Estimation-centric AI reshapes aerospace defence costs

The aerospace and defense industry is at a turning point, not because demand is slowing, but because the cost of getting decisions wrong has never been higher. With global defense spending rising and space programs accelerating, estimation errors now ripple across supply chains, manufacturing plans, and long-term national priorities. Traditional planning methods are struggling to keep pace, creating a gap between how quickly decisions must be made and how confidently they can be defended.

Why AI is exposing the limits of traditional cost estimation

As Artificial Intelligence is introduced into aerospace and defense planning workflows, it is placing unprecedented pressure on how cost estimates are created, explained, and defended. AI promises faster analysis and broader data coverage, but in highly regulated, mission-critical programs, speed alone is not enough. Leaders need to understand how an estimate was generated, what assumptions shaped it, and where human judgment influenced the outcome.

This challenge is amplified as digital supply chains, additive manufacturing, and agile software development become standard across both commercial and military programs. Traditional estimation methods were never designed to operate alongside AI-driven analysis at this scale. Assumptions are often implicit, data lineage is fragmented, and decision logic is difficult to reconstruct once models begin influencing outcomes. The result is a widening gap between the growing role of AI in planning decisions and the industry's ability to make those decisions transparent, auditable, and trusted.

Faster decisions with estimation-centric AI

Estimation-Centric Artificial Intelligence (ECAI) addresses this gap as a human-supervised, multi-agent framework that integrates four key elements: automated prompt engineering, contextual agent activation, retrieval-augmented generation, and structured human-in-the-loop review. These components transform unstructured inputs - such as Requests for Information (RFIs), Requests for Proposal (RFPs), design descriptions, or code repositories - into structured, traceable estimation outputs, allowing teams to reconstruct reasoning for audits and reviews.

ECAI aligns with emerging expectations for responsible AI outlined by frameworks such as the EU Artificial Intelligence Act (2025), the UNESCO Recommendation on the Ethics of AI (2021), and the Republic of Korea's Framework Act on AI (2024), emphasizing transparency, accountability, and human oversight as prerequisites for AI adoption. In addition, ECAI can be applied across workflows, highlighting how AI agent-supported interpretation, data origin capture, and human oversight work together to improve transparency and performance. 

AI in cost estimation use cases

Cost estimation and consulting leader Galorath Inc. assessed four cost estimation use cases to establish a repeatable framework for evaluating estimation transparency as modernization continues across the industry. These centered on proposal management, hardware system estimation, manufacturing process analysis, and software development planning. The use cases mirror realistic estimation challenges using internal benchmarks drawn from prior estimation cycles, proposal timelines, and engineering review feedback.

Proposal management

Managing RFIs and RFPs is slow and error-prone, as teams manually comb through hundreds of pages of requirements, contracts, and evaluation criteria to build compliance matrices. This labor-intensive process often causes delays, inconsistent ownership, and poor traceability, making it difficult to justify how requirements were interpreted or assigned during reviews or audits. 

ECAI streamlines the process by extracting and classifying requirements, mapping evaluation criteria, and generating a fully sourced, auditable matrix with assigned owners. In testing with real solicitations, it cuts RFI and RFP development time by 60-75% while eliminating missed clauses and creating a defensible, review-ready deliverable. 

Hardware system estimation

Work Breakdown Structures (WBS) are critical for managing complex aerospace and defense programs, but creating them is often slow and heavily reliant on expert judgment. Engineers typically assemble WBS elements by manually interpreting requirements, design documents, and interface definitions - an approach that depends on institutional memory and leaves teams unable to defend their rationale during audits or cost reviews. 

ECAI streamlines this process by parsing technical documents into functional and physical components, aligning them with proven breakdown patterns from past programs, and applying estimation-oriented logic to suggest structure and identify gaps. The result is a WBS draft with full traceability, where each element carries its source data, analog basis, and reviewer decisions. In testing, development time was cut up to 70%, with time shifted from questioning the structure itself to validating its documented reasoning. 

Manufacturing process analysis

Manufacturing estimation poses unique challenges, as it must determine how a product will be built and at what cost, all the while considering material choices, production methods, tooling, and batch sizes. Traditional Design-to-Cost (DTC) and Design for Manufacturing (DFM) approaches often rely on static checklists, leaving cost and manufacturability reasoning undocumented and leading to late-stage rework, missed sourcing opportunities, and reduced production readiness.

ECAI addresses this by integrating DTC and DFM analysis into a structured workflow. It ingests product structures, BOMs, routing data, and CAD files, identifies cost drivers, and evaluates manufacturability logic to simulate alternative configurations. In beta tests across aerospace and precision-machining programs, estimation time decreased by 40-60% while linking every cost and manufacturability factor to its source. The result is a transparent, auditable process that strengthens supplier collaboration, preserves trade-off documentation, and embeds manufacturability and cost reasoning directly into the design workflow. 

Software development planning

Software estimation is challenging because development methods evolve rapidly. Yet many organizations rely on informal sizing techniques that mix functional scope with team velocity, making estimates difficult to validate, audit, or reuse. Assumptions about complexity, reuse, and integration are rarely documented, creating gaps between engineering, finance, and program stakeholders.

ECAI addresses this by separating functional sizing from delivery methods, parsing requirements, design artifacts, and user stories, and applying standardized models such as Function Points or COSMIC. It logs assumptions and reviewer decisions, linking each function to its source and historical benchmarks. Pilot tests showed a 35-50% reduction in time to produce defensible sizing baselines, improved first-pass acceptance of effort and schedule projections, and the creation of a fully auditable, reusable estimation process that grounds software sizing in functionality rather than guesswork.  

When AI becomes accountable 

As aerospace and defense programs grow larger, faster, and more interconnected, estimation can no longer be treated as a back-office activity or a one-time deliverable. The scale and complexity of modern programs are forcing a rethink of how Artificial Intelligence is used in decision-making, not just to accelerate planning, but to make it accountable. Estimation-centric AI does not replace human judgment; it captures it, tests it, and makes it defensible under scrutiny. In an industry where trust, accountability, and repeatability determine long-term success, the future of AI in planning will belong to organizations that can explain not just what they decided, but why. 

Charles Orlando is Chief Strategy Officer at Galorath Incorporated. He leads global strategy and AI development for SEER and SEERai, focusing on explainable, policy-aligned agentic AI for cost, schedule, and risk estimation in defense and manufacturing.