For much of the last decade, fashion companies relied on scale, low sourcing costs and tactical promotions to grow profit. Today, these levers no longer provide the same advantage.
Costs along the value chain are rising, driven by tariffs — most notably US-imposed — together with inflation and supply chain disruptions. It is estimated that tariffs introduced in 2025 will drive price increases of 35 percent for apparel and 37 percent for leather goods in the short term. To add to this, fashion’s reliance on low-cost labour in sourcing countries is under growing scrutiny, raising questions about fair pay and sustainability.
Consumers are more price-sensitive, constraining margins and limiting the ability to pass on costs. Seventy percent of shoppers intend to spend less across apparel categories from Q3 to Q4 2025, and almost 80 percent say that when prices go up, they will not buy at full price but will wait for a sale, buy a cheaper alternative, postpone their purchase or buy secondhand.
Cash is under pressure as faster trend cycles and uneven demand result in overstock and tie up working capital in inventory. The number of days fashion companies held inventory before turning it into sales rose 4 percent from 2023 to 2024, inflating warehousing and logistics expenses.
Executives recognise these pressures and say changing margin, cost and cash strategies will be the second most-significant theme to shape the industry in 2026, second only to tariffs and trade disruptions.
Leading companies are pioneering technology to reshape their economic models
Automation of cost-intensive processes frees up resources
By automating parts of processes such as sourcing, inventory management and physical sampling, companies can save money to dedicate to customer-facing differentiators, such as personalisation, product innovation and enhanced store experiences. Generative and agentic AI are evolving quickly and promise to transform processes like these.
Leading players are overcoming challenges in scaling AI use cases
Research suggests that up to 90 percent of initiatives fail to scale beyond the pilot phase. This is caused by structural barriers (such as weak governance, poor data quality and fragmented tools) and cultural barriers (such as absence of incentives to reward thoughtful risk taking), both of which constrain experimentation at scale.
Digital tools, backed by strong data foundations are enabling leading players to overcome these hurdles. Coach-parent Tapestry has used generative AI design tool Adobe Firefly to scale its use of “digital twins,” which are virtual replicas of real products. Digital twins were first created to aid product development, reducing the need for physical samples, then expanded to applications like content creation for social media campaigns and in-store merchandising.
Digitisation can unlock double-digit reductions to sourcing costs
Rising input costs, geopolitical tensions and shorter trend cycles are making improvements to supply chain agility a top priority for cost savings.
While many fashion brands still rely on legacy systems, top-performing brands are digitising their sourcing practices to improve flexibility in response to changes in demand and supply chain shocks.
Brands are pursuing digitisation in different ways: Many partner with third-party providers such as product lifecycle management platforms, while others build proprietary systems. Shein, for example, has begun offering its digitised supply chain platform as a service to external brands.
Looking ahead, the next wave of innovation will be driven by AI, enabling companies to leapfrog from early foundational sourcing models to advanced, AI-driven models. Using tools such as AI agents, companies can link systems like product lifecycle management and enterprise resource planning to create a unified view of sourcing, enabling real-time analytics and unlocking double-digit cost savings.
AI can offer an edge in a price-sensitive market
The hunt for the lowest prices is intensifying, driven by rising price sensitivity, the surge of dupes and use of both established consumer tools such as Google Shopping and Lyst as well as newer entrants like Phia. This shift means brands must accurately align prices with perceived customer value to remain competitive.
AI is unlocking new levels of precision and agility in this task. Leading companies are leveraging AI to integrate cost, competitor and customer data in real time to optimise pricing, either by building proprietary pricing engines, or leaning on specialist vendors that deliver similar capabilities. In doing so, they may not only optimise prices but also build richer insight to guide future pricing decisions.
Demand-driven strategies are gaining traction as a lever to reduce inventory pressures
Inventory challenges continue to weigh heavily on the fashion industry. The number of days inventory outstanding — meaning the average number of days a company holds stock before converting it into a sale — reached all-time highs in 2024, based on analysis from the McKinsey Global Fashion Index.
Upcoming legislation will intensify the financial burden of inventory
In 2026, legislation such as the Ecodesign for Sustainable Products Regulation in the EU and the Responsible Textile Recovery Act in California will penalise unsold or obsolete stock, impose recycling and take-back obligations, and raise the costs of non-compliance.
Demand-driven inventory optimisation is one way to protect margins
Technologies such as digital textile printing, combined with AI tools that analyse demand and optimise production workflows, are making on-demand solutions more feasible. Initiatives such as Nike’s SNKRS Reserve system, which allows a select group of customers to preorder sneakers before they are made, illustrate how companies are beginning to experiment in this space.
Some brands are even developing their own inventory management platforms that generate insights from data across operational and commercial functions to improve accuracy and speed.
How should executives respond to these shifts?
Prioritise the most impactful areas to scale innovation
Evaluate where margin gains can be unlocked by assessing the full company P&L — for example, improving cash flow through better inventory management or boosting revenue by offering fewer discounts.
Target high-return technologies. Focus on solutions that tackle the biggest inefficiencies first. Use a pilot-test-learn-scale approach to manage risk, measure impact and scale promising solutions across the business.
Support innovation with enablers such as data sharing and change management
Enable data sharing between functions for faster, more-informed decisions. External partners such as AI and cloud providers should also be integrated.
Support employees with adequate training and change management to enable widespread adoption of new processes. Strong governance structures are needed to sustain momentum, with clear ownership to ensure accountability, defined decision rights to enable effective collaboration and aligned incentives across functions to prevent pilots from stalling or remaining siloed.
Encourage experimentation and treat failures as learning opportunities. Leadership should embody the culture they want to build, communicate openly and signal that AI is a priority that offers development opportunities.
Reinvest freed-up resources into customer-facing differentiators
Redirect savings from cost efficiencies into strategic customer-facing investments that will enable the brand to stand out in a highly competitive market. Study customer preferences and benchmark against competitors to prioritise differentiators.
For luxury brands, invest further into clienteling to offer highly personalised services to VICs, while sustainability-focused brands could channel investment into blockchain traceability to reinforce brand values with eco-conscious consumers.
This article first appeared in The State of Fashion 2026, an in-depth report on the global fashion industry, co-published by BoF and McKinsey & Company.
