Will AI Replace BIM Teams? Why BIM Data Quality Decides

The Architecture, Engineering, and Construction (AEC) industry is about to achieve a breakthrough. For over a decade, it was considered an industry with a slow adoption rate of digital technologies. However, things are undergoing a major shift, and the industry is bringing improvements to its reputation. Areas such as BIM, cloud computing, and artificial intelligence are the main milestones to be attained. The above statement about the construction industry is supported by 51.4% of the 559 construction experts, according to the NBS Digital Construction Report 2025.

However, we all know that every step forward is accompanied by a new setback. Even with such progress and confidence in new technologies, issues such as poor information management remain unresolved in the industry. The reason behind this is that AI is only as accurate as the information it collects as input. Good and poor data lead to good and poor outcomes, respectively. In short, the main concern is not the adoption of powerful tools like AI, but rather the information we need to provide to it for automation, i.e., consistent naming conventions, standards, and complete and precise BIM models.

AI Adoption Statistics

Over a period of five years, the incorporation of AI in daily construction activities has become more important than ever, as shown below:

• 2020 – 9%
• 2023 – 22%
• 2025 – 42.5%

It is evident from the above figures that AI is more than just a tool. It would not be wrong to call it a necessity in our construction workflows. What’s more is that there is a remarkable number of professionals who are willing to become adopters in the upcoming years. A total of 37.7% plan to introduce AI in their firms, out of which 19%, 14.8%, and 3.9% are about to begin within one, three, and five years, respectively. Conversely, there was a significant change in the percentage of professionals with no intention of AI adoption, which dropped from 43% to 8.4% from 2023 to 2025. Within two to three years, according to the survey, most industry professionals, about 88.8%, strongly believe that AI might bring fundamental or significant transformations in their regular operations. 23% of the respondents foresee AI bringing fundamental transformations, while the rest of the experts, 65.8%, expect significant transitions.

The proportion of UK architects rose from 41% to 59% according to the RIBA AI Report 2025 regarding the utilisation of AI tools in project-related tasks, demonstrating the growing impact of AI in the design and architectural sector.

Listed below are some of the most widely used AI models in the built environment sector:

• ChatGPT – 86.8%
• Microsoft Copilot – 57.9%
• Google Gemini – 36.8%
• Company-developed AI solutions – 21.7%
• Claude – 15.1%
• DeepSeek – 2%

Overall, the above stats indicate a positive contribution of AI in the construction industry. It will soon be one of the most important tools that help make better decisions, enhancing overall project efficiency.

What AI Is Already Being Used For

What used to be called an experimental technology is now helping individuals in the Architecture, Engineering, and Construction (AEC) industry perform day-to-day activities.

A survey study shows areas where AI is potentially assisting users, as follows:

Technical Information Search (71.1%)

AI can quickly provide the document one is looking for by searching large databases within minutes. For instance, if an engineer needs building codes, regulations, technical documents, or project data, AI will do the job, eliminating the need for manual searches, which waste hours.

Text Drafting / Review (63.8%)

AI also assists when it comes to drafting and reviewing content, reports, technical documents, or any sort of writing tasks, while saving a lot of time.

Data Analysis (61.8%)

AI is provided with enormous amounts of data to analyse. It is used to recognise patterns in information and generate insights that can be used in project planning and better decision-making.

Summarising Documents (57.9%)

The best use of AI comes when contracts, reports, guidelines, etc., have more content than a human could examine within hours. AI summarises content in a way that one can get a thorough understanding of what the document is about without reading it as a whole.

Calculations (46.1%)

Accuracy is what every stakeholder targets, and it is only achievable with precise figures. That is where AI becomes a necessity, helping individuals calculate everything possible to the best of its ability faster and more efficiently.

Design Generation (40.8%)

Design finalisation is no longer a time-consuming task. With AI, engineers can instantly generate multiple alternatives and choose the optimal design that fits project requirements more rapidly and effectively than conventional approaches.

Other (5.3%)

A number of respondents mentioned using AI for specialised operations, such as automating repetitive tasks, improving quality control, and managing projects in a more customised way.

Overall, practically speaking, it is fair to state that many industry professionals, whether project managers or quantity surveyors, are using AI in their workflows to get instant and precise results. Ultimately, it contributes to project productivity.

“I use AI in my QAQC process on projects. I am looking for and extracting data, that is tedious to get to, in no time. I am also automating mundane and repetitive tasks to save time and get results faster.”
~ BIM Specialist

AI and Environmental Performance Tracking

As concerns regarding environmental and climate change continue to rise, the construction industry has encountered many challenges, which in turn have substantially increased the importance of digital tools for sustainability management and environmental performance tracking over the past year.

Through its capabilities, AI now provides architects with enough time to make optimal design choices rather than wasting time on complex calculations. In short, AI handles complex environmental data, and for a sustainable facility, architects optimise space utilisation.

Why BIM Data Quality Matters

Even with the backing of 85.2% of industry professionals stating that AI will prove vital for construction improvements and 88.8% having expectations of a considerable productivity increase, these statements are dependent on data which definitely should be precise and well-organised.

Human judgement and practical construction experience are two essential factors that AI lacks. The only approach it adopts is learning patterns from data such as drawings and BIM models. That is why outcomes, in this case, are based on data quality. High-quality data results in precise outcomes, while poor or messy data leads to chaotic and inaccurate results.

