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    Home » AI & Automation – the Future of Citizen-Centric Manufacturing MSMEs
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    AI & Automation – the Future of Citizen-Centric Manufacturing MSMEs

    EditorBy EditorSeptember 8, 2026No Comments8 Mins Read
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    India’s manufacturing MSMEs are not merely business enterprises; they are engines of employment, innovation, exports and inclusive economic growth. They supply critical components to large industries, support domestic value chains, and provide livelihoods to millions of families across urban and rural India. Their success is therefore inseparable from the nation’s broader development goals.

     

    Today, however, MSMEs operate in an increasingly demanding environment. Rising input and labour costs, evolving customer expectations, global competition, stringent quality standards, and compressed delivery timelines are placing unprecedented pressure on traditional manufacturing models. To remain competitive and resilient, MSMEs must modernise their operations. The question is no longer whether to adopt digital technologies, but how to do so in a manner that is affordable, inclusive and citizen-centric.

     

    Artificial Intelligence (AI) and automation provide a transformative opportunity. When implemented responsibly, they can improve productivity, strengthen competitiveness and create better quality jobs while enhancing the sustainability of enterprises and the welfare of workers.

     

    Moving Beyond Automation for Automation’s Sake

    The national conversation around AI often centres on technological capability. For MSMEs, however, the focus should be on practical outcomes.

     

    Technology must ultimately serve people. A citizen-centric approach to AI adoption prioritises safer workplaces, higher productivity, stable employment, stronger enterprises and enhanced economic opportunities for communities. The objective is not to replace human workers, but to equip them with tools that enable them to work more effectively and create greater value.

     

    AI’s immediate advantage lies in its ability to transform operational data into actionable intelligence. By analysing machine performance, production records and supply-chain information, AI systems can identify patterns, predict disruptions and enable proactive decision-making. Problems that were once detected after causing losses can now be anticipated and prevented.

     

    At the same time, automation technologies such as collaborative robots, automated material-handling systems, robotic welding and intelligent packaging solutions are increasingly taking over repetitive and physically strenuous tasks. This allows workers to focus on quality assurance, process optimisation, maintenance oversight and customer engagement, areas where human judgement remains indispensable.

     

    The Emergence of Agentic AI

    A particularly significant development is the rise of agentic AI systems. Unlike conventional software that merely reports information, these systems can monitor operations, analyse alternatives and execute routine decisions within predefined limits.

     

    For example, a production-planning agent can continuously balance customer orders, machine availability, workforce schedules, inventory levels and delivery commitments. It can identify bottlenecks before they affect production and recommend corrective actions in real time.

     

    Similarly, a procurement agent can monitor inventory, forecast material requirements, compare supplier options and prepare purchase orders for managerial approval. Such capabilities help MSMEs reduce stock-outs, avoid excess inventory and improve working-capital efficiency.

     

    These technologies allow business owners and managers to focus less on routine coordination and more on growth, innovation and customer relationships.

     

    From Productivity Gains to National Competitiveness

    The benefits of AI extend well beyond operational efficiency

     

    Predictive maintenance can reduce costly downtime and improve asset utilisation. Intelligent inspection systems can lower defect rates, reduce waste and enhance product quality. AI-driven inventory management helps optimise working capital, while automation of routine administrative processes enables finance and operations teams to focus on strategic decision-making. Through predictive maintenance, case studies show downtime reductions approaching 30% (source: The Times of India)

     

    Collectively, these improvements strengthen the competitiveness of Indian manufacturing and support the broader objective of building resilient domestic supply chains.

     

    For the nation, stronger MSMEs translate into increased exports, improved industrial productivity, greater job creation and sustained economic growth.

     

    Modernisation Without Massive Capital Outlays: A Policy Imperative

    One of the most enduring misconceptions surrounding Artificial Intelligence and industrial automation is that adoption requires substantial capital investment. Today, however, advances in cloud computing, Software-as-a-Service (SaaS) business models and industrial AI platforms have significantly lowered the entry barrier, making these technologies increasingly accessible to MSMEs.The AI ecosystem has matured significantly, offering solutions designed specifically for industrial applications such as

    • Siemens Industrial Copilot: An AI-powered industrial assistant developed to support engineering, maintenance and manufacturing operations. It helps optimise workflows, accelerates troubleshooting and supports data-driven decision-making across the industrial value chain. 

    • PTC ThingWorx: A leading industrial IoT and digital twin platform that enables real-time asset monitoring, predictive maintenance and operational intelligence, helping manufacturers improve productivity and asset utilisation. 

