AI can be a productivity powerhouse, but we should be careful not to replace critical thinking with convenience. The value of professional expertise lies not in accepting easy answers, but in applying judgement, experience and context to determine whether those answers are appropriate.
McKinsey estimates that artificial intelligence could automate 39% of non-physical work in construction, and as much as 50% in architecture and engineering.
But having access to AI tools is not the same as understanding how to use them effectively. Construction professionals must understand what AI can and cannot do, how outputs should be validated and where its limitations exist.
AI systems can produce remarkably convincing responses with great confidence. Yet confidence should never be mistaken for accuracy. Professionals therefore have a responsibility to approach AI with healthy scepticism. Outputs should be challenged, assumptions tested and conclusions independently verified.
Perhaps the greatest risk is not that AI gives us the wrong answer, but that AI slowly stops us asking the right questions.
Construction has always rewarded curiosity. Many advances in the way we build began with someone questioning an accepted constraint: Why must this be built on site? Why demolish rather than adapt? Why does this material need to be specified this way? For quantity surveyors, the question can be simpler still: Why does it cost that much?
The best quantity surveyors, engineers and project managers do not simply know the answers, they know which questions to ask.
If AI becomes our first response instead of our second opinion, we risk losing one of the profession’s greatest strengths: intellectual curiosity.
The competitive advantage of the future may not belong to those with access to AI, but rather to those who know how to question, validate and effectively apply AI generated insights.
AI flags that a package is 12% above benchmark. The curious QS asks: Is the benchmark genuinely comparable? What assumptions sit behind it? Has the specification changed? Is the market moving? What has the model missed?
Or AI recommends Option A as the lowest-cost solution. The curious professional asks: Lowest cost when? Does it increase program risk, maintenance costs or carbon? What happens if the underlying assumptions change?
At RLB, one of our updated values encourages us to Look to Tomorrow. To me, that means more than embracing emerging technologies. It means having the courage to ask better questions and challenge established thinking.
That requires personal curiosity, a commitment to continue asking difficult questions, even when a machine gives an easy answer, and never allowing convenience to replace critical thinking.
Less time searching for information and more time interpreting it. Less time producing data and more time challenging assumptions. Less time documenting decisions and more time helping clients make better ones.
It sounds like a productive utopia. Provided, of course, we remain curious.
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