6 Fashion Industry Trends That Make Product Data Critical

The biggest risk for fashion brands in 2026 is not tariffs or inflation.
It’s product data chaos.

The State of Fashion 2026 report makes one thing clear: the industry has accepted that permanent volatility is the new normal. Trade tensions, cost pressure, changing consumer behavior, and unstable supply chains are no longer exceptions — they have become the rules of the game.

The problem is that many brands are still trying to respond to this reality using fragmented, inconsistent, and hard-to-update product data.

In such an environment, even the best strategy quickly loses effectiveness. If a company cannot efficiently update prices, descriptions, attributes, or product messaging across channels and markets, it loses its ability to adapt. And without adaptability — in a low-growth environment with tight margins — business risk grows exponentially.

That’s why in 2026 the key question is no longer how to deal with market volatility, but whether the organization has the operational foundations to respond quickly and without chaos. And those foundations start with a well-structured product catalog.

Below, we look at how six key trends highlighted in the McKinsey report translate directly into product data management challenges in the fashion industry.

Trend #1: Constant change instead of a “return to normal”

Business agility starts with product data

Fashion industry leaders are talking less about “uncertainty” and more about a market that is permanently demanding and constantly changing. Companies are no longer waiting for stability — they are learning how to operate in a state of continuous adjustment.

In practice, this means the product is no longer a closed, static entity. Within a single season, it may change in terms of:

• Price (market differences, cost pressure, promotions),
• Descriptions and messaging (adapted to channels or trends),
• Attributes (compliance, sustainability, logistics),
• Sales context (DTC, marketplaces, wholesale).

Change itself is not the problem. The real challenge is the speed and scale at which these changes must be handled.

When every product data update:

• requires manual work across multiple systems,
• involves several teams,
• creates a risk of inconsistencies between channels,

the organization loses its ability to adapt faster than the market can stabilize.

This perspective is also reflected in the WILLOW model proposed by the Samsung Fashion Research Institute. It assumes that demand volatility, short trend cycles, and the lack of a stable baseline are the new norm — not a temporary crisis. Instead of forecasting the future based on historical data, the model focuses on continuously reading market signals and adjusting decisions in real time.

As a result, in 2026 the role of PIM goes far beyond being just a “central data repository.” A well-designed PIM becomes:

• a single source of truth for all channels,
• a data versioning layer for different markets,
• a tool for fast reaction, not manual firefighting.

PIM platform capabilities also become critical. Tools built into Ergonode — such as bulk actions, automations, and AI-powered content enrichment — enable fast creation, editing, and adaptation of product content without spending hundreds of manual hours. This is a practical response to the real needs of the fashion market.

Conclusion:

Fashion brands that want to operate in a world of permanent change must design their product catalog as an adaptive system. Agility doesn’t start in campaigns or sales — it starts with data.

Trend #2: Cost pressure and low growth

Data quality as a margin protection layer

McKinsey leaves little room for doubt: 2026 will not be a year of easy growth. Cost pressure, unstable supply chains, and increasing regulatory requirements keep margins in the fashion industry under constant strain.

In this environment, product data errors stop being a “minor issue.” They quickly turn into real, measurable costs:

• returns and complaints caused by inaccurate product descriptions,
• sales blocks on marketplaces,
• delayed product launches,
• teams spending time fixing data instead of scaling the business.

Individually, each of these problems looks operational. Together, they create a systemic margin leak.

This is why PIM is increasingly taking on the role of a data quality control layer:

• defining rules for data completeness and consistency,
• validating product data before publication,
• eliminating duplicated work across teams.

As a result, organizations move from reacting to errors after the fact to preventing them in the first place.

Conclusion:‍

In a low-growth environment, product data quality is no longer about order and hygiene. It becomes a direct tool for protecting profitability.

Trend #3: Trading down and the rise of the mid-market

Klarowny katalog wygrywa z brandowym szumem

A clear product catalog beats brand noise

More cautious consumers and tighter budgets are making the mid-market the fastest-growing segment. Customers are increasingly less likely to buy into a “brand promise” and more likely to compare products in very concrete terms.

The slowdown in the luxury segment confirms this shift, as highlighted in the Deloitte report: the global fashion and luxury goods market is gradually entering a phase of more balanced growth. In the current climate of economic uncertainty, it is not just growth speed that matters, but resilience and the ability of brands to adapt to new conditions.

