AI Leaders in Industrials Grow Revenues More Than Twice as Fast Laggards

• New BCG Analysis Shows That AI Leaders Also Achieve Over Twice the Share of AI-Enabled Cost Savings
• BCG Modeling Suggests That Fully AI-First Companies in Three Subsectors Could Lift EBIT Margins by 2.5 to 5.6 Percentage Points
• Only 12% of Industrial Companies Have the Data Capabilities Needed to Be Future-Built for AI, Versus 18% in Other Sectors

BOSTON, Nov. 10, 2026 — Industrial companies leading in AI grew revenues more than twice as fast as AI laggards in the sector, according to new research from Boston Consulting Group (BCG). The research points to a divide between the companies that redesign how they operate around AI and the majority that simply add AI tools to existing workflows. BCG modeling of three industrial subsectors suggests that fully AI-first companies could lift their EBIT margins by roughly 2.5 to 5.6 percentage points at maturity.

These are among the findings of a new report from BCG, For Industrial Companies, Winning with AI Takes More Than Tech. The report draws on BCG's Applied AI Index 2026, a survey of 1,330 senior executives across more than 20 sectors, and BCG analysis of the potential financial impact of AI-first operating models in the chemicals, aerospace and defense, and automotive subsectors.

"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. "The bigger opportunity is to rethink how plants operate, how aircraft are maintained, and how production is planned. It's the difference between using AI to save time and using AI to change the economics of the business."

Reshaping Around AI Is Where Leaders Are Pulling Ahead

BCG calls the approach of redesigning processes, functions, and operating models with AI at the center "reshaping." It differs from "deploying" AI, where a company adds tools such as copilots to individual workflows but the organization stays the same. BCG estimates that deploying AI can save individual workers 10% to 15% of their time on tasks, whereas reshaping core functions and processes can deliver a 30% to 50% improvement in efficiency and effectiveness.

AI leaders in industrials are already taking the reshaping approach. They grew revenues more than twice as fast as the sector's AI laggards from 2022 to 2025 and delivered roughly double the three-year total shareholder return, BCG's survey finds. They also achieve over twice the share of AI-enabled cost savings."

Most industrial companies, however, remain at the deploying stage, and only a few run dedicated, multiyear AI transformation programs. For example, almost 70% of industrial companies in the US that adopt AI make no organizational, data, or process changes, and only 12% build new workflows, according to US Census Bureau data cited in the report.

Modeled EBIT Margin Gains Vary Across Three Subsectors

BCG modeled what becoming AI-first could mean for the P&L in three industrial subsectors:

• Chemicals. Large, global, predominantly strong-performing chemical companies could improve their EBIT margin by approximately 2.5 to 4.5 percentage points.
• Aerospace and defense. At full maturity, established AI-first players could improve their EBIT margin by up to 3.3 percentage points in OEM operations and 5.6 percentage points in aftermarket operations.
• Automotive. 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.

These estimates represent illustrative modeling of established companies following comprehensive AI transformations. They are not forecasts or projections for individual companies.

Data and Trust Gaps Make Reshaping Harder for Industrials

Industrial companies face a tougher AI challenge than other sectors. Only 12% have the data capabilities needed to be future-built for AI, compared with an average of 18% across all other sectors, with the biggest gap in data strategy. Industrials are also less likely to expect AI agents to have decision-making autonomy by 2030

The report points to siloed information—know-how that sits in senior managers' heads rather than in documented form—and multitiered supply chains as factors complicating the data challenge. Safety and durability requirements mean that companies must set a high bar before giving agents decision-making authority.

The report sets out two ways in which companies can respond:

• An Enterprise Cortex to Guide AI Decisions. This connective layer in the AI tech stack holds a company's business rules, decision logic, and operational context. It gives agents guardrails and helps companies avoid what BCG calls "cognitive lock-in" to external models.
• Trust Built in Stages. Agents advise first, then act with approval, and only later act on their own within defined limits. Explicit rules give companies something concrete to check agents' reasoning against.

Five Priorities for Industrials Leaders

The report identifies five priorities for industrials leaders: set an ambitious target that reflects AI's full potential across the business; ensure that CEOs and boards take ownership of the transformation and resolve cross-functional obstacles; redesign organizations and workflows around collaboration between people and AI agents; manage the enterprise cortex as a strategic asset; and bring employees along on the journey by addressing their concerns, building their confidence, and helping them adopt new ways of working.

"Industrial companies face a tougher AI challenge," said Decker Walker, a BCG managing director and senior partner and coauthor of the report. "They operate complex physical assets, rely on highly specialized knowledge, and make decisions where mistakes can have serious consequences. AI's potential requires connecting that knowledge, establishing clear decision rights, and fundamentally changing how people and AI work together. These organizational changes typically take at least 18 months to cascade throughout an organization, so leaders need to start now."

Download the publication here:
https://www.bcg.com/publications/2026/how-industrial-companies-win-with-ai

Media Contact:
Bruce Wraight
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SOURCE Boston Consulting Group (BCG)