6 Best Process Mining Tools for Enterprise Teams in 2026

Process mining tools show enterprises how work moves through their business processes so teams can find bottlenecks and pick targets for improvement or automation. How well a tool delivers depends on its data source, because the capture method decides which work the tool can see and what stays invisible.

Currently, process mining tools take one of three broad approaches for extracting data: system and event log mining, recorded session and task mining, and continuous desktop observation. Each method emerged to cover a blind spot the one before it left open, and each still leaves part of the job undone.

This guide compares six leading tools across the three approaches, then introduces Fluency, a work intelligence platform that captures work but sits outside the category because it continues past observation into deployment and measurement.

TL;DR

• System and event log mining: Celonis and SAP Signavio reconstruct processes from the transaction data that enterprise resource planning (ERP) and customer relationship management (CRM) systems already store, which suits standardized, system-heavy workflows

• Recorded session and task mining: UiPath Task Mining and Mimica record desktop activity during defined projects, exposing the manual work between system transactions

• Continuous desktop observation: Worktrace and Skan AI observe work across applications on an ongoing basis, keeping the process picture current as work changes

• Beyond process mining: Fluency isn’t a process mining tool; it observes work at the point of execution the way continuous observation tools do, then deploys AI agents on the best opportunities and measures results against an observed baseline.

How process mining tools capture data and why it matters

The three capture methods aren’t interchangeable. Each one draws a different boundary around what the tool can analyze, and each newer method exists because of what the previous one couldn't see.

System and event log mining

Log-based mining reconstructs workflows from the timestamped transaction data backend platforms like ERPs and CRMs already store. Celonis and SAP Signavio both work this way, using connectors, data models and transformation pipelines to turn source-system events into structured process views.

For standardized, high-volume transactions, such as order-to-cash or procure-to-pay, the visibility is deep. Every case, timestamp and variant the system recorded is available for analysis.

But the design is also the method's boundary. Work appears in the model only when it produces an event in a connected system. A reconciliation built in a spreadsheet, an approval settled over email, and a workaround a team invented to route around a broken step all leave no event trail, so the model treats them as if they never happened.

The model is also a reconstruction. It shows what the systems logged, not how a person moved through the work on screen.

Recorded session and task mining

Task mining exists because of the blind spot log mining leaves on the desktop. Instead of reading backend events, it records screen-level activity to surface the manual work between system transactions.

UiPath Task Mining records desktop actions and applies AI to flag repetitive tasks and automation candidates, while Mimica captures clicks, keystrokes and application interactions, then converts them into process maps and documentation.

The limitation with this approach is in its sample. A task mining study reflects the users who were recorded, the applications in scope and the weeks the recorder ran. The map is accurate for the window and the group it observed. Once the project ends, the picture stops updating, so any process change that follows stays invisible until someone sets up the next study.

Continuous desktop observation

Continuous observation replaces the recording window with a persistent desktop agent that watches cross-application work on an ongoing basis. Worktrace runs in the background to identify recurring workflows and automation opportunities without backend integrations. Skan AI observes work across enterprise applications and turns the activity into process maps and an up-to-date model of how work moves.

Of the three methods, this one captures the most. It sees the work between systems, keeps seeing it as processes drift and picks up exceptions and workarounds.

Data capture, though, is only half the job. These tools only produce a picture: maps, ranked candidates, blueprints for someone to build from. What happens after the map is where process mining tools lose their value.

Best process mining tools at a glance

Before comparing vendors, buyers need to know whether a tool's data source captures enough of the work they need to understand. The table below compares six platforms by capture method, time to value, infrastructure, automation deployment and strongest fit.

Each tool is strongest in a particular environment, process type or existing tech stack. But none of them fix the problem enterprise buyers are trying to solve today: Given everything that happens across an operation, what should get automated with AI first and how?

Fluency was built around this decision. It captures work at the point of execution, then goes past the map to rank opportunities, deploy AI agents and measure the results.

