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Process Mining and Agentic AI: Why You Need Both (and the Order Matters)

Process mining tells you what to automate. Agentic AI executes the automation. Skip the first layer and you scale faulty processes faster. Skip the second and you have a beautiful map of inefficiencies you cannot fix. Here is how the two layers actually work together in 2026, and the architectural sequence that determines whether AI investment compounds or stalls.

Kognitos 14 min read
Process mining vs agentic AI in 2026: the three-step discover-redesign-execute sequence that compounds, the convergence driven by Salesforce's acquisition of Apromore and Celonis's Agent Mining launch, and the architectural patterns that distinguish AI strategies that scale. By Kognitos.

TL;DR

In 2025-2026, two enterprise software categories that had developed independently for a decade started converging at speed. Process mining (Celonis, Apromore, Signavio, IBM Process Mining, KYP.ai, Skan) had spent ten years building tools to discover, visualize, and analyze how business processes actually run. Agentic AI (Kognitos, UiPath, Microsoft Copilot Studio, Salesforce Agentforce, and others) had emerged to execute autonomous decisions inside those processes. By late 2025, the strategic recognition that the two needed each other became visible across the industry:

  • Salesforce acquired Apromore ahead of Dreamforce 2025 (deal expected to close Q4 2025) to bring process intelligence into Agentforce
  • Celonis launched the Orchestration Engine, Agent Mining, and the first MCP server for process intelligence at Celosphere 2025 (November 4, 2025)
  • Celonis + Microsoft Agent 365 integration entered private preview on May 1, 2026
  • ServiceNow integrated process and task mining into its AI agent workflows
  • Apromore’s CPO Marlon Dumas publicly argued that “process intelligence can make agentic AI more powerful” by discovering manual processes, monitoring agent activities, and providing the context agents need to make smarter decisions
  • Celonis CEO Alex Rinke stated the thesis directly: “There’s no AI without PI”

The convergence is happening because the two categories answer different questions:

  • Process mining answers: “What should we automate?” — by visualizing how processes actually run, identifying bottlenecks, and surfacing inefficiencies that the organization didn’t know about
  • Agentic AI answers: “How do we automate it?” — by executing the automated decisions, handling exceptions, and producing the audit trails that satisfy 2026 regulatory standards

The order matters. Process mining without agentic AI produces beautiful diagnostic reports the organization cannot fix. Agentic AI without process mining produces fast automation of processes that should have been redesigned before automating. The strongest 2026 enterprise AI strategies pair the two layers in a specific sequence: discover with process mining, redesign with process intelligence, execute with agentic AI, and continuously monitor with the loop closing back to process mining.

This post walks through what each category does, why the convergence is happening, the three-step sequence that makes both layers compound, the common mistake of skipping either layer, and what enterprise leaders should evaluate when stacking the two together.

What process mining actually does

Process mining is the diagnostic layer of enterprise operations. The platforms extract event logs from systems of record (ERP, CRM, ITSM, custom databases), reconstruct the actual paths processes take, and visualize where the friction lives.

A traditional process documentation exercise asks people how they do their jobs and produces a diagram of how the process is supposed to work. Process mining bypasses the people and looks at the data. It reads the timestamp of every PO creation, every invoice receipt, every approval action, every payment, and reconstructs the actual sequence — including the rework loops, the manual workarounds, the unexpected detours, and the cases that never followed the documented path.

The 2026 process mining landscape includes:

  • Celonis — the ERP-centric leader, with deep extraction for SAP, Oracle, and Microsoft Dynamics, and a Process Intelligence Graph that creates a digital twin of business operations
  • Apromore — academically-rooted process mining (founded 2009 by Marlon Dumas and the Queensland University team), acquired by Salesforce in 2025 to power process intelligence inside Agentforce
  • SAP Signavio — SAP’s process intelligence and BPM suite, native to SAP estates, with strong integration into S/4HANA workflows
  • IBM Process Mining — process mining inside the IBM Cloud Pak for Business Automation portfolio
  • KYP.ai — activity-based process intelligence that captures how people work across all applications, not just ERP event logs
  • Skan — task mining specialist focused on capturing desktop activity (keystrokes, clicks, application context)

What all of them do well: discovery and visualization. What none of them do, by design: actually execute the automation that fixes the discovered inefficiencies. The platforms hand off to RPA (UiPath, Automation Anywhere), iPaaS (Workato, MuleSoft), workflow tools (Microsoft Power Automate, ServiceNow), or — increasingly — agentic AI platforms.

