Can AI Solve the Construction Productivity Problem?

Will artificial intelligence reduce hours worked or time taken to complete a project?

There has been little or no growth in construction industry productivity for many decades, and there have been many different reasons given. These include larger and more complex buildings, longer and more global supply chains, increased regulation and compliance requirements, temporary project teams, the cycles of boom and bust in construction work, increasing use of prefabrication and offsite manufacturing, the number of low productivity small firms, low industry capital intensity, and a lack of workforce skills and training. All these and more contribute to a complex problem.

Over the decades various solutions have been proposed, such as total quality management in the 1980s, lean construction in the 1990s, BIM in the 2000s, and digital twins in the 2010s. Often criticised as top-down, technocratic solutions when imposed by governments, with BIM mandates for example, the reality is the great majority of firms in the industry, particularly small and medium sized ones, have not adopted them unless required by clients. Clients, in turn, have not been prepared to increase costs or risk innovation on their projects to benefit the industry, a classic catch 22 situation.

Now there is a new solution to the productivity problem. Since the launch of Chat-GPT in November 2023 there have been dozens of artificial intelligence (AI) models released, and widely used software from majors like Microsoft, Oracle, Trimble and Autodesk now has AI incorporated into their systems. Will this time be different, or will AI follow BIM and the other solutions proposed in the past, and be adopted piecemeal when required without changing industry productivity and performance?

In the previous post around 100 companies with construction related AI systems were included, divided into six areas: preconstruction and estimating; project and document management; site monitoring and safety; equipment management and maintenance; design and planning; and materials. This post discusses the potential of AI to solve the construction productivity problem. It looks at how AI might reduce the number of hours worked or decrease the time taken to deliver projects, considering its potential effects in equipment management and maintenance, preconstruction and estimating, project and document management; and site monitoring and safety. Issues in worker and workplace monitoring are discussed, because there is also the possibility AI will make the industry a worse place to work, which has been the experience of call centres, warehouses and Uber drivers, industries where AI is already intensively used.

Can AI Improve Construction Productivity?

Labour productivity is the ratio of an industry’s inputs, usually the number of hours worked, to output, the value of production minus the cost of inputs. To improve productivity the relationship must change, for example increasing output faster than hours worked or decreasing hours worked while maintaining output will both increase productivity. Although that seems pretty straightforward, there are many issues that complicate measurement, ranging from how data is collected and managed to adjustments for price and quality effects. Those data issues are more difficult for capital productivity, which measures the contribution of buildings, structures, plant, machinery and equipment to output, adjusted for age and depreciation.

Also, productivity does not change much from year to year, currently increasing by less than one percent a year for the Australian economy as a whole, and so is gradual and cumulative, and works over the long-run. Even in high growth periods and economies, annual productivity increases of around three percent are common. Therefore, productivity is not a good target for industry policy. Finally, the statistics used for construction include onsite work and people employed by contractors and subcontractors only, and thus exclude important activities like offsite manufacturing and prefabrication, and design, technical and professional services.

There are three factors that will affect improvement in industry productivity from AI. First is the adoption rate of AI by firms, which will be the key driver because the more firms that use AI the bigger the effect. However, second, AI productivity gains may be concentrated in a few firms, lowering the overall level of industry productivity improvement. Third, some or many physical or manual tasks may be affected by AI through robotics, which is outside the discussion here. Onsite construction robots were covered in this recent post.

A November 2024 OECD publication on the productivity effects of AI over the next decade included industry estimates of the share of tasks affected by AI using two measures, a baseline exposure for the share of tasks that time for completion substantially decreases using Generative AI, and an expanded capabilities measure that includes tasks where gains are achievable if other software is developed on top of current systems. The largest gains are in ‘knowledge-intensive services that rely strongly on cognitive tasks, such as Finance, ICT services (including telecoms), Publishing and Media, and Professional services. The least exposed sectors include sectors with a strong manual component, such as Agriculture, Mining and Construction.

Figure 1. Share of tasks affected by AI

Source: Miracle or Myth? Assessing the macroeconomic productivity gains from Artificial Intelligence. OECD Working Paper. 2024: 19.

The OECD study looked at tasks and the extent to which AI can do or help with. High exposure means it has a large share of tasks that AI can assist with, and there are only half a dozen high exposure industries. Although construction is at the lower end of the range, 20 to 30 percent of tasks could be AI assisted, which is comparable to metal and plastic manufacturing and warehousing, and not far behind transport, fabrication, motor vehicles, and water and waste. There are, therefore, plenty of potential productivity gains for construction from AI.

