Manufacturers pin profit hopes on AI amid readiness gap
Manufacturing executives expect artificial intelligence to become a major driver of profit margins within the next two years, even as most say their plants are not yet ready for large-scale deployment, according to new global research from Tata Consultancy Services and Amazon Web Services.
The study of 216 senior leaders in North America and Europe found that 75% expect AI to rank among the top three contributors to operating margins by 2026. It also found that only 21% consider their organisations fully prepared for AI.
The survey covered executives in automotive, industrial machinery, aerospace and defence, process industries, chemicals, and heavy equipment. It examined investment plans, data strategies, workforce skills, and supply chain practices.
Margin focus
Manufacturers are directing a growing share of technology budgets into AI and automation. Respondents said that 51% of manufacturing transformation spending over the next two years is now allocated to AI and autonomous systems.
Almost nine in ten leaders anticipate that AI will capture at least 5% of operating margin. Many see this potential emerging from new levels of precision in production, better planning, and fewer quality failures.
Agents based on AI are a particular area of focus. Executives expect these systems to influence the way decisions move across plants and networks.
"Manufacturing is an industry defined by precision, reliability, and the relentless pursuit of performance. Today, that strength of foundation becomes multifold with AI in orchestrating decisions-delivering transformational business outcomes through greater predictability, stability, and control. At TCS, we see this as a defining opportunity to help manufacturers build resilient, adaptive, and future-ready enterprise ecosystems that can thrive in an era of intelligent autonomy," said Anupam Singhal, President - Manufacturing, TCS.
Autonomous decisions
The study reports a clear shift towards autonomous operations on the factory floor. Around 74% of leaders expect AI agents to manage between 11% and 50% of routine production decisions without human approval by 2028.
The report also notes early movement in AI-based use cases inside plants. Nearly 40% of organisations say they are embedding AI into quality and planning processes and are already seeing measurable gains.
More than 30% of respondents forecast meaningful productivity improvements from what they describe as AI-led modernisation. Many link this to self-adjusting workflows and continuous optimisation of lines and assets.
The trend extends beyond automation of single tasks. It moves towards systems that can sense conditions, run analytics, and trigger responses without waiting for operators.
Data and skills gap
The study highlights structural barriers that hold back wider deployment. The low share of companies that describe themselves as fully AI-ready points to issues with data infrastructure and integration across plants and supply chains.
Executives cite security and governance as the most common concern. About 52% say these issues are major obstacles when they attempt to scale AI at plant level.
Talent is another constraint. Around 47% point to skills gaps as a barrier to AI adoption on the shop floor.
These findings suggest that many manufacturers intend to expand AI use but face hurdles in data quality, system interoperability, compliance, and workforce training. Many are still upgrading legacy systems and aligning operational technology with IT and cloud architectures.
Supply chain resilience
Supply chain disruption remains a central theme for the sector. Companies are using both traditional and digital approaches as they adjust risk strategies.
About 67% of respondents report better real-time visibility across their supply chains. Nearly half, or 49%, say they use AI for dynamic inventory optimisation.
However, 61% continue to rely on conventional risk mitigation tools such as higher safety stock. This suggests that AI-enabled resilience is emerging alongside older, more reactive tactics.
The combination of improved data visibility and AI models is beginning to give planners earlier signals of disruption. It also supports more frequent adjustments of orders, production plans, and logistics.
Human-AI collaboration
The research indicates that AI rollouts will change the role of workers rather than remove humans from processes. Around 89% of leaders anticipate more collaboration between humans and AI systems on the factory floor as adoption expands.
Many expect operators and engineers to supervise AI agents, interpret insights, and intervene in exceptions. This implies new training needs in data literacy and digital tools.
Cloud infrastructure is a common foundation for these efforts. It provides a way to connect machines, sensors, and applications across multiple sites and supply partners.
Ozgur Tohumcu, General Manager - Automotive and Manufacturing, AWS, said "Manufacturers today are facing unprecedented pressure - from tight margins to volatile supply chains and workforce gaps. At AWS, we are revolutionizing manufacturing through AI-powered autonomous operations, shifting from manual, reactive processes to intelligent, self-optimizing systems that operate at scale. By embedding artificial intelligence into every layer of the operation and leveraging cloud-native architecture, manufacturers can move beyond simple automation to true autonomous decision-making - where systems predict, adapt, and act independently with minimal human intervention. This enables not just faster response times, but fundamentally transforms operations with AI-driven predictability, resilience, and agility. This study makes it clear: the future of manufacturing is not just digital, it is autonomous - powered by AI that learns, evolves, and operates continuously."
The findings point to a manufacturing sector that is investing heavily in AI, yet still working through the practical challenges of data, governance, and skills as it moves towards more autonomous and intelligence-led operations.
