28 Sept 2026

Executive summary

Output and Performance-based Road Maintenance (OPRMC) pays for road condition rather than road activity, and Bihar’s programme shows it works when every defect becomes evidence. Since the Integrated Command & Control Centre went live on 20 July 2022, defect closure within the service-level window rose from 69.84% to 91.80%, and pavement defect-free chainage moved from 93.33% to 98.27%.

That result rests on three stacked layers. The performance model defines the outcome as a service level. The digital layer, the RCD Road Maintenance App, gives every defect a unique ID, geo-tagged photographs and a timestamped approval trail. The intelligence layer, Rodic Nxt AI, extends observation from sampled inspection points to continuous, network-wide condition grading.

This paper describes each layer, reports the operational evidence from Bihar, and sets out how AI-based condition assessment can move road asset management from reactive repair to predictive maintenance, while keeping accountability with engineers rather than algorithms.

Keywords: performance-based maintenance, OPRMC, computer vision, GIS, human-in-the-loop AI, road asset management

1. Context: why conventional maintenance falls short

Conventional road maintenance pays for effort, not condition, so repair usually arrives on the expensive side of the deterioration curve. Road networks deteriorate continuously, but most regimes observe them only intermittently.

Defects follow a known progression. Cracks and ravelling have low impact at first. Left unattended for 45–60 days, they become heavy ravelling and potholes. After a further 30–45 days they turn into structural distress and cyclic deterioration.

The cost of arriving late is borne by the road user in safety, comfort, travel time, vehicle operating cost, fuel and emissions. For the road authority, input-based maintenance brings high O&M cost, slow rectification, selective attention and cumbersome oversight.

Before digitisation, even an outcome-focused regime faced five monitoring gaps:

2. What OPRMC is and how it works

OPRMC changes what the road authority buys: a defined service level on the road, maintained over time, instead of a schedule of works executed. The maintainer carries the outcome, and non-compliance with the service level has consequences.

2.1 Service levels defined defect by defect

An outcome model is only auditable if the outcome is defined precisely. In Bihar, ordinary maintenance is specified through seven defect families, each with its own code. That shared vocabulary is what lets field engineers, maintainers and machines describe the same problem in the same way.

2.2 A structured inspection regime

Accountability is allocated by role and road, with minimum inspection frequencies built in. Junior Engineers make four visits per road per month and Assistant Engineers two. Executive Engineers visit their roads monthly, and Superintending Engineers cover one-third of allocated roads each month. Programme-level decision makers monitor the performance of both engineers and maintainers.

A mandatory visit that cannot be verified is a visit that may not happen. Geo-fencing and geo-tagged evidence are what close that gap.

3. From evidence to intelligence: the digital and data framework

An output model is unenforceable at scale without evidence. Bihar’s digital layer turned the service-level definition into a measurable regime, and the intelligence layer now extends that evidence from sampled points to the whole network.

3.1 The digital layer: RCD Road Maintenance App

The Integrated Control & Command Centre, inaugurated on 20 July 2022, runs on mobile and web applications that put every stakeholder in one ecosystem. The mobile app serves field engineers and maintainers: mandatory visits, raising and approving defects, geo-fencing and geo-tagged photographs at source. The web app serves headquarters: compliance checks, dashboards, defect maps, inspection reports and performance analytics.

Every defect travels the same four-stage path, and each stage leaves a timestamped photographic record.

A rejection sends the defect back to the maintainer under its original ID, so closure is never taken on assertion. On the network map, six states are shown: open (crust), open (other than crust), closed pending verification, rejected or re-opened, closed and verified, and no defect.

3.2 The intelligence layer: Rodic Eye

Rodic Eye is an AI-driven Project Management Unit (PMU) layer that connects road data capture to an actionable maintenance record. It runs in three stages, capture, assess and act, and feeds the same workflow already used for OPRMC monitoring rather than a parallel system.

An initial 2,000 km deployment yields roughly 20,000 graded 100 m segments per survey cycle, each tied to its chainage and coordinates.

3.3 Condition assessment method

The core shift is from recording defect events to measuring condition, so the network becomes comparable segment by segment and over time.

• Rodic Nxt AI Index (REI): a 1–5 grade from good to poor, with a severity level assigned to every detection rather than a simple present-or-absent flag.

• One vocabulary: every detection is coded to the same OM family and sub-type used in manual inspection (for example OM_102_A, OM_601_B).

