AI Readiness Assessment

Evaluate your organisation's readiness for AI implementation across four critical dimensions and get a personalised roadmap for AI-driven improvements.

⏱️ Time to Complete

5-10 minutes

🎯 Target Audience

Engineering, Construction & Manufacturing Leaders

📊 Four Key Areas

AI Adoption, Data Readiness, Workflow Automation, ROI Potential

📋 Personalised Results

Detailed scoring with actionable recommendations

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Welcome to Your AI Readiness Assessment

This comprehensive evaluation will help you understand your current AI capabilities and identify the highest-impact opportunities for your business.

What You'll Discover:

🚀 AI Adoption Journey

Your current AI experience and organisational readiness level

💾 Data Infrastructure

How well your data foundation supports AI implementation

⚙️ Automation Maturity

Your current workflow efficiency and automation opportunities

💰 ROI Potential

Expected return on investment and business impact timeline

Section 1: AI Adoption Journey (25 points)

Understanding your current AI experience and organisational readiness

1.1 Where is your organisation currently in its AI adoption journey?

Exploring
We're researching AI possibilities but haven't started implementation
Piloting
We're running small AI experiments and proof-of-concepts
Implementing
We're actively deploying AI solutions in specific areas
Scaling
We have multiple AI solutions and are expanding across departments
Leading
We're an industry leader with mature AI integration across operations

1.2 Which AI tools and technologies are you currently using? (Select all that apply)

ChatGPT/Copilot Assistants for document generation and analysis
Power BI with AI plugins for predictive analytics
Microsoft AI agents
Predictive maintenance systems for equipment optimisation
AI-powered CAD/design tools (generative design, automated drafting)
Smart scheduling systems with resource optimisation
AI-driven cost estimating and bid optimisation
Digital twins with AI simulation capabilities
Computer vision for quality inspection and progress monitoring
Other AI applications
None currently

1.3 What's your biggest challenge with AI implementation? (Select all that apply)

Lack of technical knowledge - Limited internal AI expertise
Budget constraints - Unclear return on investment
Integration complexity - Existing legacy systems limitations
Data quality issues - Poor data availability and consistency
Change management - Employee resistance to technology adoption
Compliance concerns - Regulatory and data security requirements
Performance measurement - Difficulty measuring AI business impact

1.4 How familiar is your leadership team with AI applications in your industry?

Novice
Limited understanding of AI capabilities
Aware
Basic knowledge but uncertain about implementation
Informed
Good understanding with evaluation experience
Experienced
Strong knowledge with hands-on AI project experience
Expert
Deep expertise in AI strategy and implementation

Section 2: Data Readiness & Infrastructure (25 points)

Evaluating your data foundation for AI success

2.1 Where is your primary project data stored?

On-premises servers
All data hosted internally with limited cloud connectivity
Hybrid approach
Mix of on-premises and cloud storage with some integration
Cloud-based
Majority of data in cloud platforms (Office 365, Google Workspace, AWS)
Fully integrated cloud
Complete cloud ecosystem with real-time data synchronisation
Edge computing
Advanced cloud infrastructure with real-time processing

2.2 How would you rate your document management and file organisation?

Poor
Inconsistent naming, scattered storage, difficult to locate files
Basic
Some structure but relies heavily on individual knowledge
Good
Standardised naming conventions with centralised storage
Excellent
Automated organisation with metadata and search capabilities
AI-Ready
Intelligent document classification with automated workflows

2.3 Can you easily extract and analyse data across your core project systems?

No integration
Manual data extraction and consolidation required
Limited connectivity
Some data export but requires manual processing
Basic integration
Automated reports but limited cross-system analytics
Good integration
Real-time data flow between most systems
Advanced analytics
Seamless integration with predictive analytics capabilities

2.4 How consistent are your data standards across projects?

Inconsistent
Different coding, naming, and classification per project
Somewhat consistent
General standards but significant variations
Mostly standardised
Clear standards with occasional deviations
Highly standardised
Strict adherence to data standards across all projects
AI-Optimised
Machine-readable data with standardised taxonomies

2.5 What percentage of your project data is captured digitally vs. manually?

0-25%
Primarily paper-based with manual data entry
26-50%
Mix of digital and manual processes
51-75%
Mostly digital with some manual processes
76-95%
Predominantly digital with minimal manual entry
95-100%
Fully digital capture with automated validation

