AI and Automation in Fashion: 2026 Industry Summary

The Business of Fashion State of Fashion 2026 report outlines a clear direction for the industry. AI and automation are becoming embedded across core functions, shifting from isolated initiatives to part of everyday operations.

The scale of investment reflects this shift. According to the report, 92% of fashion executives plan to increase spending on AI, signaling broad alignment on its importance. At the same time, adoption maturity remains low. Only a small portion of organizations — often cited at around 1% — consider their AI capabilities to be at scale. The gap between intent and execution is still significant.

The report notes that “AI is moving from experimentation to execution across the value chain,” with use cases expanding rapidly across product creation, merchandising, and customer engagement.

Adoption Across Core Functions

AI is being applied across multiple areas of the business.

In product creation, brands are using AI to automate tagging, enrich product data, and support content generation. These applications reduce the time required to prepare products for internal use and external channels.

In merchandising and planning, AI is increasingly used to support demand forecasting, pricing decisions, and assortment planning. Teams have access to more immediate insights, allowing them to adjust strategies with less delay.

Customer-facing functions are also evolving. AI is supporting search, personalization, and service interactions, improving how consumers engage with brands.

Despite this progress, the report emphasizes that most organizations are still early in their journey. Many implementations are limited in scope or confined to specific functions.

Workforce and Talent Shifts

The impact on workforce structure is becoming more visible.

The report highlights that “automation will continue to reshape entry-level roles,” particularly those centered on repetitive tasks such as data entry, reporting, and coordination. These tasks are increasingly handled by automated systems.

At the same time, new roles are emerging. Organizations are hiring for positions that combine business expertise with data interpretation and system oversight. These roles require a broader skill set and a higher level of cross-functional understanding.

Reskilling is a central theme. The report references broader estimates that up to 40% of workers in developed markets may need to transition or significantly update their skills by 2030. For fashion organizations, this translates into a need to train teams to work alongside AI tools and incorporate them into daily workflows.

The shift is not limited to technical capabilities. Teams are expected to make decisions with greater speed and confidence, supported by system-generated insights.

Productivity Gains and Constraints

Early results show measurable productivity improvements.

The report notes that AI can “significantly reduce time spent on manual processes,” particularly in areas such as data preparation, reporting, and content creation. Teams are able to produce more output with the same or fewer resources.

In practice, this means:

• Faster access to product and performance data

• Reduced reliance on manual reporting

• More time allocated to analysis and decision-making

• However, these gains are not consistent across the industry.

Many organizations are still operating with fragmented systems. Product data, imagery, pricing, and inventory information often reside in separate platforms, limiting the effectiveness of AI. The report highlights that data quality and integration remain among the top barriers to scaling AI.

In addition, some companies are running AI alongside existing workflows rather than integrating it fully. This approach introduces redundancy and limits efficiency gains.

Decision-Making and Speed

One of the more immediate impacts of AI adoption is the effect on decision-making.

The report points to a shift toward faster, more data-informed decisions. Teams have access to real-time or near real-time insights, reducing the need for manual consolidation and validation.

This has implications across the organization.

Merchandising teams can respond more quickly to performance trends.

Planning teams can adjust inventory and allocation decisions with greater accuracy.

Product teams can iterate faster based on market feedback.

The report notes that “companies that successfully integrate AI into decision-making processes are better positioned to respond to market volatility.”

This is particularly relevant in the current environment, where demand patterns, supply chain conditions, and cost structures continue to fluctuate.

External Pressures and Business Context

The push toward AI adoption is not happening in isolation.

The report describes the broader operating environment as one of continued uncertainty, where “turbulence is the new normal.” Brands and retailers are navigating margin pressure, cost volatility, and shifting consumer behavior.

In this context, AI is viewed as a lever to improve efficiency and support more responsive operations. It enables organizations to reduce manual workload, improve accuracy, and allocate resources more effectively.

The financial implications are clear. Companies are looking for ways to protect margins while maintaining flexibility. AI provides a pathway to do both, provided it is implemented effectively.

Functional Impact Across Teams

The report highlights how these changes are materializing within key functions.

Merchandising teams are working with more immediate performance data. This allows for quicker adjustments to assortments, pricing, and promotional strategies.

Planning and operations teams are improving visibility into inventory and demand. AI-supported insights help guide allocation decisions and reduce excess or shortage risk.

Product and design teams are accelerating development cycles. Faster access to historical data and automated content workflows support more efficient iteration.

Across all functions, the common outcome is a reduction in manual coordination and an increase in system-supported execution.

Organizational Readiness

A consistent theme throughout the report is the importance of organizational alignment.

Technology adoption alone is not sufficient. The report emphasizes that “the benefits of AI depend on how well it is integrated into workflows and decision-making processes.”

Organizations that are seeing stronger results share several characteristics:

• Aligned data across systems

• Clear ownership of AI-driven processes

• Teams trained to interpret and act on outputs

• Companies that have not addressed these areas are progressing more slowly, even when they have access to similar technologies.

Closing Summary

The State of Fashion 2026 report presents a clear picture of where the industry is heading.

AI and automation are becoming embedded across the value chain. Investment is increasing, and use cases are expanding. At the same time, execution remains uneven, with many organizations still in early stages of adoption.

Workforce structures are evolving. Roles are shifting toward higher-value activities, and reskilling is becoming essential.

Productivity gains are visible, but depend on integration and data quality. Decision-making is becoming faster, with improved access to information and reduced reliance on manual processes.

For fashion leaders, the focus is moving from exploration to execution. The priority is integrating AI into the workflows that drive merchandising, planning, and product development.

The pace of adoption and the effectiveness of implementation will determine how organizations perform in the coming years.