Indian Textile Industry Embraces AI But Struggles With Digital Integration: CITI-NITRA Study

Artificial intelligence may be entering India’s textile factories, but the industry is still far from becoming truly digital.

A new study by the Confederation of Indian Textile Industry (CITI), supported by the Northern India Textile Research Association (NITRA), reveals a striking technology gap across India’s textile and apparel sector. While companies are increasingly experimenting with AI and automating repetitive factory-floor operations, digital systems remain poorly integrated, investment costs are high and skilled manpower is in short supply.

The study, titled AI, Automation & Digitalisation Readiness in the Indian Textile & Apparel Industry, assesses technology adoption across the textile value chain and examines investment intentions, workforce implications, skills requirements and the support industry expects from the government.

Its findings suggest that India’s textile industry has entered the technology race — but is still some distance away from the smart-factory model being seen in leading manufacturing economies.

AI adoption has started, but remains early
About 43% of the participating textile and apparel companies are either already using AI or testing it through pilot projects, while another group is still planning adoption. However, 35% have not started using AI at all, highlighting the uneven nature of the transition.

Interestingly, AI adoption is not confined to traditional technology-heavy functions.

Production and quality are currently the leading areas, with 43% adoption each. Sales, finance, HR and design follow at around 41% each.

Companies are exploring AI for production planning, machine optimisation, quality analysis, predictive maintenance, demand forecasting, inventory optimisation, customer analytics, design support and sustainability reporting.

The implication is significant: AI is beginning to move from being an experimental technology to a tool that can influence decisions across both the factory and the boardroom.

But the study makes clear that adoption remains fragmented. The next challenge is not simply getting more textile companies to run AI pilots, but connecting those applications with the data and systems that already run their businesses.

Automation is strongest where machines can take over repetitive work
The industry’s automation journey is further ahead than its AI journey.

Machine monitoring has emerged as the most automated production activity, with 62% adoption, followed by machine setting at 54% and material handling at 51%.

In quality functions, laboratory testing leads with 60% automation, followed by defect detection at 54% and fabric or yarn inspection at 51%. Shade matching, which requires greater human judgement, has a lower automation level of 46%.

The pattern shows where Indian textile manufacturers are most comfortable deploying technology: areas where the task is repetitive, measurable and relatively easy to standardise.

Production scheduling, however, is automated at only 35%, suggesting that important planning decisions still depend heavily on human intervention.

The same trend is visible beyond manufacturing. Preventive maintenance has reached 49%, while predictive maintenance and spare-parts management are at 46% each.

In warehouses, barcode and RFID systems have the highest adoption at 54%, while inventory handling stands at 51% and warehouse management systems at 49%. Automated storage systems, by contrast, remain at just 30%.

In logistics, shipment planning leads at 57%, followed by vehicle tracking at 51%.

The study therefore points to a clear transition: Indian textile companies are adopting information and monitoring technologies faster than expensive physical automation.

The bigger problem may be the lack of connected systems

This is perhaps the most revealing finding of the study.

ERP is the most widely used digital system, with more than half of the companies surveyed using it. CRM, barcode systems and digital dashboards are also gaining ground.

Yet 38% of companies reported having no digital system at all.

Even among companies that have adopted digital tools, integration remains weak. Only around 14% of respondents said their systems were fully integrated. Most have systems that are either partially connected or are still awaiting integration.

That creates a fundamental problem for AI.

A company can install an AI-based quality inspection system, for example, but its value remains limited if production, inventory, ERP, sales and supply-chain information continue to sit in separate systems.

The report points out that operational data is still frequently maintained through Excel sheets and local servers. Each accounts for roughly a quarter of responses. Hybrid systems account for about one-fifth, while true cloud platforms account for only 13.5%. Another 16.2% of companies continue to rely on paper records.

In other words, the industry’s AI ambitions are running ahead of its data infrastructure.

Cost is the biggest roadblock

The study identifies high investment cost as the biggest barrier to technology adoption.

It is followed by lack of skilled manpower, difficulty integrating new technology with existing machines, uncertainty over return on investment and cybersecurity concerns.

Other barriers include lack of suitable technology, poor connectivity, management resistance and worker acceptance.

For India’s large number of textile MSMEs, the problem becomes even more significant.

The industry is being asked to invest in automation, AI, digital platforms and modern machinery at a time when many smaller units already face constraints in accessing affordable institutional credit.

The CITI-NITRA study says the financing challenge is linked to the sector’s historical volatility, cyclical demand and past stress periods, which can lead banks to adopt conservative lending practices.

MSMEs also face higher borrowing costs, collateral requirements and credit assessment norms that may not adequately reflect the textile industry’s working-capital intensity, seasonality, longer receivable cycles and cluster-based operations.

This could become a major determinant of how quickly India’s textile industry can modernise.

