Infrastructure demand is outrunning the industry's capacity to deliver it. New evidence from owners, engineering firms and policy shows where AI is closing the gap - and where it is not.

At a glance

• Global construction output was about $15 trillion in 2025 and projected demand could reach $22 trillion by 2040. Yet labour productivity has stagnated: construction productivity improved by only 10 percent, or 0.4 percent annually, from 2000 to 2022, compared with 90 percent, or 3.0 percent annually, in manufacturing.

• AI’s addressable scope is large. McKinsey’s research claims AI has the potential to automate 39% of non-physical work in the construction sector, compared to 50% in the architecture and engineering sectors, across 150 workflows in 25 AEC-related domains.

• Adoption lags potential. Only 27% of firms have actually adopted AI, and few of those report measurable financial results. What decides the outcome is not the technology but data, process and trust.

• India’s asset owners are moving ahead of the consulting industry that serves them. NHAI is transitioning highway maintenance from reactive repairs to a predictive model that uses artificial intelligence, drones and automated monitoring to identify road defects before they become critical failures, across a network spanning over 1.4 lakh kilometres.

• Policy is now a demand signal. Indian Railways’ Rail Tech Policy, approved on 26 February 2026, provides 50:50 cost-sharing between Indian Railways and innovators for prototype development and trials.

• The winners will be defined by data ownership, encoded judgement and pricing model, not by tool adoption. Firms that control proprietary project data, decision-making workflows and the ability to charge for outcomes rather than work processes will see benefits - which is why RODIC is building an ecosystem rather than a single product.

The productivity gap is structural, and it is widening

Infrastructure is the one sector where demand is guaranteed to grow and capacity is not. The arithmetic is unforgiving. On its current trajectory, construction output could fall short of demand by up to $40 trillion cumulatively by 2040. India’s situation is a concentrated version of the global one: public capital expenditure for FY27 is projected at Rs 12.2 lakh crore, an 11.5% rise and the highest allocation to date, and the Economic Survey notes that the roads and highways sector is transitioning from a focus on rapid network expansion to an intensive concentration on logistics efficiency and quality.

That transition changes what an engineering consultancy is paid for. The expansion decade rewarded the ability to mobilise people and produce documents at scale. The efficiency decade rewards evidence: verified asset condition, defect lead time, lifecycle cost. Those are data outputs. The firms that can generate them at network scale will capture the margin.

Global construction output stood at roughly $15 trillion in 2025, with demand projected to reach up to $22 trillion by 2040. Over 2000-2022, construction productivity grew 10 percent cumulatively, or 0.4 percent a year, against 90 percent, or 3.0 percent a year, in manufacturing. The cumulative shortfall in output by 2040 could reach $40 trillion. In India, public capex for FY27 is Rs 12.2 lakh crore, up 11.5 percent on the prior year. (Sources: McKinsey, July 2026; Union Budget 2026-27.)

Where the value pools sit across the asset lifecycle

AI’s impact on infrastructure is not evenly distributed. The evidence points to three concentration points: the design front end, where iteration cost collapses; the construction phase, where progress and quality verification move from sampling to census; and operations, where inspection moves from periodic to continuous.

On design, the mechanism is well understood. Instead of creating a single design option, AI generates multiple solutions based on engineering requirements and project constraints. Engineers define factors such as material type, loading conditions, budget, and design objectives; the software then evaluates numerous alternatives before recommending optimised structural solutions. The competitive half-life is short, however: early adopters are reporting productivity gains from design, modelling, and construction-feasibility workflows, though these advantages will likely soon be table stakes.

On construction, the measured cases are specific. ALICE Technologies delivers 17% duration reduction and 14% cost savings on scheduling. Balfour Beatty achieved 20% waste reduction and 94% budget accuracy through predictive analytics. Buildots users report up to 25% faster completion through AI progress tracking. Across broader implementations, industry research shows efficiency improvements of 10-15% in project costs and 10-30% reduction in engineering hours.

On operations, the digital twin is the organising concept. Unlike static digital models, true digital twins update in real time and adapt based on sensor feeds, historical data, and analytical outputs to reflect their physical twins’ states and behaviours. The economics are material: reduction of unplanned downtime by up to 45 percent is cited as an achievable outcome, and market projections suggest the global digital twin market could expand by approximately USD 163 billion by 2029, with compound annual growth rates close to 65 percent.

• Plan and appraise. Lever: multi-source geospatial fusion and feasibility screening. Evidence: option studies compress from weeks to days. Track: survey-to-decision days.

