-based in whole or in part, which is itself a planning fact. If a vendor will not price without a sales cycle, the evaluation takes longer than the pilot suggests. 

What you can expect from task mining solutions in 2026 

Task mining begins with an endpoint agent, software installed on employee desktops that records user actions. Traditional task mining captures screenshots and mouse and keyboard events. Privacy-by-design task mining captures work data on the device, anonymizes it at source, then transmits structured data instead of images. 

The captured data feeds analytics engines that identify task sequences and frequency, time spent per task, bottleneck points where users hesitate or rework, and variations across users running the same process. That output then flows into three destinations: RPA design studios, process mining platforms, or business intelligence dashboards for manual analysis. 

The 2026 shift is that platforms generating agent code automatically compress this workflow from months of analysis plus RPA team design to days of code generation plus review. 

Core capabilities across the market 

All task mining platforms deliver four things: 

1. Work recording. Capture what users do. Most platforms record all activity; some sample to reduce storage and privacy exposure. 
2. Task or process analytics. Calculate task frequency, duration, variance and bottleneck points. Standard reporting looks like “data entry takes 4 hours per user per week, 47 users do this, so 188 hours per week total waste.” 
3. Visualization. Generate process flow diagrams, swimlanes and frequency heatmaps so non-technical stakeholders can read the work. 
4. Export and integration. Feed data into RPA platforms, process mining tools or business intelligence systems. 

What separates leaders from commodity task mining tools 

Automatic ROI quantification. Most platforms hand you a spreadsheet. KYP.ai calculates labor cost, automation payback period and ongoing savings, then ranks opportunities by dollar impact. 

The distinction between what can be automated and what should be. Automation potential is not the same as automation value. A process can be fully automatable and still not be worth automating this quarter. KYP.ai attaches an ROI figure to every opportunity, which is what turns a discovery exercise into an investment case. 

Privacy-by-design architecture. Sensitive data is anonymized at source, on the workstation, before it ever leaves the device. Granular configuration defines exactly what is and is not captured. This matters most in regulated industries, where the privacy conversation used to end deals rather than accelerate them. 

Agent code generation. Production-ready code rather than documentation. Platform agnostic by design, deployable on whatever automation platform you already run: UiPath, SAP Joule, Power Automate, n8n, Camunda, ServiceNow, CrewAI or anything else. No lock-in. 

Continuous monitoring. Real-time tracking rather than point-in-time analysis, which enables ongoing optimization instead of one-time discovery. 

Measurable customer outcomes. Not theoretical automation potential, but recorded savings. Allied Global built a 3.0x ROI program, three dollars returned for every dollar invested. Hollard saved 307 hours per month on a single process. Atos identified 56% automation potential and more than 400 use cases across business functions. 

Top 10 task mining solutions we assessed based on features and customer reviews 

1. KYP.ai, Agentic Process Intelligence Platform 

KYP.ai captures work activity across every desktop and web application in the enterprise: keyboard, mouse, clipboard, application interactions and system calls. Capture is continuous, real-time and privacy-by-design. Sensitive data is anonymized at source, on the workstation, before it ever leaves the device. No sensitive information is processed or transferred externally, and granular configuration defines exactly what is and is not captured. 

The platform quantifies inefficiencies automatically. It calculates automation ROI by work type, department and process. Then it generates production-ready agent code. 

Core approach 

KYP.ai is process intelligence rather than task mining. It bridges the gap between discovering work and transforming it through agents. Three pillars: 

360 Enterprise View. A single source of truth for how people, processes and technology interact. Not task snapshots, but continuous activity data across the workforce. KYP.ai captures work at the desktop, normalizes it, and surfaces process patterns automatically. 

Business Transformation Engine. Quantifies inefficiency in minutes rather than months. Calculates automation ROI, identifies bottlenecks, predicts payback periods and prioritizes which processes to automate by dollar impact. Results come framed in business terms, hours saved and cost reduced, not tasks per hour. 