• Full metadata: road name, chainage from and to, latitude and longitude to six decimals, bearing, date, time, the annotated image and the raw source frame.

That metadata is what lets a detection be verified, mapped, compared across surveys and, if accepted, acted on. An index that specifications can be written against is worth more than a dashboard.

3.4 Human-in-the-loop governance

AI accelerates detection; people stay accountable for acceptance, verification and action. The system is an evidence chain that makes engineering decisions faster and more defensible, not autonomous decision-making.

4. Implementation approach

Scaling AI-enabled monitoring works best in three phases: platform set-up, a baseline survey, then continuous monitoring. Core OPRMC functionality continues unchanged throughout, because the AI capability is an additive layer, not a replacement.

Phase B matters most for what follows. Change detection needs a reference point: without a high-quality baseline, later surveys can record condition but cannot reliably measure deterioration.

5. Benefits and outcomes

Bihar’s results are recorded, not asserted: the digital layer has closed 99.5% of the defects it has recorded since go-live on 20 July 2022. Figures below are cumulative, taken from the OPRMC programme dashboards.

The programme reports three benefits: transparency through geo-tagged, evidence-based inspection; accuracy through unique defect IDs and timestamped closure evidence; and faster decisions through dashboards, map views and analytics. The Command Centre has hosted NHAI leadership, Asian Development Bank delegations and road agencies from Odisha and Chhattisgarh.

5.1 What AI adds

• Network-wide visibility: every surveyed 100 m segment is assessed, not only sampled inspection points.

• Earlier detection: deterioration surfaces between routine inspection cycles.

• Prioritised action: severity, condition grade and spatial patterns focus engineering attention.

5.2 The road asset intelligence loop

The lasting value lies in a compounding cycle, where each stage produces the input for the next.

[embed: node/65563adf-5cfd]

Every inspection is a potential training label. The durable asset is the verified history of what was detected, what was fixed and how it held up; imagery can be recaptured, but that history cannot.

6. Challenges and recommendations

The architecture and operating model are proven; model performance in the field still needs to be measured and published. Five questions should guide field validation.

For road authorities considering OPRMC, three lessons carry over from Bihar. Define service levels code by code so they can be audited. Make every defect a geo-tagged, timestamped record before adding AI. Then keep a named engineer accountable for every machine finding that becomes a defect.

7. Conclusion

OPRMC made road condition a matter of evidence; Rodic Nxt AI aims to make that evidence intelligent. By linking capture, condition assessment, GIS, the defect workflow and engineering governance into one loop, the approach extends performance-based maintenance from sampled inspection to continuous, network-wide monitoring.

The design keeps accountability where it belongs: machines observe at scale, engineers decide, maintainers act and the command centre oversees. With a sound baseline and repeated survey cycles, the growing record of verified outcomes offers a practical path from reactive repair to predictive road asset management.

About the author

Anushree Srivastava is Lead Digital Advisor at Rodic Digital & Advisory, where she works on digital and AI-enabled monitoring for road asset management.

Source

Adapted from Rodic Digital & Advisory material prepared for the Government, Road Departments, Operational figures are from the OPRMC programme dashboards.

At a glance

• Design commits the money early. Extensive research indicates that 80% of a product’s life-cycle cost is determined during the initial stages of design and development, a finding that recurs across engineering disciplines.

• Design errors are the largest single source of rework. Research consistently shows that over 70% of rework traces back to design errors, including coordination conflicts, omissions, and inconsistencies in construction documents. On industrial projects studied by the Construction Industry Institute, design changes, errors and omissions averaged 78% of the total number of deviations and 79% of the total deviation costs.

• Rework itself is material. Across project types and regions, rework accounts for 1-20% of total project cost, with most studies clustering between 4-10%, and research by Love (2002) found that rework contributes to an average of 52% of total cost growth on construction projects.

• India’s largest highway owner has made design quality a procurement criterion. NHAI has rated 56 DPR consultants on a scale of 100, with the top-rated consultant having a score of 80.75, while the lowest-rated consultant scored 23.33, and this rating is expected to become a deciding factor for awarding DPR contracts.

• Simulation is changing from a verification step to an exploration engine. A February 2026 systematic mapping of 3,345 relevant papers found that Optimization and Design remains the largest application area, while Structural Performance Prediction and FEM Acceleration/Surrogate Modeling show the fastest growth.

• The design firms that will lead are those that treat design as a data and decision system - surveys, codes, precedent and simulation connected - rather than a document to be produced.

