SOLUTIONS30 easier to keep this quality – that’ s something on our roadmap.”
This focus on data foundations reflects hard-won experience.
AI systems learn from historical patterns, so errors or inconsistencies in training data will produce unreliable algorithms.
For a company handling safety-critical work, this isn’ t just about efficiency: it’ s about preventing dangerous mistakes.
The company’ s approach involves standardising data collection and storage processes across its acquired companies.
This creates consistent formats that AI systems can process reliably while supporting broader business intelligence needs.
Jerzy’ s team has several AI proof-of-concept projects in development, but he emphasises the importance of comprehensive testing before production deployment.
“ AI has two sides, so I would compare it to a knife. It can ease your daily activities, but it can also be very dangerous,” he warns.
“ We need to approach AI very carefully. We need to test it, and we need to prepare it in such a way that it’ ll not harm us or any of our partners.”
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