Agentic AI Enabler. Generates agent code ready for production, platform agnostic by design and deployable on whatever automation platform you already run: UiPath, SAP Joule, Power Automate, n8n, Camunda, ServiceNow, CrewAI or anything else. No lock-in. The code carries context, error handling, escalation logic and exception routing. Through the MCP Gateway, process intelligence is also programmatically accessible to AI agents at runtime, so agents can query process ground truth without human mediation. 

Standout capabilities 

Speed, which front-loads the decisions. Setup in minutes. Live in days. Statistically relevant insights in 3 weeks. Measurable returns in 90 days. Teams expecting a long discovery phase will need to act on findings sooner than legacy programs condition them to. 

Ground truth, not event logs. KYP.ai captures how work actually gets done at the desktop, including the emails, spreadsheets, decisions and manual steps between transactions that event logs never see. For everything humans do across applications, event logs are a proxy. 

Conversational access to the data. KYP AI Concierge answers questions like “which processes should we automate next?” with ROI-ranked, data-backed recommendations, without requiring technical expertise. 

Limitations 

KYP.ai has a smaller customer base than the category leaders. As a newer entrant than Celonis, IBM or Microsoft, it has a smaller installed base and a smaller implementation partner ecosystem. Buyers weighting vendor longevity and third-party delivery capacity heavily will find more of both elsewhere. KYP.ai is recognized as a Forrester Wave Strong Performer and an Everest Group PEAK Matrix Leader and Star Performer, which is third-party recognition rather than scale.

It is not an ERP transaction miner. If the requirement is deep SAP or Oracle transaction analysis from system logs, a system-native platform covers that narrower case with more pre-built extractors.

VDI needs configuration. Non-persistent virtual desktop environments require additional configuration to maintain data persistence across sessions. KYP.ai provides guidance and tooling for these scenarios, but it is work to plan for.

Best for 

KYP.ai is the strongest fit where the requirement is continuous process intelligence rather than point-in-time task documentation, and where the discovery work has to feed an agentic AI program. Where the requirement is an RPA pipeline feeding an existing UiPath or Automation Anywhere estate, those platforms are the better choice. 

KYP.ai: process mining and task mining in one platform for full work visibility, and the process intelligence layer for agentic AI readiness. 

2. Celonis Task Mining 

Celonis pioneered commercial process mining and has grown into the category leader, with the largest customer base, the most extensive partner network and the deepest capabilities in traditional process mining and task mining. 

Core approach 

Celonis connects discovery to value realization through its execution management stack. The desktop client captures user actions and fuses them with process mining, so manual steps, variants and bottlenecks can be quantified and linked to execution actions. 

Standout capabilities 

Linkage to process mining and execution management. Aligns desktop-level task data with server-level process mining to create visibility from individual user actions through to system transactions. 

Variant and bottleneck analysis. Prioritizes process variants by business impact, so improvement effort goes to high-value opportunities rather than getting lost in complexity. 

Enterprise client controls and allow-listing. Granular controls over which applications and activities are captured, with allow-listing that lets you set capture boundaries against security and privacy requirements. 

Limitations 

Windows-centric desktop client. The task mining client primarily supports Windows, which limits deployment options for Mac or Linux workstations. Heterogeneous desktop estates may face coverage gaps. 

Non-persistent VDI needs special handling. Virtual desktop deployments that do not persist user data across sessions require additional architectural work to maintain continuous monitoring. 

Desktop agent and local state add admin overhead. The client maintains local state and requires ongoing administration for updates, configuration and troubleshooting. 

Best for 

You already run Celonis for process mining and need granular desktop context to enrich root-cause analysis and drive execution-ready improvements. 

3. Automation Anywhere (Task Mining) 

Automation Anywhere is one of the leading RPA platforms globally, offering task mining as an integrated discovery capability within its intelligent automation ecosystem. The platform emphasizes speed to automation, targeting organizations that want to build bot pipelines with minimal friction between discovery and deployment. 

