Fashion Is Done With Remote Work

KEY INSIGHTS

  • Five days in the office is no longer an outlier, as retailers, publishers and fashion brands tighten attendance requirements and fully remote roles fade.
  • Leaders frame the shift as an operational fix, not an ideological one, pointing to communication breakdowns and slower decision-making on complex work rather than simple productivity losses.
  • Execution will determine whether mandates succeed, with companies that clearly explain the rationale, preserve some flexibility and lead by example more likely to retain talent when the labour market shifts again.
Max Ma, chief executive of 7thonline, says nearly every misstep the retail planning software provider made between 2020 and 2023 can be traced back to a single culprit. “All the mistakes we made during that time – they’re all attributable to one thing. working from home,” he said.
 
Like most companies, the New York-based firm – which counts Patagonia, PVH Corp. and Birkenstock among its clients – closed its office early in the pandemic and allowed employees to continue working from home longafter the lockdowns were lifted. Ma said the setup was functional, but that relying primarily on Slack and Zoom made it easier for key details to slip through the cracks. In 2024, the company returned to a five-day-a-week office schedule.
 
“When you’re in the same room, you can stop and ask, What do you mean by that?” he said. “Sometimes even a facial [expression] tells you certain things.”
 
Two years ago, 7thonline was an outlier. Today, its policy is increasingly common.
 
Over the past year, retailers from Amazon to Gap Inc.have mandated full-time returns to the office. In publishing, Conde Nast and Penske Media have moved to four-day mandates, while Instagram is set to require five days starting in February. Across fashion, three days in-office has become the baseline, with many companies planning to push further this year.
 
Fashion executives point to familiar concerns: culturaldrift, weaker collaboration and uneven productivity when teams aren’t physically together.
 
There are other dynamics at play. In a softer job market, employees have less leverage to resist back-to-the-office mandates. And some struggling fashion companies may be using stricter in-office policies to reduce headcount without formal layoffs – effectively nudging employees to fire themselves.
 
Whatever the rationale, how companies execute that shift matters. Offering employees a clear reason for the move – and baking in some flexibility, even if the goal is five days a week in the office, can help ease the transition.
 
“If you’re having very positive employee morale .. at one to two days a week [then] why shake that?” said Damian Chiam, a partner at executive search firm Buro Talent. “But, if you think moving to four days is going to get you triple, quadruple the growth, then it might make sense.”
 

Determining the ‘Why’

 
The data on remote work’s impact on productivity is mixed. Research from the US Bureau of Labor Statistics shows that in some industries – like publishing or data processing – output can be higher when people work remotely. Other research, including Gallup’s latest analysis, suggests employees in remote roles are spending less time working – roughly an hour less per day than before – even as productivity remains roughly steady.
 
Up for debate is whether that hour might have been spent on informal interactions – spontaneous problem-solving after bumping into a colleague in the break room – or covertly scrolling TikTok.
 
Whatever the data shows, the debate over remote work is largely settled in fashion: beyond a narrow set of roles in areas like IT and engineering, companies overwhelmingly want employees in the office, said Paula Reid, an executive coach and president of the talent search firm Reid & Co.
 
“The swing is towards wanting to have people in the office working as a team, developing deeper relationships, having those spontaneous conversations” Reid said.
 
The question is how much: if three days is the consensus, it’s not such a stretch to ask for four. Requiring five days feels more consequential. Vague justifications – “I paid for this space so show up” – are unlikely to be well received, Chiam said.
 
Ma’s case to 7thonline’s employees was more practical than philosophical. As a software provider, the problems that surface at the margins – the bugs, miscommunications or flawed decisions that might affect a small group of users – can be harder to catch on Zoom or Slack. He believes those edge-case failures largely disappear when teams are working side by side five days a week.
 
“We keep evolving our software so we need our developers to discuss what the customer wants,” said Ma. “They have to analyse what this customer said, compared to other customers.. that’s what needs face to face interactions.” (Ma does allow a couple of hands-off roles to remain fully remote, specifically in the engineering function.)
 
Gen Z workers are sometimes proving unexpected advocates for returning to the office. Pandemic-era stereotypes cast younger employees as champions of “quiet quitting” and “cameras off” culture, but in reality many are eager for more face time with managers and senior leaders.
 