Design is where the cost is committed, not where it is spent

The economics of infrastructure design are asymmetric. Design fees are a small fraction of capital cost; the decisions taken in design determine most of it. The design phase is widely acknowledged as the most influential stage of the project lifecycle because decisions relating to project scope, building configuration, materials, engineering systems, procurement strategies and construction methodologies are established at this stage and determine a significant proportion of the project’s final cost and overall performance. The same logic governs time: to discover incompatible designs at a later stage is far more detrimental in terms of time and cost as compared to during the initial design phase.

For an infrastructure owner, this means the cheapest point at which to fix a highway alignment, a bridge foundation or a drainage scheme is before the tender is issued. The most expensive point is after the contractor has mobilised. Every other conversation about productivity - construction technology, monitoring, AI on site - is a conversation about managing the consequences of what design did or did not get right.

Roughly 80 percent of lifecycle cost is committed by decisions taken in the earliest design stages, while the cost of changing those decisions rises steeply as a project moves from concept to detailed design to construction. Design-related deviations have historically accounted for around 12 percent of total installed cost on complex industrial projects and 1-9 percent across broader samples, with the range narrowing as digital coordination has improved. Rework overall sits at 4-10 percent of project cost in most studies and explains about half of cost growth. (Sources: Construction Industry Institute; PlanRadar 2025 review; Love 2002; arXiv 2601.12276.)

What actually goes wrong in design

The rework literature is unusually consistent about causes. Design-related errors and deviations account for 1-9% of total project cost, including design omissions, inaccurate drawings, and late design changes. Communication compounds them: a joint report by PlanGrid and FMI found that 26 percent of rework on U.S. construction sites is linked to poor communication, with an additional 22 percent attributed to inaccurate or incomplete project information.

In Indian highway practice the failure modes are specific and public. NHAI’s consultant rating followed concerns over flawed DPRs including poor surveys, weak geometric design and drainage planning which have contributed to unsafe roads, accidents and cost overruns. The Union Minister for Road Transport and Highways has said that consultants involved in preparing defective detailed project reports are “culprits” behind accidents and deaths on highways. NHAI’s own framing is corrective rather than punitive: DPRs are the foundation of any highway project, and weaknesses at this stage often cascade into execution delays, safety issues and cost overruns.

Four root causes recur across sectors.

Surveys that do not match the ground. Topographic, geotechnical and hydrological inputs that are thin, old or extrapolated. Every downstream quantity, from earthwork volumes to foundation depths, inherits the error.

Geometry designed for the drawing, not the driver. Horizontal and vertical alignments that meet code minima on paper but produce sight-distance, superelevation and junction conflicts in the field.

Drainage and hydrology treated as a chapter, not a system. Cross-drainage sized without catchment modelling; longitudinal drainage that ignores adjacent land use; culverts placed by chainage rather than by flow.

Coordination failures between disciplines. Structures, pavement, utilities and land acquisition designed in sequence rather than together, so that a bridge approach, a utility corridor and a drain occupy the same space on three different drawings.

None of these are exotic. They are the predictable output of a design process built around producing a document on a deadline rather than around resolving a physical problem with verified data.

The good news: digital coordination has already cut design error cost

The trend line is encouraging where digital methods have been adopted. Since the early 1990s, the cost impact of design-related errors has fallen from around 9% to just 1-2% of total project cost, reflecting the positive effects of digitisation, BIM adoption, and improved design coordination workflows. Studies using the Construction Industry Institute database of 359 projects found that utilising BIM reduces the workload throughout design and construction stages and is effective in reducing the impact of rework on total project cost and duration.

Two conclusions follow. First, design quality is not a talent problem alone; it responds to tooling and process. Second, the frontier has moved. Coordination is now table stakes. The next gains come from changing what design can explore, not just how cleanly it is documented.

Simulation is moving from verification to exploration

For most of its history, structural and geotechnical simulation was a check performed on a design already chosen. Finite-element analysis was expensive enough that engineers ran it on one or two candidates, late. That constraint is dissolving.

The mechanism is surrogate modelling. AI-accelerated finite element analysis is the integration of machine learning, neural networks and surrogate modelling techniques into traditional computational structural and physics simulation workflows to dramatically reduce solve times, improve design iteration speeds, and enable real-time optimisation. The dominant architecture uses FEM simulations to generate labelled training data, then trains ML models to predict FEM outputs at inference time without re-running the solver. The 2026 systematic mapping of the field found that a subset of the studies integrate surrogate or neural network models directly into the optimisation loop to reduce the computational cost of repeated FE evaluations, with objectives that commonly include weight reduction, stiffness improvement, or increased buckling resistance under predefined load cases.

