Download PDF

AI and Construction: Practical steps, legal risks and opportunities

July 2026

Adoption of artificial intelligence (AI) tools is growing rapidly, moving from experimentation to everyday use across the construction and engineering sector. While digital technology is not new, recent advances in generative AI and data-driven systems are accelerating implementation and reshaping how projects are designed, managed and delivered. This shift is also attracting closer scrutiny from clients, insurers and regulators, with AI-related requirements now appearing in public procurement processes, tenders and contracts.

Against this backdrop, construction businesses should focus on opportunities, as well as appropriate governance and risk management and effective practical implementation. Below, we outline the key factors emerging in the market and provide practical pointers for consideration.

Governance and risk: getting the fundamentals right

A consistent theme across the market is the importance of robust AI governance frameworks. Businesses should take steps to ensure that AI policies are:

• Updated and regularly reviewed;
• Clearly communicated and easily accessible; and
• Supported by training and change management processes.

This will help avoid policies or AI tools being poorly understood, inconsistently used, or misapplied.

This also highlights the importance of robust information management and document governance. As AI tools access and analyse large volumes of information and data, controls around how information is stored and managed become increasingly important. Although many businesses are now using enterprise versions of tools, poor document control (e.g., incorrect storage locations or permissions) may still lead to inadvertent exposure of confidential or commercially sensitive information. This aligns with growing concerns around confidentiality and data protection when using AI platforms, particularly where client or project data is input into external or open tools.

Alongside these issues, cyber and fraud risks are evolving. With the rise of sophisticated scams and deepfakes, simple practical mitigations such as agreed team verification processes (e.g., verbal password protocols for sensitive instructions) can help to provide an additional layer of protection, particularly in high-risk scenarios such as urgent requests for payment authorisation.

AI in practice

AI is not simply about efficiency gains for existing tasks. While automation of research or design creation and document review is valuable, real impact lies in transforming processes and decision-making.

AI will not fix broken or inefficient processes or poor-quality data. Businesses may wish to focus on priority areas where automation or data insights can deliver meaningful improvements, and where outputs can be easily checked and verified prior to use. We have already seen issues arise in the legal profession, where AI hallucinations have led to non-existent case law being cited or relied upon on matters. Businesses should also clearly identify at the outset the specific use case or problem they are seeking to address before developing or adopting any AI tool.

In our experience, AI applications are more effective where businesses have already established well organised data, document management systems, and processes. Poor‑quality inputs will typically result in weaker or less reliable outputs.

Key construction sector use cases

Several practical AI-driven applications are emerging across construction and in-house legal functions:

1. Automated onboarding, governance and compliance: Systems can streamline onboarding processes, applying consistent checks and governance requirements across projects and counterparties.
2. Intake and triage systems: AI-enabled tools can help manage legal or compliance workflows, routing queries effectively and identifying when specialist input is required. Such systems can generate valuable management data, including trends in client or contract types, workload pressures, risk areas and timelines. AI can also support the development of ‘self-service’ tools, enabling business teams to deal with lower-risk matters relatively independently while escalating higher-risk issues for specialist legal support earlier. This not only improves efficiency but also enhances commercial understanding across the organisation and supports project delivery.
3. Contract management: AI tools can assist with:

• Contract administration and risk management issues;
• Understanding conflicting definitions, terms or issues;
• Tracking key dates and notice periods; and
• Identifying renewal triggers.

Such solutions can combine generative and rules-based technologies, so careful consideration should be given to which tools are appropriate for each task.

4. Health and safety training and monitoring: Businesses use technology to help teams identify and avoid health and safety risks, including completion of induction and site processes.

Evolving contractual obligations

AI is becoming a contractual and commercial issue as much as a technological one. Construction contracts may include express:

• Consent or disclosure obligations regarding AI use;
• Requirements concerning certain tools/platforms or cyber accreditation;
• Requirements to comply with client AI policies;
• Indemnities or protections for breaches of confidentiality, data protection or other client requirements; and
• Termination rights for non-compliance.

This reflects broader uncertainty around liability, particularly where AI contributes to design decisions, project management or safety monitoring.

Stakeholder engagement

Successful AI adoption and implementation depend heavily on early and effective stakeholder engagement across the businesses and with external parties. Businesses should therefore take steps to:

• Identify key pain points or use cases across teams;
• Involve users in design and testing;
• Pilot solutions before full rollout; and
• Focus on usability, not just outputs.

Equally, change management with teams and clients requires careful thought. Embedding new tools requires behavioural shifts, supported by clear and continuous communication, training and feedback.

Building trust with clients, suppliers and insurers

Businesses must be prepared to clearly articulate their use of AI, both internally and externally. Clients are increasingly asking questions about AI use in tenders and project delivery, particularly in public sector procurement. Being able to explain the following is critical to building trust and maintaining a competitive advantage:

• What tools are being used (and for what);
• How opportunities and risks are managed; and
• What governance and measures are in place.

Practical observations

AI clearly presents significant opportunities for the construction sector, from improved efficiency to design optioneering and enhanced decision-making. However, these benefits must be managed against evolving legal, contractual and operational risks.

Businesses will therefore need to take steps to:

• Understand their governance and data control processes;
• Understand the terms on which the AI tool is provided (and how this aligns with their client or contractual obligations), even when using an approved enterprise version;
• Focus on practical, value-driven use cases;
• Engage stakeholders early and seek feedback;
• Align technology adoption and implementation with contractual and regulatory requirements; and
• Ensure that there is always a human in the loop, e.g. to check outputs created using AI.

In doing so, businesses can move beyond initial experimentation and harness AI as a strategic tool, while effectively managing the associated risks that come with it.

Beale & Co’s lawyers have experience on advising on the items covered above. If you wish to discuss any of these issues or practical considerations further, please contact the authors, James Hutchinson, or the Contracts and Project Advisory Team.

Download PDF