To see how Fluency finds, deploys and measures AI automation across your operation, book a demo.

The best process mining tools by category

The six tools below fall into three categories based on how they collect process data and the type of visibility they provide. Product drawbacks are based on G2 and Gartner customer reviews except where otherwise noted.

System and event log mining

Celonis and SAP Signavio work best when the processes you need to analyze are already well represented in enterprise system data. However, both also inherit this method’s main drawback, seeing only what connected systems log.

Celonis

Celonis analyzes transactional data from ERP and other enterprise systems using case-centric and object-centric process mining. Case-centric mining follows a process around a defined case, such as an order or invoice, while object-centric mining connects related business objects and events across processes involving multiple systems. Implementation timelines vary by scope, with Celonis case studies showing ranges from under 12 weeks to four months from contract signing to go-live.

Key features:

• Object-centric and case-centric process analysis

• Conformance and performance analysis

• Enterprise connectivity for systems including SAP, Oracle, Salesforce and ServiceNow

Drawbacks:

• A steep learning curve for beginners, especially for more powerful features

• Some users report missing or limited features, particularly around flexibility and advanced functionality

• Slow performance when handling large datasets

• Manual integrations per app and data source, long IT projects that typically last 6+ months

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SAP Signavio

SAP Signavio Process Intelligence combines process mining with SAP's broader business process transformation suite. It's a natural fit in SAP-heavy environments because it comes with preconfigured content for common SAP processes, and teams can also connect data from non-SAP systems.

SAP Signavio uses prebuilt value accelerators to reduce setup time for core processes like procure-to-pay, order-to-cash and record-to-report. Custom implementations or non-SAP connections require more data-pipeline configuration, which can extend setup timelines.

Key features:

• Process mining based on connected source-system data

• Preconfigured process content for supported SAP environments

• Process modeling and analysis within the wider Signavio suite

Drawbacks:

• A steep learning curve for more complex configurations

• Limited advanced reporting and customization for complex setups

• Slower performance when working with larger process models

• Limited intelligence across tools and apps outside of SAP

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Recorded session and task mining

UiPath Task Mining and Mimica expose the manual work happening on employee desktops between major system transactions. Both work in projects, so their maps describe the people, applications and weeks each study covered.

UiPath Task Mining

UiPath Task Mining records desktop activity and applies AI to identify repetitive tasks, process improvement opportunities and automation candidates. It connects to UiPath's wider automation platform, so tasks identified through Task Mining can feed directly into an existing UiPath automation program.

UiPath doesn't publish a standard time-to-value estimate for Task Mining, with timelines depending on the scope of each recording project.

Key features:

• Desktop capture of employee task activity

• AI-assisted identification of repetitive tasks and automation opportunities

• Direct connection to UiPath's automation environment

Drawbacks:

• Initial setup and fine-tuning can require substantial work

• Complexity can make implementation harder for teams without UiPath experience

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Mimica

Mimica specializes in task mining, capturing desktop activity and converting it into process maps, documentation and automation recommendations. Its desktop recorder works across applications without requiring direct integrations into each backend system.

Mimica says it can deliver actionable process maps and automation recommendations within two weeks of installation.

Key features:

• Desktop capture across applications

• Automatically generated process maps and documentation

• Automation opportunity analysis and scoring

Drawbacks:

• Process-map production can take more than two weeks

• The Miner interface can become crowded as more projects are added

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Continuous desktop observation

Worktrace and Skan AI maintain an ongoing view of cross-application work, which suits processes that change frequently or run outside structured system data. Where the two differ is in what happens after discovery.

Worktrace

Worktrace observes activity across desktop applications to identify recurring workflows, quantify automation opportunities and generate blueprints for building AI automations. The builds themselves happen on third-party automation platforms, with Worktrace positioning the blueprint as the handoff.

Worktrace reports first insights within a week of deployment.