Section 3: Workflow Automation Maturity (25 points)

Assessing your current process automation and efficiency

3.1 How would you describe your current workflow automation level?

Manual processes
Heavy reliance on spreadsheets and manual data entry
Semi-automated
Some digital tools but significant manual intervention required
Well-integrated
Connected systems with automated workflows for key processes
Advanced automation
Sophisticated workflows with minimal human intervention
AI-driven
Intelligent automation with predictive capabilities

3.2 Which areas still require manual effort? (Select all that apply)

Weekly/monthly progress reporting and KPI compilation
Project data entry (timesheets, material receipts, inspection results)
Document approval workflows and change management
Cost estimation and bid preparation processes
Quality control inspections and compliance documentation
Procurement activities and vendor management
Schedule updates and resource allocation
Risk assessment and mitigation planning

3.3 How many person-hours per week does your team spend on repetitive, manual tasks?

0-10 hours
Minimal manual effort, highly automated processes
11-25 hours
Some manual tasks but generally efficient workflows
26-50 hours
Moderate manual effort impacting productivity
51-100 hours
Significant manual workload affecting project delivery
100+ hours
Overwhelming manual processes limiting growth capacity

3.4 How quickly can you generate accurate project reports and performance analytics?

Days/Weeks
Manual compilation requiring significant effort
1-2 Days
Semi-automated process with manual validation
Same Day
Automated reports with minimal manual review
Real-time
Instant dashboards with live data feeds
Predictive
Real-time analytics with forecasting and trend analysis

3.5 Where do you see your biggest operational pain point or bottleneck?

High costs
Operational expenses eating into project margins
Quality department
Rework or administrative tasks slowing down project handover
Schedule delays
Consistently missing project milestones
Commercial and finance
Unable to forecast or predict cash flow fluctuations with accuracy
Operations
Labour efficiency, task allocation / scheduling
Information gaps
Design issues or technical expertise

Section 4: ROI Potential & Business Impact (25 points)

Determining the value proposition for AI investment

4.1 What's your company size by employee count?

1-50 employees
Small contractor with local/regional projects
51-200 employees
Mid-size company with diverse project portfolio
201-1000 employees
Large regional contractor with complex projects
1000+ employees
Major contractor with national/international presence

4.2 What percentage of your employees use AI to improve productivity?

0-5%
Very few employees currently using AI tools
6-15%
Small group of early adopters using AI tools
16-30%
Growing adoption across different departments
31-50%
Widespread adoption with regular AI tool usage
50%+
Majority of workforce actively using AI for productivity

4.3 Where does innovation sit on your leadership team's agenda, and do they view technology as a strategic advantage or a potential risk?

Innovation-Driven Leadership
We've created an internal innovation team and set aside budget each year for piloting new technologies
Progressive but Structured
We're keen to innovate and have modernised a lot of our systems, but we make sure any new tech aligns with our operational goals
Open but Risk-Aware
We've made some investments in cloud systems and dashboards, but we take a conservative approach to major changes
Inconsistent Innovation
We usually wait to see what others in the industry are doing before we act
Resistant or Risk-Averse
Our current systems work well enough, why fix what isn't broken?

4.4 What's your timeline for seeing ROI from technology investments?

Immediate
Need ROI within 3-6 months
Short-term
Expect ROI within 6-12 months
Medium-term
Comfortable with 12-18 month ROI timeline
Long-term
Strategic investment with 18+ month ROI horizon

Get Your Personalised AI Readiness Results

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🎯 Your Complete AI Readiness Analysis Includes:

  • Detailed scoring breakdown across all four dimensions
  • Industry-specific benchmarking and comparison
  • Prioritised roadmap with actionable next steps
  • ROI projections and timeline estimates
  • Customised AI implementation strategy
  • Expert guidance on quick-win opportunities
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Your AI Readiness Assessment Results

0/100
Assessment Complete

🚀 AI Adoption Journey

0/25
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💾 Data Readiness & Infrastructure

0/25
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⚙️ Workflow Automation Maturity

0/25
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💰 ROI Potential & Business Impact

0/25
Calculating...

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