Will AI replace textile workers?
The study does not paint a simple picture of mass job losses, but it does indicate that the workforce is already being affected.

Approximately one-third of the enterprises assessed reported a reduction in employment, while a considerably smaller proportion reported employment increases. A significant number of enterprises, however, have not yet experienced a clear employment impact from automation and digitalisation.

This suggests that the bigger workforce story may be about changing jobs rather than simply eliminating them.

As machines take over repetitive monitoring, inspection, testing and handling activities, workers will increasingly need to operate, maintain and interpret technology.

The study therefore calls for practical, role-based training in data literacy, automation, AI applications, digital manufacturing, systems integration and technology-enabled decision-making.

But there is another gap here: nearly half of the enterprises surveyed rely primarily on internal training, while relatively few use online learning, equipment-supplier programmes or external training institutions.

The industry’s technology transition could therefore create a new divide between companies that can develop digital skills internally and those that cannot.

China remains the benchmark
When it comes to international competition, India still has ground to cover.

The study identifies China as the advanced benchmark, particularly in smart factories, industrial internet, AI-based inspection, intelligent equipment and digital twins.

Türkiye and Vietnam are positioned at emerging-to-intermediate levels, while India is described as progressing but constrained by gaps in advanced technology adoption, MSME readiness and implementation capacity.

That comparison matters because India is already a significant player in global textiles.

The study estimates India’s textile and apparel exports at US$ 36.95 billion in 2025, making India the world’s sixth-largest exporter and accounting for about 4% of global textile and apparel exports.

Yet the report argues that India’s position remains below its potential, given the country’s raw-material base, manufacturing capabilities and depth of the textile value chain.

The technology gap could therefore become an increasingly important competitive issue.

From AI pilots to connected factories
The study’s message for Indian textile companies is straightforward: the next phase cannot be about isolated technology projects.

Instead, companies need to connect ERP, production, quality, inventory and supply-chain data and move towards integrated operations.

The report identifies several immediate opportunities including computer-vision inspection of yarn, fabric and garments, predictive maintenance in spinning, weaving and processing, AI-based demand forecasting, inventory optimisation, digital traceability and digital product passports.

For factories, real-time machine monitoring and production dashboards could provide greater operational visibility, while automated warehouse systems and compliance documentation could reduce manual intervention.

The study also sees potential in AI-based body measurement and fit analysis, which could improve apparel manufacturing as well as the customer experience.

The larger objective is a shift from isolated automation to smart manufacturing, where machines, production systems, inventory and supply chains communicate with one another.

Industry wants government to share the risk
The report’s recommendations are particularly relevant for policymakers.

The strongest demand from industry is for demonstration factories and common digital platforms.

Around three-fourths of the enterprises surveyed identify demonstration factories as important, while about seven in 10 see common digital platforms as a priority.

The logic is simple. Textile companies, particularly MSMEs, are often reluctant to commit substantial capital to technologies without knowing whether those technologies will deliver measurable returns.

Demonstration facilities would allow manufacturers to test AI, automation and digital systems in real textile production environments before making major investments.

Around two-thirds of respondents also identify industry-specific AI tools as an important area of support. AI Centres of Excellence, structured training programmes and testbeds or pilot projects each receive support from around three-fifths of enterprises surveyed.

The report also calls for standards covering areas such as digital traceability, data exchange, cybersecurity, ESG reporting and digital product passports.

India’s textile advantage needs a digital layer
India already possesses many of the ingredients needed to become a major global textile hub.

The country has a diversified raw-material base, an end-to-end textile value chain, a large domestic market and a combination of modern manufacturing and traditional craftsmanship.

The domestic textile and apparel market is estimated at around US$ 180 billion, with roughly 75% coming from the domestic market. The industry currently provides about 45 million direct and 55 million indirect jobs, according to the study.

The report notes that India is targeting a textile and apparel market of US$ 350 billion by 2030, including US$100 billion in exports.

But achieving that ambition may require more than additional factories and production capacity.

It will require factories that can produce with greater speed, consistency, traceability and resource efficiency and companies capable of responding to increasingly sophisticated global buyers.

That is where AI, automation and digitalisation could become less of a technology choice and more of a competitiveness imperative.

The CITI-NITRA study ultimately makes a clear case that India’s textile industry is ready to adopt technology, but not yet ready to fully integrate it.

The first wave has been about automating individual tasks monitoring machines, detecting defects, testing materials and tracking inventory.

The next wave will have to connect those individual pieces.

For India’s textile industry, the race may therefore no longer be simply about buying smarter machines. It is about building the digital infrastructure, skills, financing and ecosystem needed to make those machines work as part of one intelligent manufacturing network.

The companies that make that transition successfully could gain a significant advantage in the global textile market. For the rest, the cost of remaining digitally fragmented may become increasingly difficult to ignore.