Industrial artificial intelligence adoption is widespread, yet measurable value remains elusive across construction and industrial sectors.
Nearly nine out of ten organisations globally now use AI in at least one business function. However, despite this level of adoption, most industrial AI initiatives fail to deliver measurable return on investment or scale into core operations. Instead, they remain trapped in pilot mode, creating rising costs and limited business impact.
Why industrial AI struggles to scale beyond pilots
In large industrial organisations, AI deployment typically involves multiple Proofs of Concept running in parallel. These pilots often target safety management, quality assurance, predictive maintenance, energy optimisation, robotics, and site productivity. While individually promising, they frequently remain disconnected from operational decision-making and core business processes.
GREÏ, an AI-powered operational intelligence platform for organisations managing large physical sites, observes this pattern consistently across industrial AI deployments. The challenge is rarely technical capability. Instead, it is a structural issue rooted in how AI initiatives are selected, governed, and measured.
“What we see again and again is not a lack of ideas, but a lack of focus,” says Giedrė Rajuncė, CEO and co-founder of GREÏ. “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.”
The cost of ‘Pilots for the sake of Pilots’
According to Rajuncė, industrial organisations repeatedly fall into the pilot trap for three reasons: fear of missing out on AI innovation, top-down pressure without frontline involvement, and insufficient internal capability to manage pilots and organisational change.
“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.”
This approach helps explain why approximately 95% of enterprise AI initiatives fail to deliver measurable profit and loss impact. When pilots are driven by perceived innovation value rather than operational necessity, integration challenges and stakeholder resistance follow.
Focusing industrial AI on high-cost operational problems
Rather than starting with what AI technology can achieve, Rajuncė argues that organisations should identify a single operational issue that is both costly and persistent. Examples include recurring safety incidents, production bottlenecks, asset downtime, or site inefficiencies affecting 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.”
GREÏ applies this discipline internally by requiring baseline data, defined success thresholds, agreed next steps, dedicated on-site users, accelerated IT support, and clear stop dates. This structure ensures industrial AI pilots deliver results quickly and build credibility with frontline teams.
“Trust is critical, especially when a startup is the implementation partner,” Rajuncė adds. “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.”
For digital construction leaders, industrial AI success depends on focus, governance, and ownership, turning pilots into scalable operational systems that deliver measurable value, cost control, and long-term performance improvements.
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