The practical consequence for a design office is that the number of alternatives evaluated per week stops being limited by solver time. Instead of creating a single design option, AI generates multiple solutions based on engineering requirements and project constraints, and engineers define factors such as material type, loading conditions, budget, and design objectives. That is the design-side counterpart of what continuous monitoring is doing on the operations side: sampling becomes census.

Simulation is also reaching into the built asset. Recent bridge research proposes a “mechanical-core” digital twin built by integrating computer vision sensing with automated finite element model updating, so that the analysis model synchronises with the as-built state of the physical structure. The design model no longer ends at handover; it becomes the seed of the operating twin.

Traditional practice: one or two candidate designs, full finite-element runs late in the process, results used to confirm rather than choose. Emerging practice: hundreds of candidates screened by surrogate models in minutes, full analysis reserved for a shortlist, optimisation loops that trade weight, stiffness, cost and constructability simultaneously, and analysis models that persist into operations and update from sensor and vision data. Growth evidence: surrogate modelling and structural performance prediction are the fastest-growing application areas in AI-plus-FEM research, and industrial patents now explicitly replace or eliminate FEM solver calls during design optimisation loops. (Sources: Buildings 16(3):644, February 2026; Patsnap landscape, April 2026; Applied Sciences 16(6):2920, March 2026.)

The limits are real and should be stated

The evidence is equally clear about where surrogate and generative methods fail. AI predictions are highly dependent on the training dataset and can falter on geometries or loading cases outside that domain, and deterministic predictions, though fast, lack insight into model confidence. Current frameworks approximate equilibrium and compatibility but do not strictly enforce them, which is why physics-informed neural networks that embed physical constraints are the active research direction.

For infrastructure this matters more than for consumer products. A bridge in a seismic zone with unusual soil is by definition out of distribution. The design rule that follows is simple: surrogates screen, solvers certify, engineers sign. Any workflow that collapses those three roles into one is not faster; it is unsafe.

The modern design stack, and what holds it together

Digital design in infrastructure now rests on five connected capabilities. Firms that have all five connected outperform firms that have all five in separate tools.

Verified ground data. Drone photogrammetry, LiDAR, network survey vehicles and satellite imagery feeding a single georeferenced model. NHAI already requires drone video recording on a monthly basis, uploaded to its Data Lake portal; the same capture discipline applied at DPR stage removes the largest source of downstream error.

Requirements management. One of the most impactful trends recently is the increased focus on requirements management across rail and broader AECO organisations. Traceability from owner requirement to design decision to as-built is what makes a design auditable - and what a code-checking engine runs on.

Codified standards. IRC and IS codes, MoRTH specifications and owner-specific conditions expressed as machine-checkable rules rather than PDFs on a shelf, so that a drawing can be checked continuously rather than reviewed once.

Simulation and optimisation. Surrogate-accelerated analysis with full solvers for certification, connected to the same model the drawings are produced from.

Institutional precedent. The firm’s own history of what was designed, what was reviewed, what was built and what failed - searchable by the engineer at the desk. This is the capability most firms lack and the one that compounds fastest.

Ground truth first: no alignment or structure is designed on survey data the team has not verified in the current season. Codes as rules: every drawing checked against codified standards before human review, so reviewers spend time on judgement rather than compliance. Options, not answers: at least several alternatives evaluated with cost, constructability and lifecycle impact before one is recommended. Coordination in one model: structures, pavement, drainage, utilities and land in the same georeferenced space, with clashes resolved before tender. Signed accountability: every output carries the engineer who checked it and the evidence they checked it against. Design carries forward: the design model becomes the handover model and the operating twin.

Owners are pricing design quality

The single most important shift in the Indian market is that design quality now has a commercial value attached to it. NHAI’s first-of-its-kind rating evaluated consultants on technical soundness, completeness of surveys, design accuracy and adherence to prescribed standards, with the objective of aligning incentives with long-term project outcomes rather than short-term contract wins. Rankings will influence eligibility and preference for future DPR contracts.

That changes the economics of design investment. A firm that spends more on survey verification, simulation and code-checking is no longer just spending more; it is buying eligibility. The parallel with continuous monitoring on the operations side is exact: the owner now measures, so the consultant must too.

