Most manufacturers now view AI as essential to competitiveness, but few have the data infrastructure or workforce readiness to back that belief. Galorath’s 2025 report captures an industry racing to modernize its planning systems even as legacy tools, skill gaps, and fragmented data slow its progress.
Confidence Without Connection
The 2025 State of Software and Manufacturing Report from Galorath shows an industry caught between urgency and inertia. Nearly 70% of surveyed firms prioritize real-time data integration, and 71% list automation as a top goal. Yet fewer than one-third have fully integrated systems, and most professionals still work with siloed data. Cost volatility, cited by 89% as the leading operational threat, continues to erode margin predictability as material, labor, and financing costs shift faster than legacy systems can adapt.
The gap between executive confidence and operational capability is widening. Leaders often express faith in their project estimates, but frontline teams, cost engineers, analysts, and program managers, report persistent data delays and incompatible tools. This mismatch reflects what Galorath calls a “recalibration era,” where assumptions built for stability no longer fit markets defined by disruption. Similar findings have surfaced in recent studies by MIT and the World Economic Forum, both of which identify disconnected data and inconsistent modeling as hidden drivers of financial inefficiency across industrial supply chains.
Automation Promise Meets Human Limits
While 56% of organizations are planning to deploy AI-driven tools for forecasting or estimation, adoption remains patchy. Among cost professionals, 76% report no use of AI in their workflow. Engineering teams show greater experimentation, 45% report using AI in at least one process, but most deployments are limited to reporting or analysis tools rather than decision automation.
Galorath’s data reflects a growing problem: the technology exists, but the workforce is unprepared to use it at scale. Training deficits, inconsistent implementation, and mistrust of AI-generated data are common barriers. This echoes a broader manufacturing challenge: according to McKinsey, fewer than 30% of digital transformation projects reach full adoption due to skill gaps and misaligned incentives. As firms race to digitize their planning and cost estimation processes, human capability, not hardware or algorithms, has become the decisive constraint.
From Integration to Accountability
As AI and automation take root, manufacturers face a shift from proving efficiency to proving accountability. Regulators in the EU and U.S. are already moving toward requiring audit trails for algorithmic decisions in cost and risk estimation, turning data governance into a compliance function, not just an operational one. The firms that align AI deployment with verifiable, auditable systems will not only reduce volatility but also strengthen their position in markets where digital accountability is fast becoming the new license to operate.
