A latest study by Boston Consulting Group ( BCG ) shows that among industrial enterprises, the revenue growth rate of AI leaders exceeds that of the AI laggards in the industry by more than twice. The study indicates that it is those companies that have redefined their operations around AI that truly create a gap, rather than those that simply add AI tools to their existing workflows. Modeling of three industrial sub-sectors by BCG shows that enterprises that fully adopt the “AI priority” model are expected to see their EBIT profit margins increase by about 2.5 to 5.6 percentage points once they mature.

The above findings are from the latest report BCG titled "For Industrial Companies, Winning with AI Takes More Than Tech". The report is based on BCG's " Applied AI Index 2026", which surveyed 1,330 executives in over 20 industries, and combines with BCG's analysis of the potential financial impacts of AI preferred operating models in the three sub-industries of chemicals, aerospace and defense, as well as automotive.

"Industrial enterprises will not unleash the full potential of AI simply by handing over the co-pilot tools to engineers and planners," said Carsten Schaetzberger, Director General Manager and Senior Partner of BCG, and co-author of the report. "The greater opportunity lies in rethinking how factories operate, how aircraft are maintained, and how production is planned. This is different from saving time with AI or using AI to change business economics."

Reengineering operations around AI is the key for leaders to widen their gap.

The method in which BCG redesigns processes, functions, and operational models centered around AI is referred to as "reengineering" ( reshaping ). This is different from "deployment" ( deploying ) AI : the latter involves adding tools such as co-pilots to a single work process, while the organization itself remains unchanged. BCG estimates that deploying AI can save individual employees 10% to 15% of their task time, whereas reengineering core functions and processes can lead to a 30% to 50% increase in efficiency and effectiveness.

Liders in the industrial field who are AI are already taking steps to reshape their paths. A BCG survey shows that between 2022 and 2025, the revenue growth rate of these companies exceeded that of the AI laggers in the industry by more than twice, and their total return on shareholders over three years was also approximately twice that of the latter. The proportion of AI driven cost savings they achieved also exceeded twice that of the laggers.

However, most industrial enterprises are still in the deployment phase, with only a few companies advancing specialized, multi-year AI transformation projects. For example, data cited in the report from the U.S. Census Bureau indicates that among American industrial enterprises that have adopted AI, nearly 70% have not made any organizational, data, or process adjustments, and only 12% have established new work processes.

The profit margins of the three major sub-industries EBIT have increased to varying degrees.

After BCG became the preferred enterprise over AI, a model was developed to assess the impact on the income statements of the three major industrial sub-sectors:

• Chemical Industry: For large, global chemical companies with strong overall performance, EBIT the profit margin can be increased by about 2.5 to 4.5 percentage points.
• Aerospace and National Defense: At the mature stage, established AI priority enterprises can achieve a maximum increase of 3.3 percentage points in EBIT profit margins in OEM business, and a maximum increase of 5.6 percentage points in after-sales business.
• Automobiles: In the long run, mature automobile manufacturers can achieve an increase in EBIT profit margins of about 3 to 5 percentage points, with procurement and research and development contributing the majority of this potential value.

These estimates are merely illustrative models for established companies after a comprehensive AI transformation, and they are not predictions or estimations for individual companies.

The gap between data and trust makes it more difficult for industrial enterprises to reshape themselves.

Compared to other industries, industrial enterprises face more severe AI challenges. Only 12% of industrial enterprises possess the data capabilities needed to build for AI in the future, while the average for all other industries is 18%. The biggest gap lies in data strategy. The report also indicates that industrial enterprises are less likely to expect to have decision-making autonomy by 2030.

The report suggests that dispersed information—i.e., knowledge being held in the minds of senior managers rather than documented—along with multi-tier supply chains, both exacerbate data challenges. Security and durability requirements also mean that companies must set very high thresholds before granting decision-making authority to agents.

The report suggests that companies can respond in two ways:

• Establish an enterprise-level Cortex to guide the decision-making of AI. This connection layer is located within the AI technology stack, carrying the business rules, decision-making logic, and operational context of the enterprise. It provides a safeguard for proxies and helps enterprises avoid what BCG refers to as "cognitive locking" towards external models.
• Build trust in stages. First, let the agent provide suggestions, then execute after approval, and finally take autonomous actions within clear limits. Clear rules also provide a concrete basis for enterprises to examine the agent's reasoning process.

Five Priorities for Industrial Enterprise Leaders

The report outlines five priorities for industrial enterprise leaders: setting ambitious goals that reflect the full potential of AI across all business areas; ensuring that CEO and the board of directors lead the transformation and overcome cross-functional barriers; reshaping the organization and work processes around people and their collaboration with AI agents; treating enterprise-level cortex as a strategic asset; and leading employees through the transformation by addressing their concerns, building their confidence, and helping them adopt new ways of working.

"Industrial enterprises are facing more severe AI challenges," said Decker Walker, Director General and Senior Partner of BCG, and co-author of the report. "They operate complex physical assets that rely on highly specialized knowledge, and any mistakes in decision-making can have serious consequences. The potential of AI requires connecting this knowledge, establishing clear decision-making authorities, and fundamentally changing the way people collaborate with AI. Such organizational changes usually take at least 18 months to be implemented throughout the organization, so leaders need to start taking action now."

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Source: Boston Consulting Group ( BCG )