Industrial companies that redesign their operations around artificial intelligence are pulling ahead of competitors that simply introduce AI tools into existing workflows. A new report from Boston Consulting Group (BCG) finds that industrial AI leaders grew revenues more than twice as fast as sector laggards between 2022 and 2025. The research argues that the largest gains will come not from adding more copilots, but from rebuilding core business processes, decision-making structures and operating models around AI.
Industrial AI Leaders Outpace Rivals as BCG Calls for Operational Reinvention
The findings come from BCG’s report, For Industrial Companies, Winning with AI Takes More Than Tech, which draws on its Applied AI Index 2026 survey of 1,330 senior executives across more than 20 sectors. The analysis also models the potential financial impact of AI-first operating models in chemicals, aerospace and defense, and automotive manufacturing.
BCG estimates that industrial companies completing a comprehensive AI transformation could increase earnings before interest and taxes (EBIT) margins by approximately 2.5 to 5.6 percentage points at maturity. These are illustrative estimates for established companies, not forecasts of individual businesses’ future performance.
The report identifies a growing divide between two approaches to enterprise AI: deploying tools to make existing tasks faster and reshaping the organization so AI becomes part of how work is designed and executed.
Why adding AI tools may not be enough
BCG describes the first approach as deploying. Companies introduce copilots and AI assistants into individual workflows, helping employees complete existing tasks more quickly without substantially changing the underlying processes.
The consulting firm estimates that this approach can save individual workers 10% to 15% of their time on tasks. However, the larger opportunity comes from reshaping: redesigning entire functions, workflows and operating structures around AI. BCG estimates that this could improve efficiency and effectiveness by 30% to 50% in transformed processes.
The distinction is important for industrial businesses, where value is often distributed across complex operations involving engineering, procurement, production, logistics, maintenance and customer service. Improving one employee’s productivity may have limited financial impact if the rest of the process remains unchanged.
BCG’s survey suggests that companies taking a broader approach are already seeing stronger business results. Industrial AI leaders achieved more than twice the revenue growth of sector laggards from 2022 to 2025 and delivered roughly double the three-year total shareholder return. They also captured more than twice the share of AI-enabled cost savings, according to the research. BCG+1
Yet adoption remains uneven. BCG cites US Census Bureau data showing that almost 70% of US industrial companies adopting AI make no organizational, data or process changes. Only 12% build new workflows. This suggests that many businesses are still treating AI as an additional technology layer rather than a reason to reconsider how operations are structured.
AI-first models could change industrial margins
The potential financial impact varies by subsector, reflecting differences in operating costs, production processes and revenue models.
In chemicals, BCG estimates that large, global companies with established operations could improve EBIT margins by approximately 2.5 to 4.5 percentage points. AI applications in sales, customer segmentation and operational services could help companies identify new opportunities and move beyond traditional product-based business models.
In aerospace and defense, the modeled margin improvement reaches up to 3.3 percentage points for original equipment manufacturers and 5.6 percentage points for aftermarket operations. Maintenance, repair and overhaul activities offer opportunities to coordinate parts, technicians, inspections and service schedules more effectively.
For automotive manufacturers, BCG estimates a potential long-term EBIT margin improvement of 3 to 5 percentage points. Sourcing, research and development, sales and operations planning are among the areas expected to contribute substantially.
These scenarios depend on comprehensive transformation and established AI-first operations. They should not be interpreted as near-term returns from individual AI deployments.
Data fragmentation and operational risk remain barriers
Industrial companies face challenges that differ from those in sectors with fewer physical assets and less complex operating environments. Their AI systems must work with engineering records, maintenance histories, production data, supplier information and operational knowledge that may be scattered across departments and legacy platforms.
BCG reports that only 12% of industrial companies have the data capabilities needed to be considered future-built for AI, compared with 18% across other sectors. The report identifies weaknesses in data strategy as a particularly significant gap.
The problem extends beyond data infrastructure. Critical operational knowledge may exist primarily in the experience of senior engineers, plant managers and technicians rather than in structured, accessible systems. Multitiered supply chains add another layer of complexity, particularly when suppliers do not consistently share information.
Safety and reliability also place limits on autonomous decision-making. An AI agent recommending a maintenance action or preparing an engineering assessment is different from one authorized to execute a safety-critical decision.
To address these issues, BCG proposes an enterprise architecture it calls an enterprise cortex. This connective layer would bring together company-specific business rules, decision logic and operational context, giving AI agents the information and constraints needed to work within organizational requirements.
The report also recommends expanding agent autonomy gradually: agents initially advise employees, then execute tasks with human approval, and eventually operate independently only within clearly defined limits.
CEOs and boards must own the transformation
BCG argues that industrial AI transformation cannot remain an IT-led initiative. It requires executive ownership because redesigning processes often means changing responsibilities, organizational structures and established ways of working.
The report identifies five priorities for business leaders: set ambitious enterprise-wide AI targets; make CEOs and boards accountable for transformation; redesign workflows around collaboration between people and AI agents; treat the enterprise cortex as a strategic asset; and help employees adapt through communication, training and organizational support.
That final priority is especially relevant as agentic AI moves from experimentation into operational environments. Employees need to understand how responsibilities are changing, which decisions remain under human control and what skills will be required in redesigned roles.
BCG estimates that major organizational changes can take 18 to 24 months to implement. This makes workforce readiness and change management important parts of the transformation timeline, rather than activities to address after technology deployment.
The broader message is that industrial competitiveness may increasingly depend on how effectively companies combine AI capabilities with operational knowledge, reliable data and human expertise. Buying AI tools may improve individual tasks, but the companies most likely to capture sustained value will be those prepared to redesign how the business works.
Market Landscape
Industrial AI is moving beyond predictive maintenance and isolated automation toward integrated operating models that connect engineering, production, supply chains, procurement and customer operations.
Three developments are shaping the market:
- From copilots to agentic operations: AI agents are beginning to coordinate multi-step workflows, although safety-critical industrial environments require explicit permissions, human oversight and staged autonomy.
- Data infrastructure as a competitive advantage: Connecting operational technology, enterprise applications and specialist knowledge is becoming essential for reliable AI deployment.
- AI transformation as a business-model decision: Companies are exploring new service models, faster product development and more responsive production planning rather than limiting AI investment to back-office efficiency.
Technology providers including Microsoft, Google Cloud, Amazon Web Services and NVIDIA are part of the broader enterprise AI ecosystem. However, industrial companies still need to connect these capabilities to their own systems, operational data and decision-making processes.
For industrial technology providers, the opportunity is expanding across AI platforms, industrial data infrastructure, digital twins, robotics, edge computing, predictive analytics and agent governance.
Top Insights
- Industrial AI leaders grew revenues more than twice as fast as sector laggards between 2022 and 2025, according to BCG’s survey.
- BCG estimates that reshaping core processes around AI can improve efficiency and effectiveness by 30% to 50%.
- Modeled AI-first transformations could lift EBIT margins by 2.5 to 5.6 percentage points across three industrial subsectors.
- Only 12% of industrial companies have the data capabilities BCG associates with being future-built for AI.
- Enterprise AI transformation requires executive ownership, process redesign, codified operational knowledge and carefully governed agent autonomy.
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