How Do You Evaluate Process Intelligence Software for Enterprise AI Agents?

Only 13% of CIOs say their enterprise AI, according to a Gartner survey of 2,002 CIOs and technology executives published in October 2026 (Gartner, 2026). In our view, most enterprise AI lacks an accurate picture of how claims, onboarding, and lending work runs inside the enterprise, and process intelligence vendors, whose software analyzes how work actually runs, now offer that context in claims that sound identical.

To evaluate process intelligence software, score every vendor on your own work data: weight the criteria by where your work runs, ask demo questions that force live evidence, run a proof of concept with pass/fail metrics, test whether the output can ground an AI agent, and price the result against a 3-to-5-year total cost of ownership.

Why is process intelligence software so hard to compare?

Vendors from two different capture traditions, process mining and task mining, now describe their products with the same words, which makes their claims hard to tell apart. Gartner defines process intelligence platforms as “solutions that combine development and runtime tools to mine, analyze, model, design, and monitor processes,” and it lists process and task mining among their capabilities.

Forrester states that “process and task mining are converging” and that most process mining vendors have added task mining.

The traditions still differ underneath: process mining reconstructs end-to-end flows from enterprise resource planning (ERP) and customer relationship management (CRM) event logs, which gives depth but requires integration with each system of record, while task mining records user interactions on the desktop without backend integration but typically stays at the task level.

A Springer overview of process mining notes that “data extraction and data cleaning may take 80% of the time” when a team starts out. A credible process intelligence vendor evaluation tests which trade-off each product makes.

Which evaluation criteria matter most for process intelligence software?

In our suggested weighting, data capture coverage carries the most weight, because every downstream insight and agent depends on what the tool can see. The weights below reflect our view, not a neutral standard, so adjust them to your process mix:

Does the tool capture work across your real application stack?

Ask each vendor to show capture across the applications your operators use, including those that lack clean logs.

An arXiv paper on business process automation states that “not all tasks within a process are visible in an event log,” citing email attachments and Excel edits (arXiv, 2025). Virtual desktops create a second gap. Everest Group’s “Digital Interaction Intelligence Products PEAK Matrix Assessment 2024” (Everest Group, 2024) reports that most task mining providers capture data on persistent virtual desktop infrastructures (VDIs) but do not support non-persistent VDIs.

Per UiPath documentation as of November 2026, the Task Mining prerequisites for Automation Suite 2023.4 state that “VM is being captured only with screenshots, without any metadata.”

Skan AI takes a different approach to address this gap, building a Context Graph of Work process activity rather than logs. Skan Virtual Assistant (Skan VA) is a lightweight, privacy-safe desktop sensor that uses computer vision to capture screens, clicks, and application switches across mainframe, Citrix, VDI, legacy desktop, web, and office applications, capturing work with zero integrations, application programming interfaces (APIs), or logs.

Some task mining tools also deploy without integrations, so test whether a product delivers end-to-end depth without them.

How deep does the process view go?

Score depth on one complete process: discovery, variants, root cause, deviation from the documented procedure, rework loops, and drift over time. A digital twin of operations adds what-if analysis on observed processes. It differs from the digital twin of an organization (DTO) that

Gartner evaluates in its July 2026 Magic Quadrant for Digital Twin of an Organization Platforms (Gartner, 2026), which Gartner describes as “a dynamic model to help enterprises plan, monitor, and scale complex initiatives.” Skan Intelligence builds the Context Graph of Work from observed work, including end-to-end process maps, variant analysis, bottleneck and rework detection, and drift detection.

Does insight convert to action?

Check whether you can inspect the logic behind each automation recommendation, including the vendor’s own. Skan Intelligence scores each task separately for robotic process automation (RPA) and agentic automation. Skan AI also runs agents, so ask Skan AI and every other vendor to show the inputs and logic behind each score. Vendr’s 2026 UiPath pricing page reports that UiPath’s process and task mining modules are often sold as add-ons to robot licenses, so the same question applies there.

What will it cost to own?

Licensing is only one part of the estimate. TechTarget reports that most process mining pricing rests on business volume or data capacity, and that some vendors also offer per-user, per-process, enterprise-wide, or value-based licensing. Deloitte states that process mining “is not a plug-and-play technology” and that extraction and transformation “can demand a lot of effort.”

Add implementation services, data engineering, connector maintenance, and training to every quote, and ask for time to first insight in weeks, with a reference customer who can confirm it.

Can the output ground your AI agents?

In Skan AI’s view, agents need operational context they can read, trust, and audit, and a vendor’s output has to supply it in a form an agent can actually use. Gartner predicts that by 2027, 40% of enterprises will demote or decommission autonomous AI agents due to governance gaps identified only after production incidents occur (Gartner, 2026).

Apply these tests to every shortlisted vendor to see how their output can help ground your AI agents:

• Machine-readable model: The output provides a queryable context model.
• Programmatic access: Agents consume the context through an API or Model Context Protocol (MCP) server.
• Current data: The model updates continuously and flags drift.
• Exception paths: The model includes workarounds, escalations and the happy path.
• Audit trail: Every input, decision and action is logged for regulators.
• Human baselines: Agent performance can be compared against observed human performance on the same work.