“Funnily enough, this whole being-in-the-office thing has been less of an issue for junior talent, because they feel that’s how their careers will grow and thrive,” Chiam said. “They want the camaraderie and the career development they missed during remote work.”
 
It is often middle and senior managers – juggling children or ageing parents – who are more reluctant. Requiring those leaders to be present, experts say, helps set expectations for the rest of the business.
 

What Not to Do

 
While companies no longer have to yield to employees who insist on working from the beach half the year, offering some flexibility remains critical for morale – and, experts say, for staying competitive if the labour market swings back in workers’ favour.
 
“People want to be treated like responsible, contributing adults who are going to deliver results,” Reid said. “That means that if your child has a winter concert and you want to slip out at four o’clock, all somebody’s going to care about is ‘did you get the work done? Then go.”
 
In practice, that’s why four days in the office has started to land as a workable compromise, Reid said. It was a tough sell earlier in the pandemic, but by 2026 many people see it as a reasonable middle ground, she said.
 
Five days is trickier. With layoffs rising across fashion, companies may feel confident enforcing it in an employer’s market, but that confidence can evaporate quickly when the balance of power shifts, Chiam said.
 
Another common mistake: treating return-to-office mandates as a cure-all. As fashion companies deal with slower growth, some are defaulting to what Reid describes as the “low-hanging fruit” – pulling everyone back in, full time, on the assumption that proximity alone will fix deeper issues.
 
“If done well, it can have .. a ‘we’re all in this together” uniting feeling.” she said. “Done poorly, which is how I think most organisations do it, it can make everybody think the building’s on fire.”
 
What matters most, Chiam said, is whether leadership is willing to model the behaviour they’re asking for. CEOs can’t be phoning it in from a vacation home while telling everyone else to show up – and they need to be realistic about how their own flexibility and job demands differ from those of the people they manage.
 
“It’s ultimately their business, so they’re going to dictate,” Chiam said. “But, we also know that without change and being flexible, that could be your future demise.”
 
Ma says he expects five days in office will continue to yield the best results for his business but he always leaves wiggle room to assess his employees’ needs on a “case by case” basis.
 
“We are very flexible – we don’t tie people to their desks,” he said, adding that some of his employees have been with the company for 20-plus years. “Flexibility means we have to consider [that] everybody has their family life.”
 
To learn more about how an AI-powered Similarity Matrix can scale your intuition, email us at info@7thonline.com or book a demo with the team.

From Retail Intuition to Intelligence: How AI and a Similarity Matrix Are Redefining Retail Planning

2026 will be remembered as the year AI moves beyond the headline. As the market becomes saturated with “AI-powered” promises, businesses are reaching a critical inflection point: the need to replace novelty with a cold, hard requirement for ROI. 

In a rush to stay on top of tech trends, many made the mistake of forcing AI into their existing, often fragmented, workflows—treating AI like a shiny new engine they tried to bolt onto a horse-drawn carriage. The results are predictably underwhelming. True transformation occurs when companies prioritize AI investments that address specific, high-stakes business problems. For the retail industry, fashion in particular, the benefit of modern intelligence largely lies in back-end operations: supply chain planning, demand planning and forecasting, inventory management, etc.  

The Retail Example: Escaping ‘Excel Hell’

An industry still reliant on the manual spreadsheet—the greatest enemy of a modern operating model—retail has been stuck in a recurring nightmare I like to call “Excel Hell“. Between complex supply chains and global operations, Excel builds added complexity and frustration across teams through time-consuming manual entries and inevitable inaccuracies.

But the biggest offense of Excel Hell isn’t just the labor; it’s the creation of data silos. When critical information is trapped in a static cell or outdated sheet, it prevents real-time decision-making—relying on manual processes is no longer just inefficient, it’s unsustainable. Automated, AI-powered end-to-end solutions act as a sophisticated nervous system, detecting trends and empowering retailers to respond with nuance. 

The World Economic Forum predicts that this AI use case can improve on-shelf availability by 15-25% and reduce inventory carrying costs by 10-15%. Brands like H&M, Zara, Burberry and more are amongst the many that have adopted AI in their operations, supply chain and inventory management processes. H&M, in particular, created an entire division devoted to AI; since 2018 this department’s expressed goal is to apply AI in different segments of the company to be as data-driven as possible.

