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AI-Powered Road Insights - Your Next Step

The Road AI Opportunity Audit by Maintain-AI

A Quick, Insightful Tool to Exploring Opportunities of AI in Road Maintenance

In an age where data-driven approaches are transforming industries, road maintenance shouldn't be an exception. The AI Opportunity Audit is an interactive assessment tool created to empower road maintenance professionals. By answering just 10 questions, you'll gain valuable insights into how AI can support your existing maintenance practices. We're not here to replace your expertise but to augment it, helping you make the most informed decisions for your network's budget and long-term health.

Unlock the AI Advantage: Why Our Assessment is Your Next Step

This 10-question tool is your first step towards understanding the untapped potential of AI in your operations. In just a few minutes, you'll gain actionable insights into how AI could transform your current road maintenance practices.

What Drives Maintain-AI?

We're guided by a singular belief: "Good Roads Should Cost Less". In a world where limited resources and outdated methods often dictate road quality, we see a better path forward. One paved by data-driven decisions, automation, and most crucially, Artificial Intelligence.

Critical Concept

The AI Efficiency Spectrum

Traditional Methods Lag
Time Consumes Budget
Data is Underutilised


Thus, inefficiencies in Road Maintenance aren't just costly - they're exponential.

The challenge with traditional road maintenance approaches, manual or LiDAR based, is that they often miss opportunities for optimisation, leaving a gap between what is done and what could be possible.


This 'Efficiency Gap' is marked by wasted time, underutilised data, and, ultimately, increased costs.


The AI Opportunity Audit serves as an eye-opener to these gaps, offering a quick assessment to show you where AI can bridge these inefficiencies and add value.


Why This Matters: A Self-Evaluation to Explore AI's Potential in Your Operations

1. Quick Assessment:

No downloads, no complicated forms. Just straightforward questions that get to the heart of your current operations.

2. Immediate Insights:

Get instant feedback on how AI could specifically improve your road maintenance practices, right from data collection to cost-efficiency.

3. Drive Conversations:


Use your results to spark informed discussions among your team and with stakeholders about moving towards AI-driven solutions.

Current Inspection Practices

Current Inspection Methodology

The success of your maintenance philosophy can be significantly impacted by the quality of the data obtained and the inspection strategy you choose. The approach selected affects not just the breadth and depth of insights gained to make data-driven factual decisions, but also the timeliness of the data available for making informed decisions.


According to the Federal Highway Administration (FHWA), advanced and objective data collection methods can provide a more comprehensive understanding of road conditions. This question aims to gauge where you are on the spectrum of manual to automated inspections, providing insights into the potential for optimisation through AI.

What are the current methods you are using for road inspection? 

Score - 1

1: Manual inspections only (visually have people inspect all or part of the network and record defects)

3: Semi-automated inspections (a hybrid of manual inspections and some type of digital surface scanning approach of certain parts of the road network)

5: Fully automated inspections (digital surface scanning of your complete road network)

Data Collection Frequency

Frequency of inspections is crucial for timely maintenance and resource allocation.


Studies from the American Society of Civil Engineers (ASCE) and Permanent International Association of Road Congresses (PIARC) consider that more frequent inspections lead to early defect detection and cost-effective repairs. This question aims to assess your current inspection cadence to identify opportunities for optimising schedules through AI-driven approaches.

How often do you conduct road inspections? 

Score - 1

1: Rarely (less frequent than annually) or only when issues arise

3: Periodically (e.g., annually or bi-annually)

5: Regularly scheduled, (e.g. weekly or monthly inspections)

Budget and Resources

Maintenance Budgets

Budget allocation goes beyond just numbers; it also should consider strategic timing and treatment application.


According to the Federal Highway Administration (FHWA) and current research, effective budgeting should be agile enough to cover both reactive and preventive maintenance while meeting or improving performance benchmarks like the Road Quality Index (RQI). This question aims to gauge how well your current budget adapts to real-time needs and paves the way for AI-driven enhancements, that allow more informed decisions, and help identify the right treatment at the right time, ultimately reducing overall road lifecycle costs.