Core approach 

Automation Anywhere pairs discovery with rapid bot delivery in one ecosystem. It captures user actions, clusters variants, and auto-generates documentation and bot candidates for handoff to development. 

Standout capabilities 

Auto-generated PDDs and user stories. Generates Process Definition Documents and user stories directly from captured activity, reducing the documentation effort between discovery and development. 

Direct pipeline to robots. Identifies automation candidates, generates the documentation and hands ready-to-develop opportunities to the bot development team. 

Unified stack from discovery to run. Discovery, development, deployment and monitoring inside a single platform, which reduces tool sprawl. 

Limitations 

Discovery skews toward RPA-ready work. The platform optimizes for identifying automation opportunities, which can mean overlooking process improvements that do not involve RPA. 

Endpoint agent rollout required. Desktop agents have to be deployed across the user population, which requires IT coordination, change management and user communication. 

Privacy and consent programs must be explicit. Desktop activity monitoring requires clear privacy policies and consent mechanisms before rollout. 

Best for 

You are prioritizing fast discovery-to-automation handoff within a single vendor platform and aiming for quick RPA wins. 

4. Microsoft (Power Automate Process Mining) 

Microsoft entered the process and task mining market through acquisition and native development, integrating these capabilities into the Power Platform. The solution targets Microsoft-centric organizations wanting unified governance and familiar tooling. 

Core approach 

Microsoft integrates task capture with Power Automate’s low-code automation and process mining, so discovery, analysis and automation happen inside the Microsoft ecosystem. 

Standout capabilities 

Native Microsoft 365 integration. Connects with Power Platform, Teams and Dynamics 365, with insights flowing into Power BI. 

Low-code automation development. Business users can build automation workflows through visual interfaces without programming expertise. 

Governance within familiar Microsoft tooling. Uses Microsoft’s established governance framework, including Entra ID, compliance tooling and security policies. 

Limitations 

Optimized for Microsoft-centric environments. Organizations with diverse technology stacks find limited support for non-Microsoft applications, which creates blind spots outside the Microsoft ecosystem. 

Thinner on heterogeneous landscapes. A mix of legacy systems, third-party applications and non-Microsoft platforms will meet limits in capture depth and analytics sophistication compared with specialized vendors. 

Desktop capture still maturing. Microsoft’s task mining functionality is newer to market than dedicated vendors, and feature depth continues to evolve. 

Best for 

You operate a Microsoft-centric environment and want a unified toolchain from discovery to low-code automation with straightforward governance and published pricing. 

5. IBM (Process Mining with Task Mining) 

IBM brings decades of enterprise software experience to process intelligence, positioning its solution for organizations requiring governance, regulatory compliance and integration with IBM’s automation and AI portfolio. It resonates particularly in heavily regulated industries. 

Core approach 

IBM combines process mining with task-level capture to provide governed discovery of manual work and conformance within complex, regulated processes. 

Standout capabilities 

Governance and compliance features. Audit trails, role-based access controls and compliance frameworks built for regulated industries. 

Integration with IBM’s automation portfolio. Connects with IBM’s broader stack, including RPA, workflow automation and watsonx AI services. 

Support for regulated industries. Designed for financial services, healthcare and pharmaceuticals, where control requirements are stringent. 

Limitations 

Implementation complexity extends timelines. Comprehensive enterprise capabilities come with deployment timelines longer than lighter-weight alternatives. 

May require broader IBM investment. Integration with IBM’s portfolio is a strength, but realizing full value often means investing across the IBM automation stack. 

Steeper learning curve for business users. Technical depth makes it harder for non-technical users to extract insights independently. 

Best for 

You are standardizing on IBM and need governed discovery of manual work and conformance within complex, regulated processes. 

6. UiPath (Task Mining) 

UiPath established itself in RPA before expanding into task mining to complete its automation lifecycle. The platform creates a closed loop where discovery feeds bot development, deployment and optimization inside the UiPath environment. 

