Large infrastructure projects stand or fall by clear, consistent and manageable requirements. In practice, however, requirements are captured in fragmented documents scattered across email, PDFs, spreadsheets and a range of requirements and project data management tools.
Artificial intelligence (AI) is now delivering demonstrable improvements. Validated figures from the sector show efficiency gains of 10 to 35 percent, cost reductions of 10 to 15 percent, a reduction in deviations of 10 to 20 percent and 10 to 30 percent fewer engineering hours. These effects are particularly achieved when AI is applied to a logical and well-defined starting point: requirements management.
AI structures information, performs quality checks and highlights risks at an early stage. This improves decision‑making and demonstrably reduces the risk of rework, delays and budget overruns.
The sector is approaching a tipping point. Investments in AI are rising rapidly, while many organizations are still only modestly prepared for structural adoption. Precisely requirements management offers a controlled, scalable entry point to unlock value quickly.
What defines predictable project delivery?
Predictable project delivery requires an up‑to‑date and unambiguous overview of all requirements, including their interdependencies, changes and compliance status. In practice, however, infrastructure projects work with information that is dispersed across multiple documents and a mix of on‑premise and cloud‑based project data management tools. As a result, teams lack visibility into changes, dependencies and compliance requirements.
AI removes these bottlenecks by automating document processing and information extraction, giving teams immediate insight into the most relevant details. Validated results demonstrate significant gains in efficiency and cost reduction. AI‑supported requirements management creates a stable foundation for planning, design decisions and risk control.
Complexity and fragmentation of information
In many projects, requirements are managed in separate documents and partially overlapping requirements and project data management tools, without central coherence. This leads to duplicate, conflicting or outdated versions. Ensuring consistency becomes labour‑intensive and error‑prone, resulting in delays, differing interpretations and additional administrative overhead.
Manual verification
Teams spend a substantial share of their time searching for, comparing and checking requirements. This manual approach slows down decision‑making and increases the risk of errors, especially when changes follow one another in rapid succession.
What project managers consistently observeProject managers see the same recurring patterns:
The impact is tangible. Poor document control and low data quality are estimated to cause around 31 billion USD in rework per year in the US construction sector. Similar dynamics are at play in European infrastructure projects.
AI delivers both immediate and structural improvements by reducing manual work and increasing the quality of requirement sets.
Direct improvements from AI applications within requirements management include:
This results in a single validated source of truth, eliminating the need for teams to reconstruct context from fragmented information.
Clarity and coherence
AI generates structured outputs that reduce ambiguity and strengthen shared interpretation. Conflicts become visible at an early stage. The uniform extraction method ensures consistency regardless of source format. This creates alignment across disciplines and project phases, which is crucial in complex engineering environments.
AI‑supported requirements management delivers demonstrable operational and financial benefits. Validated insights show, among other things:
AI reduces search effort and makes risks visible sooner.
Teams identify conflicts, dependencies and compliance issues early in the process, reducing the likelihood of change orders and late rework.
Key benefits:
The capacity that is freed up can be redirected to planning, coordination and quality assurance.
An effective assessment focuses on three aspects: alignment with work processes, governance and scalability.
Alignment with existing work processes
An AI solution must fit within the processes of the infrastructure and energy sector and integrate with methodologies such as Agile, Waterfall and model‑based Systems Engineering (MBSE). Integration with existing requirements and project data management tools and document management systems is essential, preferably via an API‑first architecture.
A modular setup prevents organizations from having to build large internal data teams and simplifies integration with design, verification and validation platforms.
Governance and scalability
Reliable AI follows standards such as INCOSE and ISO 15288 and supports full traceability. Solutions that do not generate training data from customer input safeguard data ownership and privacy.
Model selection should be driven by speed, efficiency and cost control, with human review remaining indispensable. A pilot phase offers control, visible results and low risk for the initial implementation.
An effective starting point
A proven approach consists of three steps:
Iterative development, technical reviews and cross‑disciplinary collaboration increase implementation quality. Clear governance and communication prevent noise and misunderstandings during the transition.
Long‑term success is achieved when pilot results are translated into broad adoption. By documenting and actively sharing successes, organizations build momentum for scaling up.
AI supports this by consistently applying quality checks, for example by automatically comparing output with design models or predefined rules. This safeguards accuracy as usage grows.
Technical reviews, multidisciplinary involvement and clear governance remain crucial to guarantee reliability and compliance as requirements continue to evolve.
AI strengthens requirements management by structurally improving clarity, consistency and traceability. It addresses core challenges such as fragmentation, manual processing and limited visibility across documents and requirements and project data management tools. By automatically extracting requirements, flagging inconsistencies, highlighting compliance risks and accelerating change management, AI reinforces the foundation for predictable project delivery.
The sector is at a tipping point. While 56 percent of construction investors are increasing their AI budgets, 45 percent of organizations still have limited capabilities and 29 percent have no concrete plans. This makes requirements management a logical starting point to create immediate value and build digital maturity.
The contrast between rising investments and limited readiness underlines why AI‑driven requirements management now deserves priority: it delivers measurable results and provides a controlled pathway to broader adoption and improved project performance.