IN Brief:
- Revizto research found 96% of construction CIOs are concerned about data ownership and control.
- The study drew on 600 CIOs across the US, UK, Europe, Australia, and the Middle East.
- AI readiness remains constrained by regulatory uncertainty, skills gaps, integration problems, and poor data foundations.
Revizto has published research showing that data ownership and control have become central concerns for construction technology leaders as the sector prepares for wider use of AI.
The company’s Bridging the Gap Report found that 96% of CIOs in construction are concerned about data ownership and control across their technology stacks. The study draws on responses from 600 CIOs across the US, UK, Europe, Australia, and the Middle East.
The findings show a shift in digital construction priorities. Cost and functionality remain important, but control over project data is becoming a core concern. Companies are assessing who owns the information generated by project platforms, how easily that information can move between systems, and what happens when vendor relationships change.
Revizto’s research also shows a gap between interest in AI and the operational foundations needed to use it. Regulatory uncertainty was cited by 24% of respondents as the biggest barrier to gaining value from AI, followed by limited digital skills at 23%, lack of integrations at 17%, and poor data foundations at 15%.
Only 10% of respondents said they were already seeing value from AI with no barriers remaining. CIOs are also split on how to manage technology stacks over the next 12 to 18 months. Revizto said 41% plan to expand their tech stack, while 39% plan to consolidate.
That split reflects a persistent tension in construction technology. Project teams need more capability, but too many disconnected tools can create duplication, inconsistent data, and weaker control. Adding software can increase workload when information is not structured, governed, and integrated properly.
The report also states that 92% of respondents experience cost overruns of 6% or more. Technology alone does not prevent overruns, but fragmented information makes them harder to detect, explain, and manage. Poor data flow can affect design coordination, issue management, change control, procurement, commercial reporting, and handover.
Construction has particular difficulty with data ownership because projects are temporary, multi-party organisations. Clients, contractors, consultants, subcontractors, manufacturers, technology vendors, and facilities teams all create and use information at different points in the asset life cycle. Models, drawings, RFIs, issues, approvals, inspections, change records, commissioning data, and handover files can move through several systems before an asset is complete.
That creates commercial and legal complexity. If project information is locked into a single platform, changing vendor or integrating with another system can become difficult. If data is poorly structured, AI tools may reinforce confusion rather than improve decision-making. If ownership is unclear, disputes over access, retention, and future use can continue after the original project team has left site.
AI tools are moving further into design review, risk detection, cost forecasting, schedule analysis, and asset management. Those systems depend on reliable inputs. A project with inconsistent naming, incomplete issue histories, uncontrolled file versions, and weak audit trails will struggle to extract useful AI output, regardless of the sophistication of the software.
Regulatory pressure is also increasing. Building safety, product traceability, public procurement rules, cyber security, data protection, and contractual evidence requirements are pushing construction towards stronger information governance. Sustainability reporting adds another layer, requiring accurate product, carbon, waste, transport, and operational data.
The next phase of digital construction is likely to be shaped as much by governance as by platform selection. Data ownership, integration, access, auditability, and retention are becoming project controls alongside cost, programme, quality, and safety. Companies that build those foundations will be better placed to use AI in live delivery rather than layering automation over unreliable information.