The 2026 strategic shift is that the handoff from discovery to execution has become the most important architectural question in the broader process automation strategy.

What agentic AI actually does

Agentic AI is the execution layer. The platforms take autonomous or semi-autonomous actions across business workflows, with varying levels of reasoning, deterministic execution, and audit-trail completeness.

A traditional automation platform executes a scripted workflow: when condition X happens, perform action Y. Agentic AI executes reasoning-based workflows: when the system encounters an unexpected condition, it reasons about what to do, applies a business policy, and either resolves the case or escalates with a structured explanation.

The 2026 agentic AI landscape includes:

  • Kognitos — deterministic neurosymbolic agentic AI with English-as-code policies and audit-ready trails by design
  • UiPath with Agentic Automation — RPA platform extending into agentic capabilities
  • Microsoft Copilot Studio — agentic AI capabilities across Microsoft 365 and Dynamics
  • Salesforce Agentforce — agentic AI inside the Salesforce ecosystem (now augmented by Apromore process intelligence)
  • Workato Genie and other iPaaS platforms with AI agents layered on
  • Specialized agentic platforms like AppZen Agents (finance audit), Numeric (cash matching), Opstream (procurement), and many others

What all of them do well: execute decisions in production. What most of them require to operate effectively: knowing which decisions to automate in the first place. Without that input, agentic AI risks the failure mode the industry has documented repeatedly in 2025-2026: scaling broken processes at machine speed.

The 2026 strategic insight that drove the Salesforce acquisition of Apromore is that the execution layer’s value depends on the quality of the discovery layer that precedes it.

Why the two are converging in 2026

Five developments in late 2025 and early 2026 signal that the two categories are no longer parallel investments but a single, coupled architectural decision.

1. Salesforce acquired Apromore. Announced before Dreamforce 2025, with the deal expected to close in Q4 2025. The strategic rationale Salesforce stated publicly: bring “deep domain expertise in process intelligence and optimization directly into the Salesforce platform” and “provide a foundation to target optimal automation use cases using process intelligence.” The acquisition is the strongest possible signal that agentic AI vendors recognize they need process mining underneath their platforms.

2. Celonis announced the Orchestration Engine at Celosphere 2025. Process intelligence is now extended to coordinate AI agents alongside people and systems as they execute end-to-end processes. Celonis also released “the world’s first model context protocol (MCP) server built for process intelligence to feed AI agents with the dynamic operational context they need to make relevant decisions and take effective actions.” Process mining is no longer just diagnostic — it is becoming the context layer that agentic AI executes against.

3. Celonis + Microsoft Agent 365 integration entered preview on May 1, 2026. Celonis Agent Mining analyzes the autonomous reasoning and logic behind every agent decision, with Microsoft providing management and security while Celonis provides decision insight. This is the first major integration where process mining is positioned as the observability layer specifically for AI agent activity.

4. ServiceNow integrated process and task mining into its AI agent workflows in its latest platform release, following the same architectural pattern.

5. The narrative shift among industry analysts. Process Excellence Network’s November 2025 article was titled “Process intelligence tipped to rescue failed AI projects in 2026” — capturing the emerging consensus that the 95% AI pilot failure rate documented by MIT’s Project NANDA (July 2025) was significantly attributable to organizations automating processes that hadn’t been discovered, understood, or redesigned first.

The strategic implication for enterprise buyers: choosing process mining and agentic AI as separate procurement decisions, on separate timelines, is increasingly unlikely to produce the compound effect the strongest 2026 deployments achieve. The two layers belong together. For the broader strategic framing, see How Enterprise Leaders Build a Long-Term AI Automation Strategy That Scales.

The order matters: the three-step sequence that compounds

The convergence is not just about owning both layers. It is about applying them in the right sequence. The strongest 2026 enterprise AI deployments follow a specific three-step pattern that makes the layers compound rather than compete.

Step 1: Discover with process mining

Before any agentic AI deployment, the organization should know what the process actually looks like, not what it was designed to look like. This is the diagnostic phase that process mining is uniquely positioned to deliver.

