Ada Nwadigo is a multi-award-winning civil engineer and technologist working at the intersection of infrastructure delivery, artificial intelligence and innovation. She is an HS2 Project Engineer, Founder of Eng Trepreneur and currently pursuing an MSt in Construction Engineering at the University of Cambridge. She is also the ICE National STEM Ambassador of the Year and a WES Top 50 Women in Engineering winner.

In this short interview, Ada cuts through the pilot project noise and explains where AI is already being used on live construction projects (quietly embedded into everyday workflows) and what she believes will become unavoidable as we move through 2026.

AI is already influencing construction delivery, not because sites are suddenly fully automated, but because AI functions are being embedded into routine tools that teams use every day: computer vision to verify progress and quality, forecasting to surface schedule risk earlier and pattern detection across unstructured records (photos, diaries, incident narratives) to identify emerging issues before they become claims, delays or harm. The practical shift is from retrospective reporting to predictive decision support, where the value is not AI as a product, but AI as a capability that reduces manual reconciliation, improves signal-to-noise and enables earlier intervention.

Interview: Ada Nwadigo on AI in construction (real use, real impact)

1. A lot of AI in construction still feels like pilot projects and demos. Where are you seeing real AI being used on live projects today?

While it’s true that many AI solutions are still at pilot stage, there are now clear examples of AI being deployed on live construction projects with measurable impact. We’re seeing real adoption in areas such as progress monitoring using computer vision, where site imagery and drone data are analysed to track work completed against programme. AI is also being used to automate quality checks, flagging potential defects or non-compliances earlier than traditional inspections.

In project controls, AI is increasingly embedded in planning and forecasting tools, helping teams identify schedule risks, predict delays, and prioritise interventions. These applications may not always be branded loudly as AI, but they are quietly becoming part of everyday delivery workflows on major projects.

2. From your perspective, where is AI having the biggest impact right now: safety, productivity, cost control or decision-making?

Right now, AI is having its biggest impact on decision-making, with a strong knock-on effect on productivity and safety. AI enables more data-driven decisions by processing large volumes of information that humans simply cannot analyse efficiently in real time. This allows teams to move away from reactive decision-making toward more predictive and preventative approaches.

From a productivity perspective, AI reduces time spent on manual reporting, data reconciliation, and repetitive administrative tasks, allowing teams to focus on higher-value engineering and leadership activities. In safety, there is huge untapped potential. AI-driven solutions can identify unsafe conditions, patterns in incidents, and behavioural risks, helping to address some of the most persistent safety challenges facing the industry.

3. What’s one example of AI being applied in a practical way that most people in construction would be surprised by?

One surprising application is the use of AI to analyse unstructured data such as site photos, incident reports, and daily diaries to identify early warning signs of risk. Instead of relying solely on lagging indicators like accident statistics, AI can detect subtle patterns (such as recurring near-miss descriptions or repeated site conditions) that signal emerging issues before they escalate.

Another example is AI being used to support temporary works and sequencing decisions by analysing historical project data, constraints, and construction methodologies to suggest safer and more efficient approaches. These applications often operate in the background but can significantly improve outcomes.

4. What do you think contractors and developers misunderstand most about AI in construction?

The biggest misconception is the belief that AI will replace people and make roles redundant. In reality, AI is far more likely to create new roles and augment existing ones rather than eliminate them. It can simplify complex processes, reduce cognitive overload, and support better decision-making, ultimately improving quality of life for construction professionals.

For contractors and developers specifically, a key challenge is not the technology itself, but readiness. This includes data quality, organisational culture, skills gaps, and integration with existing workflows. AI is not a plug-and-play solution; it requires thoughtful implementation, leadership buy-in, and change management to deliver real value.

5. Looking into 2026, where do you think AI will become unavoidable on major projects?

By 2026, AI will become unavoidable in areas such as project controls, risk management, and safety assurance on major projects. Clients and regulators will increasingly expect predictive insights rather than retrospective reporting. AI-enabled forecasting, automated compliance checks, and real-time performance dashboards will become standard rather than optional.

We will also see AI embedded more deeply in procurement, carbon tracking, and whole-life asset decision-making, particularly as sustainability and efficiency requirements continue to tighten.

6. For construction leaders who want to start using AI properly, what should they focus on first?

The first priority should be upskilling and understanding, not technology procurement. The construction industry is seeing increasing interest in AI, but real benefits will only be realised if people understand what AI is, what it isn’t, and how it can be applied in their specific roles.

Leaders should start by becoming familiar with the different applications of AI across the construction lifecycle and identifying clear use cases linked to real problems whether that’s safety, productivity, or decision-making. Building strong data foundations, encouraging experimentation, and fostering a culture that supports learning and innovation are critical early steps toward successful AI adoption.

Key takeaways for project teams

• AI adoption is already happening quietly inside progress tracking, quality checks and project controls tools.
• The near-term win is predictive decision support, earlier intervention beats better reporting.
• Unstructured data (photos, diaries, narratives) is an underused risk signal.
• Readiness (data + culture + integration) matters more than buying software.
• By 2026, predictive controls, risk management and safety assurance become baseline expectations on major projects.

Expert Verification & Authorship: Mihai Chelmus Founder, London Construction Magazine | Construction Testing & Investigation Specialist