René Wolf, CEO of RIB Software, explores why construction firms risk wasting AI investment and eroding profitability if they fail to address fragmented data, disconnected workflows and inconsistent processes
Artificial intelligence is rapidly climbing the construction industry’s agenda. From predicting project delays to automating reporting, its potential is compelling. A RICS research report suggests that 37% of respondents plan to increase AI investment over the next 12 months, underlining significant momentum across the sector.
But with pressure mounting on firms to demonstrate an AI strategy, there’s a danger that the industry becomes so focused on the technology that it overlooks a fundamental challenge: getting its data in order.
The hidden cost of disconnected data
Projects are becoming more complex, with information residing in disparate platforms, spreadsheets, email chains, messaging apps and files. The challenge is growing – as PMI’s 2026 Pulse of the Profession research highlights that 81% of project professionals believe projects have become more complex in recent years.
Against this backdrop, disconnected information and inconsistent processes can add further friction, making it harder for teams to identify risks, coordinate decisions and protect project value.
Investing in AI without addressing these fragmented systems and disconnected workflows risks creating another layer of complexity when it should be improving productivity. The workarounds needed just to keep the lights on carry a real cost. Value is lost when teams spend time reconciling information rather than managing projects, controlling costs or identifying risks. In the meantime, slower reporting, delays and inconsistencies create opportunities for expensive errors.
There is also the risk of wasted AI investment. A pilot programme might demonstrate impressive results within a controlled environment but scaling AI projects with incomplete, outdated or inconsistent information risks generating unreliable insights.
Without consistent data and standardised processes, it is difficult to scale the solution without further integration work, additional resources and potentially expensive reconfiguration.
Data readiness comes before AI readiness
These challenges are, of course, not a reason to delay AI projects. But it is important for organisations to establish whether their infrastructure and workflows can support the AI outcomes they want to achieve – with data confidence, standardisation and connectivity at the centre of strategy.
For the purposes of avoiding unnecessary expenditure and building a stronger foundation for investment, it’s crucial to establish a trusted view of project information. Teams need confidence that they are working with live, accurate information. Decision-makers need it to identify issues earlier so they can be resolved before they become too expensive.
Reducing duplication, improving traceability and ensuring updates reach the right people can cut time spent checking records and reconciling conflicting versions.
Standardisation helps to scale this confidence. When teams capture information differently or follow inconsistent approval procedures, automation becomes harder to implement and maintain. Clear standards for data capture, ownership, access and quality reduce ambiguity and create the consistency that AI depends on.
They also help organisations understand where information comes from and whether it is reliable enough to support critical decisions.
Connectivity across workflows brings visibility across design, procurement, programme delivery and compliance, creating a comprehensive view of project performance and collaboration. Instead of manually assembling updates from multiple sources, teams can access connected information and spend more time acting on it. For AI applications, this context is essential to identify meaningful patterns, anticipate risks and support better forecasting.
Prioritising solid digital foundations might not grab headlines the way AI benefits have done but they can help prevent technology investments from becoming open-ended exercises in integration, correction and maintenance. Ultimately, this is what will unlock real business value from advanced technology and AI.
Making the business case measurable
In building momentum around digital transformation, organisations must be able to link technology investments to measurable business outcomes.
For example, AI might help commercial teams identify emerging cost pressures earlier, accelerate document reviews or improve forecasting accuracy. But organisations must assess these benefits against the full cost of implementation, which includes data preparation, system integration, ongoing maintenance and the human resources needed to validate outputs. Only then can they develop a realistic understanding of the initiative’s return on investment.
Building value before scaling innovation
Construction firms cannot afford to treat data readiness as a secondary consideration in their AI strategies. The cost of getting it wrong is higher than purely wasted technology investment – it also perpetuates the operational burden of disconnected systems and the financial risks associated with poor decision-making.
Before committing to the next ambitious AI project, construction leaders must ask themselves: are we investing in technology that will solve a business problem or simply adding another layer of cost? The difference between failure and success will be AI becoming an expensive experiment that ultimately erodes shareholder value or AI genuinely driving innovation, productivity and profitability.
The post Building an AI-ready construction industry: The devil is in the data appeared first on Planning, Building & Construction Today.
