Quantity Surveying x AI: current state

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In this first article, James and Yoshi share their views on the current state of generative AI use in the quantity surveying profession and will build on this in their second article focused on the future of generative AI in the quantity surveying profession.

The Data Skills Gap: Quantity Surveyors Unprepared for the AI Revolution

The quantity surveying profession stands at a critical crossroads as generative AI technologies rapidly transform the construction industry. A significant challenge hampering adoption is the widespread lack of training and awareness among quantity surveyors regarding essential data and AI skills. Many professionals who have spent decades perfecting traditional QS methodologies now find themselves facing a technological paradigm shift without adequate preparation.

This skills gap manifests in several ways: many quantity surveyors lack fundamental data literacy, there's limited understanding of how AI systems actually work, and many firms have yet to develop coherent strategies for integrating AI into their workflows. Professional bodies are beginning to incorporate data science into QS qualifications, but these efforts often focus on theoretical knowledge rather than practical application. The most successful firms are those investing in continuous professional development, creating internal training programs that blend traditional QS expertise with modern data skills.

Prompting Problems: Using Generative AI as a Glorified Search Engine

Another significant barrier to effective AI implementation is the widespread misuse of generative AI tools. Many professionals approach these sophisticated systems as they would a search engine – inputting vague queries and expecting perfect results.

When quantity surveyors input prompts like "estimate the cost of this building" without providing specifications, materials, location data, or project constraints, they receive generic responses of limited value.

Effective prompting requires precision, context, and clarity. The most successful applications of generative AI in quantity surveying come from professionals who understand how to structure their prompts effectively – providing comprehensive context, specifying desired output format, clarifying assumptions, and iteratively refining prompts based on results. Some forward-thinking firms are developing internal prompt libraries and best practices, treating effective prompting as a valuable skill to be cultivated and shared.

AI Washing: Navigating the Flood of "AI-Powered" Construction Tech

The construction technology marketplace has become increasingly crowded with products claiming to be "AI-powered," creating significant challenges for quantity surveyors attempting to identify truly valuable solutions. This phenomenon, known as "AI washing," involves companies exaggerating or misrepresenting their AI capabilities to capitalize on market hype.

Many so-called AI solutions for quantity surveying offer little more than basic automation or rule-based systems dressed up with AI terminology. True AI systems demonstrate capabilities like learning from new data, adapting to novel situations, and improving performance over time – characteristics absent from many products currently marketed to quantity surveyors.

Quantity surveyors can protect themselves by developing a more sophisticated understanding of AI capabilities and limitations, asking vendors specific questions about how their AI works, what training data was used, and how the system improves over time. Industry associations could play a valuable role by establishing certification standards or evaluation frameworks for AI tools in quantity surveying.

The Promise of Agentic Workflows in Quantity Surveying

Despite these challenges, there's significant reason for optimism regarding AI's potential in quantity surveying, particularly through the emerging concept of agentic workflows. Unlike basic generative AI that simply responds to prompts, agentic AI systems can autonomously plan and execute actions using specialised tools and reflect on results – capabilities perfectly aligned with the process-driven nature of quantity surveying work.

Quantity surveying involves numerous structured workflows that typically require gathering information from multiple sources, applying standardised calculations, making professional judgements, and producing formatted outputs – precisely the kind of multi-step tasks where agentic AI excels. Industry leaders like Trimble are already exploring how agentic AI could transform construction workflows. As Aviad Almagor, vice president of technology innovation at Trimble, explained in a recent interview, "It's about humans providing an instruction – where we want to get to in this complex construction process – and letting the agent play with estimating, scheduling, material and so on to bring an optimised solution into our hands."

Final Reflections

The current state of generative AI in quantity surveying presents a mixed picture. Significant barriers remain: a widespread skills gap, ineffective prompting techniques, and market confusion caused by AI washing. However, emerging technologies like agentic workflows offer tremendous potential to transform the profession for those willing to invest in understanding and properly implementing these tools.

The most successful quantity surveying firms will be those that address these challenges head-on – investing in data skills development, establishing effective prompting frameworks, carefully evaluating technology claims, and thoughtfully integrating AI into existing workflows. These organisations recognise that AI isn't a replacement for quantity surveyors but rather a powerful tool that can handle routine tasks while freeing professionals to focus on higher-value activities requiring human judgement and expertise.

In next week's article, we will build on the current state by exploring what we think the future holds for generative AI in the quantity surveying profession.

Keep an eye out for that, and in the meantime, enjoy the rest of your week!

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