The construction and engineering sector may be dazzled by visions of robots on site and smart glasses on foremen, but the real AI shift is happening in less visible places: finance, procurement, planning and project controls. According to industry leaders at IFS, the immediate prize lies not in theatrical automation but in digital agents that can take pressure off the back office and help firms keep large jobs moving to schedule and within budget.
That argument lands at a diffi...
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cult moment for the industry. Censuswide research cited by IFS found that 41% of construction and engineering projects are already missing both budget and completion targets, while 54% are failing to deliver expected margins. In a business where profit buffers are often slim, that combination leaves firms exposed to inflation, supply disruption, labour shortages and severe weather.
The response, according to the same research, is a stronger focus on speed and cost control. Delivering work quickly and delivering it on budget were each identified by 35% of respondents as the key ways to stand out from competitors. For many firms, that means technology change is no longer optional.
Deloitte’s 2026 Engineering & Construction Industry Outlook says leading organisations are turning to advanced digital tools, including AI-driven analytics, live project management platforms and connected jobsite systems, to improve productivity and defend margins. IFS research points in the same direction: 91% of construction and engineering organisations expect to increase AI investment from 2025 onwards, and among early adopters, 89% report profitability gains while 83% say quality has improved.
Yet the article argues that the industry is still confusing spectacle with substance. Standalone tools such as design automation, wearable safety tech or robotics may have their place, but they do not solve the bigger problem: fragmented business systems and scattered data.
The central issue is the absence of a reliable data foundation. Many firms still depend on spreadsheets and disconnected systems to manage complex, often international, projects. In that environment, there is no consistent view of projects, materials, labour or subcontractors. Without that “single source of truth”, AI cannot operate with enough accuracy to be useful. The article’s conclusion is blunt: there is no meaningful AI programme without a modern ERP backbone.
That view appears to be gaining traction. Censuswide research suggests more than 63% of construction and engineering businesses are looking to replace their ERP systems within the next two years. The reason is straightforward: firms want consistent processes and a stronger digital core before layering on more intelligent automation.
Once that base is in place, the most valuable AI use cases are likely to sit away from the job site and inside everyday operations. One major opportunity is project financial forecasting. At present, many teams still spend weeks each month gathering information manually to update forecasts. AI agents could instead monitor live project data continuously and flag likely end-of-job outcomes earlier, giving managers time to act before margin erosion becomes irreversible.
Another area is spend approval. In many organisations, a purchase order or subcontract variation still needs multiple sign-offs, creating delays and encouraging box-ticking rather than proper review. AI agents can apply rules consistently against budgets, contracts and project requirements, speeding up approval while reducing error and non-compliance.
Procure-to-pay is also a natural fit. Construction involves large volumes of high-value purchasing, making it well suited to automation of invoice checks, payment processing and related workflows. Supporters say that could ease administrative strain, improve supplier relations and strengthen cash flow.
The broader message is that construction’s AI future is likely to be built less on dramatic on-site hardware than on disciplined digital infrastructure. Firms that modernise their ERP systems and unify their data may find that the most important AI revolution is not visible from the scaffold, but from the control room.
Source: Noah Wire Services