What discovery surfaces:

  • The actual sequence of activities in the process (often very different from the documented sequence)
  • The rework loops where the same activity gets repeated due to errors, exceptions, or handoff failures
  • The bottlenecks where cycle time concentrates (often a single approval step that accounts for 40% of total cycle time)
  • The variance across cases (the “happy path” might represent only 30% of actual cases, with 70% taking some variant)
  • The manual workarounds where people bypass the system entirely (the spreadsheet that lives alongside the ERP)
  • The cases where the documented process is being violated routinely (sometimes because the documented process is wrong)

The output of Step 1 is a fact base about how the process actually runs. This is dramatically different from a process documentation exercise, which captures how people say the process runs. The fact base is what makes Step 2 possible.

Step 2: Redesign before you automate

The biggest mistake in enterprise AI deployment is automating processes that should be redesigned first. The Apromore Chief Product Officer captured the risk: “If you don’t fix the process first, you’ll automate the chaos.”

Step 2 uses the process mining fact base to decide:

What stays manual? Some activities should remain human-only because they involve judgment, relationship management, or strategic decision-making that AI cannot reliably replace.

What should be eliminated entirely? Some activities exist only because of historical reasons (a control that was added in 2017 to address a problem that no longer exists) and should be removed, not automated. Automating a useless step makes the step run faster but doesn’t add value.

What should be redesigned, then automated? Many activities exist in their current form because of system constraints (the ERP forces a particular sequence, the approval routing was set up before microservices, etc.). These should be redesigned, then automated. Automating the existing form perpetuates the constraint.

What is ready to automate as-is? Some activities are well-designed and ready for direct automation. Process mining identifies which ones, by ruling out the first three categories.

The output of Step 2 is a portfolio of automation candidates that have been validated as both valuable and ready. This is what gets handed to the execution layer in Step 3.

Step 3: Execute with agentic AI, then close the loop

Step 3 is where agentic AI takes over. The validated automation candidates from Step 2 become the production scope for the agentic AI platform. The platform handles:

  • The autonomous decisions inside the process (matching invoices, classifying claims, routing tickets, reconciling transactions)
  • The exception handling when the AI encounters cases that don’t fit the policy
  • The audit trail that demonstrates each decision is defensible to regulators and external auditors
  • The integration with systems of record where the decisions take effect

But Step 3 is incomplete without closing the loop back to Step 1. Process mining doesn’t stop being useful after automation. Once agentic AI is running, process mining becomes the observability layer that monitors agent performance:

  • Are the agents making decisions at the volume and accuracy expected?
  • Are new exception patterns emerging that the original policy didn’t anticipate?
  • Are downstream processes (customer service, accounting, audit) seeing the expected improvements?
  • Are there process changes upstream that mean the agentic AI’s logic needs to be updated?

The loop closes because process mining detects changes in process behavior — whether due to new product launches, organizational changes, regulatory updates, or vendor changes — that should trigger updates to the agentic AI’s policies.

The strongest 2026 deployments treat the three steps as a continuous cycle, not a one-time project. Discover, redesign, execute, observe, redesign again. This is the pattern that produces compound improvement rather than one-time efficiency gains that erode over time.

Why skipping either layer fails

The dominant failure modes in 2026 enterprise AI all come from skipping one of the three steps.

Failure mode 1: Agentic AI without process mining (“automate the chaos”)

The pattern: an enterprise picks an agentic AI platform, identifies a workflow that’s painful (typically AP processing or customer service routing), and deploys the AI to automate it. The deployment goes live. The pain reduces somewhat in the short term. Over 6-12 months, the underlying inefficiencies that nobody mapped before automation start to compound. The agent is now executing the broken process at higher speed.

The MIT Project NANDA July 2025 finding (95% of enterprise GenAI pilots deliver zero P&L impact) is significantly attributable to this failure mode. The pilots were not technical failures; they were strategic failures from automating processes that shouldn’t have been automated as-is. The AP-specific version of this pattern is documented in our Why Most Agentic AP Pilots Stall at 70% Touchless post, and the broader category in The 7 Places Generative AI Quietly Fails in Accounts Payable.

The fix is Step 1 (process mining first) before Step 3 (execution).

Failure mode 2: Process mining without agentic AI (“the beautiful map you cannot use”)

The opposite pattern: an enterprise invests heavily in process mining, produces detailed visualizations of every major business process, identifies dozens of automation opportunities, and then stalls at the handoff to execution. The traditional automation platforms (RPA bots, iPaaS workflows) can handle the simpler automation candidates but cannot handle the reasoning-heavy, exception-heavy workflows where the highest-value opportunities live.

