10-Point AI Readiness Checklist

Are your critical project data sources digitized and accessible?

Consider:

• Project management systems (schedules, costs, resources)
• Site documentation and inspection records
• Equipment and asset data
• Supply chain and procurement records
• Safety incident logs and HSE data

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: AI systems require quality data. Poor data = poor AI outcomes. Construction firms often have data scattered across Excel, PDF, paper, and legacy systems—making AI implementation difficult.

Quick win: Start digitizing one critical workflow (e.g., safety inspections, quality checklists) using mobile forms that feed structured databases.

Do you have governance frameworks for AI use in regulated environments?

Consider:

• CDM 2015 compliance implications
• Building Regulations and Building Safety Act requirements
• GDPR for worker/subcontractor data
• ISO 19650 (BIM information management)
• Contractual liability and insurance implications

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Construction is heavily regulated. Using AI without considering Principal Designer duties, Building Safety Act requirements, or data protection risks creates legal exposure.

Quick win: Document which regulations apply to your planned AI use cases and flag risks for legal review.

Do you have the technical foundation to deploy AI systems?

Consider:

• Cloud infrastructure or on-premise servers
• Internet connectivity at sites
• Camera/sensor infrastructure for monitoring
• Integration capabilities with existing systems (ERP, PM software)
• Mobile device availability for site teams

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: AI solutions need infrastructure to run. Computer vision for safety monitoring requires cameras. Predictive maintenance needs sensor data. Poor connectivity = limited AI capabilities.

Quick win: Audit existing infrastructure at one pilot site to identify gaps before broader rollout.

Does your team understand AI capabilities, limitations, and risks?

Consider:

• Executive understanding of AI opportunities
• Site managers’ ability to interpret AI insights
• IT team capability to support AI systems
• Workforce awareness of AI in their workflows
• Training programmes for AI tool adoption

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: The best AI system fails if your team doesn’t trust it, understand it, or use it properly. Construction teams need practical AI training—not academic theory.

Quick win: Run a 2-hour workshop for leadership on AI use cases specific to construction, with real examples.

Have you identified which AI use cases deliver the highest ROI for your business?

Consider:

• Current pain points (safety incidents, delays, cost overruns)
• Strategic objectives (margin improvement, risk reduction, growth)
• Quick wins vs. long-term transformation
• Resource availability for implementation
• Competitive differentiation opportunities

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Not all AI use cases are equal. Starting with low-value or overly complex projects wastes resources and creates skepticism. CEOs need to focus on profit-driving use cases first.

Quick win: List your top 3 business challenges and research which AI solutions specifically address them.

Can you assess AI vendors objectively without getting locked into unsuitable solutions?

Consider:

• Understanding of vendor landscape (niche vs. platform solutions)
• Criteria for evaluating vendor claims and case studies
• Contract negotiation points (data ownership, exit clauses)
• Integration requirements with existing systems
• Support and training offerings

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Vendor lock-in is expensive. Many AI vendors overpromise and underdeliver. Construction firms need independent guidance to avoid costly mistakes.

Quick win: Create a vendor evaluation scorecard with non-negotiable requirements before talking to vendors.

Do you have a plan to drive adoption of AI tools across your organization?

Consider:

• Executive sponsorship for AI initiatives
• Communication strategy for workforce
• Pilot project approach (start small, prove value, scale)
• Feedback loops to improve systems
• Incentives aligned with AI tool usage

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Construction has a history of technology resistance. AI adoption requires clear communication, proven value, and addressing “Will this replace my job?” concerns.

Quick win: Identify an internal champion (site manager, project lead) who will advocate for AI and run your first pilot.

Have you established processes to test and validate AI systems before full deployment?

Consider:

• Pilot testing approach (controlled environments first)
• Validation of AI accuracy (false positives/negatives)
• Fallback processes if AI systems fail
• Incident response plans
• Liability and insurance considerations

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: AI making incorrect predictions on a construction site could cause safety incidents, cost overruns, or regulatory violations. Testing is non-negotiable.

Quick win: Define acceptance criteria for AI accuracy before deployment (e.g., 95% accuracy for safety monitoring).

Have you allocated realistic budget and resources for AI adoption?

Consider:

• Initial capital investment (software, hardware, integration)
• Ongoing operational costs (subscriptions, support, updates)
• Internal resource time (project management, training)
• External expertise (consultants, implementation partners)
• Contingency for unexpected challenges

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: AI is not “set and forget.” Successful adoption requires investment in technology, people, and processes. Underfunding AI initiatives leads to failure and wasted effort.

Quick win: Model a 3-year total cost of ownership for your priority AI use case, including all hidden costs.

Do you have KPIs defined to measure AI impact on your business?

Consider:

• Baseline metrics before AI implementation
• Clear success criteria (% reduction in incidents, cost savings, time savings)
• Regular reporting cadence
• Attribution of results to AI (vs. other factors)
• ROI calculation methodology

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: If you can’t measure it, you can’t manage it. CEOs need concrete proof that AI investments deliver bottom-line results—not just “interesting technology.”

Quick win: Define 3 KPIs for your first AI pilot that directly tie to profit impact (e.g., reduction in rework costs).