• Design and DPR. Lever: generative alternatives, code checking, quantity extraction. Evidence: 10-30 percent reduction in engineering hours. Track: hours per DPR chapter, revision cycles.

• Tender and bid. Lever: document intelligence on RFPs, rates and risk clauses. Evidence: half-day estimating tasks reduced to minutes. Track: bid-preparation days, pricing variance against award.

• Build and monitor. Lever: vision-based progress and defect detection, schedule optimisation. Evidence: 14-25 percent cost and duration gains in reported cases. Track: RFI and NCR turnaround, deviation lead time.

• Operate and maintain. Lever: predictive deterioration models, condition-triggered work orders. Evidence: up to 45 percent reduction in unplanned downtime. Track: cost per lane-kilometre per year.

The adoption gap is wider than the headlines suggest

The distance between potential and realised value is the central finding of every credible survey. McKinsey estimates AI and automation can cut construction costs by up to 20% and schedules by 30%. The share of firms capturing anything close to that is small: about 24% of engineering and construction firms globally report having AI adopted at scale, according to the KPMG Global Construction Survey 2025/26. Across all industries, the proportion actually achieving financial impact from AI is reported at 5.5%.

Three explanations recur in the research. First, data harmonisation, system interoperability, and organisational alignment are critical success factors - and infrastructure data is fragmented across owners, contractors, consultants and their tools. Second, most firms have deployed AI as a task-level productivity tool rather than redesigning the end-to-end process; McKinsey advises builders to use the technology to transform domains, otherwise known as end-to-end processes. Third, the firms that do succeed are disciplined about measurement: they started with a specific use case, measured against a baseline, and expanded based on evidence.

The next phase raises the bar further. The next wave is not about AI copilots that answer questions or draft documents. It is about agentic AI, where autonomous software agents perform tasks, make recommendations and coordinate work across entire projects with human oversight. McKinsey believes this evolution could generate up to $228 billion in annual value for the US AEC industry and a further $126 billion across Europe by 2030. The transition will occur in stages: companies that establish robust data governance, invest in workforce capability and integrate AI into core business processes today are likely to be better prepared as autonomous systems become more capable.

Potential cost reduction: up to 20 percent; potential schedule reduction: up to 30 percent (McKinsey). Firms that have adopted AI: 27 percent (Bluebeam 2026 survey). Engineering and construction firms with AI at scale: about 24 percent (KPMG Global Construction Survey 2025/26). Organisations achieving financial impact from AI, all industries: 5.5 percent. Engineering firms using AI for design and automation: 40 percent (Bentley, January 2026).

Policy has become the demand signal in India

The most important development in Indian infrastructure AI over the past twelve months is not a product launch. It is that the largest asset owners have changed what they procure.

Highways. NHAI has deployed network survey vehicles across the national highway network under O&M to collect pavement condition data such as roughness, rutting, cracking and structural distress. Drone analytics monitoring systems are being used to create digital inventories of highway assets, monitor structures and identify encroachments, while falling weight deflectometer testing assesses the structural health of pavements and detects weakening sections before visible failures emerge. Layered on top, NHAI will introduce an AI-powered dashcam monitoring system for operations and management on around 40 thousand kilometres of the national highway network, with cameras mounted on route patrol vehicles conducting weekly surveys, and the AI-generated results integrated into the central NHAI Data Lake platform. The stated aim is a unified digital ecosystem that integrates data from survey vehicles, drones, dashcams, and pavement testing, providing a continuously updated view of asset conditions across the national highway network.

Railways. Indian Railways has published an explicit innovation framework. The Rail Tech Policy aims to support scalable and cost-effective solutions, particularly those based on AI and data-driven technologies, and provides 50:50 cost-sharing between Indian Railways and innovators for prototype development and trials, along with support for extended testing and scaling of successful solutions. The outcome evidence for technology-led safety is already visible: consequential train accidents declined from 135 in 2014-15 to 31 in 2024-25, and further to 11 in 2025-26 up to November. Deployed AI systems include a Machine Vision Inspection System designed to detect hanging, loose or missing components in moving trains and AI-enabled intrusion detection using distributed acoustic sensing, operational over 141 route kilometres with tenders issued for nearly 1,000 additional kilometres.

Planning. The Economic Survey highlights that the institutionalisation of integrated planning under the PM Gati Shakti initiative has been a defining feature of the transition, strengthening project execution and reducing transaction costs.

The implication for the engineering and consulting industry is direct. When the owner runs continuous condition monitoring and a central data lake, the supervision consultant who delivers a monthly progress report is no longer adding information. The consultant’s role shifts from observation to interpretation - and that requires the consultant’s own data capability.