How AI Might Improve Construction Productivity

There are two ways AI might affect construction productivity: it could reduce the total number of hours worked on a project or it could decrease the time taken to complete a project. Note, however, if AI had both of these effects they would cancel out and productivity would not change.

The most straightforward use of AI is for equipment management and predictive maintenance. If these increase the availability and reliability of machinery and equipment there will be an increase in productivity from less downtime for operators, hours worked might not change but output would increase, and that would also decrease time taken to complete. The impact and importance of this on productivity depends on how unreliable equipment currently is. If contractors and operators are aware of the liabilities and costs of breakdowns and already do adequate maintenance, any improvement from AI will be at the margin, however both labour and capital productivity would improve.

In preconstruction and estimating, AI could cut time and reduce mistakes. All construction firms do estimates, and using AI and image recognition for automated quantity take-offs from drawings, PDFs and BIM, and scanning drawings to count items and check for missing information and risk assessment seems an obvious way to save time. That data can be given to estimating systems that also do BQs, cost plans, bidding automation, indirect costs, KPI analysis, and predictive analytics. For larger firms doing design and build work, AI systems that do generative design, site layouts, materials selection, planning and code compliance checks would be useful. Although important for individual firms, the effect on overall industry productivity of reducing hours worked in these offsite functions would be offset by the much larger number of hours worked onsite. AI would make a major difference to productivity if it improves project management.

Because of the large number of different project documents used, copilots, AI assistants and chat bots that search, summarise and analyse those documents on request is another obviously useful task for AI that would be relevant to most firms in the industry. If site and project managers spend much time searching for information and retrieving documents, and suppliers and subcontractors have to wait for instructions, this would be an efficiency gain that could reduce both hours worked and completion time for projects.

Document management can also include project management functions like bidding, billing, payments, RFIs, claims and compliance management. Other AI assisted PM offers are task plans and schedules, risk assessments, status reports, workflows, resource management, ITPs and quality assurance, and site management. Some systems have collaboration and communication tools. How much difference could using AI for these make to PM and productivity? Tasks like scheduling, work plans and status reports are time intensive, so AI can improve productivity by reducing the hours required. Better onsite collaboration and communication can potentially reduce the time to complete, particularly on large projects where coordination is more important.

For AI enhanced PM to improve industry productivity it would have to become widely used, and that depends on how much it improves the capability and competitiveness of firms using AI. If there are significant benefits to early adopters of AI they will be able to out-compete other firms, that will then have to become fast followers to survive. Whether or not PM with AI will deliver meaningful reductions in hours worked, and those significant benefits are available and achievable, is an open question at this point.

Finally, site monitoring and safety. Reality capture and computer vision systems match site work to digital twin and BIM models to track progress. These design vs as-built comparisons can be done by drones or hand-held cameras and provide daily or real-time progress tracking, with time saved on reports and documentation a useful improvement, and possibly leading to an important reduction in disputes and defects. However, the main effect on productivity will depend on how well this information is used to improve project management, particularly collaboration and communication.

Safety systems monitor sites and detect hazards, and dangerous behaviour by people, and can prepare safety plans. While important, this will not have much effect on productivity, although reducing injuries and time off work for recovery would have an indirect effect. Some of these systems also identify people using sensors, cameras and wearables like beacons and badges, and can also be used for access control, headcounts, timesheets, task and workforce management. Again, there can be time savings in these administrative functions, however, worker and workplace monitoring raise a number of important issues.

Monitoring and Algorithmic Management

In Australia workplace monitoring and surveillance is covered by a mix of federal and state laws. The Privacy Act 1988 doesn't explicitly address workplace surveillance, but has 13 Australian Privacy Principles that employers must follow when handling personal information and data collected through monitoring. Employers can monitor employees to ensure they are doing their work and using resources appropriately, but should only monitor employees to the extent necessary to achieve a legitimate business purpose. Employees have to agree to have their data shared. The states have two laws that regulate workplace surveillance, one is a Privacy Act requiring compliance with the Australian Privacy Principles (in NSW a Workplace Surveillance Act), and the other a Surveillance Devices Act that regulates the use of surveillance devices in public places and workplaces [2].

