Celonis Alternatives for Mid-Market Insurers: When Enterprise Process Mining Fits and When It Doesn’t

Key takeaways

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• Celonis is the established leader in enterprise process mining. This company won a  Leader spot in Gartner's Magic Quadrant for Process Intelligence (May 2026) and in The Forrester Wave™: Process Intelligence Software, Q3 2025.
• The deciding factor is what the tool needs from you, not how good it is. Log-based process mining reads event records out of your systems. Before it maps anything, you need a connection into each system, a way to pull the data out, someone to reshape it into a process, and someone to keep that running.
• The alternative set includes UiPath, Skan AI, Mimica, Soroco, Apromore, KYP.ai, StereoLOGIC, and Insightful. Most of these read from desktop-level capture rather than system logs. This is a different starting point, but not necessarily a better one.
• Ask every vendor the same six questions: Where its data comes from, what infrastructure it assumes you have, how fast you get a first process map, who owns it after go-live, whether it captures work that happens between systems, and whether it can produce a before-and-after baseline.

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Lists of Celonis alternatives and competitors tend to rank vendors against one another. Process intelligence platforms differ less in quality than in where they source their data, and that difference determines the fit.

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Celonis reads event data extracted from your source systems. That architecture is what makes it strong at enterprise scale, and it's also what sets its prerequisites: usable event logs, a way to extract them, and someone to own that pipeline. Where those exist, Celonis is the strongest platform in the category. Where they do not, the decision turns on which platform draws from data you can already hand it.

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What Celonis is, and what it is good at

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Celonis is an enterprise process intelligence platform built on process mining. It reconstructs how a process actually ran by reading event data extracted from your source systems. Then it models the process as objects and events so you can see variants, conformance gaps, bottlenecks, and rework at scale.

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Celonis is at its strongest in enterprise resource planning (ERP) environments such as SAP and Oracle EBS, as well as the transactional systems that support them. In those environments, it maps a process end to end across millions of cases and measures the financial impact of every scenario.

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It ships more than 100 pre-built process connectors, each with extraction and transformation logic already written. That’s a material advantage over building the layer yourself. Its object-centric data model also lets one dataset serve as a base for many process questions.

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If you’re reading Celonis reviews to judge whether the platform delivers on its claims, its user base has some answers: 4.4 out of 5 from 739 ratings on Gartner Peer Insights, with 95% of reviewers giving it four or five stars. Everest Group has also named it a Leader in its Process Mining PEAK Matrix® for seven consecutive years.

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When Celonis is the right choice

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Celonis is the right choice when these are true of your organization:

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1. You are a large enterprise, with process volumes and a transformation budget that justify a platform of this scope.
2. You have a mature log infrastructure. Your core systems emit usable event data: case identifiers, timestamps, and activity names consistent enough to use in process reconstruction.
3. You have someone who owns the extraction. Building the pipeline, writing the transformations, and versioning the data model is their job. Celonis treats this as a discipline and certifies a Data Engineer role for it.
4. Your processes are centralized in an ERP environment or a combination of systems of record, so the work you care about is largely within the logs.
5. You can commit to a phased implementation and sustain executive sponsorship through it. Deloitte's Global Process Mining Survey 2025 found management support cited as a barrier by 41% of respondents, up from 26% in 2021. That barrier is organizational rather than technical, and it's often what decides whether a program lands.

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If you recognize yourself in this list, Celonis is the right choice.

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When a mid-market insurer should look at something else

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What happens when mature log infrastructure is not in place? For most mid-market P&C carriers, at least one of the following applies:

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You have no one to own the extraction. This is a staffing reality rather than a choice. Deloitte's research into companies with $250 million to roughly $1 billion in revenue found that 37% reported difficulty attracting engineering talent, and 35% reported difficulty finding data scientists. Log-based discovery depends on that capacity from the start. With no owner for the pipeline, extraction is inevitably beset with failures, inaccuracies, and compliance gaps.

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Your work spans desktop applications, not a single ERP. A P&C claims file rarely resides in a single system. It can move between policy admin, a claims platform, a document repository, email, a TPA portal, and a spreadsheet a senior adjuster built years ago.

