The 5 Best Skan AI Alternatives in 2026

Skan AI is built for operations and transformation teams at large enterprises, especially banks and insurers. It shows them how work gets done across their departments, including the steps in email, spreadsheets, and browser tabs that never show up in a system report. Its sensor runs on each employee's desktop and captures work across the applications they use. From what it sees, it maps how each process runs, recommends which tasks to automate, puts a projected dollar value on each one, and sells AI agents to do the work.

Most Skan AI alternatives you'll come across are process mining tools. They build their picture of each process from the records your ERP and CRM systems keep. Those records leave out much of the work that happens between systems.

Another alternative, Fluency, fully maps each process from what it observes on the desktop, including the exceptions and variations that make one case run differently from the next. It then builds AI agents to automate the work your team approves. The agents update themselves as the process changes, so they keep saving time instead of breaking. Fluency measures those savings in time and money against a baseline it records before each automation goes live.

Below, we compare five Skan AI alternatives on what each one can see, how long it takes to set up, and how it finds work to automate. By the end, you'll know which kind of tool fits your operation and which ones belong on your shortlist.

TL;DR

• Fluency is the closest match to Skan AI. It observes desktop work continuously, then builds AI agents on the work worth automating and measures what each one saves. Setup takes under an hour.
• UiPath Process Intelligence is a good fit if your automation already runs on UiPath. It combines process mining with desktop recording and turns what it finds into UiPath robots and agents.
• Celonis suits large companies whose key processes run inside ERP and CRM systems like SAP, as long as they have data engineers to set it up. It analyzes the records those systems keep in depth.
• IBM Process Mining builds a working model of each process from your ERP and CRM records. You can simulate a change on that model, like removing an approval step, and see how it affects the process before you pay to build it.
• SAP Signavio fits operations that run on SAP. It comes with ready-made analyses for processes like procure-to-pay and tools to map how each process should run.

Why enterprise teams look for Skan AI alternatives

Skan AI does two things particularly well. It processes raw screen data inside your own network, which helps if you work at a bank or insurer with strict rules about where data can go. It also connects activity from different people and systems into a clear map of each process.

Teams that move away from Skan AI usually do it for one of three reasons:

• The rollout can take months. Skan AI processes your data inside your own network, so your IT team has to set up the infrastructure Skan AI runs on before it can start. You've signed the contract, but you won't see any results until that IT project is done.
• The full cost is hard to predict. Skan AI sells Blueprint, Process Intelligence, and Agents as separate products, and some features are add-ons or need Skan to switch them on. If you start with the discovery products, Blueprint and Process Intelligence, you'll need to buy Agents and get another budget approval before any of the work they find is automated.
• Complex work is difficult to customize. Teams whose workflows have lots of exceptions find Skan AI difficult to customize around the way they work.

If you like how Skan AI collects workflow information but these problems are too significant, Fluency also observes work at the desktop, installs in under an hour, and builds and maintains the AI agents itself.

A process mining tool is the other option. It builds each process from the records your ERP and CRM systems already keep. However, setting up these tools is typically a heavy lift because someone has to connect each source system, pull out the records, and model the data before you see anything.

Best Skan AI alternatives at a glance

The table below puts Skan AI next to the five alternatives. Each row shows how the tool finds work to automate, what setup it needs before a first result, how it deploys automation, and where it fits best.

The best Skan AI alternatives in 2026

Below, we break down the five tools by their key features, who each one suits, and what you'd gain or give up by moving from Skan AI. Fluency is the only adjacent software that observes work similarly to Skan AI, while the other four are process mining tools.

1. Fluency: Best for finding and deploying AI automation

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 to automate the work, and measures results against an observed baseline. It suits teams that like Skan AI's continuous view of work but want to get insights and working automations faster.

Key features:

• Work Explorer maps how work runs across systems, teams, and handoffs.
• Opportunities ranks every automation opportunity by time saved, frequency, rework rate, and estimated cost.
• Automations deploys AI agents on the opportunities your team approves.

Fluency's desktop agent installs in under an hour with no backend integrations, so you can see insights within hours. The agent deletes anything irrelevant to work (like personal emails or banking) before it leaves your computer, and doesn’t tie anything back to an individual.

Everything it captures goes into a work ontology, a live map of every task, handoff, and document in your operation. The map includes the unwritten steps your teams take to get work done, so the automation opportunities Fluency ranks for you accurately reflect how work in your organization runs today.

When you approve an automation opportunity, Fluency builds AI agents to automate the process. As the process changes, the agent adapts, instead of needing to be rebuilt, like a scripted RPA bot. Each agent's savings are measured against the baseline Fluency recorded before it went live, making it easy to understand the ROI on automation deployment for a process or operation.