Two further pressures reinforce this. Lowest-bid awards are producing tenders 20 per cent or more below estimated cost, putting commercial viability and execution quality under pressure, which means design errors that once absorbed in contractor margin now surface as claims and disputes. And at the front end, projects are stalling because no consultant has come forward to prepare the DPR - a signal that the supply of high-quality design capacity, not demand, is the binding constraint.

How RODIC approaches design

Design sits at the front of the RODIC infrastructure AI ecosystem for the reason this article sets out: it is where the largest share of an asset’s cost and risk is committed, and where verified data and encoded judgement have the highest leverage. Four commitments shape the firm’s design practice.

Ground truth before geometry. Survey, geotechnical and hydrological inputs are captured and verified with current-season drone, LiDAR and field data before alignment or structural design begins. The design model is georeferenced from day one so that structures, pavement, drainage, utilities and land are coordinated in one space rather than reconciled after tender.

Codes as executable rules. IRC and IS provisions, MoRTH specifications and owner conditions are expressed as machine-checkable rules within the firm’s rules and compliance engine, so that drawings and DPR chapters are checked continuously during design rather than reviewed once at the end. Human reviewers spend their time on engineering judgement, not clause hunting.

Simulation to explore, solvers to certify, engineers to sign. Surrogate-accelerated analysis is used to screen many alternatives on cost, constructability and lifecycle performance; full solvers certify the shortlist; a named engineer signs the recommendation. The design model carries forward into construction monitoring and, ultimately, into the operating twin of the asset.

Precedent at the desk. The firm’s knowledge layer makes precedent designs, review history and code interpretations from two decades of delivery searchable by the engineer drafting a new bridge, tunnel or interchange. Design quality compounds because every reviewed project makes the next one better.

Alongside its own practice, RDA works with academic partners to publish research on design quality, safety and lifecycle performance, because the owner’s new rating regimes will reward tested evidence over assertion.

Implications for leaders

For asset owners. Extend outcome-based rating from DPR consultants to the design inputs themselves: require verified current-season surveys, coordinated single-model deliverables and machine-checkable compliance evidence in DPR contracts. The cost of a better DPR is small against the cost of the rework it prevents.

For design consultancies. Treat survey verification, code automation and simulation capacity as capital investments that buy eligibility, not overheads that reduce margin. Measure design quality internally before the owner does - error rate at review, clashes found before tender, revision cycles per package.

For contractors. Engage in design early where the contract allows. The constructability knowledge that lives on site is the input most designs lack, and the one most likely to reduce claims later.

For policymakers. The DPR rating framework is the right instrument. The next step is a common data standard for DPR deliverables - georeferenced models, structured quantities and compliance evidence - so that ratings can be computed from the deliverable itself rather than from post-hoc review.

The guardrails

Design is where a mistake costs the least to fix and the most to miss. Three rules apply. Surrogates screen, solvers certify, engineers sign - no AI-generated design leaves the office without a named engineer’s verification against a full analysis. Every recommendation carries its data source and its confidence, and out-of-distribution cases are flagged for full analysis by default.

Infrastructure productivity debates tend to start at the construction site. The evidence says they should start earlier. Most of an asset’s cost, safety and lifecycle performance is fixed in design, most rework traces back to design, and India’s largest owner has now made design quality a condition of eligibility. Simulation and AI are turning the design office from a document factory into a decision engine, provided the guardrails hold. The firms that connect verified ground data, codified standards, precedent and simulation into one design system will be the ones owners rate highest - and the ones whose assets perform.

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.

For over 25 years, Rodic Consultants has powered the backbone of global civil engineering and public infrastructure advisory. Managing a multi-sector engineering portfolio with over 2,500 domain experts across 200+ landmark mega-projects, we operate at the precise intersection of physical construction, public policy, and data-driven project governance.

We are launching Rodic Engineering Intelligence to deliver field-tested data analysis, structural frameworks, and quantitative engineering insights directly from the frontline of infrastructure delivery across emerging economies.

The Macro Infrastructure Deficit: Data, Scale, and Liability

Global public works face an unprecedented operational paradox. Capital allocation toward transportation, energy, and municipal utility networks continues to scale (anchored by India’s ~₹11 lakh Cr+ annual public infrastructure spend), yet traditional execution models struggle against systemic friction points:

• The Schedule & Capital Variance: Mega-projects historically suffer 20–40% cost overruns and severe delays driven by fragmented ground-truth data, siloed site communication, and static, second-hand reporting.

