This is like putting a smart energy coach on every excavator, crane and truck on a construction site. The AI watches how and when machines are used, then tells teams how to run them in a cleaner, more efficient way – cutting fuel use and emissions without stopping the work.
Construction sites run many heavy machines that burn fuel inefficiently, creating high CO₂ emissions and cost. Today, usage is often poorly measured and managed, so companies struggle to know where waste occurs, how to reduce idling, or when to switch to cleaner equipment. This AI application analyzes equipment data to identify wasteful patterns and recommend actions that reduce emissions and fuel consumption while maintaining productivity.
If Skanska is combining proprietary telemetry from its equipment fleet, historical project data and operational know‑how into the AI, the moat lies in that operational dataset plus embedded workflows in construction project management. This is hard for generic tech vendors to replicate without similar scale of projects and machinery data.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Collecting, cleaning and standardizing real-time telemetry from heterogeneous equipment fleets and sites; potential bottlenecks in streaming, storage and labeling of machine-usage data across projects and geographies.
Early Majority
Compared to generic industrial analytics, this is tailored to construction equipment and site logistics, likely integrating with Skanska’s project planning and sustainability reporting to tie equipment usage patterns directly to project schedules, cost codes and CO₂ targets.