The fix is Step 3 (agentic AI execution) after Step 2 (redesign). Without execution capability, process mining produces analysis that the organization cannot act on. For an evaluation framework that surfaces whether your candidate platforms have the right execution capability, see The Agentic AI RFP Template: 30 Questions to Ask Every Vendor in 2026.

Failure mode 3: Both layers, but no closed loop

The most subtle failure mode: an enterprise invests in both process mining and agentic AI, completes Step 1, redesigns in Step 2, deploys in Step 3, and then treats the deployment as finished. Six months later, the process has shifted (new vendor relationships, organizational changes, product launches), but the agentic AI’s policies still reflect the original design. The agent’s accuracy drifts. The process mining team isn’t monitoring agent behavior. By the time someone notices, remediation work is substantial.

The fix is treating the three steps as a continuous cycle with process mining serving as ongoing observability for agent activity. Celonis’s Agent Mining capability, Apromore’s process intelligence integration with Agentforce, and ServiceNow’s task mining inside AI agent workflows are all responses to this specific failure mode. For the audit-trail backbone that makes that observability loop work, see AI Audit Trail Requirements: A 2026 Checklist.

What the strongest 2026 deployments look like

Across the enterprises we observe in 2026, the most successful AI automation programs share four operational patterns that combine process mining and agentic AI deliberately.

1. They sequence the investment, not parallelize it. The first 90 days are process mining only. The next 90 days are redesign and small-scale agentic AI piloting on the validated candidates. The next 90 days are scaled deployment with process mining continuing as observability. Programs that try to run process mining and agentic AI deployment in parallel from the start typically struggle because the agentic AI piloting team makes assumptions about the process that the process mining team is still analyzing.

2. They explicitly classify automation candidates into four buckets. Manual-only, eliminate, redesign-then-automate, automate-as-is. Programs that skip this classification usually find that 40-60% of their initially identified “automation candidates” should have been in one of the first two buckets. The classification work is uncomfortable but produces dramatically better ROI than direct automation.

3. They use process mining’s output to write the agentic AI’s policy. The most efficient deployment pattern uses the process mining diagnostic to inform the English-language policy that the agentic AI executes. When process mining identifies that 35% of AP exceptions come from master data drift (per the four-quadrant exception mix documented in our agentic AP pilot post), the agentic AI’s policy can be written to handle master data drift explicitly. This is more efficient than discovering the exception pattern through agentic AI deployment.

4. They maintain process mining as ongoing observability, not a one-time exercise. The Celonis Agent Mining capability, the Apromore-Agentforce integration, and the broader convergence of process mining with agentic AI are all infrastructure investments that enable the continuous loop. Organizations that treat process mining as a “do once, hand off” exercise miss the compound benefit. The HITL design pattern matters here too — see The Hidden Cost of Human in the Loop for the operational design that keeps the loop running.

How Kognitos fits into the discover-redesign-execute sequence

Kognitos is positioned in Step 3 of the three-step sequence: the execution layer where validated automation candidates become production agentic AI deployments. The platform is designed to work alongside process mining tools (Celonis, Apromore, Signavio, KYP.ai, Skan) rather than competing with them.

The architectural fit:

English-as-code policies make process mining outputs directly executable. When process mining identifies that vendor master drift causes 35% of AP exceptions, the policy that handles vendor master disambiguation can be written in plain English: “When the vendor name on the invoice resolves to multiple ERP records, route to AP supervisor unless the invoice references a PO whose vendor record is unambiguous, in which case match to that record and update the vendor name normalization.” The policy uses the same language the process mining diagnostic produces. For the deeper architecture, see What is English as Code?

Deterministic execution preserves the process integrity that process mining established. Once a process has been mined, redesigned, and validated, the execution should produce the same result every time. Probabilistic AI can drift from the validated process behavior in ways that process mining will eventually detect; deterministic AI maintains the validated behavior consistently. The underlying architectural pattern is covered in What Is Neurosymbolic AI?

Audit trails support the continuous-loop observability that process mining requires. Every Kognitos decision logs with the 12-field audit trail standard (covered in our AI audit trail requirements post). This event log is exactly the data process mining platforms ingest for continuous observability of agent behavior. The two layers connect naturally. For the auditor’s view, see What Your SOX Auditor Will Ask About Your AI Automation.

