Addressing The Challenges of AI in the Construction Industry

AI has transformed from a buzzword to an undeniable reality – and is set to significantly disrupt the construction industry. When implemented effectively, AI will boost productivity, accuracy, and safety on projects across every sector.

However, there are also challenges around adopting AI across construction. While the construction sector has historically approached digital transformation with caution, the emergence of AI presents an exciting opportunity to tackle construction’s long-standing productivity challenges and usher in a new era of unprecedented efficiency.

This guide breaks down some of the core challenges of AI in the construction industry and what construction leaders can do to address them.

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Written by Alex Hall.

Posted 04/09/2025

Construction’s Data Dilemma

A single construction project generates a huge wealth of data, but many construction firms don’t have the data standards in place that would make this data effective for AI tools.

Siloed Data

The construction sector struggles with siloed and inconsistent data. Rather than using the same systems and procedures for each construction project, data is often spread across multiple disconnected platforms, which can often change from project-to-project.

In fact, many construction companies are still relying on paper processes. A 2023 McKinsey report revealed that 37% of construction companies cite gaps in core tech as an impactful barrier to growth.

Even construction companies with digital systems in place often encounter data silos, using several unconnected software solutions spread across departments, rather than one connected system for all project data. It’s common to have separate tools for project management, procurement, and accounting with limited, if any, integrations.

Different departments are likely to store their data in different formats or use different naming conventions. When this data is put into an AI tool in bulk, it will likely generate inconsistent or inaccurate results.

Poor Data Quality

AI outputs are only as good as the data the AI is trained on, and construction companies can struggle with collecting consistent, good quality data.

Some of the most common data quality in construction issues include:

Missing form fields
Outdated information
Human errors
Duplicate data
Conflicting entries

Addressing Data Challenges for an AI-Powered Future

To get the best possible results from AI tools, construction leaders need to implement consistent and accurate data management. This starts with adopting an Enterprise Resource Planning (ERP) platform.

An ERP serves as a ‘single source of truth’ across an organization. Rather than using separate software programs for each aspect of a construction project, ERP provides a centralized hub for project management, finance, HR, and more. Centralizing this data leads to consistency, accessibility and accuracy.

ERP platforms can also integrate with other tools to automate data capture. This reduces the risk of human error that comes with manual data entry.

When switching from legacy systems to an ERP, construction companies need to invest time in data cleansing to improve the quality of existing data. This involves reviewing existing data sets for errors, duplicates, or conflicting entries. While the data review process is time-consuming, it will help avoid more serious errors in the long run, especially as firms transition to AI.

Finally, construction companies should implement clear policies for data strategy and governance. These policies should outline practices for safe and consistent data collection, storage and usage. They should also be distributed and explained clearly to all team members to limit confusion and human error. Not only are data strategy policies helpful for AI adoption, but they can also support data privacy and compliance efforts.

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Cultural Roadblocks to AI in Construction

The construction industry has its own unique culture, and when it comes to technology adoption, what works in other sectors doesn’t always work for construction teams. Many construction professionals have been working in the industry for decades and are used to existing policies and procedures. Because of this, team members may be resistant to new technologies, worrying that they could disrupt tried-and-true processes.

Although AI has the potential to make construction work easier and more efficient, team members might be more focused on the possible downsides than the benefits of new technology.

A pivot towards AI tools could fall flat if leaders don’t address these downsides and consider the existing culture of their teams.

An Aging Workforce and Traditional Mindsets

Employees are staying in the workforce longer than they used to — often well past age 65. While these employees have a wealth of knowledge and experience to bring to the table, they also tend to be resistant to new technologies. This resistance isn’t unique to experienced team members - younger generations working in construction also have concerns around job displacement and environmental factors.

Where younger generations learned how to adapt to new technologies as part of their education, older professionals have used manual processes to get the job done for decades. Additionally, some older workers struggle with a lack of digital literacy and will require more training to use AI tools.

Professionals of any age may feel initially skeptical about adopting a new and unfamiliar tool. There are many legitimate concerns about AI technology teams might have. For example, 30% of employees across a range of fields are concerned about possible redundancies.

Other common concerns include biased or inaccurate outputs and the environmental impact of AI. It’s important for construction organizations to address these concerns and help workers feel comfortable before going all-in on AI adoption.

Addressing the Cultural Challenge of AI in Construction

These cultural roadblocks create an uphill battle towards AI adoption. However, there are many approaches leaders and technology advocates can take to help construction teams embrace AI adoption.

Strategic change management

Before implementing AI technology, organizational leadership should develop a comprehensive road map to guide this shift.

This plan needs to outline where the company plans to use AI across operations, the type of training offered to team members, and the data management processes and quality checks that will be in use. Having a plan in place will make the transition smoother and limit disruptions to operations.

H3:Training and upskilling programs

AI works best when it augments human skills, rather than replacing them completely. To help construction teams do their best work with AI, leaders need to provide comprehensive training. Bring in consultants to help teams develop the necessary digital literacy before incorporating AI into day-to-day operations. After launching AI, leadership will need to provide ongoing training sessions to help teams keep up with new features and applications.

Showcase tangible benefits

Even when providing AI training and resources, construction companies will still need buy-in to make the new systems work. The easiest way to do this is by showcasing how AI can create quick wins for the team. For example, highlighting how AI can speed-up tedious and repetitive tasks, reduce errors and improve safety.

Championing from leadership

Leadership will need to set the tone for the entire organization. In order to successfully transition to AI, construction companies will need strong, visible buy-in from leadership teams. Over time, this buy-in can help shift company culture, even if employees are initially skeptical.

Foster a data-driven mindset

Effective AI use starts with clean construction data. As construction transitions to AI-powered operations, teams also need to make a shift toward a more data-driven culture. Focusing on data will make the shift to AI easier for everyone.

Encourage teams to use data to support their decision-making, rather than relying solely on established processes or norms. Additionally, build data management and quality checks into day-to-day operations. Data review and analysis should happen across departments for every construction project to minimize errors and support better AI outputs.

Proving the ROI of AI in Construction

In the long run, adopting AI has a number of huge cost-saving benefits for construction organizations.

However, the upfront cost of AI adoption can be high, especially for construction businesses that already struggle with cash flow. The cost of software licenses, employee training, and technical implementation can quickly add up and eat into budgets.

In this situation, it’s important to weigh these short-term expenses against long-term ROI. In most cases, the initial stress on a budget is worthwhile, as it leads to significant long-term gains.

Addressing the Cost Challenge of AI in Construction

Although AI implementation can come with a high price tag, there are approaches construction leaders can take to make these cost challenges more manageable.

Phased implementation: Instead of starting with full-scale AI implementation, test tools out with more affordable pilot projects.
Focusing on high-impact use cases: When deciding where to implement AI first, start with the areas that will have the biggest impact on the business and address leading pain points. For example, if a company is struggling with costly delays or material waste, it can focus efforts on this area first to see the biggest positive financial impact, then branch out.
Strategic partnerships: Working directly with technology providers can help construction companies develop cost-effective solutions with a positive impact.

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Access Coins helps construction teams gain real insight from their data using role-specific dashboards, project analytics, cost forecasting and automated workflows.

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Alex has over a decade of experience working within both the construction and construction technology sector. He has now brought his rich experience and insights to Access Construction, where he manages pre-sales teams for Access Coins ERP, covering the end-to-end process in the AEC sector across the USA, UK&I, and Australia.