Top 10 AI Process Mining Tools 2027
In 2027, the best AI process mining tools combine three things: questions in natural language, agents that run analyses on their own, and a data model that ties the AI’s answers to real process data. Almost every vendor now offers a copilot – the difference lies in what the AI can access and whether its statements can be verified. This list rates ten tools on exactly those criteria. It reflects the state of October 2026 and looks ahead to the developments that will shape 2027.
Updated October 2026 · Published by Noreja · Methodology disclosed
The Top 10 at a Glance
| Rank | Solution | Best for |
|---|---|---|
| 1 | Noreja | Verifiable AI on a causal graph |
| 2 | Celonis | Broad AI ecosystem in large enterprises |
| 3 | SAP Signavio | AI assistance in SAP landscapes |
| 4 | UiPath Process Mining | From finding to orchestrated agent |
| 5 | Microsoft Power Automate Process Mining | AI entry point in Microsoft environments |
| 6 | ServiceNow Process Mining | Agentic AI for service workflows |
| 7 | Pega Process Mining | Mining as a basis for redesign |
| 8 | IBM Process Mining | AI assistance in regulated industries |
| 9 | QPR ProcessAnalyzer | Process data for external agents via MCP |
| 10 | Skan AI | Agents built from observed desktop work |
How We Ranked
Grounding in the data model
What does the AI rely on: a graph of events, objects and relationships, an object-centric log or a case-centric log? The richer the context, the fewer plausible-but-wrong answers.
Agent capability
Can the AI plan and execute multi-step analyses, or does it only answer single questions? We assess in-product agents and how they fit into workflows.
Openness to external AI
Can your own agents and models be connected, for example via MCP or open interfaces, and is the choice of language model free?
Traceability
Can AI statements be traced back to the underlying cases, KPIs and relationships? Without that, recommendations remain assumptions.
Transparency note: this ranking is published by Noreja, and Noreja is part of the list. The criteria above are disclosed and structurally favor causal, graph-based approaches. Information on other vendors is based on publicly available sources (as of October 2026); feature scope and packaging change frequently – the respective vendor’s documentation is authoritative. Trademarks belong to their respective owners.
The Top 10 in Detail
Noreja
Best for: Verifiable AI on a causal graph
Noreja’s AI assistant Minerva 2.0 reads the causal process graph – cases, variants, KPIs and deviations from the expected order – and combines it with company knowledge from the Context Hub. Specialized agents take on recurring analyses, while Frontier Agents work continuously in the background. The language model is freely selectable, from cloud models to locally operated ones.
- Event Knowledge Graph as grounding: AI answers highlight the activities they mention directly in the process graph
- Agent Library with ten specialized analysis agents, for example for root causes, waiting times and working capital
- Free LLM choice up to on-premises operation; knowledge can be restricted to local models
Keep in mind: Minerva only reads and does not change source data or analyses on its own – execution steps run through connected systems with human approval.
Celonis
Best for: Broad AI ecosystem in large enterprises
Celonis aligned its platform with AI early on: Process Copilot answers questions in natural language and creates reports, while AgentC connects agent platforms such as Copilot Studio, Bedrock and Agentforce. With the MCP server introduced at Celosphere 2025 and the Orchestration Engine, the approach extends from analysis to automated follow-up actions.
- Process Copilot as an LLM assistant for questions and reports without technical background
- AgentC connects process data with leading agent platforms of major hyperscalers and software vendors
- MCP server and Process Intelligence Graph provide process context to external agents
Keep in mind: The AI relies on frequency-based process models, so root-cause statements rest on correlations in sequences rather than on a causal model.
SAP Signavio
Best for: AI assistance in SAP landscapes
With Joule, SAP has brought its AI assistant into the Signavio suite; Joule with SAP Signavio has been generally available since February 2026. According to SAP, Joule Agents help with content discovery, process analysis, creating value cases and onboarding. A Dashboard Analyzer agent that interprets event logs and KPIs was introduced as a beta in November 2025.
- Joule as SAP’s end-to-end AI assistant – generally available in the Signavio solutions as well
- Several Joule Agents work together in a multi-agent framework, according to the vendor
- Tight integration of AI analysis with SAP process content, modeling and benchmarks
Keep in mind: Some agents are still in beta or early-adopter phases, and their value unfolds mainly in SAP-dominated landscapes.
UiPath Process Mining
Vendor: UiPath
Best for: From finding to orchestrated agent
At UiPath, process mining is the entry point to agentic automation. With Maestro, the platform orchestrates agents, robots and people together in BPMN processes, so insights from mining can flow directly into automated workflows. Here, AI mainly serves to find and implement automation candidates.
- Maestro coordinates agents, robots and people in one shared BPMN process
- Combined view of system, desktop and communication data as context for automation
- Short path from analysis to productive agent and RPA implementation
Keep in mind: The analytical depth of root-cause analysis is subordinate to automation; full value emerges together with the UiPath platform.
Microsoft Power Automate Process Mining
Vendor: Microsoft
Best for: AI entry point in Microsoft environments
Microsoft anchors process mining in the Power Platform and lets users query process data through integrated language models. Teams already working with Power BI, Microsoft 365 and Copilot find a familiar interface and a low entry barrier. Insights can be turned directly into low-code automations within the Microsoft stack.
- Conversational queries of process data via integrated language models
- Close connection to Power BI, Azure and Microsoft 365
- Low-code automation as the natural next step after analysis
Keep in mind: The AI works on case-centric, frequency-based models and is designed for accessibility rather than analytical depth.