Speaking to Supply Chain Dive, Arti Zeighami, former Global Head of Advanced Analytics and AI at H&M dives into the impact AI and predictive analytics has on demand planning. Zeighami stated it’s about “how you make sure the right product is in the right place at the right time and is transported into the warehouse. Utilizing data analytics allows us to do that. And we’re thinking of ‘how can we do this for our entire production?’… We’re working very specifically on being able to calculate and quantify how many cases you’re going to buy [of any item]”.

 

Scaling Retail’s Sixth Sense for Merchandising

With or without AI, the goal is to precisely align supply and demand. Historically, retailers have relied on their intuition, a “sixth sense” great merchants have to determine which style will pop or which color will trend. But as retail planning becomes more and more complex in a globalized, multi-channel world, the harder it is to scale that instinct—at thousands of SKUs and hundreds of locations, it’s essentially impossible.

To solve this, we developed 7thSense.

Launching at NRF 2026, this technology reshapes how 7thonline is approaching AI. By introducing a multi-dimensional similarity matrix to scale merchant intuition at a granular level, merchants can uncover hidden affinities, repeat successful patterns and make confident merchandising decisions. Behind the scenes, AI is constantly analyzing data and evaluating hundreds of product and location attributes, such as fabric, silhouette and region, to rank new and seasonal products against past winners.

 

Solving the “Cold Start” Problem

One of the most specific business problems in retail is the “New Item” dilemma. How do you plan for a product that has never been sold? Traditionally, this involves guesswork or best-guess proxies. For products without performance history, which seasonal items often lack, AI is able to draw insights from similar products to predict how these products are likely to perform. AI empowers retailers to make confident decisions and repeat winning patterns by predicting trends based on a matrix of attributes, product behaviors, market shifts and customer preferences. 

 

Consider three ways this digital “instinct” redefines the retail operating model:

  • Autonomous Reordering: Instead of a manual scramble to restock, AI monitors real-time sales velocity to project order quantities for new items at various store locations. It recognizes early performance patterns and automatically recommends reorder quantities adjusted for seasonality and lead times. It’s the difference between reacting to a stockout and preventing one.
  • Strategic Promotions: Enhance promotional forecasting with AI that predicts the ideal cadence and sales impact before a promotion goes live. Determined by the historical lift of similar styles, AI is empowering dynamic promotional decisions and maximizing profit impact while protecting margins with clear visibility on the potential sales life of various promotional activities.
  • Hyper-Localization: AI allows brands to finally execute winning assortments down to the style, color and size at the local store level—something that was previously too labor-intensive to be profitable. With detailed insights from previous winners, assortment planning teams can replicate proven strategies for new and seasonal items at individual locations. 

 

The Human-Centric Future

As we prepare to showcase these advancements at NRF Booth #6823, the message is clear: AI is not here to replace, it is here to liberate. The future of business belongs to those who don’t just “use” AI, but those who integrate it into a smarter, more intentional way of working. 

For retail, extinguishing the fires of Excel Hell and handling the granular, repetitive analysis of millions of data points, empowers merchants and planners to return to what they do best—creative strategy, brand storytelling and high-level decision-making. From intuition to intelligence, the “sixth sense” is no longer a mystery—it’s a matrix.

 

To learn more about how an AI-powered Similarity Matrix can scale your intuition, email us at info@7thonline.com or book a demo with the team.

The Disconnect in Retail Tech on the Corner of Fifth StreetTalk Podcast

With insight into the fashion, apparel, beauty and retail markets, Corner of Fifth’s StreetTalk Podcast unpacks recent headlines and trends top-of-mind for retailers. Max Ma joins host Arthur Zaczkiewicz to discuss how fashion brands can escape the “dark cycle” of low margins and leverage technology to navigate today’s increasingly complex retail market. 

Some market background —

This year, Cyber Monday landed at the start of December, closing out November with the largest turnout the five-day Thanksgiving weekend has seen. According to the National Retail Federation, an estimated 202+ million U.S. consumers shopped over the long weekend, setting a new record. Defined as a time period spanning November and December, NRF estimates spending upwards of $1 trillion in 2025. Analysis from Adobe reported that while spending was up, inflation and tariffs pushed the volume of goods purchased down, indicating shoppers are being more frugal.

Against the backdrop of the latest, broader retail environment, Max and Arthur drill into the sobering reality of “Excel Hell” and the consequences of having a large tech stack that doesn’t integrate. Many retailers rely on Excel for their retail planning, leading to a fragmented approach to planning, slower decision-making and an increased risk of costly errors. But rather than managing spreadsheets, the right technology empowers them to run their businesses. 