When an agent decides how to handle a case, banks, insurers, and healthcare payers need that decision grounded in how their work actually runs.

Skan AI, an agentic AI platform for regulated enterprises that 3 in 5 of the top US insurance carriers use, builds a Context Graph of Work from real process activity, an end-to-end map of how work moves across applications, and that graph serves as the record of how work runs. From it, Skan AI generates agentic operating procedures (AOPs), which are machine-readable definitions of steps, decision logic, exceptions, policies, and escalation thresholds.

The sequence runs Skan AI Blueprint, Skan Intelligence, and Skan AI Agents: Blueprint identifies opportunities, Intelligence analyzes and drives process transformation, and Agents execute under AOPs after simulation against human baselines.

What should you ask in every vendor demo?

Each question below forces evidence instead of a scripted walkthrough:

1. Can you show results on live data from our systems, not your demo tenant?
2. How long until our first insight, and what work does our team do before then?
3. How much effort does event log extraction and mapping take for our ERP, CRM and core banking or claims systems?
4. What do you capture on mainframe green screens, Citrix and non-persistent VDI sessions?
5. How do agents consume your output, and what format and access method do they use?
6. Where does raw screen or log data go, and what leaves our environment?

What does a real proof of concept look like?

A proof of concept should end in a yes or no decision. Structure it in six steps:

1. Pick one process. Choose a high-volume regulated process such as claims, KYC onboarding or loan origination.
2. Scope the data. List every application the process touches, including spreadsheets and email.
3. Set the baseline. Record cycle time, rework rate and variant count before the pilot. Public benchmarks give context: J.D. Power’s 2026 U.S. Property Claims Satisfaction Study reports an average of 40.7 days before U.S. homeowners customers receive final payment, down from more than 44 days from first notice of loss to final payment in its 2025 study. ICE’s May 2026 Mortgage Monitor reports that the average purchase loan closed in 36.8 days in March 2026.
4. Define pass/fail metrics. Fix thresholds for capture coverage, variant accuracy validated by your process owners, and agent-readiness tests before kickoff.
5. Set the timeline. Plan in weeks, matched to the scope.
6. Hold a decision gate. Compare results against thresholds and decide to expand, retest or stop.

Integration and security requirements to set upfront

Settle deployment and data handling before any demo reaches procurement:

• Deployment: Ask whether the vendor deploys on-premises inside the customer firewall, in private cloud, or vendor hosted. Skan AI offers all three, including Skan AI hosted.
• Data handling: Skan PRISM, an on-premises gateway, applies pixel-level redaction before transmission, so raw screen data stays in the customer environment and only anonymized metadata reaches analytics.
• Privacy controls: Controls include application allow-listing, pseudonymized identities, customer-defined retention with automated deletion, and right to erasure.
• Assurance: Skan AI’s security materials list a SOC 2 Type 2 report, ISO 27001 and ISO 42001 certifications, and HIPAA, GDPR and CCPA compliance.

Test the endpoint footprint, meaning the software that runs on each employee’s machine, on your Citrix, VDI, and terminal-emulation images, and design for privacy by design, with analysis focused on work patterns rather than individuals. In Germany, §87 BetrVG gives works councils co-determination rights over technical equipment capable of monitoring performance, so engage them early. Map logging to the EU AI Act, which requires providers and deployers to retain high-risk system logs under their control for at least 6 months, unless other applicable law provides otherwise.

How to build a vendor shortlist

Apply the scorecard to each candidate, and let fit follow where your work runs. Vendor documentation describes these capture approaches:

• Celonis: Per Celonis documentation as of November 2026, extractors connect to systems such as ServiceNow, Oracle NetSuite and JDBC databases. A Task Mining client captures desktop events, and the developer changelog lists an Agent Tools (MCP) asset released in Private Preview.
• SAP Signavio: Per SAP documentation as of November 2026, the feature scope description lists connectors for SAP ERP, ServiceNow, Snowflake and other sources. Task mining comes through the partner product SAP UEM by Knoa, and the October 2026 release added a read-only MCP server as a beta feature in Lab Space.
• UiPath: Per UiPath documentation as of November 2026, Process Mining loads data through CData Sync and Theobald Xtract Universal, and the Task Mining Recorder captures screenshots and metadata.
• Skan AI: Desktop computer vision provides end-to-end depth with zero backend integrations, with on-premises deployment available.

Two further names belong on many shortlists: Apromore, which Automation Today lists as part of Salesforce among the providers in Gartner’s 2026 evaluation, and Mimica, which HFS Research covers as a task mining vendor adding agentic AI.

Next steps for your evaluation

Start your scorecard with a process your operators run outside clean systems of record.

Download Process Intelligence: A Buyer’s Guide for the full criteria set and proof of concept templates.

Request a demo to explore how observed workflows become context for governed AI execution.