Pakistan Textile Leaders: AI, Omnichannel, Growth

December is when export teams feel the pressure most: tighter shipping windows, end-of-year audits, and buyers who want faster sampling for Spring/Summer. The uncomfortable truth is that many textile and garment businesses are still trying to scale with the same playbook they used five years ago—more follow-ups, more spreadsheets, more meetings.

A recent leadership profile of Yash Dongre (President & COO at House of Anita Dongre) landed on a simple message that applies far beyond fashion retail: digital-first execution is a management discipline, not a “tech project.” He helped a 25-year-old brand move into e-commerce and omnichannel operations, while also staying cautious-but-optimistic about AI and sustainability.

This matters for Pakistan because the same forces are squeezing our mills and garment exporters: shrinking lead times, compliance complexity, volatile demand, and buyers expecting digital visibility. The opportunity is real—but only if leadership treats AI as operational infrastructure.

The real lesson from omnichannel: one view of demand, one view of inventory

Omnichannel isn’t about selling on more platforms; it’s about building one decision-making system. That’s the transferable insight from brands that successfully went digital-first.

For Pakistan’s textile and garment industry, “omnichannel thinking” looks like this:

• One demand signal: combine export order history, sampling requests, buyer forecasts, and inquiry pipelines.
• One inventory truth: greige, dyed, finished goods, trims, and WIP shouldn’t live in separate universes.
• One delivery promise: the date you commit to must be backed by capacity, material availability, and QC readiness.

Where AI fits (and where it doesn’t)

AI is useful when it reduces uncertainty in day-to-day decisions:

• Demand forecasting for styles, colors, fabric bases, and size curves
• Capacity planning (line loading) based on real SMV, learning curves, and absenteeism patterns
• Exception detection: highlight orders likely to miss the critical path before the merchandiser finds out the hard way

AI is not a magic layer you add on top of messy data. If your ERP, spreadsheets, and WhatsApp approvals conflict, AI will simply automate confusion.

Snippet-worthy truth: If you can’t trust your data on a Monday morning, you won’t trust AI on a Friday night.

Leadership stance: “AI is an opportunity” is not enough

Dongre’s view—AI is a huge opportunity, but it needs responsible use and awareness of its footprint—is the right starting point. But in Pakistan’s export environment, leaders must go one step further: translate optimism into governance.

Here’s what that governance looks like in practical terms.

1) Pick 3 AI use-cases tied to hard KPIs

Most companies get this wrong by starting with “we need AI” instead of “we need to fix this.” Pick use-cases that map to export survival metrics:

1. Reduce fabric rejection and claims (computer vision for fabric defect detection and shade sorting)
2. Cut sample cycle time (digital product development: 3D, auto-measure checks, spec validation)
3. Improve OTIF (On Time In Full) using predictive alerts from the production critical path

If you can’t measure the impact monthly, it’s not a priority.

2) Assign an owner who can change processes

AI projects fail when they’re run like IT rollouts. Your owner should be someone who can change how work happens—typically a Head of Operations, Industrial Engineering lead, or a strong Production/Quality leader.

3) Decide your “human-in-the-loop” rules

In garments, fully automated decisions are rare and risky. Define where AI can recommend and where humans must approve:

• Auto-flag defects and risk lots? Yes.
• Auto-release bulk cutting without QC signoff? No.
• Auto-suggest marker efficiency improvements? Yes.
• Auto-change buyer-approved specs? Never.

AI in Pakistan textile & garments: the high-ROI applications in 2026 planning

The best AI implementations in apparel aren’t flashy—they’re boring and profitable. They reduce rework, prevent claims, and compress timelines.

AI for quality control: from “4-point system” to image-based consistency

Fabric and garment defects are expensive because they show up late:

• you cut before shade segregation is done,
• you sew before measurement drift is detected,
• you pack before final inspection catches repeat defects.

Computer vision systems can:

• detect common fabric defects (holes, slubs, contamination)
• classify defect severity consistently
• help with shade banding and roll allocation

That translates into fewer buyer claims and fewer shipment delays.

Action you can take in 30 days: start defect data discipline. Even without AI, standardize defect codes, defect location reporting, and link it to roll IDs and lots. AI needs this foundation.