“Accuracy is the issue, the majority of the AI Hype Bubble at the moment is surrounding LLMs which hallucinate and generate inaccuracy but in very credible and believable ways (until someone checks the fine detail). This has to be resolved first before any AI really becomes of use within the Construction Industry where accuracy is an absolute requirement.”
~ Technical Product Specialist

That is why Building Information Modelling (BIM) is fundamental as it serves as a core system for managing construction information in a reliable and structured way.

An evolution in the perception of BIM in the industry is observed:

• BIM is all about “3D models with data”. This view was held by 14.6% and 26% of industry professionals in 2025 and 2023 respectively.
• 30% of them see BIM as compliance with established standards such as BS EN ISO 19650.
• 27.4% of experts consider BIM as the foundation of digital transformation.
• 26% define BIM as “better information management”.

AI’s Dependency on Structured Information

Structured and clear data is the key to a well-functioning AI. If it lacks these requirements, it might lead to improper functioning of AI. Despite the fact that 72.3% of the construction industry now uses BIM, the report still pinpoints problems regarding data management, which makes it complicated for AI to produce precise and effective results.

1. Classification Gap

When it comes to assisting automation tools to connect 3D model objects with the written specification, a unified classification system works best.

The figures back in 2023 increased from 46% to 58.9%, indicating industry professionals who incorporate Uniclass as their core classification system. Conventional CAWS systems are still employed by 14.6% of the organisations, whereas the percentage of organisations utilising custom systems is 13.2%. The stats show that 20% of individuals are unaware of the classification systems their organisation is putting to use.

With this problematic approach of identifying elements using different classifications, AI may fail to cross-check and recognise them, resulting in errors and inaccurate outcomes. The same inconsistency undermines BIM coordination and clash detection, where automated checks rely on elements being identified the same way across every discipline.

2. Supply Chain Disconnects

To ensure precision in project data, pooling efforts are of utmost importance. 88.3% of building consultants work with manufacturer-provided BIM data, though fewer suppliers deliver it (73.4%).

This gap between manufacturers and designers compels them to use tentative or unverified data. Ultimately, it can result in incorrect calculations and approvals when the data is processed by AI.

3. Internal Data Silos

Product Information Management (PIM) systems enable enterprises to securely store all information in a properly structured centralised hub. However, 18.4% of product suppliers are uncertain whether their company utilises a PIM system. This is an indication of a communication and data management gap, which is a potential issue. If such problems are not addressed, AI will encounter difficulties while processing data, and the results will be inaccurate.

How Firms Can Prepare

Even for the adoption of something general, strategy and roadmap are the foundation, and the same applies to AI. By focusing on these five core areas, companies can incorporate AI into their workflow effectively:

1. Establish a Cloud Operating System

For AI to operate efficiently, it requires quick access to data and strong computing power, which is only possible with cloud technology. As of today, the figure has risen from 80% back in 2023 to 86.3%, indicating that construction companies use cloud computing. Cloud systems have been trending in the industry, as they allow team members to store, access, share, and manage project files and information from anywhere, regardless of their location, seamlessly. Keeping projects in CDE platforms ensures fast access for AI to up-to-date information. Below are some figures showing the use of cloud systems in different areas:

• For information storage – 92.9%
• For real-time collaboration – 85.1%
• For client information sharing – 79.5%
• For digital administration – 61.7% (increased from 32% in 2023)

2. Enforce a Single Corporate Classification Code

A single agreed standard system should be enforced across all teams including documentation and modelling in order to ensure confusion-free and reliable data. Training should be conducted to help teams implement the Uniclass system.

With this measure, consistency and clarity in the information can be improved, allowing AI tools to gather accurate information and produce credible outputs.

3. Audit the Data Supply Chain

As time passes, the need for firms to acquire accurate and complete digital data from their suppliers is becoming non-negotiable. As industry rules are becoming stricter, particularly with the EU Construction Products Regulation 2024 and the forthcoming Digital Product Passports (DPPs), they are leading the industry towards transparency and reliable product information.

Digital Product Passports are considered a benefit, as they build trust and competitiveness in the market. Among experts familiar with DPPs:

• 83% support the adoption of DPPs
• 15.2% are neutral
• 1.8% oppose them

This demonstrates firm support for supply chain transparency in the construction industry.

4. Prioritise Formal Data Education

Organisations, particularly smaller ones, should first be familiar with AI data requirements and should afterwards employ it in their tasks to obtain the best possible results. They should not only provide employees with hands-on structured training in AI but also teach them about:

• Construction data formatting, validation, and organisation.
• Compliance with industry standards such as BS EN ISO 19650.

5. Set Up Human Oversight Frameworks

Without human oversight, sole reliance on AI might end up in legal, professional, and business risks. In accordance with RIBA research, it has been clearly stated that AI tools without supervision may lead to insurance issues, plagiarism concerns, or even unreliable designed structures.

In short, AI performance under human monitoring seems to be the best approach to adopt to ensure safety and trustworthiness.

Is Your BIM Data Ready for AI?

AI will not replace BIM teams. What it will do is expose whichever teams are working from inconsistent naming conventions, mixed classification systems, and incomplete models. The firms that get value from AI over the next few years will be the ones that fixed their information management first.

If you want models, drawings, and data that are structured to a consistent standard from the outset, get in touch for a no-obligation quote.