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The work you most need to see happens between systems. This includes handoffs, re-keying, lookups, exception handling, and the workaround a team invented because a field in policy admin wasn’t editable. These kinds of activities consume time from adjusters and underwriters. Log-based discovery reads what the system committed, so steps in between fall outside its scope.

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Data readiness is the project before the project. Gartner found 63% of organizations lack or are unsure of the right data management practices for AI, and predicts 60% of AI projects unsupported by AI-ready data will be abandoned through 2026. The same dependency governs log-based process discovery. If your data isn’t ready, your first functional process map is quarters away rather than weeks.

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What actually drives cost in enterprise process mining

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Searches for Celonis pricing rarely return a figure: Celonis doesn’t publish list pricing. That’s normal for platforms scoped per environment, and why two carriers of similar size get very different quotes.

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Scope drives the number, and that scope is mostly a function of:

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• Data volume as a licensing dimension. Consumption is metered by data volume, so every added process, source system, year of history, and refresh cycle moves it.
• Extraction and integration work. Event data must be pulled from each source system before analysis can begin. Cost scales with the number of systems and the consistency of the data.
• Transformation, modeling, and the engineering time to do it. Non-standard sources need a custom transformation into the event and object model. That work sits outside the license.
• Connector maintenance. A pre-built connector moves the data, but you still configure what it extracts and how often. Every time a source system is upgraded, that work needs redoing.
• Implementation services and scope definition. You supply the requirements and KPIs. If you define them too loosely, the scope grows after you’ve committed.
• Contract term. Multi-year commitments price differently from annual ones.

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Ask for the extraction and ownership costs alongside the license quote. That combined figure is what to compare between vendors.

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The alternative set for mid-market insurers

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These are the Celonis competitors and alternatives a mid-market carrier is most likely to encounter, listed in no particular order. For the vendor-by-vendor breakdown, see our article about the mid-market process intelligence guide for insurers.

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1. UiPath: Ships a log-based process mining product and a separate desktop-level task mining product, feeding discovered opportunities into an automation pipeline.‍

Fit: Organizations building an automation backlog.

2. Skan AI: Desktop-level observation using computer vision, which the vendor states operates without log integrations.

Fit: Very large enterprises, concentrated in financial services.

3. Mimica: Desktop-level capture of user interaction, with automatic anonymization of personal data, producing scored improvement opportunities and process documentation.

Fit: Large enterprises assessing automation and AI opportunities.

4. Soroco: Builds a "work graph" primarily from human-machine interaction data, supplemented by log files.

Fit: Large enterprise operations functions mapping cross-system journeys.

5. Apromore: Process discovery from uploaded event logs and extracted transactional data, plus task mining. Became part of Salesforce in November 2025.

Fit: Organizations that already hold usable event logs.

6. KYP.ai: A desktop capture agent with on-device anonymization, paired with an enterprise integration database.

Fit: BPOs, shared services, and back-office operations in banking and insurance.

7. StereoLOGIC: Process and task mining for regulated industries, including insurance. The vendor states that task extraction is performed without desktop installation.

Fit: Carriers standardizing service and back-office procedures.

8. Insightful: A work data platform capturing activity and interaction data at the desktop and processing it into workflows.

Fit: Mid-market carriers needing a measured baseline without first building a log extraction layer.

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How to evaluate any of them

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Ask every vendor the same six questions as a diagnostic:

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1. What data source does the discovery read from? System and server event logs, desktop-level capture, uploaded event logs, or a combination. The answer determines every decision downstream.
2. What infrastructure does it assume you already have? Which systems must be connected, what data they must produce, and what must be true of your data before anything works.
3. How long to the first process map? Every vendor quotes a timeline. Ask what that timeline assumes about your data.
4. Who owns and maintains the platform after it goes live? Name the role. If it doesn’t exist at your organization today, the answer is a hire, a partner retainer, or different architecture.
5. Does it capture work that happens between systems? Ask specifically about handoffs, re-keying, exception handling, and work contained in spreadsheets and email.
6. Can it produce a before-and-after baseline? If you can’t measure the same process the same way before and after a change, you can’t prove it worked or move on to the next phase comfortably.