Monitoring with AI becomes controversial when it is used for ‘algorithmic management’, where workers are monitored, ranked, rated, given instructions, potentially harmed or auto-fired by an AI system. In Australia, the current dispute between Woolworths and the United Workers Union a worker performance management program called ‘The Coaching and Productivity Framework’ that was introduced across warehouses is a major issue. The December 7^th Sydney Morning Herald article on the dispute said the ‘point of contention is the disciplinary action workers face if they fail to achieve 100 per cent adherence to pre-determined pick rates. What was previously a non-enforced goal is now a mandatory requirement.’

In the US the Federal Trade Commission has authority to block unfair trade practices, and a recent speech by commissioner Alvaro Bedoya argued algorithmic management is an illegal unfair trade practice. Because it causes substantial injury, can’t be reasonably avoided, and isn’t outweighed by a countervailing benefit, Bedoya says algorithmic management satisfies the three criteria for an unfair trade practice.

He gives three examples. First, on substantial injury, Bedoya describes a warehouse worker injured while working for ecommerce sites monitored by an AI that required him to pick and drop an object off a moving belt every 10 seconds, for 10 hours a day. Workers are tracked by a leaderboard, supervisors punish workers who don’t make quota, and the algorithm auto-fires them if they fail to meet it. Second, Bedoya describes the experience of New York rideshare drivers when the apps start randomly suspending them, telling them they can’t book a ride. Drivers who stop for coffee or use a bathroom are locked out for hours as punishment. Third, on average a call-centre worker is subjected to five forms of AI, with AI video and voice monitoring to measure empathy, an AI timing calls, and two more to analyse calls. AIs produce transcripts of calls, but workers with accents find them ‘riddled with errors’ and their performance assessment is based on the transcripts.

Given these considerations, construction employers who hope AI monitoring of workers will improve productivity should approach this idea with great caution. Workers have the right to know what data is being collected, who it’s being shared with, and how it’s being used.

Conclusion

Industry productivity is the combined effect of both capital and labour productivity, and AI adoption can lead to broad and long-run gains in construction productivity as AI assistants take over time consuming tasks like report writing and document management, and AI systems improve resource and project management. The potential productivity gains from AI are significant, but there are factors that could limit the effect, such as the willingness of firms to adopt AI, their capabilities, skills, and access to data. Also, AI adoption across the industry is likely to be uneven, and the productivity benefits of AI will tend to go to firms that use other advanced technologies and have IT skills.

Two construction tasks that AI could significantly improve are estimating and project management, because it can synthesise, summarise and interpret data, and provide insights and suggestions, although it cannot replace expertise and requires supervision and checking of results. AI would also make a major difference to productivity by saving time used for document management, through quicker access to information, by reducing mistakes, disputes and defects, and could lead to more and better collaboration and communication.

To improve construction industry productivity, AI has to increase efficiency and reduce the hours worked or decrease the time taken to complete a project, although if AI had both of these effects they would cancel out and productivity would not change. Therefore, productivity is not the most appropriate metric to use for industry-wide effects of AI, rather it should be used to measure productivity improvements in specific tasks where AI can assist workers.

In addition to productivity there are other labour market implications not discussed here, such as the effects of AI on recruitment, retention and wages, and ongoing demographic changes such as the ageing workforce. Also, the history of technologies like electricity, internal combustion engines, computers and the internet show it typically takes two to three decades for a new technology to become widely used and to significantly affect the economy and productivity. AI may be different, but probably not. Modern AI began with Alpha Go in 2016, so we are almost a decade on already, and the next decade could see rapid uptake of the AI systems surveyed in the previous post.

At this stage it is impossible to know whether AI will deliver on its potential productivity gains. That will depend on how the technology continues to develop, how quickly AI is adopted and successfully applied, associated innovation-boosting effects, and how government policy on incentives, digital infrastructure, AI regulation and data develops.

---

[1] Progress in AI may be slowing down. GPT-4 is nearly two years old, but The Information said OpenAI’s new models have only incremental improvement, and Reuters quoted Ilya Sutskever, co-founder of OpenAI, saying results from scaling up pre-training have plateaued.

[2] For more details see the Office of the Australian Information Commissioner about workplace surveillance: https://www.oaic.gov.au/privacy/your-privacy-rights/surveillance-and-monitoring/workplace-monitoring-and-surveillance