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2. UiPath Process Intelligence: Best for turning discovery into UiPath automation

UiPath Process Intelligence is the tool within UiPath that finds work to automate. UiPath is an automation platform best known for robotic process automation (RPA). It now sells AI agents too. If your company already runs its automation on UiPath, Process Intelligence is the natural place to start, because anything it finds can go straight into a UiPath robot or agent without switching tools.

Key features:

• Process Mining reads the logs from systems like SAP, Oracle, Salesforce, ServiceNow, and Workday.
• Task Mining records specific desktop tasks your team selects, which fills in work the logs miss.
• Maestro coordinates the people, robots, and AI agents that run each workflow.

The biggest difference from Skan AI is how much desktop work UiPath sees. Skan AI's sensor captures work for as long as it's installed. UiPath's Task Mining only records the tasks your team picks. Someone records each one as a separate trace, and UiPath's guidance is to keep each trace under an hour. Task Mining then merges the traces into a picture of the workflow, so a task nobody thought to record won't show up.

For the work Task Mining doesn't record, UiPath relies on Process Mining, which rebuilds each process from system logs. Each system logs only its own steps. An order might be created in Salesforce, invoiced in SAP, and closed in ServiceNow, leaving three separate records. Those records have to be joined before you can follow one order from start to finish and see where the time goes.

UiPath only sells Process Mining and Task Mining in its Standard and Enterprise tiers, so you'll need one of those plans.

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3. Celonis: Best for enterprise-scale process mining

Celonis is a process intelligence platform built on the event logs that ERP and CRM systems like SAP, Oracle, and Salesforce record. It fits large enterprises whose key processes run inside those systems and that have data engineers to model them.

Key features:

• Object-centric process mining tracks related orders, invoices, and shipments together.
• Conformance checking compares how a process runs with how it's supposed to run.
• Action Flows trigger alerts and third-party automations in connected systems.

Moving from Skan AI to Celonis trades the desktop view for a deeper look inside systems like SAP, Oracle, and Salesforce, where Celonis can follow an order from entry to shipment and flag each step that departs from plan.

But Celonis can't see the work that happens between those systems. When someone updates a spreadsheet or approves an invoice over email, it doesn’t record it. That hidden work is often where the delays and manual effort hide.

Celonis needs a lot of setup before you see results. Your team has to connect each source system, pull out the event records, and build a data model first, which can take months.

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4. IBM Process Mining: Best for simulating process changes before building them

IBM Process Mining is IBM's process analysis platform built for teams that want to test a process change before paying to build it. It turns ERP and CRM data into a digital twin of each mined process.

Key features:

• What-if simulation predicts the effect of a process change before anyone builds it.
• Compliance monitoring flags where work deviates from the intended process.
• RPA bot scaffolding gives teams a head start on bots for mined processes.

Simulation is the main reason to choose IBM. You can try out a change, like removing an approval step or moving work to another team, and see how it would affect the process before you spend money building it.

The trade-off is the same one you'd make with Celonis. IBM only sees what your connected systems record, so the work people do between those systems stays hidden. Before your first analysis, your IT team also has to pull the event logs out of each system and clean them up.

IBM starts at $4,250 a month for SaaS and $2,885 a month on premises. A published price is easier to budget for than Skan AI's custom quote. If keeping data inside your own network is one reason you looked at Skan AI, IBM's on-premises version does that too.

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5. SAP Signavio: Best for SAP-centric processes

SAP Signavio is SAP's suite for analyzing and redesigning business processes. It mines system data to show how each process runs and gives teams modeling tools to document how it should run. It’s a good fit for operations whose core processes run through SAP, although it does mine processes outside of SAP as well.

Key features:

• Prebuilt value accelerators cover processes like procure-to-pay and order-to-cash.
• Process modeling lets teams document each process and govern changes to it.
• Connected actions send findings to SAP Build Process Automation or third-party RPA tools.

If your processes run through SAP, Signavio can show you how they're performing sooner than a tool you set up from scratch because of its value accelerators, which are ready-made packages for common SAP processes like procure-to-pay. Each one comes with the data connections, performance metrics, and industry benchmarks already built, making it easy to see how long invoices take to get paid and where they stall, without building any of it yourself. You can also document how each process should run in the same suite.

If a process runs on systems other than SAP, like Oracle or Salesforce, most of those ready-made packages won't apply. Your team has to connect each system, pull out the data, and set up the metrics itself. That's the same setup any process mining tool needs before it can show you how long each step in a workflow takes and where a process gets stuck.

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How to choose between observing work and mining logs

Fluency and Skan AI observe work as it happens on the desktop. Celonis, IBM, SAP Signavio, and UiPath mostly rebuild it from the records your business systems keep. The approach that suits you depends on where your work happens, how soon you need results, and what you want to do once you know what to automate.

It's easier to decide with a specific process in mind. Consider a supplier invoice that needs a manager's approval. Someone enters it in the ERP, the manager approves it by email, and a clerk updates a tracking spreadsheet before the payment goes out. With that process in mind, work through the three questions below, then pick the approach that matches your answers.