• NHAI: predictive O&M built on network survey vehicles, drone inventories, FWD testing, dashcam analytics and a central Data Lake; dashcam rollout of about 40,000 km on a 1.4 lakh km network. Implication: consultants must analyse owner data streams, not just produce reports.

• Indian Railways: Rail Tech Policy and portal with 50:50 cost-sharing for prototypes and trials. Implication: a funded path for AI and drone inspection solutions.

• Indian Railways: machine vision, acoustic sensing and Kavach; 141 route km of acoustic sensing live and about 1,000 km tendered. Implication: continuous inspection replaces periodic inspection.

• Union Budget FY27: public capex of Rs 12.2 lakh crore. Implication: the volume of projects to monitor rises faster than engineering headcount.

• PM Gati Shakti: integrated multimodal planning across 16 ministries on one platform. Implication: data interoperability becomes a procurement expectation.

Data quality is becoming a financial variable

A quieter shift is that infrastructure data now has a price outside the project. Bentley’s January 2026 analysis reports that firms with high-quality infrastructure data are securing better financing and ESG ratings. For India’s infrastructure investment trusts and hybrid annuity concessionaires, this creates a new buyer for verified, structured asset histories - and a new product line for the consultancies that can produce them.

It also reframes the digital twin. In practice a twin is less a 3D model than a governed data record with a physics-informed prediction layer on top. Digital twins integrate simulation models, sensor data, and operational systems into a coherent digital representation of physical assets. The value of that record compounds with time, which is why the firms that begin structuring asset data now will be hard to displace later.

What the leading engineering firms are doing differently

The evidence points to a consistent operating pattern among firms capturing value rather than piloting indefinitely.

They own the data layer. Firms that control proprietary project data are the ones McKinsey identifies as beneficiaries. In infrastructure that means structured records from every DPR, site diary, measurement book and claim - not PDFs in a shared drive.

They encode engineering judgement. Decision-making workflows are the second asset McKinsey names. Firms that have written down how their engineers check a design against a code, or decide when a pavement section needs intervention, can put that logic into a system. Firms that have not cannot automate anything meaningful.

They redesign the process, not the task. The two camps McKinsey describes are those who leverage AI to automate core tasks, and those who use it as a superficial productivity tool. The first group treats AI as an operating model. The firms that view AI as an operating model rather than another software tool are likely to set the pace for the next generation of construction and engineering.

They decide deliberately between build and buy. High-profile builders have stepped into the arena to develop their own tools in-house to solve pain points. For an engineering consultancy the calculus is domain-specific: buy commodity capability (document extraction, computer vision primitives), build where the firm’s own data and judgement are the differentiator.

They measure from a baseline. Vendor-reported figures set the ceiling: automated quantity takeoff and estimating reach 85-90% accuracy and turn half-day tasks into minutes. Firms that report real financial impact are those that established the “before” number first.

How RODIC is building an ecosystem for infrastructure AI

The research is clear that a tool, or even a platform, does not by itself close the adoption gap. Value shows up when data, engineering judgement, delivery workflows, partners and research reinforce one another. RODIC’s response is therefore not a single product but an ecosystem - a connected set of assets and relationships in which each part makes the others more valuable.

RODIC’s starting position is two decades of delivery across highways, bridges, tunnels, railways, metros, hydropower, urban infrastructure, water and mining, for NHAI, MoRTH, Indian Railways and state governments. That is a proprietary base of project data and engineering decision workflows - the two assets the evidence identifies as the source of advantage. The mandate of Rodic Digital & Advisory (RDA), the firm’s digital arm, is to build the ecosystem around that base.

The ecosystem has eight connected layers.

• Engineering practice: live delivery across sectors and owners, with engineers who sign every output. AI in infrastructure only works with domain experts in the loop; practice is the source of ground truth.

• Data foundation: structured records from DPRs, site diaries, measurement books, surveys, inspections and claims. Proprietary project data is the first asset McKinsey names, and data quality now affects financing and ESG ratings.

• Knowledge layer: institutional knowledge - precedent designs, code interpretations, review history - made queryable by engineers and by workflows. Encoded decision-making is the second asset; without it nothing meaningful can be automated.

• Lifecycle delivery platform: spanning planning and DPR, tender, construction, handover, and operation and maintenance, on a shared spine for contracts, finance, compliance and assets. Value comes from transforming end-to-end domains, not isolated tasks.

