Enterprise AI is moving rapidly from experimentation toward real business use. As AI agents become part of everyday operations and decision-making, one question is becoming critical: how does AI understand how the business actually operates?
In Part 1 of our CEO Interview Series, QPR Software CEO Matti Erkheikki shares his view on where the Process Intelligence market is heading, why Process Intelligence is already a critical part of enterprise AI infrastructure, and how innovations such as MCP are opening new ways for organizations to bring real operational context directly to AI.
With more than 20 years of experience in enterprise technology and the Process Intelligence market, Matti has seen many technology shifts. He says the current momentum around AI — and the opportunities it is creating for Process Intelligence — stands out.
There is a lot of energy and excitement around QPR right now.
We are working closely with customers and partners, and bringing important new innovations to the market.
What excites me most is the response we are seeing around AI. I have worked in this market for more than 20 years, and during my time at QPR I have rarely seen this level of excitement around new innovations and the possibilities they create.
Organizations are looking for practical ways to make AI truly useful in their own business. Process intelligence has a critical role to play in making that possible.
I would group them into three areas.
The first is AI-ready Process Intelligence. Process Intelligence is no longer used only by human analysts. It can now provide operational context directly to AI applications and agents.
One of our key innovations here is the QPR MCP Server. MCP, or Model Context Protocol, gives AI applications and agents a standardized way to access external systems, data and tools. AI agents can access Process Intelligence directly in QPR and use it to understand process design, delays, deviations, bottlenecks and other operational issues.
The second area is our Snowflake-native architecture, which allows Process Intelligence to operate where the customer’s data already resides — reducing unnecessary data movement and connecting naturally with the customer’s wider data and AI environment.
And third, object-centric Process Intelligence and AI-supported root cause analysis help organizations understand increasingly complex processes and move faster from identifying a problem to understanding why it happens.
What connects these innovations is one idea: Process Intelligence provides the operational context that both people and AI need to understand how the business actually operates.
Because customers immediately understand what it makes possible.
During my time at QPR, I cannot remember another innovation creating this kind of immediate interest from customers and partners.
Traditionally, Process Intelligence has often been consumed through dashboards and analyses designed for people. With MCP, that intelligence can also become directly accessible to AI agents.
Instead of only asking an AI generic questions, you can ask: Why is this process delayed? Where are the bottlenecks? Which cases deviate from the expected process? What should we investigate first?
And the answers can be grounded in the organization’s actual process data.
That opens up a completely new way to use Process Intelligence.
Process Intelligence is already a critical part of enterprise AI infrastructure.
AI models can understand language, documents and patterns, but they do not automatically understand how a specific organization operates.
They need operational context: what should happen, what actually happened, where deviations occur, why they occur and what the downstream impact is.
Process Intelligence provides that understanding.
And the closer AI gets to real decisions, recommendations and autonomous actions, the more important this becomes.
Knowing that an invoice exists is data.
Knowing that it has been waiting for approval for ten days gives you more information.
But understanding that invoices from a particular supplier repeatedly stall at the same stage, that this differs from the normal process and that the delay creates problems further downstream — that is operational context.
The same principle applies across procurement, finance, supply chain, customer journeys and many other areas.
AI becomes significantly more useful when it understands not just individual data points, but what is happening around them and why.
We are not simply adding an AI assistant on top of traditional process mining.
We are making Process Intelligence part of the enterprise AI architecture itself.
MCP gives AI agents access to Process Intelligence. Snowflake-native architecture brings the analysis closer to enterprise data. Object-centric Process Intelligence helps customers understand complex business reality. And by combining process mining with process modeling, we can understand both how work was designed to happen and how it actually happens.
Together, these innovations enable QPR ProcessAnalyzer to give AI something it fundamentally needs: an understanding of how the business actually operates.
That is where I believe QPR has a very strong opportunity in this market.
Our ambition is clear: we want QPR to be a leading provider of the operational context layer for enterprise AI.
This market is moving quickly. Organizations that connect AI with real operational understanding early will be able to make better use of AI agents, automation and AI-driven decision-making than those that wait.
At the same time, we want enterprise-grade Process Intelligence to be easier to adopt and easier to scale.
Customers should be able to start with a real business problem, demonstrate value and then expand across more processes, users and AI use cases without creating unnecessary complexity or cost.
That combination — advanced technology, AI innovation, flexibility and close customer collaboration — is where I believe QPR can be exceptionally strong.
What is Process Intelligence?
Process Intelligence helps organizations understand how business processes actually operate by combining process data, analysis and process context. It can reveal delays, deviations, bottlenecks and root causes across business operations.
Why is Process Intelligence important for enterprise AI?
Enterprise AI needs more than access to documents and data. It also needs operational context — an understanding of how work actually flows, what should happen, what actually happened and where deviations occur. Process Intelligence provides that context.
What is MCP in Process Intelligence?
MCP, or Model Context Protocol, provides a standardized way for AI applications and agents to connect with external systems, data and tools. Through the QPR MCP Server, AI agents can access Process Intelligence directly from QPR ProcessAnalyzer and use it as operational context.
What does operational context mean for AI?
Operational context means understanding the business reality surrounding individual data points — for example, why a process is delayed, how a deviation affects downstream activities or which process pattern is causing an issue.
How does QPR ProcessAnalyzer support enterprise AI?
QPR ProcessAnalyzer combines Process Intelligence with capabilities such as the QPR MCP Server, Snowflake-native architecture, object-centric Process Intelligence and process mining with process modeling. Together, these help organizations bring operational context into their AI environments.