The construction industry is at a critical turning point. Demand for large and complex infrastructure, from data centers and energy assets to transmission and distribution networks, is accelerating as the fundamentals of delivery are affected.
Construction labor productivity has remained virtually flat for decades, even as project scale, technical complexity and regulatory oversight have increased¹. Owners, engineers and contractors are being asked to deliver faster, with fewer experienced resources, with tighter cost and schedule control. This occurs while navigating supply chain volatility, enabling constraints and evolving sustainability mandates.
These pressures are no longer theoretical. Over the past several years, several hyperscale data center projects in North America have reported schedule delays related to power availability, equipment delivery times, and coordination challenges with utilities². In Northern Virginia, the world’s largest data center market, network interconnection delays and power shortages have emerged as the top constraints on development timelines³. As capital continues to flow into digital infrastructure and energy transition projects, execution risk increasingly determines not only when assets come online, but how future construction spending is planned, sequenced and governed.
Why one-time solutions fall short
Over the past decade, construction has made significant progress in digitizing workflows. Cloud-based document management, BIM visualization tools, programming platforms and mobile field reporting have improved access to information and replaced paper-based processes. However, most of these tools operate as point solutions, producing fragmented datasets that require manual interpretation and reconciliation.
While teams can now see more information than ever before, turn that information into forecasting to anticipate what will happen next, identify emerging risks or understand root causes, they are still heavily dependent on individual expertise rather than systems intelligence. Industry research consistently highlights this challenge, noting that fragmented technology stacks limit the ability to extract full value from project data⁴.
The shift towards integrated intelligence
What is emerging now is a different class of construction technology. Rather than focusing solely on documentation or visualization, these platforms are designed to ingest project data across phases, understand domain-specific workflows, and generate insights that help teams act earlier and with more confidence.
This change is already reflected in industry sentiment. According to Slate Technologies’ 2025 Construction intelligence study, 74% of construction leaders believe AI and automation will have a positive impact on project cost and efficiencystill 65% report that their organizations do not currently use AI or predictive analytics in planning or execution. This reveals a significant gap between belief and adoption.⁶ At the same time, 61% of respondents say AI tools that provide predictive insights and real-time market intelligence would add clear valueemphasizing the growing readiness for more advanced systems.⁶
The implication is clear: the next phase of value in construction technology will come not from adding more tools, but from better integration of data in design, planning and execution to reduce uncertainty and disruption downstream⁵.
Reduce rework, mitigate risk, improve results
In many projects, the most costly problems are not isolated errors but systemic issues. These include misaligned domains, sequencing conflicts, or risks that appear too late to correct without significant cost. Poor forecasts and limited visibility contribute to rework, schedule slippage, and budget overruns that impact stakeholders.
Embedded intelligence systems help address these challenges by connecting data throughout the project lifecycle. In practice, this can mean:
- Pre-construction: Codify engineering standards, stakeholder input, and design logic into repeatable workflows that reduce manual effort and improve consistency.
- Execution: Capture progress directly with 3D models and schedules to create a shared, real-time view of work done and work remaining.
- Risk forecast: Learn from historical and flight data to identify recurring problems, understand their root causes, and anticipate where similar risks may arise.
AI-based software such as Slate Technologies is an example of how systems can support this approach. This enables teams to move beyond documentation to institutional learning and proactive decision-making. When applied carefully, these technologies enable earlier intervention, fewer surprises, and better alignment between cost, schedule, and quality goals.
A competitive advantage based on understanding
As construction enters an era defined by scale, speed and scrutiny, competitive advantage will increasingly depend on how effectively organizations turn project data into understanding. The companies best positioned for the next decade will not be those with the largest collection of tools, but those with systems that learn from execution, reduce blind spots, and help teams act before risks become out-of-control problems.
In an industry where margins are thin and quality is essential, connected intelligence is a prerequisite for success.
sources
¹ US Bureau of Labor Statistics, Productivity in constructionlong-term productivity trends
² Immersion in construction, Data center projects face delays amid power and supply constraints2023–2024
³ Reuters, Power shortage threatens Northern Virginia data center growth2024
⁴ McKinsey & Company, The next normal in constructioninformation on fragmented technology adoption
⁵ McKinsey Global Institute, Reinventing construction through a productivity revolution
⁶ Slate Technologies, AI in Construction: Industry Report 2025 — https://slate.ai/ai-construction-2025-industry-report/
About the author
Garrett Jones, PE, SE, is Vice President of Product and Customer Strategy at Slate Technologies, where he helps shape AI-powered solutions for the construction industry. A former McKinsey & Company consultant and licensed structural engineer, he brings more than a decade of experience in bridge engineering, product strategy and management consulting. Garrett is a graduate of MIT and the University of Texas at Austin and is passionate about applying data and technology to improve construction project outcomes.