Is your current budget consistently keeping your roads at target minimum performance levels (e.g. Road Quality Index (RQI) or similar) while also covering both routine and preventive maintenance requirements? 

Score - 1

1: Budget is mainly for reactive repairs; roads frequently fall below target minimum performance levels; often have unspent funds due to inefficiencies.

3: Budget covers routine maintenance to meet target minimum performance levels but lacks allocation for preventive measures.

5: Budget covers routine maintenance and some preventive measures but may lack technological advancements for optimisation.

Cost Efficiency and Procurement Challenges

Efficient procurement processes are the backbone of any well-oiled maintenance operation.


According to the American Association of State Highway and Transportation Officials (AASHTO), streamlined procurement can lead to quicker project delivery and reduced costs. This question seeks to understand your focus on efficiency and cost-saving in the procurement stage. It sets the stage for introducing AI-driven solutions that can further optimise these processes, from easier vendor commercial models to contract management, thereby saving both time and money.

How important are cost-saving and streamlined procurement processes in your inspection and maintenance operations?

Score - 1

1: Little to no focus on cost-saving; procurement is cumbersome and time-consuming.

3: Moderate focus on cost-saving; procurement processes are in place but could be more efficient.

5: High focus on cost-saving; procurement is relatively smooth but open to improvements for further optimisation.

Reliability and Public Perception

Reliability of Data for Decision-Making

Reliable data is crucial for effective road maintenance.


ISO 55000 standards stress the importance of data quality for asset management decisions. This question assesses how useable your current data is, as unreliable or aged data can lead to poorer maintenance choices and increased costs - both from resources and ongoing road lifecycle costs. Knowing your data's reliability is a step toward integrating AI for more consistent and actionable insights.

How reliable is the data collected from your current road inspection methods in informing key maintenance decisions?

Score - 1

1: Data is often unreliable, leading to poor maintenance decisions.

3: Data is sometimes reliable for routine maintenance but may lack depth and breadth for strategic decision-making across our entire road network.

5: Data is highly reliable but would benefit from more advanced analytics for nuanced decision-making and objectivity.

Public Feedback and Complaints

Public feedback is an essential metric acknowledged by various international bodies like the American Association of State Highway and Transportation Officials (AASHTO), International Road Federation (IRF) and ISO standards (ISO 55000) for asset management. Even the Institute of Transportation Engineers (ITE) and the Federal Highway Administration (FHWA) provide resources on the importance of public involvement in road maintenance. This question aims to gauge the effectiveness of your current maintenance strategies in meeting public expectations. A high frequency of complaints may suggest a gap that could be bridged by more advanced, data-driven methods, such as those enabled by AI.

How frequently does your organisation receive complaints from road users regarding road conditions?

Score - 1

1: Frequent complaints, consistent levels of public dissatisfaction.

3: Occasional complaints but generally manageable.

5: Rare complaints, indicating high levels of public satisfaction. When complaints occur, they are often publicised. Open to further reduction through advanced monitoring to demonstrate proactive strategies.

Data Management and User Experience

User-Friendliness of Current System

Ease of use in a road maintenance system is crucial for timely and effective decision-making.


The Transportation Research Board (TRB) notes that complex systems can create barriers to efficient road management, increasing the risk of delays or errors. This question seeks to evaluate the intuitiveness and ease-of-use of your current road inspection and maintenance systems. User-friendly and interoperable systems often facilitate quicker data interpretation and action, providing an avenue for more advanced, AI-driven tools to further streamline the process.

How user-friendly are your current road inspection and maintenance systems?

Score - 1

1: System is cumbersome and requires specialised training to use, operate and identify issues.

3: System is moderately easy to use but lacks intuitive features.

5: System is user-friendly but could benefit from more streamlined interfaces or automated features.

Data Analysis and Interpretation

Effective road maintenance hinges on a system's ability to collect, collate, analyse and report data in a manner that leads to actionable insights.