One architecture across multiple validated workflows. As process mining identifies automation candidates across AP, three-way match, vendor master, claims, reconciliation, and operations, Kognitos handles them on one platform. This reduces integration overhead at the handoff between process mining and execution, and produces a consistent observability surface for ongoing monitoring. For the 3-way-match vendor landscape specifically, see Best Procurement Automation Platforms for 3-Way Match Validation.

Customer references for this integrated pattern include Paysafe (significant AP cost optimization), JBI Interiors (3,300 hours saved annually through workflow automation following operational discovery), and a Fortune 50 food and beverage partner (approximately 23x projected ROI on operational automation). Century Supply Chain processes 50,000+ Bills of Lading per month on the Kognitos platform, demonstrating that the discover-redesign-execute pattern scales beyond AP into broader operational reasoning.

Recognized in 2026 as:

  • #1 Exemplary Provider in the 2026 ISG Buyers Guide for Automation and Orchestration
  • Most Innovative AI Product at SiliconANGLE Media’s 2026 Tech Innovation CUBEd Awards
  • Gold Globee® Winner and Best in Category for Neuro-Symbolic AI Platform (2026 Globee Awards for AI)
  • Natural Language Understanding Solution of the Year in the 2026 AI Breakthrough Awards
  • Sample Vendor in the Gartner® Hype Cycle™ for AI in Finance, 2025

Compliance and trust: SOC 2 Type II, HIPAA, GDPR, and ISO 27001 aligned (see our Trust portal). ISO/IEC 42001 alignment work underway.

Book a working session with a Kognitos solutions engineer → Or try Kognitos free →

