Over the past decade, construction has adopted robotics, drones, and IoT at impressive speed. Yet the industry still lands near the bottom of sector digitization rankings year after year. What is missing is not tools, but the data plumbing that lets information move cleanly from drawings and specs into the decisions that run a job.

In AEC, technology adoption is real, but uneven across use cases and teams. A Dodge analysis reported that 78% of civil engineers use BIM, yet nearly 60% of civil contractors do not. Adoption jumps among large civil contractors, which helps explain the split. In contrast, other technologies such as reality capture have grown quickly, and construction even ranks among the top commercial sectors for drone use. To date, however, most adoption has focused on managing field tasks and capturing point-in-time data. The harder problem is now building the software and infrastructure that moves accurate, governed information through estimating, buyout, and schedule, so decisions are timely and traceable.

Construction is built on drawings, specs, and documentation shared among owners, GCs, and trade partners. Without the software to support clean information flow, bad or missing data becomes the foundation for decisions. An Autodesk + FMI study estimated $88.69B in avoidable rework in 2020, about 14% of all rework, tied to missing, inaccurate, or siloed data. Only about one third of firms reported a formal process to identify and fix bad data. Autodesk News

Why digitization matters

The sectors that rank higher on digitization did not simply deploy more apps. They invested in a common model for their data and governed the flow of information end to end. The results show up as faster decisions, fewer surprises, and measurable cost savings. The same patterns translate to AEC, especially if we start in preconstruction.

In logistics, warehouse and transport management systems, barcode or RFID capture, and exception management created end-to-end visibility and lower working capital. The AEC translation is to link procurement and submittals to an authoritative schedule, make long-lead readiness visible in one place, and escalate risk before it becomes delay. Studies show that when the schedule is the source of truth and material status feeds it in real time, buyout decisions improve and buffers shrink. 

Manufacturing  created a digital thread that connects the value chain from design through planning and production: CAD → PLM → MES → ERP. Designs flow into bills of material, change control, and traceability, which raises first-time-right rates and shortens cycle times. This learning can be directly applied to AEC: treat model-based quantities as the BOM, govern estimate versions against explicit design deltas, and use productization or prefab so lessons carry from one project to the next instead of evaporating at closeout. 

Aviation and energy. Standard operating procedures paired with digital logs and sensors shifted work from reactive to predictive. In construction, McKinsey research suggest that the analog is design-completeness and constructability gates, along with predictive RFIs that are generated from insights in plans and specs before they create field friction.

Why this matters now is simple: data without flow does not compound. FMI has found that roughly 95% of E&C data goes unused, which describes the gap between “we captured it” and “we used it to decide.” Closing that gap is the real work of digitization. It turns one-off captures into a consistent stream of governed information and gives every stakeholder the same version of reality.

Barriers to digitization

So why has digitization lagged in construction? Four structural reasons recur across conversations with AEC professionals and are echoed in industry research.

1) Projects are one-offs and teams are temporary

Every job is a prototype with new players and rules, so knowledge rarely compounds after closeout. Technology stacks, folder structures, and naming conventions change from project to project. The result is persistent lateness and overruns from fragmented, document-driven handoffs and point solutions that work for one project but not the next. The result is disjointed processes and technology tools that are siloed and change from project to project.  This requires teams to constantly learn new tools and slows adoption and maximal ROI. Functionally, this means drawings, PDFs and emails that never become structured data in a common data environment (CDE). This often results in information that does not carry forward to inform the next pursuit, estimate or project decision.

2) Delivery methods and misaligned incentives
Construction’s delivery models involve owners, designers, GCs, subs, vendors, and regulators. The costs to digitize often land on one party while benefits accrue to another, which leads teams to deprioritize platform investments and integrations in favor of local point tools. When digitization is treated as a project line item rather than an operating-model change, benefits dissipate at the handoffs and incentives remain misaligned. In contrast, comprehensive programs that connect data and process across functions - model to estimating, estimating to procurement, procurement to schedule - consistently show stronger outcomes, clearer cost control and double-digit productivity gains.

3) Thin margins and cash-flow volatility
In AEC, low margins and pay-when-paid structures encourage deferring technology investment and change management, even when rework and delay costs are visible. Studies from Autodesk + FMI quantify the penalty and tie outcomes to data-governance maturity. Tool adoption without standards, ownership, and version control does not bend the curve; it just moves work around. The ROI improves when you start upstream in preconstruction: let model-based quantities drive the estimate, tie estimate versions to specific design deltas, feed procurement status into the schedule, and make long-lead exposure visible in one place.

4) Regulatory sprawl and liability calcification
Local code variance, audit requirements, and claims pressures encourage “PDF compliance” rather than interoperable digital systems. Across AEC, contract deliverables are often drawings and stamped documents that create a paper trail instead of enabling collaboration. Over time, this has cemented practices that trap information in formats that resist analysis and reuse and has allowed other industries to realize the benefits of digitization while AEC has yet to tap these benefits. When owners specify model-based or digital delivery, incentives shift toward data standards, traceable changes, and system-to-system visibility within projects, exactly the conditions where digitization starts to pay back.

Why the timing is different now

Three forces are changing the risk-reward math:

Owners are moving to delivery models that reward digitization.

Public and private owners are expanding design-build and IPD because they value speed, risk sharing, and coordinated data. The latest DBIA/FMI utilization work projects nearly 50% of U.S. construction spend will be delivered as design-build by 2028 (≈ $2.6T in the assessed segments). Public works lead the way in highways/streets and education, and private manufacturing is a fast-growth sector. Performance studies cited by DBIA report design-build 102% faster than design-bid-build and 3.8% less cost growth, which is exactly the kind of outcome that depends on shared information, not paper handoffs. IPD is also gaining traction with large private owners (healthcare, life sciences) who want aligned incentives and model-based collaboration throughout delivery.

Construction platforms are opening up, which lowers integration friction.

The ecosystem is shifting from “closed projects and emailed files” to open, API-first environments aligned with ISO 19650 and open formats like IFC. Autodesk Construction Cloud now lists 275+ direct integrations and an iPaaS layer (ACC Connect) to wire schedules, procurement, and docs without custom middleware. Procore advertises 500+ marketplace integrations on an open API. Trimble Connect exposes APIs for model and document services. The implication is simple: mid-market firms can now maintain a governed single record - models, quantities, versions, submittals - that other systems can read, instead of retyping data across tools.

AI allows technology to leverage previously unusable information

Finally, the biggest change of all, AI fundamentally changes the nature of data software can leverage…

How AI changes the game

AI presents AEC firms with the chance to reinvent themselves and accelerate their digitization journey. New advances in AI technology will allow forward-thinking firms to turn unstructured historical data into a knowledge graph that will be the foundation of actionable insights and faster, more accurate decision making.  Think quantity deltas linked to design changes, “what-if” schedule options generated automatically, long-lead risk surfaced early, and estimates that learn from past projects.

This transformation will have profound effects on team productivity and drive business health. Deloitte and others estimate 10–15% cost savings when AI and advanced analytics are applied to planning and execution, and multiple analyses peg potential productivity gains up to ~20% when AI informs planning and resource management. AEC teams and vendors are already demonstrating AI that reads plans/specs/RFIs to prioritize risk and streamline estimating.

AI will transform every industry. But in AEC, leapfrogging digitization will turn that transformation into a revolution.