Why does the construction, architecture and real estate industry need a clear AI strategy?

Identification starts with a systematic capture of processes: we map lead times, manual steps, sources of risk and cost blocks across the project lifecycle. In workshops with stakeholders from estimating, procurement, project management and facility management we identify recurring pain points and quantify the manual effort.

In the next step we assess data availability and quality: which plan versions, reports or emails are available digitally? Which data is structured and which needs to be enriched via OCR, NLP or semantic tagging? Only with a realistic data inventory can we judge whether a use case is technically feasible.

In parallel we conduct an economic evaluation: savings potential through time savings, lower change-order rates, faster commissioning or reduced warranty cases are translated into simple business cases. We prioritise use cases with high leverage and short time-to-value.

Practical recommendation: start with a mix of low-hanging fruit (e.g. automatic document classification) and a strategic use case (e.g. a Tender Copilot). This way you achieve quick wins while simultaneously building the foundation for larger transformations.

Compliance does not begin at the model stage but with data and processes. We define clear data provenance rules, version control and audit trails so that every model decision can be based on traceable data. For standardised checks like fire protection or occupational safety we build validation layers that compare AI results with clear rule sets.

A central element is the AI Governance Framework: roles (Data Steward, Model Owner), approval processes, test procedures and monitoring KPIs are established as binding. This prevents inconsistent model usage and creates accountability for exceptions and escalations.

Technically, we integrate explainability tools and regular retrain cycles: models document which features led to a decision and are continuously subjected to performance checks against representative control sets. This is especially important for liability issues or regulatory reviews.

Finally, change management is essential: users need to understand the limits of AI and know how to override decisions. Training, clear SOPs and a defined escalation procedure are therefore an integral part of a compliance-secure rollout.

BIM models are often the biggest lever: they contain geometric, semantic and lifecycle-relevant information that can be used for clash detection, automated quantity take-off and asset management. Additionally, bills of quantities/specifications, inspection reports, defect reports and acceptance records are particularly valuable because they provide direct indicators of risk and cost.

Drawings in different versions and email threads are also important sources, but they require clean ingest pipelines (OCR, semantic normalization) to be reliably usable. IoT data from sites or buildings expands the picture with real-time information that is critical for predictive maintenance or resource optimisation.

Quality is more important than quantity: it is better to integrate a few well-structured data sources than to link all sources half-heartedly. We therefore invest early in data foundations: data catalogs, standard metadata and automated validation rules.

Practical advice: start with a pilot based on the most accessible, highest-quality data source (e.g. BIM + inspection reports) and gradually add more sources once the architecture and governance are in place.

A typical schedule starts with an AI Readiness Assessment (2–4 weeks) and a use-case prioritization (another 2–4 weeks). A technical proof of concept for a well-defined use case can deliver tangible results within 6–12 weeks. Subsequent scaling depends on integration needs and data maturity and usually takes 3–12 months.

Success is measured not only by technical metrics but by business KPIs: shortened tender cycles, reduced manual hours, fewer change claims, shorter acceptance lead times and improved schedule adherence. In addition, usage metrics (adoption, sessions, overrides) and model metrics (precision/recall, robustness) are relevant.

To make ROI transparent, we create a business-case layer from the outset that translates time savings into monetary values and plays through sensitivities with different assumptions. This makes it clear when an investment pays off.

Important: measure continuously and iterate. Early wins should be used to secure budget and trust for the next scaling phase.

A practical AI roadmap is anchored technically and organisationally: we specify integration points to ERP, CAFM and BIM systems and define APIs, data transformation layers and authentication mechanisms. The goal is to provide AI functions as modular services that complement existing tools rather than replace them.

Organisationally, a layered roadmap is recommended: Phase 1 addresses quick, visible wins (document classification, simple chatbots), Phase 2 builds on stable data foundations and integrates BIM workflows, Phase 3 focuses on prescriptive models and predictive maintenance across the asset lifecycle.

Governance, security and compliance are cross-cutting themes of the roadmap. We define milestones for governance implementation, data stewardship and model audits and synchronise these with IT security roadmaps and architecture guidelines.

Practical recommendation: embed a product backlog and appoint a responsible product owner from the business who prioritises the roadmap and acts as a bridge to IT. This ensures technical deliverables target real user needs.

Risks include data quality, lack of user acceptance, unclear responsibilities and regulatory pitfalls. Technically, bias in training data or overfitting can lead to unreliable predictions; organisationally, interface and process breaks can erode efficiency gains.

To minimise risks, we implement staged validation steps: unit model tests, field tests with real site scenarios and pilot phases with human oversight. Governance defines clear ownership structures, escalation paths and regular audits.

Change management reduces acceptance risks: we work with end users in their daily routines, build user-centered interfaces and run training and shadowing phases in which AI is used as assistance until trust is established.

Finally, a conservative rollout strategy is advisable: start with supporting functions (assist mode) before automating decisions. This keeps liability issues transparent and controllable.