In this context, poorly structured product data works against the brand. Inconsistent attributes, different names for the same features, or incomplete descriptions reduce clarity — especially in marketplace environments and comparison engines.

A well-structured product catalog makes it possible to:

• standardize attributes and naming across the entire assortment,
• create products that are easy to compare, both for customers and algorithms,
• communicate product value in a consistent and transparent way.

Instead of relying on “brand noise,” brands start winning through clarity and precision of information.

We’ve explored how to build an effective product catalog in the fashion industry in more detail in our free e-book for fashion brands.

Conclusion:‍

In a world of trading down, the winners are not the brands that speak the loudest, but those that present their products most clearly.

Trend #4: AI as a business necessity

New technologies need well-structured data

The McKinsey report predicts that physical stores will increasingly act as showrooms and advisory touchpoints, while the actual purchase decision happens online.

Why?
Because consumers are more and more likely to ask AI-powered tools direct questions:

• “Find me a lightweight jacket for autumn under €120,”
• “Compare sneakers for city running,”
• “Which shirt should I choose for a business trip?”

In these scenarios, AI-generated answers replace traditional search results. Users no longer browse dozens of pages — assistants like ChatGPT or Gemini do it for them, along with dedicated AI shoppers or custom autonomous agents. The user receives a small set of recommendations, often with a clear explanation of why certain products were selected.

Looking ahead, these tools will not only search the web for products that best match specific criteria, but will also increasingly complete purchases on the user’s behalf.

This shift is also highlighted by research from Samsung experts, who point out that AI is reshaping the shopping journey. As AI-driven recommendation systems become more advanced, the traditional search–select–purchase model is giving way to discovery-led commerce. Consumers encounter products through AI recommendations, social media, and short-form content, and gradually delegate more decision-making to AI systems that can anticipate preferences with increasing accuracy — reducing shopping fatigue in the process. This shift is expected to have a particularly strong impact on fashion, beauty, and lifestyle categories.

The same trend will be clearly visible in the luxury segment, where personalized experiences will become one of the key sales levers.

In this model, the role of AI covers a wide range of tasks. AI:

• compares attributes and parameters, not marketing slogans,
• analyzes relationships between variants, collections, and categories,
• assesses data completeness and clarity,
• selects products that best meet the query criteria — without brand loyalty.

As a result, the advantage goes to companies that provide the clearest and most structured representation of their products.

This is where PIM becomes the foundation of AI-driven commerce. Structured product data makes it possible to:

• “feed” AI models with precise, reliable information,
• increase the chances of products being selected by recommendation algorithms,
• prepare the organization for a future where AI not only advises, but actively makes purchasing decisions.

Conclusion:‍

If AI is meant to see and recommend your product, it needs something solid to understand. And that starts with well-structured data.

Trend #5: Retention and resale

The product doesn’t end with the first sale

The State of Fashion 2026 report clearly shows that customer acquisition is becoming more expensive, while maintaining relationships is becoming increasingly critical. It’s no surprise that retention strategies are among the key topics shaping the industry in 2026.

At the same time, the resale market is growing rapidly. Secondhand is no longer a niche — it is becoming a fully integrated part of the fashion ecosystem, present in both the luxury and mid-market segments.

This changes how brands think about products. More and more often:

• the same product goes through multiple sales cycles,
• it exists in different pricing and communication contexts,
• it is discovered by new customers via secondhand channels before they ever buy firsthand.

In this model, product data must live longer than a single season. Information about materials, composition, sizing, images, and descriptions can no longer be treated as one-off marketing assets — they become long-term brand resources.

Once again, PIM plays a central role. Centralized product data management enables brands to:

• reuse product information in resale and re-commerce channels,
• maintain data consistency between firsthand and secondhand,
• build customer trust through transparent and complete information.

Importantly, resale data does not compete with primary sales. On the contrary, the report shows that consumers often use secondhand channels to discover aspirational brands — which later translates into purchases in the firsthand channel.

Conclusion:‍

In 2026, PIM becomes the product’s memory system — not just an operational tool.

Summary: 2026 is the year data defines competitive advantage

The State of Fashion 2026 report does not point to a single breakthrough. It points to constant shifts. In such a world, the winners are not those who predict the future best, but those who can adapt to it the fastest.

All the trends discussed above share one common denominator: product data as the foundation of business decisions.

Companies that treat their product catalog as a strategic asset will gain a real competitive edge. The rest will keep reacting — always one step behind.