BCG: Industrial AI Leaders Grow Revenue More Than Twice as Fast

Reshaping operations gives AI leaders an edge

BCG distinguishes between deploying AI and reshaping a business around it. Deploying involves adding tools such as copilots to individual workflows while leaving the organization largely unchanged. Reshaping places AI at the center of processes, functions, and operating models.

According to BCG, deploying AI can save individual workers 10% to 15% of their time on tasks. Redesigning core functions and processes can deliver a 30% to 50% improvement in efficiency and effectiveness. AI leaders in industrials also delivered roughly double the three-year total shareholder return of AI laggards and achieved over twice the share of AI-enabled cost savings, the research found.

“Industrial companies won't unlock AI's full potential simply by putting copilots in the hands of engineers and planners,” said Carsten Schaetzberger, a BCG managing director and senior partner and a coauthor of the report. He said the larger opportunity is to rethink how plants operate, aircraft are maintained, and production is planned.

AI-first models could raise EBIT margins

BCG modeled the potential financial impact of comprehensive AI transformations in chemicals, aerospace and defense, and automotive. The estimates describe established companies at maturity and are illustrative modeling, rather than forecasts or projections for individual companies.

• Large, global, predominantly strong-performing chemical companies could improve EBIT margins by approximately 2.5 to 4.5 percentage points.
• Established AI-first aerospace and defense players could improve EBIT margins by up to 3.3 percentage points in OEM operations and 5.6 percentage points in aftermarket operations.
• Established automotive manufacturers could achieve an EBIT margin gain of approximately 3 to 5 percentage points over the long term, with sourcing and R&D accounting for much of the potential value.

Despite this potential, most industrial companies remain at the deploying stage, and only a few run dedicated, multiyear AI transformation programs. US Census Bureau data cited by BCG shows that almost 70% of US industrial companies adopting AI make no organizational, data, or process changes, while only 12% build new workflows.

Data, safety, and trust remain barriers

Only 12% of industrial companies have the data capabilities BCG says are needed to be future-built for AI, compared with 18% across other sectors. The report identifies siloed information, including know-how held by senior managers rather than documented, and multitiered supply chains as contributors to the challenge.

Industrial companies are also less likely to expect AI agents to have decision-making autonomy by 2030. Safety and durability requirements mean they need a high bar before giving agents authority to make decisions.

BCG proposes an “Enterprise Cortex” as a connective layer containing business rules, decision logic, and operational context. It would provide guardrails for agents and help companies avoid what BCG calls “cognitive lock-in” to external models. The report also recommends building trust in stages: agents should advise first, then act with approval, and only later act independently within defined limits.

What industrial leaders should do next

BCG's five priorities are to set an ambitious business-wide AI target, ensure ownership by CEOs and boards, redesign organizations and workflows for collaboration between people and AI agents, manage the enterprise cortex as a strategic asset, and help employees adopt new ways of working.

“These organizational changes typically take at least 18 months to cascade throughout an organization, so leaders need to start now,” said Decker Walker, a BCG managing director and senior partner and coauthor of the report. The findings suggest that industrial companies seeking the financial benefits modeled by BCG will need to change how work and decisions are organized, rather than treating AI as an add-on to existing processes.