• Applied engines: scoped capabilities for rules and compliance checking, asset condition and structural health, computer vision on drone and dashcam imagery, delay and cost forecasting, field capture and owner-system integration. The firms that succeed start with specific use cases with known inputs and metrics.

• Partner network: drone, sensor, survey and geospatial providers; technology and cloud partners; startups through owner innovation programmes. Continuous monitoring depends on multi-source data, and build-versus-buy discipline requires a partner bench.

• Research and public value: joint work with academic institutions; published research, IP and insights on quality, safety and delivery. Credibility in infrastructure rests on evidence, and policy instruments such as the Rail Tech Policy reward tested, scalable solutions.

• Owner and contractor interfaces: integration with owner data lakes and monitoring programmes, and shared evidence bases with contractors. Owners now run continuous monitoring; the consultant’s role shifts from observation to interpretation.

Five design principles hold the ecosystem together.

Practice feeds product, and product feeds practice. Every project RODIC delivers generates structured data and refined decision logic; every improvement in the platform returns to the field through the same engineers. This loop is the ecosystem’s compounding mechanism, and it is the reason a delivery firm can build what a pure software vendor cannot.

Knowledge is a shared substrate, not a feature. The knowledge layer sits beneath everything - the platform, the engines, the advisory work - so that an engineer drafting a bridge DPR, a rules engine checking a contract clause and a forecasting model estimating delay all draw on the same institutional memory.

Engines are scoped to measurable workflows. Rather than a single general model, capabilities are built one workflow at a time with a known input and a known success metric - hours per DPR chapter, measurement-to-bill days, defect detection lead time. This is the only adoption pattern the evidence supports.

Partners are chosen where the differentiation is not RODIC’s. Commodity capability - sensor hardware, aerial capture, document extraction primitives, cloud infrastructure - is sourced. Capability that depends on RODIC’s data and judgement is built. The partner bench also gives clients a single accountable integrator across a fragmented supplier landscape.

Research is a product line, not a marketing activity. Working with academic partners, RDA publishes research, intellectual property and insights on infrastructure quality, safety and delivery - the kind of tested evidence that owner innovation programmes now reward and that a consulting firm’s credibility rests on. This publication is part of that commitment.

The commercial logic follows from the structure. Supervision and advisory fees are priced on effort. Platforms, data, verified asset intelligence and research can be priced on value. An ecosystem lets RODIC offer both to the same client, and move the mix toward the second over time.

Implications for leaders

For asset owners. The data you are now collecting is an asset in its own right. Specify data standards and interoperability in procurement, not just deliverables. Consider outcome-linked terms - detection lead time, data completeness - for supervision and O&M contracts.

For engineering consultancies. Treat the firm’s project archive as a strategic asset and structure it. Pick two or three workflows with clear baselines - DPR drafting hours, measurement-to-bill days, defect detection lead time - and measure before deploying. Decide explicitly what to build and what to buy. Move pricing toward outcomes where the data supports it.

For contractors. Owner-side monitoring will surface deviations faster than internal reporting. Firms that run their own progress and quality intelligence will negotiate from evidence rather than respond to it.

For policymakers. The Rail Tech Policy’s cost-sharing model and NHAI’s data lake are the right instruments: they create demand, fund trials and centralise data. The next step is interoperability - common data schemas across owners so that the same asset intelligence can move between highway, rail and urban programmes under PM Gati Shakti.

The guardrails that make this work

Governance is not an afterthought in a sector where errors are measured in structural failures. Establishing clear guidelines around AI use, along with continuous monitoring and validation of outputs, will be essential to ensuring both cybersecurity and trust in digital systems as adoption accelerates. Engineers are essential as AI augments human expertise, not replaces it, and strategic AI adoption starts with governance, data quality, and solving real business problems. RODIC’s operating rules apply across the ecosystem: an engineer signs every design output; every AI-assisted recommendation carries its source and its confidence; and client data stays within the client’s boundary.

The bottom line

The built world now generates more data about itself than at any point in its history - from survey vehicles, drones, dashcams, sensors and the projects themselves. Owners have moved first. The engineering firms that turn that data into decisions - and build the ecosystem of practice, data, knowledge, partners and research that keeps those decisions improving - will define the next decade of infrastructure delivery.

About this article. This article draws on published analyses from McKinsey & Company, KPMG, Bentley Systems, Jama Software and industry surveys through July 2026; on official releases from the Ministry of Road Transport and Highways, NHAI, the Ministry of Railways and the Press Information Bureau; and on the Union Budget 2026-27 and Economic Survey 2026. Case figures attributed to vendors are as reported by those vendors.