Nearly nine out of ten organisations worldwide now use artificial intelligence in at least one business function, yet only a fraction have successfully scaled AI into core industrial operations. While adoption is widespread, most initiatives stall at the pilot stage, not because the technology fails, but because companies begin with disconnected experiments instead of clearly defined operational problems.
In many large industrial organisations, AI adoption takes the form of multiple Proofs of Concept running in parallel, often focused on safety, quality control, predictive maintenance, energy optimisation, or robotics. Yet these initiatives often remain fragmented and disconnected from core operations, a pattern GREÏ, an AI-powered operational intelligence platform for businesses with large physical sites, sees repeatedly in industrial AI deployments.
“What we see again and again is not a lack of ideas, but a lack of focus,” says CEO and co-founder Giedrė Rajuncė. “Companies can easily list dozens of AI use cases, but many start pilots without agreeing on the pain point they are solving, what success actually looks like, or who owns the outcome. When many pilots run at once, attention gets fragmented, which leads to slower decisions.”
Why ‘pilots for the sake of pilots’ keep failing
According to Rajuncė, companies fall into the pilot trap for three main reasons.
“There is strong FOMO around AI, pressure to innovate coming from the top without involving end users, and a lack of internal competence to manage pilots and implement change,” she explains. “When those elements are missing, pilots may look successful on paper, but they rarely survive contact with real operations.”
By prioritising ideas that sound impressive over input from day-to-day operators, many organisations launch poorly integrated pilots, a pattern reflected in the roughly 95% of enterprise AI initiatives that fail to generate measurable P&L impact.
Rajuncė notes that instead of starting with what AI can do, organisations are better served by identifying one operational area that is both painful and expensive, such as safety incidents, production bottlenecks, or inefficiencies that directly affect margins.
“The most limited resource is time,” she says. “A pilot needs active management and coordination. When one person is responsible for several pilots at once, execution suffers, and ROI disappears. That’s why focusing on one high-cost problem almost always delivers better results.”
In applying their own platform, GREÏ requires clear baseline data, defined success thresholds, pre-agreed next steps, dedicated users on the ground, fast-tracked IT support, and hard stop dates to ensure pilots deliver results quickly. Rajuncė believes this disciplined approach also changes how pilots are perceived by frontline teams.
“Trust is critical, especially when a startup is the implementation partner,” she says. “I want to know the technical task upfront so I can say with confidence the pilot will work. In the end, it’s KPIs, trust, and strong participation that determine success. There is no other way to build a pilot that actually scales.”
Tristan Wood
