Construcciones Yamaro: Why practical AI is the construction industry’s next competitive edge

The road to AI-powered autonomous businesses. (Images: MYOB)

Few topics spark as much debate on construction sites and in boardrooms as artificial intelligence (AI). Across the globe, progressive teams are already utilising AI-driven tools to analyse site data, predict cost overruns, detect safety risks and streamline scheduling. But is now the time for mid-sized construction firms to act?

That question is front of mind for Valantis Vais, head of product and product marketing at MYOB, who says the answer lies in practicality: focusing on what AI can deliver for construction businesses today and how leaders can prepare their teams to use it wisely. Through MYOB Acumatica, the company is helping construction businesses connect field operations with financials, turning real-time data into faster, smarter decisions.

“There are wide interpretations and applications when it comes to AI,” says Vais. “It can range from a simple chatbot that saves a firm five minutes on basic day-to-day tasks to something as complex as predictive analytics that identify a bottleneck in a project schedule and save millions of dollars.”

AI, he adds, already ranges from simple assistance tools to intelligent systems that make decisions and handle complex objectives. The term now covers a broad spectrum, from traditional machine learning to generative models and the emerging field of agentic AI.

Imagine an AI procurement agent that tracks material usage, predicts shortages and automatically places orders at the best price, or a workforce allocation agent that monitors worker availability, certifications and productivity before reassigning crews or booking subcontractors. These are not far-off possibilities, says Vais, but early glimpses of what is already taking shape.

“This shows how far-reaching the implications of AI can be,” he says. “At the moment, the only real limit is our imagination.”

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The journey

Vais compares the journey of AI in construction to the automotive industry’s path to self-driving vehicles, from level zero with no automation to level five with full autonomy where a car drives itself completely.

In the construction industry, he sees the journey progressing from basic digitisation to autonomous back-office systems.

At level one, AI automates simple tasks such as invoicing and record-keeping, while people still make key decisions. Level two introduces automated workflows and order-to-cash processes, streamlining routine approvals and preventing delays. Low-code or no-code technology has helped businesses reach this stage, although it still depends on rule-based instructions such as “if this, then that”.

Large language models then push businesses toward level three, enabling conditional autonomy where AI can complete end-to-end processes such as accounts receivable with minimal human input. It recognises when an invoice is late, determines how to follow up, and manages the communication – much like a car that can detect lanes and correct its course but still needs a driver.

Level four emerges when multiple AI agents begin working together, sharing information across functions such as accounts and sales to manage risk and cash flow – akin to a vehicle that can navigate and overtake autonomously.

Level five, though still aspirational, represents an autonomous enterprise that runs its operations while people focus on strategy, vision and value creation. In the car analogy, the driver steps inside, sets the destination and the vehicle takes them there.

“This is a useful framework for understanding where your organisation sits on the autonomy spectrum and what is needed to progress,” says Vais. “The question is not just what AI is capable of, but where each business stands on that journey and how far it wants to go.”

For now, technology is capable of supporting most organisations up to level two, he explains. Yet many construction firms remain at level zero or one, still relying on manual processes and disconnected spreadsheets.

Impact in the field

Those ahead of the curve are increasingly using AI to enhance how work is planned, delivered and managed across the construction lifecycle, from pre-construction through to delivery and maintenance.

“Right now, the most visible benefits are in cost reduction, safer job sites, quality control and extending the lifespan of equipment. One of the most promising areas is in improving the connection between the office and the field in real time,” says Vais.

“Traditionally, project managers prepare a weekly report that summarises site activities and then email it to the team. With AI, that process can become almost instantaneous.

“The system can analyse site activity data and automatically generate a written summary of what occurred each day or week. The project manager simply reviews it, confirms the details are correct and shares it.

“This gives the back office an immediate understanding of what is happening on site, closing the communication gap that often exists between the field and the office.”

Another use case involves estimators and project managers who receive constant scope changes, client requests and email updates.

“Managing that information manually can be overwhelming,” says Vais. “AI can process those emails, compare new information against the existing project scope, and identify what has changed. This helps teams manage variations more efficiently and reduces the risk of something being overlooked.”