The stakes are even higher as tariffs cut into margins, technology investment, and store renovations. As early inventory buffers are beginning to run out, and tariff costs are expected to fully materialize in 2026, retailers will need to fundamentally rethink their inventory strategies. Max’s perspective is clear: while tariffs are unavoidable, precision in inventory management is not. By moving to a centralized system to conduct planning, brands can eliminate silos and reduce manual errors associated with it—improving efficiencies and productivity to better understand production, supply chain and trending insights. Without the right technology in place, improving net margins becomes nearly impossible. Instead, it creates a dark cycle of costly decisions, low margins, no tech—Max has seen the retail industry stay in this loop for 30+ years.

As NRF 2026 approaches and excitement builds around the latest retail tech innovations, the real challenge lies beyond the conference floor. It’s critical that attendees not only get inspired, but also carry that momentum back into their organizations, and up to senior leadership. Turning excitement into action—and action into sustainable change—will be what separates retailers who survive from those who truly thrive.

Listen to the full podcast here: https://www.youtube.com/watch?v=VxCr8TKu54o 

For more industry insights, check out our blogs or contact the team at info@7thonline.com

The Secret to Omnichannel Demand Planning on the eCommerce Edge Podcast

Discover the secret to demand planning on this episode of the eCommerce Edge Podcast with Jason Greenwood and Max Ma. From data and the rise of AI to complex omnichannel processes, Max and Jason dive into why retailers need to stop treating demand planning like a monthly checklist, and the need to build real-time feedback loops across channels to grow. In an increasingly complex omnichannel world, the right tech can turn planning challenges into strategic strengths. Here’s a quick preview of the conversation:

“It all starts, typically, with a PLM process as a backbone of the product. Once we want to go to production with it, we then have to predict for that initial order (the sizes, colors and variations), and how much on that variation curve we want to produce. Then we have to track momentum, supplier performance of that product, how much we’re going to reorder and how frequently. It’s a complex and disjointed process in many respects. What point does 7thonline come into the supply chain planning process?”

Actually, 7thonline was designed to assist before product design—to analyze who are you designing for, what was sold, who bought your product at what price point, where, etc. We use the data to guide the product design team and determine the future assortment at a very high level. The system helps retailers understand localized demand and forecasts demand to provide production teams with pointed guidance. 

Essentially, we match consumer segmentation to micro demand signals of the products that brands and retailers offer. We’re in the AI age; we use data and analyze your target audience against product characteristics/attributes to bridge the gap between supply and demand from various channels. A lack of structure in product data is often a major challenge. When attribute commonality—such as color, size, style or hem length—is unclear or inconsistent, effective demand planning becomes extremely difficult. Even the most advanced analytics or AI tools cannot deliver meaningful insights without well-structured data.

Establishing strong disciplines around data integrity, data mapping and data validation is the first step we take with every new client. A trusted, well-governed source of master data is critical to enabling accurate analysis and decision-making.

 

“You see all types of levels of maturity for business, but what size and scale of business typically starts to see benefits from what you do? ”

7thonline is an enterprise software system, designed to support organizations operating at meaningful scale. Most of our customers are larger companies—often with revenues of $100 million or more—because success with a platform like ours requires more than just ambition. It requires the right foundation.

These organizations typically have the infrastructure and data maturity needed to fully leverage enterprise software. Over time, they’ve invested in building reliable systems, establishing governance and, most importantly, treating data as a strategic asset rather than an afterthought. One of the most effective practices we see among these companies is a deliberate focus on master data. Rather than relying on fragile integrations across dozens of disconnected systems, they’ve taken the time to centralize their data. By building and maintaining a unified data warehouse, they create a single source of truth that supports consistency, accuracy and scalability.

This approach doesn’t happen overnight. It’s the result of long-term thinking and disciplined execution. But the payoff is significant: cleaner data, fewer integration challenges and a foundation that allows enterprise platforms like 7thonline to deliver real value. In our experience, companies that prioritize centralized data and strong master data management are far better positioned to turn technology investments into operational and strategic advantages.

 

“What’s on your radar for the next 6, 12, 18 months, is there anything as you look out into the market of demand planning, inventory management, supply chain management software, that you see and recognize as gaps in the industry?”