• The Gap in Asset Accountability: Strategy consultancies sell frameworks without site ground-truth; traditional engineering consultancies generate knowledge that dies inside static DPRs; software vendors offer dashboards without engineering liability.

• The Shift to Asset Observability: Infrastructure assets built today demand continuous real-time observability—shifting O&M from reactive post-failure repairs to sensor-to-decision, predictive asset longevity.

Our operational philosophy addresses these gaps directly: Tools tell you what the sensor saw. Rodic Engineering Intelligence tells you what to do — and backs it with an engineer’s professional signature.

Key Capabilities & Product Infrastructure

Our data-driven engineering framework is powered by an integrated digital engine designed to de-risk assets from concept to decommissioning:

• Rodic bRAIN (The Core Substrate): An underlying Engineering Graph built on 25 years of structural outcomes, material behavior, and project actuals across 10+ sectors to predict deterioration, cost risks, and schedule variances.

• Rodic Rules (Executable Compliance Engine): IRC codes, MoRTH specifications, IS standards, and statutory contract clauses translated into machine-executable checks—ensuring design QA and bill verification carry a signed liability signature.

• End-to-End Lifecycle Coverage: Unifying 8 critical asset stages—from land intelligence, DPR drawing extraction, and quantity takeoff in 72 hours, through drone progress capture, to post-construction Structural Health Monitoring (SHM).

Engineering in Action: Key Projects & Benchmark Portfolio

Our technical perspectives are grounded in direct execution across complex civil assets:

• Bridges & Megastructures (Guwahati–North Guwahati Bridge): Engineering a multi-lane Extradosed PSC Bridge crossing the Brahmaputra River, integrating advanced deep-foundation engineering in high-scour alluvial beds.

• Rail & Mass Transit (Mumbai Urban Transport Project - MUTP III): General Consultancy (GC) for track quadrupling across 64 RKm on the Virar–Dahanu Road line, increasing operational throughput under continuous suburban line traffic.

• Freight Corridors (Eastern Dedicated Freight Corridor): Project Management Consultancy across the Mughal Sarai to New Sonenagar sections, optimizing heavy-haul rail corridors for high axle-load operations.

• Public Healthcare & Social Infrastructure (MMDHY - BHAVYA Platform): Statewide public health delivery digitization spanning 13,000+ public health facilities across 38 districts in Bihar.

• Highways & Tunnels (NH-44 Banihal Corridor & Srinagar Ring Road): Full-spectrum engineering and construction supervision through unstable, high-altitude Himalayan geology.

What You Can Expect here

Rodic Engineering Intelligence is built specifically for institutional investors, policy makers, chief engineers, urban planners, and digital transformation leaders.

Each edition delivers rigorous, non-promotional technical breakdowns, policy insights, and research-backed analysis focusing on:

• Smart Construction & Real-Time Monitoring: How AI computer vision, drone LiDAR terrain mapping, IoT structural sensors, and machine-executable compliance engines (like Rodic Rules) replace manual field oversight.

• Policy Making, Advisory & Strategic Research: Evidence-based frameworks, state asset policies, specification reform, and published research whitepapers designed to inform government decision-making, ministry guidelines, and regulatory standards.

• AI & Frontier Technology: Deep dives into domain-specific artificial intelligence, predictive machine learning models, autonomous drone-in-a-box workflows, and digital twins across the entire infrastructure lifecycle.

• Infrastructure Economics & Policy: Quantitative frameworks evaluating Public-Private Partnerships (PPP), Hybrid Annuity Models (HAM), capital allocation efficiency, and outcome-linked value engineering.

• Advanced Structural & Geotechnical Mechanics: Engineering deep dives into long-span bridge dynamics, high-altitude tunneling methodologies, and climate-resilient material science.

• Energy, Hydro & Municipal Water Systems: Systems-level engineering analyses on power grid modernization, run-of-the-river hydropower integration, and statutory dam safety frameworks.

Join the Benchmark for Infrastructure Intelligence

Engineered infrastructure shapes economic sovereignty. Building assets capable of lasting half a century requires rigorous data discipline, continuous technological innovation, and peer-to-peer engineering dialogue.

Subscribe now to receive our bi-weekly deep dives straight to your inbox, and join the conversation shaping the future of global infrastructure delivery.