A further example of AI in practice, already built into MYOB Acumatica, is anomaly detection. Vais explains that the technology analyses data sets such as materials purchased from specific vendors, comparing prices and flagging possible overcharging.

“Pricing discrepancies can occur due to errors or outdated rates and identifying them manually would take hours of review. With anomaly detection, managers no longer need to sift through hundreds or thousands of records. AI highlights just the few that need attention,” he says.

“This kind of functionality makes activities that were once impractical due to time constraints now entirely achievable. It helps construction firms manage costs, increase transparency, and improve decision-making. That is the kind of value AI can deliver today.”

Across Australia, mid-sized construction businesses are starting to see measurable gains. In MYOB’s recent study, the top benefit reported from AI use was improved decision-making (46 per cent), followed by increased productivity (39 per cent).

“What I find most interesting about the improved decision-making result is that, even though we are not yet operating at levels three or four of autonomy, AI is already helping teams make better calls,” says Vais.

By interpreting schedules, analysing data and synthesising large volumes of information, AI is enabling project managers and business leaders to understand and act faster.

“AI can collate huge amounts of information and present it in a way that aligns with how people think and work,” says Vais. “That accessibility is what makes it powerful.”

He often hears construction described as “data-rich” – an industry with vast stores of historical information but little clarity on how to use it. MYOB calls this data overload, and AI can help close that gap. By turning data into actionable insight, it simplifies complex decision-making and puts information to work. Predictive analytics, for instance, can deliver faster and more accurate cost estimates while helping control labour, material and compliance expenses.

Turning data into value

On the value front, MYOB frames AI across three experiences: assist, automate and advise.

The “assist” layer acts as a digital helper, allowing users to query their systems conversationally. For instance, a manager could ask which projects are most profitable or which suppliers rank highest by spend. It makes it easier to access and interpret information without manual effort.

The “automate” layer streamlines repetitive processes such as invoice handling and approvals, saving time while maintaining oversight. For example, when an email with an invoice arrives, AI can read it, extract the information, and generate the bill directly in the system, linking it to the correct project. By the time the user opens it, the work is done and ready for review.

The “advise” layer provides deeper insights that guide better decision-making. Through anomaly detection and predictive analytics, AI highlights cost variations or irregularities before they escalate, without requiring someone to manually analyse lines of data. The system surfaces what matters most, enabling managers to act faster and more confidently.

“Through these three lenses, AI transforms how construction firms use the data they already have,” says Vais. “It takes them from being data-rich but insight-poor to being insight-driven and more agile in every aspect of their operations.”

Laying the groundwork

Technology alone will not carry a business up the autonomy curve. Many construction businesses still need to get their foundations in place before they can benefit from AI. Large language models and AI systems rely on information being accurate and, to some extent, structured in a way that allows them to interact with it and make informed decisions. That data readiness is one of the biggest barriers to adoption.

Data security, privacy and compliance are another concern. Sensitive project and financial information must be protected, yet AI systems need context to generate meaningful insights. This tension often slows adoption.

“At MYOB, we tackle this challenge head-on,” says Vais. “Privacy and security are built into the core of how we integrate AI into our platform.”

The platform, MYOB Acumatica, is a cloud-based enterprise resource planning (ERP) system that brings financial, customer, project and reporting management together in one environment. In the way it assists businesses, automates workflows and delivers insights, privacy remains fundamental. At its core is a secure gateway that anonymises business data before prompts are sent to large language models. This ensures context is retained for accuracy, while sensitive information never leaves the protected system.

“This gateway delivers both power and control,” says Vais. “It allows businesses to access the full capability of AI while maintaining confidence that their data remains secure and compliant.”

But the biggest barrier, Vais argues, is change management, and it starts at the top. While technology is exciting to talk about, what really determines success is how leaders use it.

At MYOB, leaders embrace large language models openly, showing teams that experimentation is not just acceptable but essential. By modelling curiosity and new approaches, they have turned experimentation into a core part of how the company works.

“What changed for us, and what I believe needs to happen in construction, is that leaders must actively use and normalise these technologies,” says Vais. “They need to demonstrate that AI tools are legitimate and valuable, creating an environment where their teams feel comfortable experimenting.”

