Walk into any product development or merchandising meeting and you will hear a familiar refrain: timelines are slipping and it feels harder to get the right product onto the floor at the right time. What often gets overlooked is where those problems begin.
In apparel, some of the most expensive mistakes happen long before production starts. They emerge in the grey area between design, sourcing and factory partners, where product data is incomplete, inconsistent or scattered across tools. Product development alone can take 10 to 18 months, with production adding another four to six weeks on top of that. When gaps or ambiguities appear at the design or sourcing stage, they don’t just create momentary friction, they compound across an already lengthy cycle, surfacing later as missed deliveries, margin leakage and quality issues when it is too late to course correct.
The industry itself has changed. Fashion once ran on a predictable four-season calendar with long lead times. Today, ultra-fast players like Shein release thousands of new styles at a pace that has reset consumer expectations, and everyone else is under pressure to keep up with more styles and shorter cycles. That increase in velocity strains development teams and exposes weaknesses in the handoffs linking creative intent to actual production.
Where the Real Gap Appears
In an ideal world, design teams would hand over complete specifications and factories would execute them exactly as intended. In reality, the most critical break often happens one step downstream, when sourcing teams hand specifications to factories that may be operating in different languages, time zones and contexts.
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When details are missing or ambiguous, factories make assumptions to keep work moving. Those assumptions can distort costs, materials or construction and often create additional work later in the process. The more styles a brand develops, the more these small gaps compound.
Many product teams rely on PLMs as their system of record, but these tools depend on constant manual updates and rarely keep up with the pace of development. The work gets done first and the system updates follow later, which is when gaps and inconsistencies tend to creep in. In fast-moving environments, that means the data is often incomplete or outdated by the time teams need it.
How AI Helps Close the Pre-Production Data Gap
The most meaningful AI applications in product creation are not about generating sketches or mood boards. They are about resolving the heavy, repetitive workflows that create friction for sourcing and development teams.
AI can interpret unstructured design inputs, translate them into structured specifications and standardize what moves downstream. This gives sourcing teams clearer inputs earlier in the process and reduces the ambiguities that often turn into delays or cost overruns. In a development cycle that is increasingly compressed, these early corrections are one of the highest leverage opportunities for reducing operational friction.
These upstream improvements quickly translate into downstream benefits for retailers, particularly those managing private label programs, and the impact tends to appear in a few key areas.
- Better early data reduces downstream risk. When product information is clearer earlier in the process, retailers see immediate improvements in timing, landed cost accuracy and the reliability of what will show up on the floor. Cleaner inputs reduce the variance that makes planning difficult.
- Standardized handoffs reduce delays and late-stage surprises. Most retailers do not control the full creation process, but they feel the consequences when specifications are incomplete or inconsistent. When brands and factories operate from validated, standardized inputs, retailers face fewer quality issues and more predictable deliveries.
- AI enables leaner workflows and faster decision-making. Some organizations still rely on multiple rounds of physical samples for decisions that could be made digitally. AI can help determine when a physical sample is truly required and when a digital representation or partial sample is enough. This eliminates weeks from the calendar without compromising quality.
- Clear “ready for handoff” standards reduce rework. Sourcing and private label teams often know what must be finalized before a style moves downstream, but deadlines make it difficult to apply those standards consistently. AI helps maintain those guardrails by flagging missing details or ambiguous instructions before they become late-stage problems.
- Less uncertainty upstream means fewer costly surprises downstream. As retailers plan assortments in a volatile environment with shifting input costs, rapid trend cycles and tariff-driven pressure, eliminating ambiguity early in the process has become one of the most effective ways to reduce downstream risk. AI helps remove the inconsistencies that often turn into expensive fixes.
The Bottom Line
The most persistent delays and cost overruns in apparel rarely start on the factory floor. They begin much earlier, when creative ideas, sourcing requirements and technical details move across teams and tools that were never designed to handle today’s pace of development. AI is not a silver bullet, but it is finally giving organizations a way to translate unstructured inputs into the clear, complete specifications that downstream partners need to deliver reliably.
Closing this gap is becoming a competitive advantage, and the retailers that act now will be the ones equipped to navigate whatever comes next.
Kathleen Chan is the Founder and CEO of Calico, a sourcing co-pilot for consumer brands that streamlines the most complex and fragmented parts of production — using AI to help teams generate manufacturable design concepts, compare regional factory pricing, source the right partners, manage costs and move from concept to production faster. A three-time founder with deep experience in fashion and retail, Chan previously built and scaled DTC brands in the jewelry and apparel space, where she oversaw everything from design to sourcing to go-to-market strategy. Her frustration navigating global production across time zones, spreadsheets and unreliable partners became the spark for Calico, which is now used by leading brands to bring speed and resilience to their supply chains. Chan began her career at Microsoft and was named one of Sourcing Journal’s 2025 Female Founders to Watch.