The American Association of State Highway and Transportation Officials (AASHTO) underscores the importance of integrated data management for informed decision-making. This question aims to gauge the robustness of your existing data management capabilities. A well-designed, integrated system can save time and resources, making it easier to adopt innovative solutions like AI to improve road maintenance strategies further.

How capable is your existing approach in collecting, collating, analysing and reporting road network data for actionable insights?

Score - 1

1: Limited capabilities in multiple stages, from data collection to analysis and reporting.

3: Tolerable capabilities but may face challenges in either collection, collation, analysis or reporting.

5: Strong capabilities in most stages but open to improvements for enhanced analysis and automated reporting.

Operational Efficiency and Environmental Sustainability

Speed of Inspection-to-Repair Cycle

Time is of the essence in the cycle from road inspections to timely repairs.


The Federal Highway Administration (FHWA) emphasises that delays in this cycle can lead to further deterioration and increased repair costs. This question seeks to evaluate the agility of your current operational model. A streamlined process that minimises delays can dramatically affect both the longevity of the road network and the cost of maintenance. As road conditions can rapidly change due to various factors such as weather and traffic, the speed of this cycle becomes even more critical. It's worth noting that consistent inspections are key to understanding the health state of your roads. Without this knowledge, there's a discreet increase in both the risk of escalated repair costs and potential safety issues.

How quickly does your current operational model complete the cycle from inspections to analysis, reporting and timely repairs?

Score - 1

1: Significant delays across multiple stages, affecting road quality and user satisfaction.

3: Average speed with some bottlenecks in either inspection, analysis, or reporting.

5: Quick cycle but could benefit from real-time analytics and automation for even faster turnaround.

Alignment with Environmental Goals and Efficient Maintenance

Environmental sustainability has become a cornerstone in public infrastructure management and public perception.


According to the Environmental Protection Agency (EPA), the environmental impacts of road maintenance can be extensive, ranging from air and noise pollution to water quality issues. This question aims to gauge how well your operations marry the need for effective, timely road maintenance with the pressing requirements of environmental stewardship. It's no longer enough to just fix the roads; modern best practices dictate that maintenance activities should be conducted in an eco-friendly manner whenever possible. Achieving this balance not only contributes to environmental sustainability but will also yield long-term cost savings through the use of green technologies and materials.

How well do your operations align with environmental sustainability goals while ensuring timely and appropriate maintenance treatments?

Score - 1

1: Limited alignment with environmental goals; inefficient maintenance approaches.

3: Moderate alignment with some sustainability initiatives; occasional use of timely treatments.

5: Strong alignment and efficient maintenance but open to further optimisation through data-driven decision-making.

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See Below.

Outcomes of the Evaluation


Great Start! Unlock More Maintenance Benefits with AI-Driven Strategies.

Areas for Consideration

Based on your responses and the score you've received, it's evident that there are specific aspects of your road maintenance and management processes that could be significantly enhanced or augmented through data-driven, AI-optimised strategies.

Here are three (3) primary areas to consider for immediate impact:

Focus 1

Focus 1 Response

Focus 2

Focus 2 Response

Focus 3

Focus 3 Response

What is the Road Ahead?

Taking steps to explore how AI can enhance your road maintenance practices is an exciting venture, one filled with both opportunities and valuable lessons. You've now got a snapshot of some areas that could benefit from a fresh perspective. We invite you to consider these insights as starting points for discussion within your team. Whether it's exploring new data collection methods, being more proactive to gather more consistent information or considering how machine learning could offer real-time insights, the possibilities are significant.

The journey toward more efficient road maintenance is collaborative and ongoing. It's about building on what's already working and being open to new approaches that could make things even better. Your commitment to this process not only stands to improve your operations but contributes to a larger goal of sustainable and safe road networks for everyone.

As you take the next steps, know that Maintain-AI is here as a resource and partner. We're excited about the innovative changes happening in this field and look forward to the opportunity to work together in making the most of them. And remember, Good Roads Should Cost Less. Let's Keep the Good Roads Good.

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