4 Recurring challenges in modern

Ahead of the NI Tech Forum Nordics, we share four patterns we commonly see across lab and test environments.

If you work with test, measurement, lab automation, or NI-based systems, you already know the real challenge isn’t getting data – it’s building systems that are reliable, repeatable, scalable, and easy to evolve over time.

At Init, we work with engineering teams to make those systems easier to build, operate, and evolve – by bringing structure to architectures, reducing manual steps, and ensuring data is trustworthy and accessible when it matters.

Below are four patterns we see across R&D and industrial test environments – lab automation, test cells, IT/OT integration, and lifecycle management. We’ll be at NI Tech Forum 31st of March (link at end), and we’d love to compare notes and talk through what these ideas could look like in your setup.

1) Lab automation: turning disconnected instruments into one coherent system

Many labs evolve organically: instruments are added over time, data ends up in separate files or databases, and key workflows remain manual. The result is often slow turnaround, poor repeatability, and limited visibility during test execution. A more modern approach is to treat the lab as a connected ecosystem:

• Integrate instruments, controllers, and subsystems into a unified setup
• Centralise control, logging, and visualisation
• Design for modularity so the system can expand without becoming fragile
• Enable cloud–edge architectures when remote access and shared data matter

The payoff is typically faster configuration, better traceability, and easier access to results for the people who need them.

2) Test cells: reduce setup time while improving accuracy and repeatability

In many validation environments, it’s still common to see separate systems for control and measurement – often combined with manual instrument handling and long setup time whenever requirements change. Integrated test-cell platforms can help by:

• Unifying control and data acquisition in a single system
• Supporting high-speed, precise measurement with synchronized signals
• Automating sequences (including calibration data handling)
• Providing intuitive HMI/SCADA interfaces for operators and engineers
• Making test data easier to access for post-processing and reporting

This is especially valuable when tests change frequently, new instrumentation is introduced, or repeatability is critical.

3) IT/OT integration: access your systems and data from anywhere (securely)

Whether you’re running field monitoring or remote test rigs, a common challenge is getting reliable, continuous data – and making it available to multiple users without compromising uptime or security. Connected monitoring solutions typically focus on:

• Edge platforms for real-time control and data acquisition
• Secure transfer to cloud and/or local storage
• User access management and dashboards for live + historical data
• Scalable architectures that can grow across multiple sites or setups

Done right, this creates dependable insight and reduces the friction of “where is the latest data?” across teams.

4) Lifecycle management: keeping labs maintainable as people and priorities change

Labs often follow episodic investment cycles: a project funds the initial setup or a major refresh, driven by a few key people with deep knowledge and the motivation to improve the lab.

Over the years, however, the lab transitions from development into long-term operations. People change roles or companies, and the team may retain enough knowledge to run the system – but not to continuously evolve it as new requirements, technology trends, best practices, and lifecycle needs emerge.

Modernising a lab can mean architecture cleanup, DevOps practices (e.g., Git + Azure DevOps), and lifecycle upgrades – all aimed at improving maintainability, reducing risk, and making the platform ready for the next phase of development.