ServiceNow Process Mining
Vendor: ServiceNow
Best for: Agentic AI for service workflows
ServiceNow integrated process and task mining into its Zurich release, which centers on agentic AI with embedded process automation. Because the process data already lives on the platform, no data extraction is needed for ServiceNow processes. Via Workflow Data Fabric, external data is made available mainly for workflows and agents.
- Process and task mining as part of a platform release centered on agentic AI
- No data extraction for processes running on the ServiceNow platform
- Real-time KPI monitoring, for example for SLA compliance
Keep in mind: Mining analysis focuses on processes inside the platform; back-end flows in ERP or production largely stay outside its view.
Pega Process Mining
Vendor: Pegasystems
Best for: Mining as a basis for redesign
Pega positions process mining as the operational context for deciding where AI agents should be deployed. According to its press release of 12 May 2026, insights feed into Pega Blueprint, where processes are simulated, optimized and turned into reworked workflows and applications. According to the vendor, Pega combines process mining, task mining and modeling in one AI-powered platform.
- Handover of mining insights to Pega Blueprint for simulation and redesign
- Process mining as a basis for deciding where to deploy AI agents
- Process mining, task mining and modeling in one platform – according to the vendor
Keep in mind: The greatest value emerges when the redesign is also implemented on the Pega platform.
IBM Process Mining
Vendor: IBM
Best for: AI assistance in regulated industries
IBM combines object-centric process mining with an LLM-powered Process Mining Assistant based on watsonx that formulates root-cause hypotheses. Through integration with watsonx Orchestrate, insights can be turned into automated workflows. Data-driven what-if simulation and on-premises or hybrid operation appeal especially to regulated industries.
- Process Mining Assistant based on watsonx for root-cause hypotheses in natural language
- Integration with watsonx Orchestrate for the step into automation
- What-if simulation and prescriptive recommendations on object-centric data
Keep in mind: Root-cause hypotheses remain methodically frequency-based, and rollout and operation require substantial implementation effort.
QPR ProcessAnalyzer
Vendor: QPR Software
Best for: Process data for external agents via MCP
In April 2026, QPR introduced an MCP interface for ProcessAnalyzer that lets AI agents such as Claude, ChatGPT or Microsoft Copilot query process analyses directly. It also offers AI-powered root-cause analysis designed to identify the operational drivers of process problems automatically. The platform runs natively in Snowflake and supports object-centric process mining.
- MCP interface brings process analyses into common AI assistants and agents
- AI-powered root-cause analysis for operational drivers of process problems – according to the vendor
- Snowflake-native operation with object-centric process mining
Keep in mind: The statements about the MCP interface come from vendor announcements; details on availability and setup are hardly documented publicly.
Skan AI
Best for: Agents built from observed desktop work
Skan AI captures how work is actually done on the desktop: screen content is processed locally on the machine, and anonymized metadata is sent to the analytics platform. According to the vendor, this picture of work produces ready-to-run agents on its own Agents platform. With the Agentic Ontology of Work, introduced in February 2026, Skan offers an open vocabulary for how humans and agents work together.
- Observation of desktop work with local processing and anonymized metadata
- Dedicated Agents product that turns observed workflows into executable agents
- Agentic Ontology of Work as an open semantic framework for human-agent work
Keep in mind: Skan relies on workplace observation rather than event data from source systems, so it complements classic process mining rather than replacing it.
What Will Matter in 2027
MCP becomes the standard interface
More and more vendors open their process data to external agents via the Model Context Protocol. In 2027, what matters is which context comes with it – plain KPIs or a model with causes and relationships.
From copilot to background agent
Instead of answering individual questions, agents monitor processes continuously and report deviations on their own. This raises the need for governance, approvals and traceable reasoning.
Grounding beats model size
The quality of AI answers increasingly depends on the data model rather than on the chosen language model. Tools that connect events, objects and enterprise knowledge deliver more reliable answers than tools built on flat logs.
Frequently Asked Questions
What is AI process mining, or agentic process mining?
AI process mining means that language models answer questions about process data in natural language, narrow down causes and create reports. Agentic process mining goes a step further: agents plan multi-step analyses themselves, monitor processes continuously and propose measures. The quality depends on how well the data model connects the AI with real process data and domain knowledge.
Can I connect my own AI agents to process mining data (MCP)?
Yes, more and more vendors are opening their platforms for this. Celonis introduced an MCP server in 2025 and connects process data via AgentC with platforms such as Copilot Studio, Bedrock and Agentforce; QPR announced an MCP interface for ProcessAnalyzer in April 2026. What matters is which context is passed on and how permissions and data protection are handled.
Do AI features in process mining hallucinate?
Language models can produce plausible-but-wrong statements – especially when they only see KPIs or flat logs. The risk drops when the AI works on a model of events, objects and relationships and its statements can be traced back to the underlying cases. AI results nevertheless remain decision support and should be reviewed before consequential decisions.
Which AI process mining tool runs with local LLMs?
Alongside cloud models, Noreja offers locally operated models that run on servers operated by Noreja or the customer; a dedicated agent variant, Minerva (On-Premise), exists for this. Companies can also specify that certain Context Hub knowledge may only be used with local models. For other vendors, check their current documentation.
What should I look for when choosing an AI process mining tool?
More important than the copilot itself is the data model underneath: can the AI distinguish causes from mere sequences and draw on enterprise knowledge? Also check whether your own agents and language models can be connected and whether every statement can be traced back to the data.
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Experience Causal Process Analysis Yourself
See how Noreja analyzes processes causally on an Event Knowledge Graph – beyond frequency-based Directly-Follows models.