    • GE Digital Asset Performance Management (APM): A widely adopted industrial platform focused on predictive maintenance, asset reliability and risk management, enabling manufacturers to reduce downtime and extend equipment life.

    • Sight Machine: A manufacturing data platform that transforms shop-floor information into actionable insights, helping businesses improve quality, increase throughput and optimise production performance. 

    • Augury: A machine health and reliability platform that uses AI-driven vibration and operational analysis to detect equipment issues before failures occur, making predictive maintenance accessible to manufacturers of all sizes. 

    • Detect Technologies and Robro Systems: Innovative Indian technology companies providing industrial inspection, safety monitoring and AI-enabled quality control solutions tailored to manufacturing environments.

     

    The focus should therefore shift from questioning affordability to creating an enabling ecosystem that accelerates adoption. Experience suggests that MSMEs derive the greatest value from phased, outcome-driven implementation rather than large-scale transformation programmes undertaken at the outset.

     

    A pragmatic adoption pathway could begin with high-impact use cases such as predictive maintenance of critical equipment, AI-enabled visual inspection for quality assurance, and intelligent production planning systems that optimise resources, inventory and delivery schedules. Once measurable benefits are realised, enterprises can progressively expand adoption across additional processes and facilities.

     

    To support this transition, policymakers may consider a multi-pronged approach:

    • Encouraging subscription-based technology procurement models that reduce upfront capital expenditure.

    • Expanding targeted grant programmes and pilot-support schemes for MSMEs adopting Industry 4.0 technologies or tax free holiday till 2047.

    • Establishing regional AI and manufacturing innovation centres at Universities & Industrial Training Institutes to demonstrate practical use cases and provide technical guidance.

    • Promoting interoperability standards to prevent vendor lock-in and ensure seamless integration with existing systems.

    • Supporting workforce reskilling initiatives to prepare employees for higher-value roles in digitally enabled manufacturing environments.

    • Encouraging youth to build a promising & lucrative career in the Academia

     

    Such measures would not only minimise financial and operational risks for enterprises but also accelerate the diffusion of advanced technologies across India’s manufacturing ecosystem.

     

    Importantly, India’s approach to industrial AI must remain citizen-centric. The objective should not be automation for its own sake, but the creation of more productive enterprises, safer workplaces, higher-quality employment and globally competitive manufacturing capabilities. When supported by appropriate policy frameworks and institutional backing, AI can become a powerful tool for inclusive industrial growth, ensuring that technological progress strengthens both economic competitiveness and social development.

     

    Managing the Human Dimension

    Technology adoption succeeds only when people are part of the transformation journey.

     

    Concerns regarding job displacement must be addressed through transparent communication and sustained investment in workforce development. Experience across industries demonstrates that successful automation initiatives often change the nature of work rather than eliminate it. New roles emerge in machine supervision, quality management, digital operations, customer engagement and data-driven decision-making.

     

    Upskilling and reskilling should therefore be treated as core components of every AI implementation programme. Workers /Employees at all levels must be empowered to participate in the digital economy, not be left behind by it.

     

    This is where a citizen-centric approach becomes essential. Modernisation must create opportunities for individuals while strengthening enterprises. Economic progress and employment generation should advance together.

     

    The Role of Policy Makers & Public Institutions

    Accelerating responsible AI adoption requires collective action. Equally important is the need to strengthening skilling across all levels in the manufacturing workforce so that smaller enterprises can confidently embrace emerging technologies. The policy makers, Central and State Public Sector Enterprises, financial institutions, technology providers and academia must work together to evolve a feedback based enabling ecosystem. Public policy can play a vital role by supporting pilot programmes through CSR interventions, facilitating technology demonstration centres and promoting industry-wide standards that improve interoperability and reduce implementation costs.

     

    A Vision for the Future

    India stands at the threshold of a new industrial era. AI and automation are no longer technologies reserved for large corporations; they are increasingly becoming practical tools that can empower MSMEs to compete globally.

     

    The path forward is not one of disruptive replacement but of responsible transformation based on the grass root feedback from the Industry. By combining technology adoption with workforce development, phased investments and citizen-centric outcomes, MSMEs can build stronger businesses while creating more productive and rewarding employment opportunities.

     

    The future of Indian manufacturing will be shaped not by those who automate the fastest, but by those who modernise with purpose, inclusivity and vision. If we embrace this opportunity wisely, our MSMEs can become more competitive globally, more resilient domestically and more valuable to the millions of citizens whose aspirations depend upon their success.

     

    By Manoj Lal 

    LinkedIn: www.linkedin.com/in/manoj-lal

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