In any industry, timing is everything. When the right technology exists, sectors must reorganise around it and commit to it. This is exactly what the aviation, farming and trading sectors have done, and itis now time for the construction industry – in particular, project controls – to follow suit. It stands at a hugely important crossroads, where making the right decision will be crucial.
Every industry that has gone through a transformational shift has followed the same pattern. Ultimately, it starts with an opportunity that has been waiting for the right technology to make it happen.
Take farming, for example. The agriculture industry mechanised because the engine arrived and it made something possible that had not been achievable before. Similarly, aviation adopted autopilot systems when computers made it technically possible, and safety unlocked a global industry that previously was unable to exist at scale.
Trading was able to algorithmise when the internet made an execution speed – that no human could match – possible. In all three cases, once the technology existed, the industries committed. Roles changed, and the professionals who adapted became more valuable.
Project controls now face a similar opportunity. Auditable AI, capable of reasoning across the complexity of a major capital project without hallucination, now exists for the first time. The big question is, ‘how far is the industry already through this shift?’
The five stages of an industry shift
The AI transformation is playing out across the project controls sector in a defined progression, with each stage creating the conditions for the next. However, it’s important to understand the industry is moving through these stages at different speeds; some teams and organisations are further ahead than others, but the direction is the same for everyone.
Stage One: Siloed and manual
This is the starting point for most organisations. Tools are often fragmented and critical data lives in separate systems. Every function has its own version of the truth. Crucially, project controls professionals will spend substantial time stitching information together manually, chasing status updates and fixing data rather than analysing it.
Decisions are frequently made on intelligence that is already out of date by the time it arrives. The majority of the industry spent the past decade here; many still do.
Stage Two: Connected and unified
Transitioning from fragmented systems to one interconnected platform is a structural process. Schedules, risks, costs and change data come together into a single validated spine. Data is unified, systems are connected and patterns that were previously impossible to see become visible because the information existed in too many places to compare.
Stage Two delivers real productivity gains. It means faster reporting, less time reconciling conflicting data, and provides a shared picture of reality across the entire project controls team. Plus, without the foundation it creates, nothing that follows is possible.
Stage Three: Synthesising and forecasting
With a trusted, unified data foundation in place, the system begins to operate in a qualitatively different way. Instead of just reporting on what has happened, it synthesises across datasets, accurately forecasts what is likely to happen, and automatically surfaces what matters.
This is ultimately the equivalent of autopilot being switched on. The system continuously undertakes tasks that previously required a specialist team working periodically. For instance, analysis that cost $10,000 per run now costs $0.50; work that took around 6,720 hours per year now takes just 828.
The economics of project controls change structurally at this stage.
Stage Four: Proactive intelligence
For the teams that have moved through the earlier stages, this is where the industry is today. The system raises risks before humans spot them and recommends actions without being asked. It will continuously monitor every workflow, identifying what needs attention and filtering out what doesn’t.
The role of project controls professionals shifts here in a meaningful way. As opposed to building the picture of reality, they govern the system that builds it continuously. They move from the execution layer to the judgement layer, making the calls that matter while the system handles everything else.
Using the aviation example, when autopilot arrived, the number of commercial pilots in the world did not fall. It grew from around 13,000 in 1930 to almost one million today. In that industry, safety unlocked demand. In the project controls sector, auditable intelligence will do the same.
Stage Five: Closed-loop decision intelligence
The full capability shift becomes visible at this stage. The system senses, reasons, forecasts and acts within guardrails crucially defined by humans. While the team governs, the system manages. Critically, projects that would previously have been too complex, too unpredictable or too risky to pursue become commercially viable.
McKinsey’s analysis of more than 300 megaprojects found average cost overruns of approximately 80% and schedule delays of around 50%. At this stage, those numbers change structurally, because the intelligence underpinning every decision is more accurate, more auditable and faster.
The crucial information project controls teams need to know now
The challenge lies in the fact that the industry is moving through this transformation at different speeds. For the project controls teams that are keen to move with confidence, rather than be pulled along, here is where to start.
Firstly, it’s important to know which stage you are in. You will need to assess where your data, your tools and your workflows sit. However, most teams overestimate their stage because skilled people compensate for fragmented systems. Ultimately, the real question is whether your systems are keeping up with your people.
The foundation must be prioritised. For instance, Stages Three, Four and Five will require a completed Stage Two. Trusted, connected and unified data is the prerequisite for everything that follows. If systems of record aren’t talking to each other, that is the first problem that needs to be solved.
Hard questions must be asked about AI. It isn’t so much about how powerful the AI is but how auditable the outputs are. For instance, can every number be traced to its source? Can the methodology be explained to an expert witness? A platform that cannot answer those questions introduces risk as opposed to reducing it.
Software is important, but so is support. According to Intuit QuickBooks, 82% of construction leaders believe employees who resist learning AI skills risk losing their jobs within five years. While this pressure is real, the majority of it comes from being handed a new system and left to figure out alone whether the information it’s producing can be trusted.
Adoption therefore depends on whether teams feel they are succeeding while using the technology. This requires an implementation approach that keeps people close to the process rather than handing them a product and stepping back.
Consider role evolution. The teams moving into AI with confidence now will define what competitive project delivery looks like for the next generation. Ultimately, the goal is to make today’s project controls professionals – as well as tomorrow’s AI-enabled experts – with human judgement at the heart of every decision.
To read the full argument for why project controls is at a critical point, and what the industries that transformed before it can teach all of us, download our whitepaper, entitled ‘What Aviation, Farming and Trading Tell Us About the Future of Construction’ here.