This shift, Vais adds, highlights a broader truth for every industry: adoption starts at the top. If leaders are not modelling the behaviour, their teams are unlikely to follow.

He describes MYOB’s approach to AI as deliberate and grounded in practicality. Rather than adding AI features for the sake of it, the focus is on where the technology can genuinely improve workflows and deliver value. Privacy remains central to that philosophy, as evidenced by MYOB Acumatica’s secure gateway. Equally important is trust and local relevance. MYOB designs its platform to meet the regulatory and compliance standards of Australia and New Zealand.

Looking ahead, the goal is to ensure customers can adapt without disruption.

“We are designing MYOB Acumatica to be open and flexible, with the gateway connecting to multiple AI models rather than locking users into a single provider. Businesses can choose the model that best suits their needs in terms of cost, efficiency and speed,” says Vais.

“That openness ensures longevity. When you commit to our platform, you are not confined to today’s technology. You are positioned to benefit from the next generation of advancements as they emerge in the months and years ahead.”

Starting smart

Vais emphasises that MYOB’s approach to AI is practical first. He says success depends on how people use it in their day-to-day work rather than on simply acquiring new technology.

For mid-sized firms ready to explore AI, Vais recommends starting with small pilot projects that prove the use case and build confidence.

“Allow teams to test the technology within a specific workflow and see how it can support their day-to-day operations,” he says.

“For example, take an upcoming request for proposal (RFP) and feed it into a large language model to extract key information, such as opportunities for the business to participate. Observe how the AI interprets the data and what insights it provides.”

Vais cautions against rushing into technology purchases without a clear plan. Too often, he says, organisations treat new systems as “all-in” investments, handing them to their teams and expecting results. That approach rarely works. The key is to create an environment where teams can test, learn and refine while ensuring data quality and accessibility are in place.

He recalls a business that used a large language model to help complete an RFP when a vendor was slow to provide technical input. The team was able to finalise its proposal faster and with greater accuracy.

“These small pilots help spark creativity and imagination within the organisation,” he says. “They show people what is possible.”

The no-regret move

Asked what message he would give to construction leaders still waiting for AI to mature, Vais is unequivocal.

“Now is the time to prepare your business and set the foundation for innovation,” he says. “The one move every leader can make without regret is to lead by example.”

His advice: use AI personally, encourage teams to test it, and be transparent about experimenting.

“When leaders demonstrate curiosity and a willingness to learn, it creates a culture where teams can test, learn and build confidence,” he says. “Leading by example through experimentation is the no-regret move every business leader should be making right now.”

He notes that leaders do not need to become technologists but do need to stay informed. Understanding when a technology is mature enough to apply within the business is part of effective leadership, he says, and awareness helps “distinguish what is genuinely useful from what is technology for technology’s sake”.

Momentum and possibility

Vais believes the next 12 months will see AI influence almost every aspect of construction. The immediate opportunity lies in improving how work is managed, enabling faster, more precise decision-making and ensuring everyone has access to reliable information.

“Imagine a site manager using AI to flag scheduling conflicts before they happen, a foreperson quickly checking material availability across locations, or a team member reviewing safety reports in seconds,” he says. “When each decision is informed by better data, the impact on efficiency and outcomes is huge.”

Yet Vais is also realistic about the unpredictability of what comes next.

“No one, not even the most experienced AI specialists, can predict with certainty what the next 12 months will bring,” he says. “Agentic AI, which was theoretical a year ago, is now appearing in real-world use cases. The pace of change will only accelerate.”

Major providers are already experimenting with autonomous models and humanoid applications, hinting at AI’s next phase.

“I expect we will see AI not only improve decision-making and efficiency for mid-sized construction businesses but also open entirely new possibilities that extend beyond what we can currently imagine,” says Vais.

AI’s evolution is continuous. Its maturity curve will never flatten in the way traditional software does. For construction, that means the race has already begun, and those who act today will be best placed to keep pace with a future defined by intelligence and data.

The post Why practical AI is the construction industry’s next competitive edge appeared first on Inside Construction.

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