The promise of AI in construction is not only technical, but also economic. One of the main motivators for its adoption is the reduction of costs by improving productivity and reducing errors.
Various studies and cases point to significant savings:
- Reduction of reprocessing: It is estimated that reworks (reworks due to errors or changes) can add between 20% and 50% to the total cost of a project, and in the worst case, even duplicate it. Much of this reprocessing comes from late-detected coordination problems or from misinformed decisions. By implementing AI in coordination (detecting shocks in design, planning optimal sequences and monitoring execution), many of these incidents are resolved virtually before materializing. For example, by identifying MEP conflicts in the model phase with Solibri or similar tools, it avoids having to re-route pipelines on site (with the associated cost of extra labour, wasted materials and possible delays). Miller Electric observed that minimizing re-work in the pre-construction phase is one of Augmenta's key benefits, since an interference-free design from the start translates into an on-site assembly without unexpected shocks.
- Time and labour savings: Time is a critical factor in construction; delays prolong overheads, equipment rentals and affect return on investment. AI tools have been shown to be able to accelerate certain tasks by 30% or more. AF Gruppen, with the optimization of planning by ALICE, shortened the duration of work by almost a fifth. Juneau Construction, another company cited in 2024, combined drones and AI to speed up pre-concrete emptying inspections, reducing 10 man-hours of manual processing for each hour of drone flight. This saved him US$40,000 in a single 31-story tower project. In design, the AI that automates modelling (like MagiCAD) frees engineers from hours spent drawing every detail, allowing them to focus on high-level decisions. The learning curve of new tools also tends to be shorter thanks to more natural interfaces (e.g., conversational assistants who answer questions in natural language). All this results in fewer hours of work necessary to achieve the same progress, or to be able to undertake more projects with the same team.
- Optimization of resources and materials: AI can help reduce costs indirectly through better use of resources. In optimized 4D/5D planning, inactivity of crews or over-allocation of unnecessary personnel is avoided. A scheduling algorithm can detect that a certain critical activity is slowed down by a shortage of staff, allowing crews from other fronts to be reorganized so as not to waste dead time. Similarly, in efficiency-oriented generative designs, AI tends to minimize lengths and the number of components, which lowers the cost of materials. Augmenta reports that its designs tend to reduce the number of elbows and meters of conduit compared to traditional designs, which implies modest savings per project but significant savings when calculating in multiple works. Likewise, with predictive maintenance (facilitated by AI and IoT), surprise equipment failures that lead to expensive repairs are avoided; on the contrary, interventions are scheduled at the optimal time that minimize cost and extend the useful life of the assets.
- Improved quality and safety: Although more difficult to quantify in money, improving quality and reducing accidents have enormous economic repercussions. The AI that assists in regulatory review prevents fines or rework for non-compliance. The computational vision that warns about unsafe conditions avoids work accidents, with its consequent impact on insurance costs, interruptions, and reputation. The construction company that adopts AI for safety may see decreases in its accident rate, which in the long run translates into lower insurance premiums and less loss of time (a serious accident can paralyze a work). Overall, a better coordinated, planned and monitored project with AI tends to be a more orderly and safe project, which delivers on time and with controlled quality, which strengthens the competitive position of the company and its profitability.
Fig 1 Data upload from ObraLink to the BIM Model
It is pertinent to note that investment in AI also has costs: software licenses, staff training, possible need for infrastructure (e.g., better workstations or cloud services), etc. However, the rapid maturation of the sector is making these technologies more accessible. Many operate under Software as a Service (SaaS) models with scalable costs according to use, and with interfaces designed for AEC users who are not experts in programming or data. In addition, the cost of not adopting AI can be high in terms of loss of competitiveness. Traditional companies have seen how competitors that use AI reduce their cost overruns and deliver projects with fewer setbacks, allowing them to win contracts by offering tighter prices and with greater confidence in compliance. In a sector where typical margins are small, cutting 5-10% of costs thanks to AI can make the difference between winning or losing a tender.
References:
Domínguez Rogers, F. J. (2023) MEP Coordination and how AI is imposing itself. LinkedIn Pulse.
Josserand, M. (2021). How to automate MEP layout using Machine Learning. Medium.
Increases (2024). Case Study: How Miller Electric Reduced their Modelling Time by 40%. Augmenta Blog.
BIM Space (2025). MagiCAD, what is MagiCAD for Revit? [Web article].
NeevIQ (2025). Top 6 AI Tools for MEP in 2025: Transforming Workflows and Efficiency. NeevIQ Blog.
AEC Magazine (2023). Fuzor 2024 uses AI for "4D automation". Greg Corke
Numalis (2024). Driving Efficiency: How AI Streamlines Construction Costs. [Web article].
Open Space (2023). Product website and documentation. [Data on 360° monitoring].
Buildots (2024). Press Release: Buildots launches AI assistant for site managers. The Construction Index.
ECLAC - ECLAC (2024). Latin American Artificial Intelligence Index (ILIA) 2024 - Press Release.
Citations:
https://en.wikipedia.org/wiki/micromouse
https://futurearchi.blog/en/ai-parametric-design-space/
