The construction industry stands at a critical inflection point. Demand for large, complex infrastructure, from data centers and energy assets to transmission and distribution networks, is accelerating just as delivery fundamentals come under strain.
Construction labor productivity has remained largely 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, under tighter cost and schedule scrutiny. This occurs while navigating supply-chain volatility, permitting constraints and evolving sustainability mandates.
These pressures are no longer theoretical. Over the past several years, multiple hyperscale data center projects across North America have reported schedule delays tied to power availability, equipment lead times and coordination challenges with utilities². In Northern Virginia, the world’s largest data center market, grid interconnection delays and power shortages have emerged as a primary constraint on development timelines³. As capital continues flowing into digital infrastructure and energy transition projects, execution risk increasingly shapes not just when assets come online, but how future construction spend is planned, sequenced and governed.
Why Point Solutions Fall Short
Over the last decade, construction has made meaningful progress in digitizing workflows. Cloud-based document management, BIM visualization tools, scheduling platforms and mobile field reporting have improved access to information and replaced paper-based processes. Yet 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, converting that information into foresight to anticipate what will happen next, identifying emerging risks or understanding root causes, remain heavily dependent on individual experience 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 Toward Integrated Intelligence
What’s 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 greater confidence.
This shift 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 efficiency, yet 65% report their organizations are not currently using 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 deliver predictive insights and real-time market intelligence would provide clear value, underscoring growing readiness for more advanced systems.⁶
The implication is clear: the next phase of construction technology value will not come from adding more tools, but from better integrating data across design, planning and execution to reduce uncertainty and downstream disruption⁵.
Reducing Rework, Mitigating Risk, Improving Outcomes
In many projects, the most expensive problems are not isolated mistakes but systemic issues. These include misaligned scopes, sequencing conflicts or risks that surface too late to correct without significant cost. Poor forecasting and limited visibility contribute to rework, schedule slippage and budget overruns that ripple across stakeholders.
Integrated intelligence systems help address these challenges by connecting data across the project lifecycle. In practice, this can mean:
- Preconstruction: Codifying engineering standards, stakeholder inputs and design logic into repeatable workflows that reduce manual effort and improve consistency.
- Execution: Capturing progress directly against 3D models and schedules to create a shared, real-time view of work performed and work remaining.
- Risk foresight: Learning from historical and in-flight data to identify recurring issues, understand their root causes and anticipate where similar risks may arise next.
AI-powered software like Slate Technologies are one example of how systems can support this approach. These enable teams to move beyond documentation toward institutional learning and proactive decision-making. When applied thoughtfully, these technologies enable earlier intervention, fewer surprises and better alignment between cost, schedule and quality objectives.
A Competitive Advantage Built on Understanding
As construction enters an era defined by scale, speed and scrutiny, competitive advantage will increasingly depend on how effectively organizations convert project data into understanding. Firms 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 runaway problems.
In an industry where margins are thin and quality is essential, connected intelligence is a prerequisite for success.
Sources
¹ U.S. Bureau of Labor Statistics, Productivity in Construction, long-term productivity trends
² Construction Dive, Data center projects face delays amid power and supply constraints, 2023–2024
³ Reuters, Power shortages threaten Northern Virginia’s data center growth, 2024
⁴ McKinsey & Company, The Next Normal in Construction, insights on fragmented technology adoption
⁵ McKinsey Global Institute, Reinventing Construction Through a Productivity Revolution
⁶ Slate Technologies, AI in Construction: 2025 Industry Report — https://slate.ai/ai-construction-2025-industry-report/
About the Author
Garrett Jones, P.E., S.E., is Vice President of Product and Customer Strategy at Slate Technologies, where he helps shape AI-driven solutions for the construction industry. A former McKinsey & Company consultant and licensed structural engineer, he brings over a decade of experience bridging engineering, product strategy and management consulting. Garrett holds degrees from MIT and the University of Texas at Austin and is passionate about applying data and technology to improve construction project outcomes.