Where does the work you want to automate happen?

A process mining tool sees what your systems record. In our example, the ERP records when the invoice was entered and when it was paid. The email approval and the spreadsheet update leave no record, so the tool can tell you how long the invoice took but can’t show the entire process.

Continuous observation in tools like Fluency and Skan AI catches the work outside your core systems, like the email approvals and spreadsheet updates, because it builds an understanding of what people do in every application as they work. These hidden tasks are often the best ones to automate because they're repetitive and take up hours of your team's time.

In the invoice example, the clerk copies the same details from the ERP and the manager's email into the tracking spreadsheet for every invoice. A repetitive step like that is easy to automate with an AI agent. A tool that only reads logs never sees it, so it won't make your list of automation opportunities.

How much setup can you take on before you see results?

Process mining tools need a connection to each system, plus someone to extract the records and model the data before you see your first process map. Depending on how many systems are involved, the setup can take weeks to months. Even after launch, a process mining model only updates when the data is refreshed, lagging behind how work is actually done today.

Desktop observation skips the system connections, but Skan AI still needs setup of its own. It processes screen data inside your company's network, requiring your IT team to set up the systems that run it before Skan AI can start.

Fluency's desktop agent installs in under an hour with no connection into your systems, and you see your first insights within hours.

What needs to happen after discovery?

Finding work to automate doesn't save time on its own. The savings come from building the automation, keeping it running, and measuring the result. If a tool doesn't handle one of those, your team has to staff and pay for it as a separate project.

After discovery, each tool handles different things:

• Fluency builds the AI agents, keeps them updated as processes change, and measures the time and money each automation saves.
• Skan AI builds and monitors agents, with a focus on regulated work like claims and underwriting.
• UiPath Process Intelligence turns what it finds into UiPath robots and agents.
• IBM Process Mining lets you simulate a change before you build it.
• Celonis and SAP Signavio check processes against how they're supposed to run and can trigger actions in your other systems.

To see an automation's ROI, compare how long the workflow took before the automation went live with how long it takes after. In the invoice example, a log-based tool can time the invoice entry and the payment, but not the approval or the spreadsheet update. Any time an automation saves on those two steps doesn’t appear in its measurement of the workflow, rendering it inaccurate. Fluency times the whole task before and after, including the in-between steps, giving you an accurate understanding of which automations paid off and by how much.

Which approach should you choose?

Pick the approach based on where your team spends its time. If most of the work you want to automate happens between systems, in email, spreadsheets, and approvals, you need a desktop observation tool like Fluency or Skan AI. If the work happens inside SAP or another ERP, start with the process mining tools.

See where to automate first with Fluency

Fluency ranks every automation opportunity based on the hours it would save, how often the task comes up, how much rework it involves, and what it costs. You approve the ones you want from the top of the list, and Fluency deploys AI agents to automate them. The agents update themselves when the process changes, and Fluency measures how accurate each automation is and how much time it saves.

For example, in one private-markets deployment, Fluency found 63 automation opportunities. Payment authorization alone took 66 hours a month, and Fluency cut a cash flow reconciliation from 60 minutes to 10.

Fluency sees what people do inside and between systems, so its list of automation opportunities includes tasks that log-based tools miss. You get an accurate picture of where your team's time goes and, with it, an accurate ROI for every automation you deploy.

Request a Fluency demo to see where your operation should automate first.

Frequently asked questions about Skan AI alternatives

What is the best Skan AI alternative?

Fluency is the closest Skan AI alternative if you want to keep observing work at the desktop and deploy AI agents on it.

For event-log mining, look at Celonis for ERP-heavy processes, UiPath for teams already on it, IBM for simulation, and SAP Signavio for SAP businesses.

What does Skan AI do?

Skan AI observes work across applications with a desktop sensor and models it as a Context Graph of Work. Its Process Intelligence, Blueprint, and Agents products run on that graph, with raw screen data processed inside the customer's network.

How is Skan AI different from process mining tools like Celonis?

Skan AI observes work at the desktop, including spreadsheet and email work between systems. Celonis rebuilds processes from ERP and CRM event logs, going deeper inside those systems after a data-modeling project. Task mining records desktops too, but only for a set period.

How is Fluency different from Skan AI?

Both observe work at the desktop. Fluency reconstructs each process end to end from that observation, including the exceptions and variations, then builds the AI agents that automate the work your team approves. The agents update themselves when a process changes, and Fluency measures the time and money each one saves against a baseline recorded before it went live. Setup takes under an hour, with no direct system access.

Skan AI processes raw screen data inside the customer’s network, so its rollout includes an IT infrastructure project. Its Blueprint product puts a projected dollar value on each recommendation, and its agents focus on regulated work like claims and underwriting.