Key features:

• Cross-application workflow capture without backend integrations

• AI automation opportunity identification

• Automation blueprints for third-party agent platforms

Drawbacks:

• Early users reported initial resistance to desktop observation before seeing its value

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Skan AI

Skan AI uses continuous desktop observation to build process maps and a current model of how work moves across enterprise applications such as Excel, email and web apps. Its platform spans process intelligence, an AI roadmap product called Blueprint and an Agents offering. Skan describes the agents as work-aware, running through its Agentic Operating Procedures (AOPs), and cites six weeks or less from observations to a deployed agent.

Key features:

• Continuous observation across enterprise applications

• Process maps built from observed workflows, including variants and exceptions

• An Agents offering built on observation data, alongside AI roadmap and ROI analysis

Drawbacks:

• Infrastructure setup can be more complex and time-consuming than expected, with some features requiring vendor involvement

• Customization and integration options can be limited, particularly for more complex workflow environments

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The inherent problem with process mining tools

Set the six tools side by side and the category's history reads as a series of patches. Task mining exists because event logs miss the desktop, and continuous observation exists because recordings go stale. Each patch fixed what the previous one couldn't see.

However, even after three rounds of patching, four problems persist: coverage, integration speed, data freshness and actual AI deployment. The first three come from how each method captures data. The fourth comes from what happens after.

Coverage depends on what the system records

System-log process mining can only analyze work captured in the systems connected to it. Spreadsheet reconciliations, email exchanges and manual workarounds disappear from the process model because those steps never produce the event data the platform relies on.

Fluency describes this as the 70% problem, estimating that structured systems represent roughly 30% of enterprise work while the rest happens across spreadsheets, email, browsers, chat and manual processes.

Integration slows time to value

Traditional process mining tools such as Celonis and SAP Signavio depend on backend data being connected, prepared and modeled before teams can analyze anything. Some traditional process mining programs require six or more months of IT integration before organizations have the visibility they bought, although individual deployments vary by scope.

Desktop-based methods cut this setup time down, which is a large part of why task mining and continuous observation exist.

Data freshness depends on the pipeline

Event-log process models are only as current as the data pipelines feeding them. Extraction and transformation pipelines refresh on their own schedules, so the process view reflects how recently the underlying data was updated rather than direct observation of work as it happens. Task mining removes the lag during a recording project, but when recording stops, the visibility into work goes stale.

Continuous observation solves this problem at the capture layer. An agent keeps the picture current, exceptions included. What constant capture can’t change is what the tool does with the picture.

Discovery without deployment

Process mining is usually bought as the discovery phase of a larger program: find the inefficiencies, then redesign, automate and measure. In practice, the map goes into a consulting engagement or an internal automation backlog, the redesign and build run as separate workstreams and the business case sits unproven until the loop closes months later.

None of the six tools deploy automation entirely on their own, though each has tried. Celonis triggers third-party bots through Action Flows. SAP Signavio triggers robotic process automation (RPA) or SAP Build workflows the same way. UiPath feeds Task Mining candidates into its own automation platform, while Mimica scores automation fit and leaves the build to another tool. Worktrace generates blueprints for third-party platforms to build from. Skan AI goes furthest, marketing work-aware agents through its AOPs.

What varies is how much integration and building still sits between a ranked opportunity and a running automation. For teams trying to decide where AI belongs, the distance between insight and deployed change is the major constraint.

Where Fluency fits beyond process mining

Fluency is a work intelligence platform that shows large enterprises where AI will have the biggest impact by finding the best work to automate first. It observes work at the point of execution, deploys AI agents directly into the workflow and measures results against an observed baseline after deployment.

Fluency shares one trait with the continuous observation tools above. Like Worktrace and Skan AI, it watches work as it happens and catches the activity in browsers, spreadsheets and chat that never writes to a system log.