Core approach 

UiPath’s task mining integrates tightly with its RPA platform, providing a closed-loop pipeline from discovery to deployment with built-in ROI tracking. 

Standout capabilities 

Integration with the UiPath automation platform. Native connectivity with UiPath RPA, AI and orchestration, so discovery insights feed automation development directly. 

ROI measurement and tracking. Built-in analytics compare baseline task execution metrics against post-automation performance. 

Governed discovery-to-deployment pipeline. Workflow management and approval processes move opportunities through discovery, prioritization, development and deployment with oversight. 

Limitations 

Focused on RPA use cases. The platform optimizes for traditional RPA opportunities, which can mean missing broader operational improvements that do not involve bots. 

Limited contextual insight beyond automation. Strong at identifying what to automate, thinner on why processes execute as they do and how human expertise contributes. 

Best value realized inside the UiPath ecosystem. Organizations without existing UiPath investment will find the value proposition less compelling than platform-agnostic options. 

Best for 

You are a UiPath customer wanting a governed, closed-loop pipeline from discovery to deployment with built-in ROI tracking. 

7. EdgeVerve (AssistEdge Digital Observer) 

EdgeVerve, an Infosys company, brings deep expertise in enterprise systems integration and legacy technology. The platform stands out in heterogeneous environments, particularly where mainframe and legacy systems remain critical. 

Core approach 

EdgeVerve specializes in capturing activity across heterogeneous environments, including legacy and mainframe systems, with analytics to guide automation decisions. 

Standout capabilities 

Legacy and mainframe support. Captures activity across mainframe terminals, AS/400 systems and legacy desktop applications that many modern task mining tools cannot monitor. 

Capture across diverse technical landscapes. Handles complex environments spanning multiple operating systems, application types and infrastructure models. 

Automation readiness assessment. Evaluates which processes are genuinely suitable for automation on technical feasibility, business value and complexity, rather than simply documenting them. 

Limitations 

Interface feels less modern than newer entrants. User experience patterns are dated compared with newer task mining solutions. 

Implementation can require specialized expertise. Deploying across complex legacy environments often needs specialized technical knowledge or Infosys consulting resources. 

Limited AI-driven recommendations. Strong at capture and analysis, thinner on AI-powered insight and automated recommendations than solutions built on newer machine learning architectures. 

Best for 

You manage heterogeneous estates including legacy and mainframe systems, where capture across every system is a prerequisite for automation decisions. 

8. Soroco (Scout) 

Soroco’s solution is built around work graphs, visualizations that map how work flows across people, teams and systems. The company positions for organizations prioritizing change management, digital adoption and collaboration patterns rather than purely automation-focused discovery. 

Core approach 

Soroco provides low-level capture and creates work-graph visualizations mapping how work flows across teams, applications and processes, to guide change programs and digital adoption. 

Standout capabilities 

Work-graph visualization. Interactive maps showing how work actually flows across the organization, revealing handoffs, collaboration patterns and dependencies invisible in traditional process maps. 

Cross-team collaboration analysis. Identifies how teams collaborate on shared processes, revealing handoff bottlenecks, communication gaps and coordination opportunities. 

Change management and digital adoption insight. Tracks how employees adapt to new systems and processes over time, giving data on adoption patterns, resistance points and training needs. 

Limitations 

Steeper learning curve for interpreting work graphs. The visualizations take time and training to read effectively. 

May require dedicated analysts. Without a conversational interface, extracting insight typically requires analytical expertise on the team. 

Less focus on the automation pipeline. Strong on work patterns and change dynamics, lighter on feeding automation pipelines directly than RPA-centric solutions. 

Best for 

You need low-level capture and cross-team work-graph exploration to guide change programs and digital adoption. 

9. Nintex (Process Discovery, formerly Kryon) 

Nintex acquired Kryon’s process discovery technology and integrated it into its process management and workflow automation platform. The solution targets organizations wanting straightforward discovery coupled tightly with workflow automation, emphasizing speed and ease of use over analytical depth. 

