Skip to main content
Submit RFP

      This article was first published in Manufacturing Today Online on 15 September 2026. Please click here to read the article.

      Artificial Intelligence (AI) is no longer a matter of choice. It has become a competitive imperative. In a world shaped by geopolitical uncertainty, volatile markets, shifting customer expectations and constantly evolving technologies, the speed of change has accelerated dramatically. CEOs today are expected to navigate an unprecedented number of variables simultaneously, making traditional decision-making approaches increasingly inadequate.

      As a result, organisations are turning to AI as a force multiplier, enabling leaders to model multiple scenarios, anticipate risks and make faster, more resilient decisions. This shift is reflected in KPMG International’s Global AI Survey, where 93 per cent of decision-makers believe organisations that embrace AI will gain a competitive advantage over those that do not, while 62 per cent report achieving ROI exceeding 10 per cent from their AI investments.

      The question is no longer whether AI can drive efficiency and growth. The real question is: how quickly can organisations harness AI, and where can they create the greatest impact?

      Applications of AI in the industrial manufacturing sector

      AI is rapidly transforming every link in the manufacturing value chain, spanning both core operations and support functions.

      Some of the most impactful applications include:

      Autonomous production lines
      • AI agents continuously monitor process parameters and dynamically adjust machine settings to maximise yield, reduce costs and ensure consistency
      • Autonomous systems identify and correct defects in real time, reducing scrap and strengthening quality control
      • AI-driven scheduling engines optimise production plans based on changing demand, resource availability and machine performance
      • Self-learning robotic systems collaborate seamlessly with human operators, improving productivity while preserving operational flexibility.
      Autonomous maintenance and asset management
      • AI agents predict equipment failures before they occur by analysing real-time sensor data and equipment behaviour
      • Digital twins powered by Agentic AI simulate wear-and-tear scenarios, enabling manufacturers to test maintenance strategies and extend asset life
      • AI analyses failure patterns, identifies critical spare requirements, redesigns inventory policies and triggers automated replenishment, minimising downtime and enhancing reliability.
      Self-Optimising supply chains
      • AI agents power intelligent commodity forecasting by tracking market indices in real time and dynamically adjusting procurement strategies to optimise cost and manage risk
      • AI acts as a smart negotiation buddy, leveraging historical negotiations, supplier performance metrics and market intelligence to recommend winning sourcing strategies
      • Autonomous supply chain agents enhance resilience by recalibrating inventory and ordering strategies in response to demand fluctuations, weather events and geopolitical disruptions
      • AI optimises share-of-business decisions by identifying savings opportunities, flagging anomalies and streamlining approvals, delivering greater efficiency and compliance across procurement
      • Predictive logistics solutions anticipate demand changes and proactively reroute shipments to improve service levels and delivery performance.
      Human-AI collaboration in Decision-Making and upskilling
      • AI-powered assistants support managers by evaluating complex scenarios and recommending optimal courses of action
      • Intelligent copilots deliver real-time guidance to shop-floor employees, improving both speed and accuracy of decision-making
      • AI-enabled learning platforms create personalised training journeys that accelerate workforce capability building and skill development.
      Enabling circular economy and sustainable manufacturing
      • AI identifies opportunities to reduce material consumption and optimise recycling across the manufacturing lifecycle
      • Autonomous sustainability engines monitor carbon emissions in real time, helping organisations maintain regulatory compliance and ESG commitments
      • AI dynamically optimises power generation and consumption by adjusting equipment operating parameters based on demand patterns, unlocking significant energy savings.
      Intelligent sales enablement
      • AI analyses dealer ordering and off-take behaviour to create right demand forecasts
      • AI uncovers customer purchase patterns, enabling organisations to develop targeted sales strategies and differentiated service offerings
      • Intelligent pricing engines simulate competitive scenarios and discount structures, helping businesses optimise pricing decisions and margin outcomes.

      Autonomous production lines

      • AI agents continuously monitor process parameters and dynamically adjust machine settings to maximise yield, reduce costs and ensure consistency
      • Autonomous systems identify and correct defects in real time, reducing scrap and strengthening quality control
      • AI-driven scheduling engines optimise production plans based on changing demand, resource availability and machine performance
      • Self-learning robotic systems collaborate seamlessly with human operators, improving productivity while preserving operational flexibility.

      Autonomous maintenance and asset management

      • AI agents predict equipment failures before they occur by analysing real-time sensor data and equipment behaviour
      • Digital twins powered by Agentic AI simulate wear-and-tear scenarios, enabling manufacturers to test maintenance strategies and extend asset life
      • AI analyses failure patterns, identifies critical spare requirements, redesigns inventory policies and triggers automated replenishment, minimising downtime and enhancing reliability.

      Self-Optimising supply chains

      • AI agents power intelligent commodity forecasting by tracking market indices in real time and dynamically adjusting procurement strategies to optimise cost and manage risk
      • AI acts as a smart negotiation buddy, leveraging historical negotiations, supplier performance metrics and market intelligence to recommend winning sourcing strategies
      • Autonomous supply chain agents enhance resilience by recalibrating inventory and ordering strategies in response to demand fluctuations, weather events and geopolitical disruptions
      • AI optimises share-of-business decisions by identifying savings opportunities, flagging anomalies and streamlining approvals, delivering greater efficiency and compliance across procurement
      • Predictive logistics solutions anticipate demand changes and proactively reroute shipments to improve service levels and delivery performance.