But the similarities end at capture. Process mining tools exist to produce the map as the sole product, while Fluency treats the map as raw material for the decisions that follow: which work to automate first, what to deploy against it, and whether the deployment paid off. Fluency’s ability to provide visibility into end-to-end work across an enterprise is only the foundation to AI strategy.

Three modules carry the loop from observation to deployment:

• Work Explorer maps recurring tasks across systems and handoffs to show where time is spent, built from observed execution data

• Opportunities analyzes those execution patterns to rank automation candidates by projected hours and financial ROI, relative to the unique context of each enterprise

• Automations deploys AI agents directly into existing workflows

After deployment, Fluency keeps collecting execution data after an agent goes live and compares live execution against the original baseline, so changes in throughput, effort and process quality are measured rather than estimated. Self-reported numbers show why an observed baseline matters. In Fluency pilot data, tasks that teams estimated at 20 to 30 minutes took 45 to 50 minutes when observed. And opportunities that surfaced for automation could reduce these tasks to around 10 minutes with an agentic process.

Instead of discovery as one project and delivery as another, both run continuously: Observation keeps the model of work current, deployment acts on the strongest opportunities, and measurement feeds the next round of decisions.

It’s a continuous cycle of business improvement and AI deployment, agents also improve over-time and self-adapt to the upstream and downstream changes within a business, so they never break unlike brittle RPA. It becomes the new control layer enterprises use to keep AI investment pointed at the right work as their operations change.

How Fluency compares side-by-side

See where AI belongs in your operation

Choosing a process mining tool means choosing what your enterprise can see, and what it can do about what it finds. A platform that only shows part of the work, or stops at a process map, puts the decision of where AI belongs and what to build first back in your hands.

Fluency captures execution across every application and carries the same data through to a deployed agent and a measured result.

Fluency observes work, not workers. Deployments run with SOC 2 Type I and II controls and automatic redaction of personal data, and analysis stays at the workflow level, not against an individual.

Request a Fluency demo to see which workflows in your organization carry the strongest case for AI automation, and deploy your first set of automations with Fluency.

Process mining tool FAQs

Quick answers on how these tools work and where Fluency fits.

What are process mining tools?

Process mining tools analyze operational data to reconstruct how business processes execute in practice. Traditional platforms use event logs generated by enterprise systems, while task mining and desktop observation tools capture user activity across applications. The capture method determines which parts of a workflow appear in the resulting process model.

What's the best process mining tool?

The right process mining tool depends on what a team needs to see. Celonis and SAP Signavio fit structured processes captured in enterprise systems. UiPath and Mimica add desktop-level task visibility during scoped projects, while Worktrace and Skan AI observe work continuously across applications. Fluency isn’t a process mining tool. It observes execution the way the continuous observation tools do, then deploys AI agents and measures results against an observed baseline.

How do process mining tools collect data?

Traditional process mining tools extract timestamped event data from ERP, CRM and other enterprise systems. Task mining software records employee interactions during defined observation projects. Continuous observation platforms such as Worktrace and Skan AI capture cross-application activity on an ongoing basis, including work that never appears in backend system logs.

How long does process mining implementation take?

Implementation ranges from weeks to months, depending on capture method, process scope and existing data infrastructure. Event-log platforms require source-system connections and data preparation, while desktop-based platforms can begin observation without the same backend integration work. Vendor-reported timelines should be evaluated against the scope of the proposed deployment.

What's the difference between process mining and task mining?

Process mining traditionally reconstructs workflows from system-generated event logs. Task mining captures user activity at the desktop, including clicks, keystrokes and application changes. Recording at the desktop gives task mining visibility into manual work between system transactions that may not appear in traditional process mining data.

Can process mining identify AI automation opportunities?

Process mining can identify bottlenecks, repetitive patterns and process variants that point to automation opportunities. Acting on them typically runs through separate automation platforms or programs. Fluency ranks opportunities by projected hours and financial ROI from execution-level data, then deploys AI agents against the strongest candidates and measures the results.