Core approach 

Nintex focuses on fast-tracking bot-suitable tasks through guided discovery and quick documentation, integrated with the broader Nintex automation platform. 

Standout capabilities 

Identification of automation-ready processes. Guided discovery surfaces processes with high automation potential based on repetition, rule-based logic and manual effort. 

Guided discovery workflows. Structured paths through process documentation, analysis and opportunity identification. 

Documentation generation. Creates process documentation, flowcharts and automation specifications from captured activity. 

Limitations 

Optimized for the Nintex ecosystem. Maximum value comes when you use Nintex’s broader automation and workflow tools. 

Narrower than a full process intelligence platform. If the goal extends beyond automation opportunities into workforce productivity or process variation across geographies, the analytics are lighter than full process intelligence platforms. 

Limited advanced analytics. Less extensive AI-driven insight, simulation or statistical analysis than broader process intelligence platforms. 

Best for 

You are using Nintex tooling and want to fast-track bot-suitable tasks with guided discovery and quick documentation. 

10. Pega (Workforce Intelligence and Task Mining) 

Pega has long held a position in BPM and case management, adding task mining to give visibility into how users interact with case workflows. It appeals to organizations already invested in Pega’s case management architecture who want integrated discovery without a new vendor relationship. 

Core approach 

Pega’s task mining integrates with its case management and BPM platform, providing a discovery-to-execution path within one environment. 

Standout capabilities 

Native integration with Pega case management. Connects desktop task data with case management flows, showing how users interact with cases from intake through resolution. 

End-to-end visibility from task to case. Bridges individual user actions and enterprise case management, showing how desktop activity affects case outcomes, SLA adherence and customer experience. 

Decisioning and workflow capabilities. Uses Pega’s decision engine to route automation opportunities, prioritize improvements and orchestrate process changes. 

Limitations 

Value maximized inside the Pega ecosystem. Organizations without existing Pega investment face significant cost to reach the platform’s full capabilities. 

Implementation complexity outside Pega environments. Deploying task mining alongside the broader platform requires substantial technical expertise and project management overhead. 

Higher total cost of ownership. Enterprise pricing and the need for specialized implementation expertise raise total cost compared with lighter-weight alternatives. 

Best for 

You are a Pega customer seeking a discovery-to-execution path inside the same platform, especially where case management is central. 

Task mining market landscape in summary

Task mining software captures how your employees interact with applications at the desktop level, giving you granular visibility into manual work that system logs and traditional process mining miss. Unlike traditional process mining approaches that only analyze server-level data, task mining shows you exactly how your teams navigate their daily workflows. 

Task mining is one of the fastest growing categories of enterprise software, with a current market size of €2 billion projected to reach €10 billion by 2033. Gartner tracks at least 25 different task mining solutions. Your best-fit task mining solution depends on your ecosystem, maturity, and objectives. 

Key use cases for task mining 

1. RPA discovery and automation pipeline. The most common use case: identify repetitive tasks that consume time and money. Relevant to organizations running RPA programs, where UiPath, Automation Anywhere and Nintex customers need a source of process candidates. Task mining replaces guesswork with actual time spent, frequency and user variance, which guides prioritization. Best platforms: UiPath Task Mining, Automation Anywhere, KYP.ai. 

2. ERP process compliance and audit. Understanding where users deviate from intended ERP processes and documenting those deviations for auditors. Relevant to regulated industries and to finance, procurement and operations teams. Timestamped activity records paired with ERP logs become audit evidence. Best platforms: Celonis Task Mining, IBM Process Mining. 

3. Continuous process optimization. Ongoing visibility into how work is performed, with automatic identification of inefficiencies. Relevant to continuous improvement programs, Lean and Six Sigma teams and transformation initiatives. Replaces one-time process mapping with real-time visibility, so problems surface as they emerge rather than in quarterly reviews. Best platforms: KYP.ai, Celonis, Soroco. 