      Human-AI collaboration in Decision-Making and upskilling

      • AI-powered assistants support managers by evaluating complex scenarios and recommending optimal courses of action
      • Intelligent copilots deliver real-time guidance to shop-floor employees, improving both speed and accuracy of decision-making
      • AI-enabled learning platforms create personalised training journeys that accelerate workforce capability building and skill development.

      Enabling circular economy and sustainable manufacturing

      • AI identifies opportunities to reduce material consumption and optimise recycling across the manufacturing lifecycle
      • Autonomous sustainability engines monitor carbon emissions in real time, helping organisations maintain regulatory compliance and ESG commitments
      • AI dynamically optimises power generation and consumption by adjusting equipment operating parameters based on demand patterns, unlocking significant energy savings.

      Intelligent sales enablement

      • AI analyses dealer ordering and off-take behaviour to create right demand forecasts
      • AI uncovers customer purchase patterns, enabling organisations to develop targeted sales strategies and differentiated service offerings
      • Intelligent pricing engines simulate competitive scenarios and discount structures, helping businesses optimise pricing decisions and margin outcomes.

      Data disconnect: The critical barrier to AI adoption

      While manufacturers increasingly recognise AI's transformative potential, large-scale implementation remains slower than expected. According to KPMG's Global AI Survey, 56 per cent of organisations cite data-related challenges as a major hurdle in AI adoption.

      The data disconnect


      Manufacturing organisations generate vast amounts of valuable data, yet much of it remains fragmented

      • R&D functions capture insights from product design, simulation models and material testing
      • Shop floors generate real-time information on production performance, quality and efficiency
      • Field service teams collect valuable data on product failures, customer usage patterns and real-world operating conditions

      Unfortunately, these data streams often operate in isolation. Insights from field performance rarely influence product redesign. Production inefficiencies go unresolved due to the lack of feedback loops between manufacturing, engineering and service teams.

      The complexity of manufacturing data


      Manufacturing data is not only siloed but also highly heterogeneous.

      • Products vary widely in complexity, requiring diverse data structures and analytical approaches
      • Data ownership is distributed across manufacturers, suppliers, distributors, dealers, customers and third-party service providers
      • Data standards and formats differ across plants, production lines and systems, making integration challenging

      Without a unified data strategy, manufacturers struggle to generate actionable insights. Consequently, they remain reactive rather than predictive, limiting their ability to optimise performance, improve quality and accelerate innovation.

      The five Bs of effective AI implementation

      Organisations may be at different stages of AI maturity, but successful implementation consistently hinges on these critical five B’s.

      Business priority identification

      Organisations need to identify the priority areas to implement AI based on the business impact as well availability of right data. This is typically done with a leadership workshop where they understand the “Art of the Possible with AI” followed by focused Functional discussions to identify areas. Then the identified areas/use cases are prioritised based on impact on business and data availability

      Baselining

      Establish a clear baseline before deployment. Define target KPIs, validate calculation methodologies, analyse historical performance and quantify the financial value of improvement. A well-defined baseline is critical for measuring success

      Blueprinting for execution

      Develop a comprehensive implementation roadmap covering data sources, training datasets, data quality requirements, AI model selection, future operating model, governance structures, project milestones and risk mitigation plans. Successful execution begins with a robust blueprint

      Benefit management office

      Track performance rigorously after implementation. Compare actual outcomes against baseline metrics, quantify realised benefits and monitor progress against planned targets to ensure accountability and value creation.

      Budget interlocking

      Embed improvements into future budgets and performance targets. By resetting baselines and incorporating productivity gains into planning cycles, organisations can sustain benefits and prevent performance regression

      Conclusion

      The manufacturing sector stands on the threshold of an AI-driven transformation that will fundamentally redefine how value is created, from product design and manufacturing to supply chains, maintenance and customer engagement. What was once primarily a physical industry is rapidly evolving into an intelligent, data-driven enterprise powered by autonomous systems, predictive analytics and AI-enabled decision-making.

      Our research indicates that leading manufacturers are no longer merely experimenting with AI. They are systematically embedding it into core operations to enhance productivity, strengthen resilience, improve competitiveness and unlock new growth opportunities.

      The winners of the next industrial era will not be those that simply automate processes. They will be the organisations that successfully combine human ingenuity with machine intelligence, creating enterprises that are not just automated, but truly intelligent. This is where AI becomes more than a technology initiative. It becomes the defining force multiplier for sustainable competitive advantage.

      Author

      S Sathish
      S Sathish

      Partner and National Sector Leader – Industrial Manufacturing

      KPMG in India

      How can KPMG in India help

      Smart manufacturing transforms traditional processes with technology

      Read more

      Organizations are moving beyond AI adoption to Trusted AI - embedding transparency, accountability and human oversight to unlock sustainable value
      Read more

      Advanced technologies are shaking up the supply chain world
      Read more
      KPMG Insights Edge

      KPMG Insights Edge

      On the go access to KPMG in India’s insights and publications