Fashion has always been a guessing game. Designers create collections months before they hit stores, buyers place orders based on instinct and experience, and everyone crosses their fingers that the public will actually want to buy what’s been produced. Some seasons you get it right. Some seasons you’re left with warehouses of unsold inventory.

In 2026, that guessing game is getting a lot more sophisticated. AI-powered trend forecasting is moving from experimental to essential for brands that want to stay competitive, and the results are reshaping how collections are planned from concept to production.

What AI Trend Forecasting Actually Looks Like

Let’s be clear about what we’re talking about. This isn’t some sci-fi scenario where a computer designs an entire collection. It’s more practical than that.

AI trend forecasting tools analyse massive datasets — social media activity, search trends, street style imagery, runway coverage, retail sales data, and even weather patterns — to identify emerging trends before they become obvious to the human eye. The technology looks at what people are wearing, searching for, talking about, and buying, then identifies patterns that would take a human team weeks or months to spot.

Companies like Heuritech are processing millions of social media images daily, tracking specific garment types, colours, fabrics, and silhouettes across geographic regions. When they detect a spike in wide-leg trousers being worn in street style photos in Sydney and Melbourne, that signal reaches brands before traditional trend forecasters have even started compiling their seasonal reports.

How Australian Brands Are Applying This

The adoption curve varies enormously. The big Australian fashion houses — your Zimmermanns and Scanlan Theodores — have the resources to invest in proprietary data analysis. Smaller labels are accessing similar capabilities through third-party platforms.

One area where AI is proving especially useful is colour forecasting. Traditionally, colour palettes for upcoming seasons were determined by industry bodies like Pantone and filtered through trend agencies. That process was slow and often disconnected from what consumers actually wanted to wear. AI tools can now track real-time colour preferences across demographics and regions, giving brands data-driven guidance on which hues to invest in for upcoming ranges.

I spoke with a Melbourne-based designer recently who told me their label reduced unsold inventory by 23% after incorporating AI-generated trend data into their planning process. They didn’t replace their design team’s creative instincts — they supplemented them with data that helped them make better decisions about which directions to pursue.

The Role of AI Consultancies

What’s interesting is how this technology reaches fashion brands. Most labels don’t have in-house AI teams. They’re not hiring machine learning engineers. Instead, they’re working with external partners who specialise in applying AI to business problems.

Business AI solutions providers are increasingly working with fashion and retail clients to build custom forecasting models. These aren’t off-the-shelf products — they’re tailored systems that connect a brand’s own sales data with external trend signals. The result is something that understands both what’s happening in the broader market and what’s happening within that specific brand’s customer base.

This consultancy model makes sense for fashion. Most brands need AI capability but don’t need a permanent team of data scientists. Working with a specialist firm means you get the expertise without the overhead.

Beyond Trend Prediction: Production Planning

The most immediate financial impact isn’t in predicting what will be trendy — it’s in predicting how much to produce. Overproduction is one of the fashion industry’s biggest problems, both financially and environmentally. The Ellen MacArthur Foundation estimates that the equivalent of one garbage truck of textiles is wasted every second.

AI demand forecasting is helping brands produce closer to actual demand. By analysing pre-order data, social media sentiment, historical sales patterns, and even economic indicators, these systems can recommend production quantities with a precision that human planners struggle to match.

For Australian brands selling into both domestic and international markets, this is particularly valuable. The seasonality differences between hemispheres, combined with varying consumer preferences across markets, create a complex forecasting challenge. AI handles that complexity better than spreadsheets.

What’s Working and What’s Not

Not everything about AI in fashion forecasting is rosy. There are genuine limitations.

What’s working: Colour trend identification, demand forecasting, identifying emerging silhouettes from street style data, optimising production quantities, and predicting which items in a collection will be the strongest sellers.

What’s not working yet: Predicting truly novel trends. AI is excellent at identifying patterns in existing data, but fashion’s most exciting moments come from genuine creative leaps that have no precedent in the data. No algorithm would have predicted the Birkenstock resurgence or the sudden mainstream adoption of gorpcore. Those movements were driven by cultural shifts that aren’t captured in purchasing data until after they’ve happened.

The best brands understand this distinction. They use AI for the quantitative decisions — how much, what colours, which price points — and preserve human creativity for the qualitative decisions that make fashion exciting.

The Privacy Question

There’s also an ethics conversation happening that the industry hasn’t fully resolved. Much of this trend data comes from social media images of real people. When a platform scrapes millions of Instagram photos to analyse what people are wearing, there are legitimate questions about consent and data use.

Australian privacy legislation is evolving, but it hasn’t kept pace with how this data is being used commercially. Brands and technology providers need to be proactive about this, because consumer backlash against surveillance-driven marketing is real and growing.

Where This Goes Next

The next frontier is personalisation at scale. Instead of creating one collection and hoping it resonates broadly, AI could enable brands to produce micro-collections tailored to specific customer segments — different colourways for different markets, slightly adjusted silhouettes based on regional preferences, and personalised styling recommendations at the point of sale.

We’re not there yet, but we’re closer than most people in the industry realise. The brands that invest in this capability now won’t just design better collections. They’ll waste less, sell more at full price, and build stronger connections with their customers.

Fashion will always need creative vision. But in 2026, the smartest designers are pairing that vision with data. And the results are hard to argue with.