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Text: ´¡²Ô³Ù³Ù¾±â€¯A¾±²Ô²¹³¾´Ç, Adjunct Professor, Department of Management Studies

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For an industry literally building the modern world, construction has been surprisingly slow to modernise itself. Productivity growth in the construction industry has stalled for decades. Projects still routinely run over time and budget. Fragmented supply chains continue to make collaboration harder than it should be. While sectors like manufacturing, retail, and finance are embracing digital transformation, construction appears stuck with outdated processes and disconnected systems. 

Recently, however, artificial intelligence has finally begun to change the above image, not just as a tool for automation, but also as a foundation for entirely new business models. At the basic level, AI can help automate routine administrative work, optimise procurement and scheduling, and provide real-time insights into project performance. But the transformative significance of AI lies in shifting companies from a reactive mindset to a predictive one. Instead of responding to delays, cost overruns, or equipment failures after they have happened, AI now increasingly enables construction firms to anticipate problems before they escalate, sometimes even before they happen at all. 

At the root of what has long challenged construction is fragmentation. A typical project involves multiple contractors, consultants, and suppliers working across separate systems with limited data sharing. Decisions are often made with incomplete information, and inefficiencies build up over time. AI has the potential to cut through this complexity by bringing together data streams, identifying patterns, and enabling faster, more informed decision-making.  

In practical terms, this means fewer delays and smarter resource use. It provides outcomes that actually address the sector’s most persistent pain points. 

AI can reshape business models 

While the above kinds of efficiency gains are compelling, one of the most underestimated impacts of AI is how it could change construction firms’ business models to be more effective than previously. Traditionally, the industry has operated on a transactional basis. Companies have focused on winning contracts, delivering projects, and then moved on. The mindset has been one-off work, with client relationships more often than not ending when construction project has been delivered. 

AI has the potential to disrupt this model by enabling ongoing, data-driven services. Digital platforms can collect performance data throughout a building’s lifecycle, allowing companies to offer predictive maintenance, operational optimisation, and long-term monitoring. In effect, construction firms can move from being purely project deliverers to long-term service partners. 

This shift opens the door to more stable and diversified revenue streams. Instead of relying solely on winning the next contract, companies can build recurring income through subscription services, analytics, and consultancy. It also strengthens client relationships, turning them into ongoing collaborations rather than short-term transactions. 

The advantage will go to early adopters 

Digital transformation rarely unfolds evenly across an industry. In most sectors, a relatively small group of early adopters gains a first-mover advantage, setting standards that others must later follow. 

The construction sector is unlikely to be any different. Companies that invest early in AI are not only improving internal efficiency, they are building digital ecosystems that connect clients, suppliers, and partners in ways that are difficult to replicate. Over time, these ecosystems can create powerful network effects. The more participants work on a particular digital platform, the more valuable both hat platform and its use become, leading to a widening gap between digitally advanced firms and those that continue to rely on traditional methods. 

In that context, AI is no longer just about operational improvement; it is becoming a strategic differentiator that will shape future market leadership. 

So why is adoption still slow?  

Despite the clear benefits of adopting AI, the industry remains cautious towards it, and there are several reasons as to why.   

Cost is an obvious factor. Implementing AI systems requires a significant amount of upfront investment in technology. Furthermore, companies in this sector often rely on outdated software that does not integrate easily with modern digital platforms, implementation complex and time-consuming. For many companies, particular the smaller ones, the amount of time needed and the financial risk can feel quite daunting. 

Data governance adds another layer of hesitation. AI depends on the availability of large volumes of project information, raising questions around ownership, privacy, and cybersecurity. Without clear governance frameworks as of yet as to what constitutes proprietary knowledge, companies are understandably cautious about sharing sensitive data. 

Rather than technology, cost, or governance, the biggest barrier remains perhaps people. Construction remains a tradition-driven industry, and people are very accustomed to their current ways of working. As a result, scepticism toward new technologies, including redesigning processes to run on new technologies and training personnel to use these new technologies, is still common in this industry.  

Leadership is key to overcome barriers

Successfully adopting AI in construction requires more than technical solutions. It demands strategic leadership and cultural transformation. Organisations must begin by aligning AI investments with clear business objectives, starting with pilot projects that demonstrate return on investment while minimising risk, moving on to high-impact use cases that deliver unquestionable measurable value.  

It is equally important to focus on workforce development. For the implementation of AI to be successful, companies need to invest in training programs that build digital literacy and equip employees with the skills needed to operate AI-enabled systems effectively. Prioritising their development, being clear that the technology is to enhance rather than replace their expertise, is essential for overcoming resistance and achieving successful implementation.  

Companies also need to ensure they have clear policies on data ownership. Establishing such policies around privacy and security can help build trust among stakeholders and encourage collaboration. 

Finally, companies must recognise that AI adoption is not something they can achieve alone. Partnerships with technology providers, clients, and supply-chain partners will be critical to building integrated digital ecosystems that deliver real impact. 

For decades, fragmentation, inefficiency, and low productivity growth have been entrenched within the construction industry – AI offers a rare opportunity to address these issues while simultaneously unlocking new business opportunities.  

It’s important to remember that technology alone is not enough; successful adoption requires strategic planning, cultural change, and strong data governance. The companies that treat AI implementation as an organisational transformation, rather than just a technical upgrade, will be best positioned to thrive.  

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