Processes - the basis of every quality management system.
Process management makes quality-relevant workflows visible. In the GxP environment it takes on a second meaning: the process is the starting point of validation. Only the process defines a system's Intended Use; requirements, risks and the scope of validation all follow from it. That is why “we don't know our processes” is not an efficiency problem but a compliance problem: without a described process there is no robust Intended Use, and without an Intended Use no defensible validation.
Who does what, when, how and with what?
Process management means capturing, structuring and putting your quality-relevant workflows into daily practice. That spans everything from the company-wide process map down to the individual business process. There is a deeply human reason why this works: people can picture a process. You can point at a modeled process and talk about it. The business, IT, the supplier and the auditor are suddenly talking about the same thing. The approach is backed by standards and by guidelines, which differ in how binding they are:
Any organization can start today with what it already has.
Purpose originates in the process, not in the system.
The regulatory framework has kept two levels apart for decades. The business process defines the purpose, the Intended Use. The technical system provides functions that must fit that purpose (fit for purpose). A system does not know its purpose; it receives it from outside, from the process. That is why suitability can only be demonstrated against the process the system serves. The code alone does not establish it.
In practice, this means requirements must be derived from the business unit's real processes. Only then do system requirements emerge from them, and only then is it even defined what the evidence is meant to show. The cascade has four steps:
- 1
Capture the business process
Record the lived reality, not the idealized version. This is where Intended Use and criticality originate.
- 2
Derive the business requirements
What the process needs from the system comes from the business unit, not from a system's feature list.
- 3
Specify the system requirements
System requirements are derived from the business requirements and remain traceable. The specification sets out exactly what will be demonstrated.
- 4
Implement within the lifecycle
Implementation always follows the lifecycle model: specify, develop, implement, verify. Evidence focuses on the critical steps.
The Investigator Notification System case study shows how this cascade works in practice, tracing one real project from the business process through the user requirements to the development specification.
Master the process, master quality.
The process map structures the company across three levels, from strategic governance through the value-adding core processes to the supporting activities; interfaces and roles cut across all three. At a more detailed level, this yields the IT process and service map. Qualification of the IT infrastructure builds on it.
Above all, this view of the process makes the data flow visible: where data originates, where it is handed over and where the flow breaks down. End-to-end processes pave the way for end-to-end data flows. Such data flows are the foundation of Data Integrity in the GxP environment and the substrate every AI works on.
Reality and model must match.
Modeling depth is not process mastery.
We capture your workflows with established methods or with whatever tool you already use. The approach is method-agnostic, the notation yours to choose. Fully modeled L1-L5 process landscapes are not the goal. What counts is making who-does-what-when-how-and-with-what unambiguous.
There is exactly one quality criterion: the reality people live and the reality you model must match. That works best when you do not model down to the last level of granularity. Granularity is not the measure.
- SIPOC: Supplier, Input, Process, Output, Customer. The whole process on a single page
- Turtle diagram: inputs, outputs, resources and metrics for each process
- Swimlane: roles, responsibilities and interfaces along the workflow
What carries quality today will carry what comes next.
AI changes the technology, not the logic: an AI-enabled system still receives its purpose from the process it serves. That holds for the entire digital transformation in GxP-regulated fields. Three things you take straight from established process work into the AI world:
Intended Use & requirements
For an AI system, too, the requirements come from the business process: which task the model takes on in the workflow, what its suitability is measured against and where its limits lie.
Human Oversight
Human Oversight is process design: review steps, roles, intervention points and responsibilities are defined in the process, not in the model. Accountability stays with people.
End-to-end data flows
A model is only as reliable as the data flow that feeds it. Fragmented processes deliver fragmented data. Processes understood end to end provide the substrate on which AI can work reliably.
Our page on the validation of AI in the GxP environment shows how that turns into robust evidence.
The process has been part of the definition since 2000.
"Computerized System: A process or operation integrated with a computer system."
ICH Q7, Glossary (2000). The GMP framework for active pharmaceutical ingredients defines the computerized system as process plus technology, separate from the computer system (hardware and software). By definition, what is demonstrated includes the process. That has been true for a quarter of a century.
Process understanding is not groundwork for validation. It is its core. That is why every validation at QFINITY starts with the process. And so does every AI rollout.
Process management that lays the foundation for GxP compliance.
From mid-sized manufacturers without a dedicated process team to global corporations with landscapes that have grown over the years: we bring the method, you bring the knowledge of your workflows. No large-scale program, no mandated tooling.
Common questions about process management.
No. AI needs well-understood processes. The process remains the place where purpose, requirements and responsibility originate, even when a model takes on parts of the work. What is new are individual control points such as Human Oversight, plus the data flows for training and operation. The logic of deriving both from the process is the same as it has been for decades.
Detailed enough for the model to capture lived reality. No deeper. A process landscape modeled down to the last level of granularity goes out of date faster than anyone can maintain it, and loses its evidential value. A deliberately limited model that is right beats a detailed one that lags behind reality.
Because criticality is a property of the process: only those who understand the process see which steps and functions carry product and patient risk. Evidence belongs where it counts, not spread across the board. That is exactly what risk-based approaches from ICH Q9 to the CSA guidance presuppose.
No. How you get there is up to you, from a facilitated workshop to simple diagrams to the BPM system you already run. The notation is secondary; what matters is that everyone involved is looking at the same picture.
Processes work best together.
Start where you are.
Whether it is your first process map or an established landscape where reality and model need to line up again: we capture your quality-relevant workflows and turn them into the basis for requirements, validation and AI deployment. The intro call is free of charge and takes about 30 minutes.
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