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For a deeper dive into the mechanics, see how process mining and task mining capture the work. Definitions for the terms used above are available in this work intelligence glossary.

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Where Insightful fits

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Insightful and enterprise process mining platforms operate at different scopes. Where the priority is ERP conformance across a large transactional environment, Celonis is the appropriate choice.

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Insightful is a work data platform that captures activity and interaction data at the desktop level and processes it into precise workflow insights. The focus of Workflow Optimization is to improve operations and drive bottom-line efficiency, starting with how people execute the work rather than what the system of record logged.

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Process mining reads system logs, while desktop-level capture reaches the work that moves between systems and happens off-system. Different starting point, different fit.

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Discover. Insightful maps how a claims workflow runs across every application an adjuster touches and shows where handle time concentrates. That gives you a measured basis for deciding which step to standardize first, and which to cut or reroute.

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Improve. Once a baseline exists, you re-measure the same workflow the same way after a change. The difference between the two is your improvement, expressed as a measured figure. McKinsey reports the average P&C expense ratio has hovered between 27% and 32% since 2005. Process improvement incrementally closes the loop using the financial signals it picks up in each iteration. .

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Automate. The same data shows which workflows and steps are worth AI investment, which are already being absorbed by AI-assisted work, and which should be left manual. Gartner's 2026 CIO and Technology Executive Survey found only 17% of organizations have deployed AI agents, but more than 60% expect to within two years. The race is on to automate efficiently, and there’s plenty of room in the market to break out.

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Insightful's Workflow Optimization integrates directly with Salesforce Service Cloud, so the baseline starts from the case activity your claims teams already generate.

Start with a measured baseline

If you are evaluating Celonis and still don’t know who will own the extraction pipeline, just measure one workflow to see where you stand. A baseline gives every subsequent conversation, including the Celonis conversation, a set of real numbers to work from.

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Workflow Optimization is available in beta for carriers running Salesforce Service Cloud. Your claim queue is mapped inside the first 14 days, and process-level data accumulates across your claims workflows from that point on. The beta runs 60 days and closes with an optimization roadmap: the route files actually take through your operation, the steps costing you handle time, and points where compliance can improve. And you don’t need to build the extraction layer first.

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Request beta access and find out where your claims capacity is actually going.

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FAQ

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What are the main alternatives to Celonis?

The main alternatives are UiPath, Skan AI, Mimica, Soroco, Apromore, KYP.ai, StereoLOGIC, and Insightful. They split into two groups: platforms that read event data from the system and server logs, and platforms that capture process data at the desktop level. The split matters more than any feature difference, because it determines what infrastructure you need before you start.

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Is Celonis worth it for a mid-market insurer?

It depends on prerequisites, not on price or quality. Celonis is worth it if your carrier has usable event data in its core systems, a named owner for the extraction pipeline, and processes centered on a small number of systems of record. If you have no dedicated data engineering resource, or your claims work moves across many desktop applications, the prerequisite gap is the thing to solve first.

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How much does Celonis cost?

Celonis does not publish list pricing, and neither do most vendors in this category. Cost is scoped per environment and driven by data volume, the number of source systems and processes in scope, years of history, extraction and transformation work, data engineering ownership, implementation services, and contract term. That is why quotes vary between similar-sized organizations. Ask for extraction and ownership costs alongside the license.

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What is the difference between Celonis and UiPath?

Both are process intelligence vendors. The difference is orientation. Celonis models event data from source systems as objects and events to analyze processes at enterprise scale. UiPath ships process mining and task mining as inputs to an automation pipeline, routing discovered opportunities into its automation tooling. Which fits depends on whether your objective is process understanding or an automation backlog.

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Do you need process mining if your processes run across desktop applications?

You need process intelligence, but log-based process mining may not be the technique that gets you there. System logs record committed transactions in the systems that produce them, so the work between those transactions never appears: re-keying, lookups, spreadsheets, email handoffs, exception handling. Desktop-level process capture starts from the execution itself, which is why it reaches that work without a log extraction layer.