Many large organisations can describe in detail how a process is supposed to work. Fewer have an accurate picture of how the same process is actually performed day to day, with its workarounds and spreadsheet macros alongside the three applications the team has to use simultaneously. Technologies that support process intelligence aim to close this gap by gathering evidence rather than asking people to describe their own work.
That matters for automation decisions, because automating a process nobody has measured tends to automate the mess. A representative picture of operations makes it easier for operations leaders to decide where to invest in automating or simplifying work.
What process intelligence is, and where this platform fits in
Process intelligence means gathering evidence about how processes actually run and analysing it to produce actionable insight. KYP.ai Process Intelligence falls under this category. The vendor's publicity material presents it as a solution that helps an organisation understand itself by collecting "real-time" data about people, processes, and technology.
Earlier tools tended to focus on one area: system logs and data for process mining, or desktop activity for task mining. The vendor positions its platform as an end-to-end view that covers all of these, so a team can see what happens within applications and across the people performing the tasks.
It is similar to the difference between a map and traffic information. The map gives an overview of the road system, while the traffic data shows where congestion forms.
What the platform captures and how it is structured
Capturing work practices across people, processes, and technology
According to the vendor's publicity material, KYP.ai Process Intelligence aims to provide a 360° view of the company. In practice, this means capturing the relevant data continuously rather than as a one-time exercise.
For an analyst, this approach supplies much-needed context. When a workflow is taking too long, for instance, a review of desktop activity might show that workers are manually entering the same data into another application. This is the type of evidence the product is designed to capture, highlighting variations and exceptions.
From evidence to a prioritised list of opportunities
Finding opportunities is only part of the task. According to the vendor, the platform transforms operational data into business cases and a prioritised list of automation candidates based on projected impact.
A ranked pipeline is important because many automation leaders have more opportunities than budget. In practice, each candidate still needs its value compared using traditional project appraisal methods. At the same time, a ranked list is only as good as the data it is based on.
Where automation leaders tend to apply it
The vendor suggests several areas of work that process intelligence can support. These include process analysis, back-office automation, standardising operating procedures, digital transformation initiatives, and preparing processes for AI agents. Across these areas, insurance, banking, outsourced-service businesses, and shared service centres are prioritised as the key markets.
The reason is simple: these areas involve large-scale operations that follow standardised rules but use multiple applications. A shared service centre, for instance, may process thousands of supplier invoices every year, so even small improvements can yield valuable results. This is the type of setting where KYP.ai Process Intelligence is designed to create value.
The mention of preparation for AI agents probably reflects the fact that agents need detailed information about how a task is performed in the real world, including the exceptions. Structured evidence about such processes is what the product is designed to collect, and the vendor positions itself as an enabler for adopting AI-based technologies.
Limitations, privacy concerns, and when it is not appropriate
Evidence about how people work is valuable but raises privacy concerns. Employees who use the monitored applications may notice the platform and consider it an invasion of privacy. This is especially relevant in European countries, where works councils and data privacy laws require companies to evaluate carefully whether and how such monitoring can take place.
It is therefore important to understand how the vendor addresses these concerns, including whether personal data is captured and how it is treated, for example aggregated or de-identified where necessary. Raise these questions with legal and HR advisors, ideally before a pilot project begins.
Other limitations relate to the practical use of the evidence. First, collecting it takes time, and the initial insights may be superficial. Second, implementing changes, whether redesigning a process or building automation, also takes time, because such projects always involve people.
Third, the figures in the vendor's publicity material should be weighed against what the company has actually achieved. The product may be a good starting point for evaluating process intelligence, but it is not necessarily better than the alternatives.
Finally, such a tool may be overwhelming for an automation leader who has to analyse the operations of a small team, where a few processes take most of the time. In that case, a time-and-motion study or procedures documented in a spreadsheet may be enough to gather the evidence needed for a sound decision about automation.
Questions to ask before selecting a platform
Before adopting a process intelligence solution, it is important to understand how and when the product will actually be useful to the automation team. The questions below are a starting point for discussions with the vendor, and they can also be included in the RFP sent to shortlisted vendors.
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Can the tool capture the types of activity the automation team wants to analyse, and are there limitations or fields that cannot be captured?
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How does the vendor ensure the evidence collected is accurate, and how does it validate that evidence before delivering insights or business cases?
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What technical infrastructure and security requirements need to be in place before the product can be used?
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What are the ownership and storage terms: who controls the data, and where is it stored?
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How are the insights delivered, and which automation or applied-intelligence tools can be used with the analytics?
When considering KYP.ai Process Intelligence, ask the vendor to describe an example business case from another company in the same sector. Put the same question to other vendors, in particular about how the opportunities were prioritised. A meaningful comparison of the options should be based on the same example.
The core focus in a short answer
What's the core focus? An evidence-based approach: understand work practices across the organisation, then use that evidence to decide what to automate, simplify, or leave as it is.
For KYP.ai Process Intelligence, this means capturing evidence across people, processes, and technology, structuring it into a prioritised list of opportunities, and then applying it to transform the operation. It is important to see the product as a tool that provides evidence rather than one that makes decisions. That role continues to belong to the automation leader.
Frequently asked questions
How is process intelligence different from process mining?
Process mining usually refers to using the logs of a business application to understand how a process works. Process intelligence covers process mining but also draws on other sources, such as desktop activity, that provide evidence of work practices. Both can be used in combination, and the distinction may vary from vendor to vendor.
What is task mining?
It is similar to process mining, but task mining focuses on capturing people's activities and grouping them into tasks. It can reveal practices that individual applications do not record, such as copying and pasting data between systems. Because it involves people's activities, it touches on personal data and must be implemented with consideration for privacy and consent.
How long does it take to see results?
It depends on the scope of the deployment, but normally the more data is captured, the more insights can be generated. A focused project covering one specific process may be enough to highlight opportunities. As with any solution, ask the vendor how long it will take to deliver an initial assessment after the software is installed.
Does it replace consultants or business analysts?
Not entirely. Software can collate and present evidence more quickly, but analysts and consultants interpret that evidence to decide where and how to intervene. Many leaders will probably use such technologies to support analysts rather than replace them.
Evidence-based discovery of work practices before automation
Evidence-based discovery of work practices is key to deciding which processes to automate. KYP.ai Process Intelligence is one tool that supports it, but there are limitations. A prudent automation leader will try the technology on one small, high-volume process to see how useful the evidence is before starting a company-wide discovery project.
To begin with, document people's current understanding of a process: how they think it should be done. Then compare this with the evidence captured by the tool and see how much of it matches expectations. That comparison shows how useful the evidence will be for the automation strategy.
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