4. Agentic AI enablement and agent design. Feeding AI agents the context they need to execute multi-step workflows without human intervention. Relevant to organizations deploying agents on UiPath, SAP Joule or Copilot Studio. Agents cannot run unsupervised without decision rules, exception handling and fallback logic, and task mining is where that context comes from. Best platforms: KYP.ai, UiPath Task Mining with Studio. 

5. Back-office labor cost reduction. Quantifying where labor hours go and calculating ROI for automation investment. Relevant to BPOs, shared services centers and finance operations. Dollar-level ROI is a more compelling case than a percentage of workload. Best platforms: KYP.ai, Celonis, Automation Anywhere. 

6. Customer service process mining. Understanding contact center agent behavior, handling time, quality and escalation patterns. Relevant to customer service leaders, BPOs and customer experience teams. Shows where agents spend time, where customers experience delay and how to redesign the workflow. Best platforms: Pega Workforce Intelligence, KYP.ai, Soroco. 

7. Digital transformation program scoping. Identifying which processes should be automated, redesigned or reimplemented before selecting new software. Relevant to enterprise software selection teams and transformation program managers. Current-state visibility prevents buying expensive systems to automate broken processes. Best platforms: KYP.ai, Celonis, Soroco. 

How to choose the best-fit task mining solution 

1. What is your primary use case: RPA pipeline, ERP analysis or continuous optimization? For an RPA pipeline, UiPath Task Mining or Automation Anywhere feed directly into execution. For ERP process analysis, Celonis Task Mining has unmatched ERP event correlation. For continuous optimization, KYP.ai provides real-time capture with automatic ROI quantification. 

2. What is your privacy and compliance stance? In regulated industries where sensitive data must never leave the workstation, KYP.ai anonymizes at source with granular configuration over what is captured. Where capture policy is less constrained, Celonis, UiPath, Automation Anywhere and Nintex all offer configurable controls. 

3. Do you need ROI quantification now, or is task documentation sufficient? If the automation business case has to stand up to Finance, KYP.ai quantifies automatically and Celonis supports manual modeling. If documentation is enough for now, UiPath, Microsoft and Nintex cover it, with ROI calculation coming later. 

4. Are you committed to a platform, or vendor-agnostic? UiPath-committed, choose UiPath Task Mining. SAP-committed, Celonis Task Mining. Microsoft-committed, Power Automate Process Advisor. Pega-committed, Pega Workforce Intelligence. Vendor-agnostic, KYP.ai, Automation Anywhere or Soroco. 

5. What is your scale? Under 500 employees, Microsoft Power Automate Process Advisor is cost-effective where it is already included in the Microsoft licensing, and Nintex prices per use case. Between 500 and 5,000, UiPath Task Mining and Automation Anywhere are proven at that scale. Above 5,000 in a regulated industry, Celonis or KYP.ai. 

The bottom line on task mining solutions

The task mining market offers solutions optimized for different use cases, but one question decides between them: which platform will produce insight your organization can act on and fund? 

Prioritize on five criteria. Speed to value, meaning results in weeks rather than months. AI sophistication, meaning conversational analytics and automated recommendations. Agentic AI enablement, meaning production-ready agent code with structured business context. Ecosystem fit with your existing automation and analytics tools. And capture breadth across your entire application portfolio, including legacy systems. 

KYP.ai is the strongest fit where the requirement is continuous process intelligence rather than point-in-time task documentation, and where discovery has to feed an agentic AI program. Live in days, statistically relevant insights in 3 weeks, measurable returns in 90 days. 

Ready to see what task mining finds in your operation? Book a demo at kyp.ai/book-demo to see where hours are going, which processes carry the ROI, and what the transformation roadmap looks like on your own data. 
To discover where you’re losing money, identify automation opportunities, and build data-driven transformation roadmaps—typically within 2 weeks of deployment. 

Video: See how Capgemini automated process intelligence at scale with KYP.ai