AI for planning: predicting delays before they become excuses

Production planning in many factories still depends on heroic firefighting. AI-assisted planning changes the rhythm:

• Identify orders at risk based on historical bottlenecks (printing, washing, embroidery, lab dips)
• Recommend line changes with the least disruption
• Forecast overtime needs earlier, when it’s still optional

A useful target: reduce late-stage expediting (air shipments, emergency outsourcing) by 20–30% over two seasons. That’s not “innovation.” That’s margin protection.

AI + 3D for sampling: fewer iterations, faster buyer alignment

When a fashion house goes digital-first, it usually invests in digital content and faster decision loops. For Pakistan’s exporters, this becomes:

• 3D garment prototyping for early approvals
• automated measurement checks against spec
• faster colorway visualization and trim placement

If you’re selling to buyers who increasingly expect digital touchpoints, 3D isn’t a luxury; it’s a speed strategy.

Expansion mindset: what scaling looks like when buyers want speed + proof

Dongre’s role includes driving global expansion and shaping strategy. For Pakistan’s textile and apparel exporters, “expansion” today doesn’t only mean adding lines or looms. It means adding capabilities buyers can verify.

What global buyers increasingly ask for

• Traceability signals: material origin, process transparency, audit readiness
• Consistency: repeatable shade, repeatable fit, repeatable finishing
• Speed: faster development, shorter lead times, reliable replenishment

AI supports each of these, but only when paired with disciplined operations.

The sustainability angle buyers can’t ignore

AI can support sustainability initiatives (energy optimization, chemical dosing, waste reduction), but there’s a catch: AI itself has a carbon footprint through compute and data infrastructure.

A sensible approach for Pakistani manufacturers:

• Start with efficiency-first AI (reduce rework, reduce waste, reduce scrap)
• Prefer edge computing where practical (on-prem vision systems) rather than sending everything to the cloud
• Track savings in kWh, water, and material waste alongside financial ROI

Strong stance: Don’t sell “AI adoption” as a sustainability story unless you can show waste reduction in kilograms and energy reduction in kWh.

A practical 90-day roadmap for textile and garment businesses

If you’re reading this as an owner, GM, or export head, you don’t need a five-year transformation plan to start. You need momentum without chaos.

Days 1–30: fix data and pick the first process

• Choose one value stream: fabric QC, sewing QC, or production planning
• Standardize defect/issue codes and reporting formats
• Define one KPI baseline (e.g., DHU, claim rate, rework %, planner schedule adherence)

Days 31–60: pilot with clear boundaries

• Run a pilot on one line, one product group, or one fabric category
• Build a “human-in-the-loop” checklist
• Train a small team deeply instead of training everyone lightly

Days 61–90: operationalize or kill it

• If the pilot improves KPI by a meaningful amount (even 5–10%), document SOPs
• Integrate into daily meetings (morning huddle, QC review, planning meeting)
• If it doesn’t work, stop and re-scope. Don’t carry a zombie project.

Common questions Pakistani teams ask (and the direct answers)

“Will AI replace merchandisers and planners?”

It won’t replace strong people. It will expose weak systems. The winners are teams that use AI to reduce manual checking and spend more time on buyer communication and risk management.

“Do we need a big ERP upgrade first?”

Not always. You need clean, consistent operational data more than you need a brand-new system. Many companies start by cleaning master data, standardizing codes, and creating a reliable data pipeline.

“What’s the fastest ROI area?”

In most factories: quality and rework reduction. Claims, re-cuts, and shipment delays are where money leaks quietly.

The leadership trait Pakistan’s textile sector needs next

The best part of Dongre’s perspective isn’t the buzzwords—it’s the discipline: build digital channels, run operations like a system, and treat AI with cautious optimism rather than blind hype.

That’s exactly where this series—“پاکستان میں ٹیکسٹائل اور گارمنٹس کی صنعت کو مصنوعی ذہانت کیسے تبدیل کر رہی ہے”—is heading. AI isn’t the headline. Execution is.

If you’re planning 2026 capacity, new buyer development, or a compliance upgrade, now’s the time to choose one AI initiative that reduces waste and improves delivery reliability. Once you get one workflow right, scaling gets easier.

Where do you want AI to help first in your business—quality control, planning, or sampling speed?