7 essential strategies for safe AI implementation in construction

Artificial intelligence is rapidly transforming the construction industry, from streamlining bid processes to automating project planning. However, the technology’s complexity and the high-stakes nature of construction projects create unique risks that demand careful consideration. Construction attorneys Christopher Horton, a partner at Smith Currie Oles LLP, and Peyton Aldrich, an associate at the same firm, have observed both the promise and perils of AI adoption across their client base.

The construction sector’s embrace of AI tools brings particular vulnerabilities—complex contracts, tight deadlines, and significant financial exposure mean that AI errors can cascade into costly mistakes. A misinterpreted contract clause or biased risk assessment could lead to selecting the wrong bid winner, missing critical compliance requirements, or exposing confidential project data. Yet when implemented thoughtfully, AI can dramatically improve efficiency in project planning, proposal review, and administrative tasks.

Success requires treating AI as a precision tool rather than a universal solution, with robust verification processes and clear policies governing its use. Here are the essential strategies construction companies need to implement AI safely and effectively.

7 essential strategies for safe AI implementation in construction

1. Treat AI as a strategic scalpel, not a universal hammer

The most successful construction companies approach AI implementation with surgical precision, targeting specific inefficiencies rather than attempting wholesale automation. AI excels at particular tasks—crafting bid package narratives, reviewing proposals for improvement opportunities, and streamlining meeting documentation—but struggles with nuanced decision-making that requires industry expertise.

Before adopting any AI tool, conduct thorough due diligence to understand its security features, capabilities, and limitations. This means testing the system with non-sensitive data first, understanding exactly what data the tool retains or shares, and clearly defining which tasks are appropriate for AI assistance versus those requiring human judgment.

2. Understand the critical difference between closed and open loop systems

AI systems operate in fundamentally different ways that directly impact data security and confidentiality. Closed loop systems process your data without sharing it externally or using it to train other models, while open loop systems may incorporate your inputs into their broader learning algorithms or share information across users.

For construction companies handling confidential project details, proprietary specifications, or sensitive client information, this distinction is crucial. Never input confidential, proprietary, attorney-client privileged, or work product information into unverified AI tools. Approved enterprise tools like Microsoft Copilot, certain versions of ChatGPT, and legal research platforms like Westlaw typically offer closed loop options specifically designed for professional use.

3. Recognize and prepare for AI hallucinations

AI hallucinations occur when systems generate false information that appears credible, such as fabricated case law, non-existent regulations, or incorrect technical specifications. In construction, these errors can be particularly dangerous—imagine an AI system recommending a building code that doesn’t exist or misinterpreting structural requirements.

Common hallucination scenarios in construction include: AI citing non-existent safety regulations, generating incorrect material specifications, creating fictional legal precedents, or producing inaccurate cost estimates based on outdated or imaginary data. Always verify AI-generated technical information, legal references, and regulatory citations through authoritative sources before making decisions based on AI recommendations.

4. Address AI bias in high-stakes decision making

AI systems can perpetuate biases present in their training data, leading to unfair outcomes in contractor selection, risk assessment, or project evaluation. In construction’s competitive bidding environment, biased AI outputs could result in discriminatory practices or poor vendor selection based on incomplete or skewed data analysis.

Bias often emerges when AI systems are trained on historical data that reflects past inequities or when they lack sufficient diverse examples. For instance, if an AI system learned from historical bidding data that inadvertently favored certain types of contractors, it might continue those patterns. Implement checks to ensure AI recommendations are reviewed by diverse human teams and regularly audit AI decision-making processes for patterns that might indicate bias.

5. Establish comprehensive staff training and usage policies

Effective AI implementation requires moving beyond ad hoc usage toward structured, policy-driven adoption. Train staff to use AI tools critically, understanding both their capabilities and limitations. This includes teaching employees how to craft effective prompts, recognize potential errors, and know when human oversight is essential.

Consider this strategic approach used by experienced legal professionals: use one AI system to help formulate better questions for another. For example, leverage a specialized legal AI tool’s prompt suggestions to craft more effective queries for general-purpose AI systems. This layered approach helps maximize accuracy while maintaining appropriate skepticism about AI outputs.

6. Create and maintain a defensible trail

A defensible trail documents how AI was used in your decision-making process, including the specific inputs provided, outputs generated, and human oversight applied. This documentation becomes crucial during litigation, regulatory reviews, or dispute resolution where you need to justify AI-driven decisions.

Your defensible trail should include: the specific AI tool used and its version, exact prompts or inputs provided, complete AI outputs received, human review and verification steps taken, final decisions made and rationale, and any modifications to AI recommendations. Without this documentation, defending AI-assisted decisions in court or arbitration becomes nearly impossible, particularly when dealing with complex construction disputes.

7. Implement a verify-first culture

The golden rule of AI implementation in construction is verification at every step. This means cross-checking AI outputs against authoritative sources, having qualified humans review AI-generated recommendations, and maintaining healthy skepticism about AI capabilities.

Verification is particularly critical for: contract interpretation and legal analysis, technical specifications and building codes, cost estimates and financial projections, regulatory compliance requirements, and safety protocols and procedures. The fast-paced nature of construction projects can create pressure to accept AI outputs without verification, but this shortcut often leads to expensive mistakes down the line.

Why construction faces unique AI vulnerabilities

The construction industry’s particular characteristics create amplified risks when implementing AI systems. Unlike other sectors where errors might be easily corrected, construction mistakes often become permanently embedded in physical structures or legally binding contracts.

Construction projects involve lengthy, highly customized contracts with complex terms that AI systems may misinterpret. The industry’s data-heavy processes—from risk assessments to subcontractor evaluations—provide numerous opportunities for AI bias or errors to influence critical decisions. Additionally, the sector’s collaborative nature means that AI mistakes can cascade across multiple stakeholders, from general contractors to specialized subcontractors.

The industry’s fast-paced environment, driven by tight deadlines and competitive pressures, can tempt firms to rely on unverified AI outputs for bid submissions or project schedules. However, these unverified outputs risk violating contractual obligations, exposing proprietary information, or creating new legal liabilities that far exceed any time savings achieved.

Practical implementation guidelines

For project managers: Focus AI implementation on administrative tasks like meeting notes and initial proposal reviews, but maintain human oversight for all client-facing communications and technical specifications.

For legal and compliance teams: Establish clear policies about which AI tools are approved for use with confidential information, and create templates for documenting AI usage in legal decision-making processes.

For executive leadership: Invest in comprehensive AI training programs and consider appointing an AI committee to evaluate new tools and establish company-wide policies.

Moving forward strategically

AI represents a powerful opportunity for construction companies to improve efficiency and reduce administrative burden, but success requires disciplined implementation with robust safeguards. The companies that will benefit most from AI are those that approach it strategically, with clear policies, comprehensive training, and unwavering commitment to verification.

As the technology continues evolving, construction firms that establish strong AI governance frameworks now will be better positioned to adopt more advanced capabilities safely. The key is building a foundation of responsible AI use that can scale with both technological advancement and business growth.