Internal rate of return is the metric that infrastructure investors and project developers use to evaluate whether a capital commitment makes financial sense. It is a function of two variables: the magnitude of the expected cash flows the project generates, and the timing of those flows relative to the investment required to produce them. Anything that increases the scale of positive cash flows, reduces negative ones, or accelerates the time to revenue materially improves IRR.
AI-assisted and generative design tools affect all three of these variables in ways that are financially significant and often underappreciated. This article explains the specific mechanisms through which better design tools improve project IRR, drawing on documented results and well-established relationships between design quality and financial outcomes.
Mechanism 1: Compressing Pre-Construction Timelines
The most direct IRR effect of faster design is earlier revenue. For water and wastewater infrastructure projects, the pre-construction phase, including feasibility, conceptual design, detailed design, procurement, and permitting, typically spans several years before the asset begins generating revenue or delivering the service outcomes that underpin regulatory performance commitments.
Every month that the pre-construction phase is compressed represents one month of earlier revenue or earlier service delivery. When Caesb reduced lift station design time from 15 days to approximately four hours, the direct effect is not just a cost saving on the specific design exercise. It is an acceleration of the project programme, which moves forward the date at which the asset is operational and generating returns.
For large capital programmes with many individual projects, the compounding effect of faster design across the full portfolio can advance the programme’s aggregate cash flow profile by months or years. Applied to an IRR calculation, that acceleration has real financial value.
Mechanism 2: Reducing the Cost of Pre-Construction Design
Design cost itself is a pre-construction expenditure that reduces the IRR of the project. Smaller design costs mean smaller initial cash outflows, which directly improves the IRR calculation. BRK Ambiental’s documented 80% reduction in conceptual design costs represents a material reduction in pre-construction expenditure that improves project economics directly.
The magnitude of this effect depends on the ratio of design cost to total project cost. For smaller facilities, design costs can represent a significant percentage of total pre-construction expenditure. For large, complex facilities, the percentage is smaller, but the absolute saving on an 80% cost reduction is still substantial.
Mechanism 3: Improving CAPEX Precision
One of the most consequential effects of generative design on project IRR is not in the design phase at all. It is in the quality of the CAPEX estimates that design produces. Infrastructure projects regularly experience cost overruns, and those overruns systematically reduce realised IRR relative to projected IRR. A project that costs 20% more to build than the investment case assumed is a project whose IRR is materially lower than expected.
The fundamental cause of most infrastructure cost overruns is not execution failure. It is scope uncertainty at the time of investment decision: design that was not sufficiently detailed at the conceptual stage to produce accurate cost estimates. When conceptual designs are generated manually under time pressure, cost estimates are anchored to experience and rule of thumb rather than to actual engineering specifications. When scope changes emerge during detailed design, or when site conditions diverge from planning assumptions, the cost of those changes is disproportionate because they require rework of decisions that were made under insufficient information.
Generative design produces CAPEX estimates grounded in actual engineering specifications: equipment lists, civil quantities, process configurations. The accuracy of these estimates is materially higher than planning-level estimates, which reduces the gap between projected and realised CAPEX and improves the reliability of IRR projections.
Mechanism 4: Optimising OPEX Through Better Technology Selection
The technology choices made at the conceptual design stage determine the operating cost profile of an asset over its entire operational life. Treatment technology, equipment specification, and process configuration all affect energy consumption, chemical usage, maintenance requirements, and labour intensity, year after year, for 20 to 30 years.
Generative design, by enabling rapid evaluation of multiple technology options with accurate OPEX modelling, makes it practical to identify the design configuration that is genuinely optimal over the whole life of the asset, not just the configuration that is most familiar or most easily specified within the available time. The compounding financial value of even a modest OPEX improvement, applied over a 25-year asset life, is substantial when calculated in present value terms.
According to McKinsey’s infrastructure digital tools analysis, digital modelling can improve operational performance of infrastructure investments by 20 to 30%. Applied to the OPEX line of a water infrastructure project, that range of improvement represents a significant positive contribution to project IRR over the asset’s operational life.
The IRR Case in Summary
The financial case for AI-assisted design in infrastructure projects is not primarily about design cost savings, though those are real. It is about the compound effect of better decisions, made earlier, with greater precision, across the full financial lifecycle of an infrastructure asset.
Faster design compresses pre-construction timelines and advances revenue. Lower design cost reduces initial cash outflows. More accurate CAPEX estimates reduce the probability of cost overruns that erode realised IRR. And better OPEX optimisation improves the cash flow profile of the asset across its operational life.
For infrastructure investors and project developers evaluating digital design investment in their project development capabilities, the framework is clear. The question is not whether AI infrastructure engineering design improves IRR. The evidence strongly suggests it does. The question is how much, and whether that improvement is being captured systematically across the project portfolio or left on the table through continued reliance on conventional design methods.
To explore how Transcend’s generative design platform supports better investment decisions across the full infrastructure project lifecycle, visit transcendinfra.com/capitalplanning.
