If the AI fits, design it: what ASOS’ move signals about the next era of retail

Fast fashion has always been built on speed. Short trend cycles, frequent drops and production models designed for constant turnaround.

Design teams are no strangers to that pressure. They already work to relentless deadlines and react quickly to emerging trends. But however fast the turnaround, experimentation still depends on human input, from sketching and sampling to refining ideas before anything reaches production.

Now, that process is being sped up too. ASOS recently partnered with AI start-up Ferma to upskill its designers in the use of generative AI, the aim being to accelerate the in-house creative process. Designers can now experiment with colour combinations, product variants and fabric options in seconds rather than hours – reducing design time by as much as 75-80%.

While AI in retail is hardly new, McKinsey reports that 35% of global retailers have already deployed generative AI across parts of their operations. But until now, it has largely been confined to basic automation. Think customer service chatbots, demand forecasting and supply chain optimisation.

ASOS’s move goes beyond this. Positioned at the heart of the workforce, its creative responsibility will rival that of the designers – which raises an important question. What happens to employee talent when technology evolves beyond a productivity driver?

What is automation without architecture?

To date, AI’s USP has been its ability to optimise workflows and reduce reliance on manual oversight. The conversation in the boardroom usually positions it as the ultimate cost-cutting measure. But too often, businesses fail to capture its full potential.

Recent research found that 42.5% of AI programmes never make it out of the pilot stage, with less than one in five scaled to the point where they generate measurable value.

The blame doesn’t lie at the technology’s door. Rather, it’s the tendency to treat AI as a bolt-on solution, layered onto outdated processes and legacy systems that impedes progress. The success (or failure) of AI hinges on the viability of the operating model around it, which helps explain the growing focus on upskilling.

Lloyds Banking Group, for example, is investing millions into its AI Academy, a scheme that will see its 67,000-strong workforce trained in the ethical and effective use of AI and the role it will play in their day-to-day working lives.

Meanwhile, the government forecasts that more than 10 million workers will have enrolled in their free AI training courses by 2030. These initiatives are indicative of a wider trend. Seamless AI adoption doesn’t happen by chance. Businesses need to shift their focus away from “Where can we force AI into the picture” to identifying where human creativity and judgement deliver the most value – and then layering technology in to enhance them, not disrupt them.

Don’t underestimate the human eye for detail

Concerns about AI’s impact on the creative industry are growing. A recent report suggested that 2.4 million creative workers in Britain could be at risk of job changes or losses. High-profile copyright disputes, such as the scrutiny on ByteDance’s Seedance technology in the wake of allegations from Disney, only serve to emphasise the complexity involved in striking the right balance between AI and creative.

So, what does the solution look like in practice? The key is to play to each’s strengths – and acknowledge their weaknesses. For all the hype surrounding it, AI continues to fall short in areas that humans instinctively excel in. One trait that is noticeably absent is empathy, a crucial component of premium customer service.

When a delivery is delayed or an order arrives damaged, consumers aren’t looking for a perfectly worded, computer-generated apology. They want accountability. They want to speak to someone who understands the inconvenience and friction caused. In these moments, it’s not efficiency that counts; it’s emotional intelligence.

The same logic applies behind the scenes. Returning to ASOS, value in fashion isn’t defined by speed alone. Shortening the design-to-sample cycle unlocks economic benefits – but this is only attractive if the business allocates the reclaimed time accordingly.

This is where cultural fluency becomes a differentiator. AI may be effective at identifying viral trends and analysing keyword spikes, but it cannot understand sentiment or nuance in the same way a human team can. Designers and strategists live and breathe these cultural moments, meaning they’re better placed to judge which trends will resonate long-term, which activations will feel genuinely “on brand” and which micro-movements are fleeting engagement traps as opposed to momentum drivers.

That type of discernment is unique to humans, and it’s what protects brand identity. It empowers teams to edit and refine ranges so that every product retains an innate sense of ASOS-ness, something no algorithm can achieve by itself.

The allocation conundrum

The generative AI versus human creativity debate is unlikely to subside anytime soon, particularly with 90% of global retailers expected to increase investment into the technology over the next 12-24 months. ASOS is far from alone in attempting to achieve harmony between meaningful innovation and effective workforce management.

Retailers must acknowledge that senior designers, buyers and merchandisers contribute far more than just greasing the execution wheels. Whether it’s interpreting culture, protecting brand identity or preserving audience relations, relegating them to menial administrative tasks not only risks employee disengagement, but also that of the consumer.

For AI to truly flourish, businesses must go back to the drawing board and design a value-based operating model. Retailers that elevate human decision-making while simultaneously using technology to accelerate experimentation and remove duplicated processes won’t just generate ideas faster – but execute them more efficiently.