Ireland’s construction and infrastructure sectors are entering a decisive decade. Climate obligations, EU directives, capacity constraints, and rising public investment are converging with rapid advances in digitalisation and AI.
Against this backdrop, a critical question emerges: how do we ensure that technology investment delivers measurable outcomes rather than further complexity? The answer lies not in 'more AI', but in better‑governed, standards‑based product data.
Today, buildings and infrastructure are developed and shaped by a supply chain of manufacturers, designers, contractors, and operators. Yet these relationships are still largely mediated through fragmented data exchanges, most notably through static PDFs and unstructured product information.
While in recent years building information modelling (BIM) has improved coordination and design quality, its full potential is constrained when product data is not digitised, structured, unique and interoperable. This 'missing link' prevents supply chains from functioning as connected systems and limits the ability to apply emerging technologies such as machine learning, agentic sourcing/procurement/three-way matching, and digital twins at scale.
Standards as the fuel for AI
OpenBIM frameworks, GS1, IFC, ISO 19650, building smart data dictionaries (bSDD) and information delivery specification (IDS) standards enable structured, interoperable representations of assets and information requirements. However, structure alone is not enough. Data must also be unambiguously identified and traceable across organisational and life-cycle boundaries for the entire life cycle of the core maintainable assets, given their contribution to operational carbon parameters.
This is where GS1 identifiers become strategically relevant to the construction industry. Global trade item numbers (GTINs), global location numbers (GLNs), and global individual asset identifiers (GIAIs) allow product, asset and location data to be reliably linked, forming the backbone for forthcoming digital product passports (DPPs) under the EU ecodesign for sustainable products regulation (ESPR).
Crucially, AI systems, whether predictive models or large language models, depend entirely on the availability of structured, contextualised data. With GS1 standards, AI & LLM-assisted platforms can support cost prediction, maintenance planning, compliance automation, and life-cycle carbon optimisation.
Alignment with regulation and national policy
This shift aligns closely with the EU's regulatory direction. Energy performance of buildings directive (EPBD) places growing emphasis on life-cycle performance and digital building logbooks' (DBLs). ESPR mandates product‑level transparency through DPPs, while the construction products regulation (CPR) is moving decisively towards digital, verifiable performance data. Together, these regulations signal a transition from document‑based compliance to data‑driven assurance.
From a cost and delivery perspective, this also reinforces the relevance of ICMS3, which enables whole‑life cost transparency by linking cost information to consistent asset and product definitions, an outcome only feasible where structured data flows exist.
Nationally, the government’s accelerated infrastructure delivery action plan underscores the same direction of travel under its four pillars:
- Legal reform.
- Regulatory reform and simplification.
- Co-ordination and delivery reform; and
- Public acceptance.
These are all dependent on reliable information flows across the asset life cycle. Data standards, product identifiers, and interoperable digital processes should be recognised as enabling infrastructure in their own right.
A practical agenda for engineers
For design engineers and project managers, the message is clear. The next phase of digital transformation is less about new tools and more about discipline: adopting open standards, demanding structured product data, and integrating supply chains digitally.
In doing so, the industry can move from fragmented BIM models towards open built intelligence, where decisions are evidence‑based, compliance is automated, and environmental impact is demonstrably reduced.