Frequently Asked Questions

Process mining is the diagnostic layer of enterprise operations. The platforms (Celonis, Apromore, Signavio, IBM Process Mining, KYP.ai, Skan) extract event logs from systems of record, reconstruct the actual paths processes take, and visualize where the friction lives. Agentic AI is the execution layer that takes autonomous or semi-autonomous actions inside those processes. The platforms (Kognitos, UiPath Agentic Automation, Microsoft Copilot Studio, Salesforce Agentforce, and others) handle reasoning, exception management, and decisions. Process mining answers “what should we automate”; agentic AI answers “how do we automate it.” The two are complementary layers, not competing categories.
Five developments in late 2025 and early 2026 made the convergence visible. Salesforce acquired Apromore ahead of Dreamforce 2025 to bring process intelligence into Agentforce. Celonis launched the Orchestration Engine, Agent Mining, and the first MCP server for process intelligence at Celosphere 2025 (November 4, 2025). Celonis and Microsoft Agent 365 integration entered private preview on May 1, 2026. ServiceNow integrated process and task mining into its AI agent workflows. The industry narrative shifted toward process intelligence as the prerequisite for AI ROI, captured by Celonis CEO Alex Rinke’s statement: “There’s no AI without PI.” The convergence is happening because agentic AI vendors recognized that scaling automation requires knowing what to automate first, and process mining vendors recognized that diagnostic insights only become valuable if the organization can execute on them.
Yes. The three-step sequence that produces compound benefits is: discover with process mining first, redesign before automating, then execute with agentic AI. Programs that skip the discovery step risk automating broken processes at machine speed (the failure mode Apromore CPO Marlon Dumas describes as “automating the chaos”). The MIT Project NANDA July 2025 finding that 95% of enterprise GenAI pilots deliver zero P&L impact is significantly attributable to this pattern. Process mining first, agentic AI second, with the loop closing back to process mining for ongoing observability of agent activity. The order is not optional; it is the difference between a strategy that compounds and one that stalls.
No. Process mining and agentic AI answer different questions. Process mining extracts event logs from systems of record and reconstructs how processes actually run, including the rework loops, manual workarounds, and exceptions that nobody knew about. Agentic AI executes decisions inside processes but doesn’t reconstruct the broader process behavior from event logs. Even with agentic AI deployed, process mining remains valuable as the observability layer that monitors agent activity, detects drift, and surfaces new exception patterns. Celonis’s Agent Mining capability and Apromore’s integration with Agentforce are explicit responses to this — process mining is becoming more important alongside agentic AI deployment, not less.
Yes. Salesforce announced the acquisition of Apromore ahead of Dreamforce 2025. The deal was expected to close in the fourth quarter of 2025. Salesforce stated that Apromore would bring “deep domain expertise in process intelligence and optimization directly into the Salesforce platform” and would specifically provide visibility across all business processes, systems, and applications, and a foundation to target optimal automation use cases using process intelligence. Apromore was founded in 2009 by Marlon Dumas and the Queensland University team and had raised $30 million in funding. Salesforce was already an investor in the company prior to the acquisition. The acquisition is one of the most significant signals that process mining and agentic AI are converging at the enterprise platform level.
Celonis Agent Mining is a capability announced at Celosphere 2025 (November 4, 2025) and expanded in the Microsoft Agent 365 integration (May 1, 2026 private preview). The capability analyzes the autonomous reasoning and logic behind every AI agent decision, treating agent activity as another data source that process mining can ingest, analyze, and visualize. Unlike traditional process mining (which monitors human and rule-based system activity), Agent Mining specifically focuses on AI agent behavior. The strategic positioning is that agents offer reasoning traces that explain why decisions were made — a critical factor for continuous learning, self-healing processes, and audit-readiness under regulatory standards like COSO February 2026 and EU AI Act Article 11.
The choice depends on your existing technology investments and the workflows you’re prioritizing. Celonis is the strongest option for ERP-centric process mining with deep SAP, Oracle, and Microsoft Dynamics extraction, plus mature integration with major agentic AI platforms (Microsoft Agent 365, MCP server for AI agents). Apromore is now positioned for buyers committed to the Salesforce ecosystem through the Agentforce integration. SAP Signavio is the natural choice for SAP-centric organizations. KYP.ai and Skan focus on activity-based process intelligence that captures how people work across all applications (not just ERP event logs), which matters if 70%+ of operational work happens outside the ERP. The right choice is the one that integrates with both your existing systems and the agentic AI platform you’re deploying for execution.
The process is ready when four conditions are met: the actual sequence has been mapped via process mining (not just documented from interviews), the rework loops and manual workarounds have been identified, the candidates have been classified into the four buckets (manual-only, eliminate, redesign-then-automate, automate-as-is), and the redesign work has been completed for any candidates in the third bucket. If any of these four conditions are missing, the process is not ready for agentic AI deployment. Common failure modes include automating documented processes that don’t match actual processes, automating activities that should have been eliminated, and automating poorly designed processes that produce automation regret 6-12 months later.
Yes, and increasingly the most valuable application. Once agentic AI is deployed in production, process mining becomes the observability layer that monitors agent behavior. The questions process mining answers in this mode: Are the agents making decisions at the expected volume? Are new exception patterns emerging? Are downstream processes seeing the expected improvements? Has the underlying process drifted in ways that require updating the agent’s policies? Celonis’s Agent Mining, Apromore’s process intelligence integration with Agentforce, and ServiceNow’s task mining inside AI agent workflows are all infrastructure for this observability use case. Treating process mining as a one-time exercise before agentic AI deployment misses 50%+ of the value.
Treating the two as parallel investments instead of a sequenced architecture. Common pattern: the procurement team buys a process mining platform and an agentic AI platform on the same timeline. The process mining team and the agentic AI team operate independently. The agentic AI team builds pilots before the process mining team has completed discovery. Six months later, the agentic AI pilots are stalling because the underlying processes have inefficiencies the team didn’t know about. The fix is sequencing: process mining and discovery first, redesign second, agentic AI execution third, with process mining continuing as observability after execution. The sequence is not optional; it determines whether the investment compounds or stalls.
Kognitos sits in the execution layer (Step 3 of the discover-redesign-execute sequence). The platform is designed to work alongside process mining tools rather than competing with them. The architectural fit is direct: process mining’s outputs (which workflows to automate, what the exception patterns look like, what the validated process design should be) become inputs to Kognitos’s English-as-code policies. Deterministic execution preserves the validated process design that process mining established. The 12-field audit trail Kognitos produces feeds directly into process mining platforms for ongoing observability of agent behavior. Customers commonly deploy Kognitos for AP automation, three-way match, vendor master cleanup, claims processing, and other operational workflows that process mining identified as automation candidates after discovery and redesign.

Related reading

Last updated: May 2026. This article is intended for informational purposes and does not constitute legal, audit, or procurement advice. Specific platform capabilities and integration patterns should be verified with vendors directly. The Salesforce acquisition of Apromore was announced ahead of Dreamforce 2025 with the deal expected to close in Q4 2025. The Celonis + Microsoft Agent 365 integration entered private preview on May 1, 2026. Process mining and agentic AI vendor positioning continues to evolve rapidly.

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