Quantity Surveying x AI: Unlocking Productivity in Construction

Author: Tom Haley

In this article, we explore the intersection between Quantity Surveying and generative AI; an area that is rapidly evolving yet remains underutilised in the construction industry.

The Productivity Problem in UK Construction

The UK construction industry faces a long-standing productivity challenge. It’s not uncommon to see the default response to problems being “throw more resources at it.” This approach may feel action-oriented but rarely addresses inefficiencies. Often, we fail to ask: how can we do this smarter?

A significant part of this issue stems not from the site or the labour force but from the upstream processes: how we design, procure, and manage projects. On one major public framework I was involved with, four contractors and a framework manager were appointed. The sheer volume of professionals (project managers, designers, quantity surveyors) ironically became a hindrance rather than a help. At some point, too many cooks really do spoil the broth.

What this results in is long lead times and, ultimately, excessive costs. Take, for instance, the staggering difference in cost per mile of high-speed rail between the UK and countries like Germany, France, and China. While some cost factors are outside the industry's control, productivity inefficiencies certainly play a role. And with mounting issues around outdated infrastructure, one must wonder: how much further could UK investment stretch if projects were delivered efficiently and without waste?

Enter AI: A Productivity Lever?

The question, then, is simple: how much time and cost could be saved if generative AI tools were embraced fully?

It’s easy to imagine the impact. Time lost to manual data processing, correcting errors, or navigating disjointed systems could be reclaimed. Anyone who’s been delayed by inaccurate information or clunky workflows understands the frustration and performance drag this causes.

What if people working on a project could focus solely on their professional functions? What if a quantity surveyor could actually spend all their time surveying quantities? This isn’t fantasy: it’s a tangible, achievable shift.

If generative AI is implemented effectively, the potential productivity uplift is enormous. We may even find that what we call a “skills shortage” is actually a misuse of skilled professionals’ time.

AI as the Saviour?

The answer is both yes and no.

Yes, AI has unlocked capabilities that the average professional doesn’t have the time or training to achieve alone. It can analyse data, synthesise information, and create outputs faster and often more accurately than we can. Used wisely, it becomes an indispensable companion, enabling professionals to focus on high-value work.

No, AI is not a plug-and-play solution. It requires investment: of time, of thought, and of leadership. Tools must be selected, tested, and adapted to workflows. In a profession stretched for time and culturally cautious, this doesn’t happen easily.

The Barriers to Adoption

The benefits of AI are evident, so why isn’t the industry rushing to implement them?

The first issue is cultural. Construction is notoriously conservative when it comes to innovation. There's a deep-rooted preference for working harder, not necessarily smarter. Many are overwhelmed with work and can’t afford the time needed to explore new tools.

Even when a monthly task could be automated, people fall back on Excel because “it gets the job done.”

To break this cycle, strong and enlightened leadership is essential. Leaders must create psychological safety for teams to try, fail, and try again. Without this shift, we risk missing out on enormous gains.

There’s also a generational dimension. Younger professionals are more likely to adopt digital tools as part of their natural working style. But unless educational bodies like RICS, and universities who deliver accredited course, adapt, this change will be slow. For example, the current emphasis on “data management” in RICS-accredited courses and APC criteria is nowhere near enough. Understanding data structure, analysis, and application must become core skills, not optional extras.

Then there's the ever-present “wait and see” mindset. Most firms are waiting for someone else to trailblaze. From a cost-efficiency perspective, that might make sense. But without parallel investment in skills, this approach may delay progress by years.

The Data Paradox

Construction is a data-rich industry, yet remains information-poor.

We've all sat in meetings where a basic question requires someone to hop between five spreadsheets, only to give an incomplete or incorrect answer. This is not a tech problem;

it’s a systems-thinking problem.

We capture data with little forethought about how it will be used. Site records, cost logs, time sheets: each one a siloed entry. Rarely are they structured in a way that facilitates meaningful analysis. So, we end up with more data than ever, but no clear insight.

The result? Quantity surveyors become DIY data scientists, using tools they weren’t trained for to make sense of fragmented information.

What we need is a foundational shift in how we collect and organise data. Only then can we unlock AI’s full potential and transform the profession.

The Role of Policy and Professional Standards

The RICS recently closed a public consultation on its draft professional standard for responsible AI use. This will be a vital step forward. Without clear guidelines, it’s impossible to know whether AI-generated outputs meet professional standards.

If a report was drafted with the help of AI, should the client be told? Absolutely. Disclosure must become the norm. And more importantly, surveyors must understand what they’re inputting into AI tools, how their data is being used, and whether they have permission to do so.

Policies around privacy, attribution, and disclosure will help build trust, both within the profession and with clients. AI isn’t going anywhere, so we must ensure its use aligns with the values and responsibilities of chartered professionals.

Final Reflections

The case for generative AI in quantity surveying is compelling. It can address productivity issues, enhance professional focus, and potentially reshape the industry. But there are very real barriers (cultural, structural, and ethical) that must be acknowledged and addressed.

For now, the technology is outpacing adoption. That may frustrate the early adopters among us, but it also offers a crucial pause and a chance to get our data in order, strengthen our policies, and ensure we implement AI responsibly.

Because in the end, it’s not just about being faster or cheaper. It’s about being smarter. And smarter starts with structure, skills, and leadership.