The 5 best practices to gather relevant insights while testing new technology in a pilot environment
At Techfinders, we never stop talking about the importance of market validation. One of the best ways to understand what the market needs and find your target is to perform test strategies where you actually work with your technology in real scenarios. Interact with customers and strive to understand their challenges and goals within an industry-specific context.
But running a pilot isn’t just about testing, it’s about learning. To get valuable insights, you need to define what you want to measure and how you’ll measure it. Without this clarity, it’s easy to miss the signals that truly matter. Challenges and barriers will inevitably arise, but having a clear measurement plan equips you to navigate them more effectively.
In this article, we share five proven practices to help you collect relevant, actionable data during your pilot testing, so you don’t just run a test. You learn from it and take the next step with confidence.
Why is collecting the right information critical during tech validation?
Piloting a new technology in a real setting is a unique and valuable opportunity. Whether you’re testing a sensing suite in a food factory or a predictive maintenance tool for X-ray equipment, you’re operating with limited time, access, and resources.
You won’t get many chances to test in a live environment, so making each one count means knowing what to look for, how to collect it, and how to act on it.
1. Define what “relevant information” means for your use case
Not all data is good data. Before diving into the pilot, clarify what “relevant” means for your specific solution or company. Focus only on the insights that can help you make real business or product decisions.
| Examples of relevant insights to prioritise | |
|---|---|
| Usability feedback | Is the technology easy to use for its intended operators? |
| Operational performance | How does it behave under real conditions? |
| Fit within existing workflows | Can it be adopted without significant disruption? |
| Purchasing signals | Do stakeholders express interest in adoption or scale-up? |
2. Prepare your information goals before testing
Your pilot should be designed with clear learning objectives. Use a hypothesis-driven approach to define what you’re testing and why.
How to prepare a hypothesis to run an experiment
- Set specific validation goals—for example:
- Does our robotic arm integrate easily into the packaging line?
- Will operators accept and trust AI-based quality control?
- Use tools like the Experiment Board to define your hypotheses and what success looks like.
- Align your metrics with your Technology Readiness Level (TRL)—what you test at TRL 5 is very different from TRL 8.
3. Use structured tools for data collection
Once you know what to measure, use tools that help you capture insights consistently and efficiently—especially in complex or fast-paced industrial settings.
Recommended tools & formats:
- Surveys & forms: Tools like Google Forms or Typeform are great for post-test feedback, especially when mixing open-ended questions with scales.
- Observation templates: Use checklists or logs during on-site pilots to capture real-time feedback.
- Interview guides: Conduct short, structured interviews with technicians or managers immediately after the test.
Analytics & sensors: For digital tools or physical systems, track usage data, cycle times, or error rates.
4. Engage stakeholders actively and promptly
Insights degrade with time, so the most valuable feedback comes immediately after the experience. To maximise both participation and quality, schedule short debrief conversations as soon as the test session ends. Encourage stakeholders to share feedback in different formats, some may prefer talking, others may feel more comfortable completing a quick form or simply rating their experience on a scale from 1 to 10.
Keep the process simple and frictionless. Use pre-filled forms, yes/no questions, and a few open fields to make it easier for participants to respond without overthinking. It’s also important to collect input from both technical and business profiles. A solution that seems promising from an engineering point of view might raise concerns when viewed through a commercial or operational lens.
5. Use the data to drive decisions
Collecting insights is only useful if you use them. After the pilot, review the data with your team and decide: Do we pivot, iterate, or scale?
How to close the loop:
- Organize your insights using a format like the Validation Learning Card: What did we assume? What did we learn?
- Compare expected vs. actual outcomes and highlight what surprised you.
- Turn learnings into actions: update your roadmap, tweak your value proposition, or prioritize new development.
| At Techfinders, we often help teams translate raw pilot data into strategic decisions. That’s where real validation happens. Want to know how? Schedule a free consultancy meeting with us . |
Pilot testing is your most realistic shot at understanding if your technology is ready for the market. But it’s not just about proving your solution works: it’s about uncovering the why, how, and what next.
The right data will help you understand your technology’s value in context, spot adoption blockers early and refine your positioning or features.
So, before you plan your next pilot, make sure you also plan your data strategy.
Want to validate your tech in real industry environments? Get in touch with us and let’s start this journey together.
ABOUT THE AUTHOR
María Páez Guerrero
Techfinders Product Owner and Product Marketing Manager
María drives product strategy and marketing at Techfinders, helping manufacturing developers craft compelling value propositions for their solutions and connect with a strong online community. Her work ensures that SMEs can seamlessly access and adopt innovative technologies.
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