AI can generate a product image in seconds. The harder question is whether that product belongs in the line, can meet margin and sourcing requirements, and deserves to be sampled.
That distinction is the focus of a new interview with VibeIQ CEO Brian Lindauer, published by The Interline as part of its AI Report 2026. In All About AI: Brian Lindauer of VibeIQ, Brian discusses trust, measurable returns and what it will take to move AI from isolated experiments into the fashion product creation process.
The short answer: AI becomes more useful when it has access to the context behind product decisions and supports teams while those decisions can still be changed.
An AI-native product decision layer is a shared environment where creative, commercial and operational teams can evaluate products before plans are finalised and costs are committed.
Traditional enterprise systems remain important. PLM, ERP, planning and sourcing platforms hold approved product records, bills of materials, costs and purchase orders. But much of the reasoning that shapes a product happens earlier.
The original creative intent may sit in a presentation. Merchant feedback may live in a spreadsheet. A margin concern could surface during a meeting, while regional demand signals arrive through email or chat. When this context is scattered across tools, AI receives an incomplete picture.
A product decision layer captures that work in progress: concepts, assortment goals, costs, feedback, constraints and the reasons behind each decision. This gives AI the grounding it needs to support judgment, rather than producing an answer that merely looks convincing.
Trust in fashion AI should be evaluated task by task.
A generated image is relatively easy for a designer or merchant to review. If it misses the brief or feels off-brand, the problem is usually visible. Technical documentation requires closer checking against construction, measurements, materials, trims and vendor requirements.
Line planning presents a different challenge. A recommendation may reflect margin targets, price architecture, carryovers, channel priorities, regional needs and assortment balance. Teams need to understand how the recommendation was reached, not simply see its final output.
That makes context, human review and an audit trail essential. Teams should be able to see what information informed an AI-assisted recommendation, who reviewed it and what changed before the decision moved forward.
Brian separates the value of AI in fashion product development into three areas: automation, speed and decision quality.
Automation can reduce the manual work involved in preparing, structuring and documenting product information. Brands can measure changes in effort, output or the number of categories and regions a team can support.
Speed should be tied to a product outcome. A company might test whether a new workflow shortens a seasonal milestone, enables earlier review or reduces the time between a concept and a commercial decision.
Decision quality is often the most revealing measure. Useful indicators include:
Sell-through and markdown performance still matter, but many variables affect those results. Upstream measures can show more directly whether AI is helping teams make better choices before development costs accumulate.
Generative AI has made it much faster to explore product concepts, colourways and campaign imagery. Teams can visualise a potential line and gather reactions before investing in physical samples.
A compelling image, however, is not yet a manufacturable product. Moving from visual concept to production requires decisions about materials, construction, measurements, trims, cost, margin, minimum order quantities, vendor feasibility and assortment fit.
The next phase of AI in fashion product creation will connect visual exploration with that product information. Technical documentation is one part of the opportunity. The bigger shift is a workflow in which design, merchandising, planning and sourcing can determine whether a concept should become a real product.
Wider adoption depends on making AI part of the product creation process.
Individual tools can help people generate images, summarise information or draft documents. But if those outputs remain isolated, the wider organisation gains little reusable context. Each team must begin again, and the reasoning behind earlier decisions is easily lost.
Operational AI works differently. It draws on shared product and assortment information, contributes to a decision, and carries the result forward for the next team. The same context becomes more useful as it moves through planning, design, sourcing and regional review.
This is the difference between adding an AI feature to an existing workflow and building a product process that is ready for AI.
Fashion companies do not need ten times as many concepts, samples or SKUs. More output without better filtering can increase complexity, consume development capacity and move risk further downstream.
The more useful goal is higher-confidence decisions made earlier: before the sample, before the buy and before the cost is locked.
Read the full interview with Brian Lindauer in The Interline for his perspective on trust, ROI and the next stage of AI adoption in fashion.
To learn how VibeIQ connects product, assortment and commercial context across the workflow, explore VibeIQ’s merchandising platform or request a demo.