How to Start and Scale Process Intelligence Across the Enterprise

CEO Interview Series — Part 2

Organizations often know that Process Intelligence could help them understand and improve their operations — but many are unsure when to start, how ready their data needs to be, or how large the initial project should be.

In Part 2 of our CEO Interview Series, QPR Software CEO Matti Erkheikki shares his perspective on how organizations can get started with Process Intelligence, demonstrate value quickly and scale it across the enterprise.

His message is simple: you do not need perfect data or a large transformation program to begin. Start with a meaningful business problem, use the data you have, prove the value and expand from there. 

1. Many organizations say: “Our data isn’t good enough yet for process intelligence.” Should they wait?

No. This is one of the most common misconceptions I hear.

Your data does not need to be perfect before you start. In fact, process intelligence often helps organizations identify where their data problems actually are.

Waiting until every system, data model and governance issue is solved can mean waiting for years.

Start with an important business question, understand what data is available, identify the gaps and improve from there.

Process intelligence can be part of the journey toward better data — it does not have to come after it.

2. So when is the right time to start?

When you have a business question that matters and you cannot answer it well enough today.

Why are orders delayed? Where are we losing revenue? Why are customers dropping out of a journey? Where is unnecessary manual work happening? What should we automate?

You do not need perfect data or a large transformation program.

Start with one meaningful problem, prove the value and expand.

Waiting for everything to be perfect usually means losing time while others are already learning, improving their processes and building AI capabilities on top of their operational data.

3. Does process intelligence have to mean a long and heavy implementation project?

Absolutely not.

One perception I would like to change is that process intelligence automatically means a massive transformation project.

It can start very focused: one process, one business question and a clearly defined outcome.

Modern architectures, ready-made integrations and no-code configuration also make it possible to move much faster than before.

The goal should be simple: get to useful insight quickly, demonstrate business value and then scale based on the results.

4. Scaling can become expensive with some process intelligence platforms. What should companies consider when choosing one?

Think beyond the first use case from day one.

A platform may work well when you analyze one process. But what happens when you want to add ten more processes, more users, new business units or AI use cases?

Process intelligence creates its greatest value when it can spread across the organization.

That is why commercial scalability matters just as much as technical scalability.

We believe customers should be able to expand process intelligence without creating a new cost barrier every time another process or use case is added.

Our goal is to provide enterprise-grade capabilities with flexibility and a highly competitive total cost of ownership.

5. Why do customers choose QPR when there are much larger vendors in the market?

Customers want advanced technology, but they also want flexibility, responsiveness and value.

We have very high customer satisfaction, and close collaboration with customers is a fundamental part of how we operate. Many of the capabilities we develop originate directly from real customer needs.

Customers also want to scale without unnecessary complexity or rapidly increasing costs.

That combination is important: enterprise-grade technology, AI innovation, flexibility, close customer collaboration and attractive total cost of ownership.

Being smaller than some of the largest vendors can also be an advantage. We can stay close to our customers and move quickly when their needs evolve.

6. What would be your advice to a company considering process intelligence today?

Don’t wait for everything to be perfect.

Start with a business problem that matters. Use process intelligence to understand what is really happening. Prove the value and then expand.

But today I would add one more thing: think beyond dashboards from the beginning.

Ask not only what process intelligence can tell your people, but also how that operational understanding could make your AI better.

The companies building that capability now are already learning how to combine process intelligence, automation and AI in ways that will be increasingly difficult to catch up with later.

Enterprise AI needs to understand the business it is expected to support.

Process intelligence provides that context.

From getting started to building operational context for AI Starting small does not mean thinking small.

A focused Process Intelligence initiative can help an organization solve an immediate business problem and demonstrate measurable value. But it can also create something increasingly important for the future: a better understanding of how the business actually operates.

As AI becomes more deeply embedded in business operations, that operational understanding can provide the context AI needs to make better recommendations, support decisions and ultimately take action.