Integrating AI into textile processing industry

The transformation imperative

The textile processing industry stands at the threshold of profound technological change. The convergence of automation, robotics, digitalization, and artificial intelligence, envisions enhanced productivity, improved efficiencies, greater occupational safety, and more rigorous quality assurance.

Yet these same technologies raise legitimate and pressing questions about their far-reaching impact on jobs, skills, wages, profitability, and production processes. For many entrepreneurs, this transformation creates an ambivalent situation where the concerns of overall uncertainty of introducing these technologies is assessed with the feasibility of potential benefits.

The calculus depends on pragmatic evaluations, deep-dive consultations, and enlightened approvals, including financial outlay and sustainable implementation.

The ecosystem reality

The ecosystem prevalent in textile processing industries, primarily medium enterprises, is usually owner-led direct management, administration, and control, and many owners are hands-on to the point of micro-managing daily operations.

A significant proportion are not tech savvy and remain reluctant to deploy digital tools in the processing of fabrics. They are equally wary of investing in these technologies and are broadly content with maintaining the status quo, and not comfortable in getting their staff and operational team to learn and adopt new platforms. But times are changing daily.

Export-oriented customers are now explicitly demanding technological modernization of the processes and, at the same time, want faster delivery cycles, tighter quality controls, and competitive processing charges. These market dynamics leave little room for complacency. The pressure is no longer optional. Hence, the per force imperatives.

The economics of automation

Technical feasibility alone does not translate automatically into the adoption of automation in the workplace. Financial realities must be factored in alongside technical potential. The initial outlay for hardware and software development, deployment, and integration is substantial, and it must be considered in conjunction with prevailing labour costs. Critics of automation frequently argue that in an environment of high unemployment, a large pool of low-cost workers renders automation financially unjustifiable. That argument has merit in the short run but is increasingly being overtaken by the structural shifts in global supply chains and buyer expectations.

The human challenge

The resentment towards introduction of these technologies also comes from employees who have been on the job for decades and are averse to any such shifts that are alien to them. Although older employees can and must learn new technology, it is prudent to gradually inculcate in them the convincing need to adapt, and ideally this orientation can be taught by the technical team or even in cases by their peers who have already made the transition. However, many older employees, even those with access to the internet and digital devices, struggle with learning or have a cynical mind-set, thus creating a negative force multiplier. They are also impervious to the oft-repeated “lifelong learning” concept despite the fact that although they have years of experience, are skilled in their tasks, and mostly have job security, they will have to reskill or up-skill themselves. Change is anathema to them, but they have to accept the new normal. Technological transformation has consistently restructured the workplace, but the turbo speed with which automation technologies are being integrated, and the magnitude at which they are disrupting industries, are essentially overwhelming and astonishing.

Building a path forward

There is often an unspoken understanding between employers and employees that neither party wishes to disturb the current equilibrium. Breaking out of that inertia will require deliberate leadership. One practical route is the progressive induction of younger talent at both the ownership and workforce level, bringing with it a natural affinity for digital tools and new ways of operating. It is not an overnight transformation, but crucial and hard-core steps have to be taken. The workers must be counselled that AI is being positioned as a tool to augment their technical and skill capacity rather than replace them. Many of the challenges around labour skills are not new, but making them understand and accept these remain problematical to address in practice. Matching employees’ skills with new job qualifications must be inculcated in ways that benefit both employees and employers. The new normal must be recognized and accepted as valuable and useful for the enterprises that need to change. It can be argued that there would be emolument issues or even constraints, apparent skill imbalances, and even migration of employees through resignations or attrition. These could emerge as greater challenges that would, in the short term, disrupt the operation of the enterprise.

Employees must be encouraged, through conversations, motivation, and training, to adopt flexibility instead of outright resistance, and they must be willing to align with the broader growth and sustainable objectives of the enterprise. Change is uncomfortable, but the pace of technological transformation in this industry is no longer something that can be deferred. Automation is being integrated at a speed and scale that is genuinely disrupting industries, and that disruption will not wait for those who hesitate. Artificial intelligence is reshaping workplaces at a faster pace than most employers and employees are able to comprehend, hence amplifying a gap between its adoption and workforce willingness.

AI in the processing unit: real applications

The finishing process in the textile sector is accepted as complex. This is due to the diversity of fabric structures, number of steps and machinery involved, types of materials, and essentially the need for creativity and precision. These different aspects, such as properties of textile materials, chemicals and dyes recipes, functioning of processing machines, organizational performance of company, plus the occupational health and safety of workers are basic requirements. AI applications are already proving viable in processing units of comparable scale and operating conditions.

AI assists in defect detection, quality control, maintenance of machinery and equipment, and process optimization that are imperative for sustainability of the enterprise and management of company resources. The importance of AI in dyeing is highlighted by an example of a dye house that caters to large orders of fabrics from institutions. Usually matching the batches becomes difficult and results in re-dyeing of number of batches. This leads to added cost of dyes and chemicals, production time, water, gas and electricity, and labour, not counting the loss of profits. AI would provide consistent results, reduction in re-dying of batches, increased productivity and efficiency, and restructuring optimal production processes. Another serious issue affecting profits is wastage of inputs, such as dyes and chemicals, auxiliaries, and water.

The honest conversation

An informal interaction with six owners of dyeing and printing mills in SITE revealed a nonchalant attitude towards integrating AI in their workplace. As one well known employer remarked, “AI is not my immediate priority. My priorities are availability of water, backbreaking rates of power and gas, FBR, and my accounts receivables. I survived and am surviving with the present ecosystem in my plant. However, one day, I will go full force in integrating AI into my plant”. Tripti Sharma, who writes on fashion, once said that “the textile industry is undergoing a remarkable transformation. The symphony of interconnected systems guided by AI’s intelligence resonates with harmony, crafting an agile and responsive industry”.

Copyright Business Recorder, 2026