High on our priority list is the development of expanded AI capabilities that can capture and scale an expert’s thought process. For years, even many of our most experienced retail customers relied heavily on Excel for demand planning. And to be fair, the industry still loves Excel. But reliance on spreadsheets also creates limitations. Knowledge remains siloed, assumptions are hard to track, and insights live in the heads of a few key individuals rather than being shared across the organization. Our goal is to increase visibility across the board and move beyond static tools toward systems that continuously learn and improve.

One example, we are using AI to determine dynamic classification that is currently human defined—it’s subjective. With AI, it becomes systematic. The result is not just better forecasts, but a more transparent, scalable and resilient planning process that evolves alongside the business.

 

Listen to the full podcast here: https://www.youtube.com/watch?v=haJknRGYX0Q&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz-_kc1LahTFMSsgWNYLb16-lcjBCBlMxpG_8LLSJ2Q1snoOfrOcaTJ86kmdcJ-R8ZXZCGKDB 

For more industry insights, check out our blogs or contact the team at info@7thonline.com

Retail101 Online: How AI is Rewriting Retail Planning on the Retail Voices Podcast

Max Ma sat down with Mark Lack on Retail Voices, a podcast where leaders from the world of retail talk about what really works: insight, innovation and the human side of the industry. In the episode, the industry veterans break down the real reason retail planning fails and how AI is transforming processes. From dirty data and omnichannel distortion to guardrails and balancing the art and science of merchandising, Max and Mark’s conversation dives into the heart of what makes modern retail operations succeed. Here are a few key insights:

“If you could print one KPI on every planner’s wall, what would it be and what’s the most overrated or underrated forecast signal you’ve ever seen?”

Print this above your computer screen as a daily reminder: increase net margins by 1% every other year. The margin challenge is a depressing reality for the retail industry—low margins restrict budget for new tools, a lack of tools keep retailers guessing and stuck in the same cycle. A percentage boost may not be realistic year over year but striving for that improvement, even in small increments, is still a positive trajectory toward the bottom line. 

The most overrated signal? Macrotrends. Businesses should look at their own data and microtrends to determine what they can conquer first. Once internal efficiencies and data quality are optimized, then retailers can look outward at other elements to improve planning. An underrated signal from planning efficiency is out-of-stock and availability; retailers tend to overlook the negative impact brought on by out-of-stock numbers: how much out-of-stock has caused in losses and the opportunity cost of it all. Nonavailability has been overlooked as consumers (reluctantly) accept the reality of out-of-stock items in favor of convenience—while purchasing the closest alternative isn’t ideal, they make due. But eventually, without the right products in the right place at the right time, customer loyalty falls and they become frustrated.

 

“Most retailers have really messy data. What do you think should be the first 2-3 steps that any retailer should take before investing in any AI planning tool?”

With retailers implementing many different systems, there are many different versions of data. The first step is to have a master sheet; master data to ensure data integrity and a single version of truth. The second step is to smooth out anomalies—the white noise that shouldn’t be considered in future decisions. Lost sales and out-of-stock needs to be backfilled into your data, and unproductive sales need to be filtered out to set an ideal base for decisions. 

 

“How do you keep store-level inventory availability honest when online and offline are competing for the same stock?”

7thonline is a unified planning solution, we pride ourselves on empowering an integrated approach that not only takes into account brick-and-mortar and ecommerce but also different distribution channels like wholesale and franchisees. Clients are able to look at the demand signals of all channels on a centralized, unified place to forecast demand more precisely. 

Our system is robust enough to account for different data formats. For instance, wholesale channels won’t display sell-through data, only sell-in data. So how do you forecast? For ecommerce, what about the media buying aspect? We have to consider the “4th dimension” to account for the promotional heavy strategy that influences the retail cube: product, time, location. A robust system can process all of these to provide a more accurate and comprehensive view of demand to precisely forecast based on the needs of individual channels. 

 

Listen to the full podcast here: https://www.youtube.com/watch?v=1YWmitUxzSc 

For more industry insights, check out our blogs or contact the team at info@7thonline.com

7thonline Unveils New AI 7thSense “Similarity Matrix” to Improve Retail Accuracy and Performance

Just ahead of NRF 2026, 7thonline announces the launch of 7thSense, a multi-dimensional AI-powered ranking engine enabling brands to gain insight on localized merchandising strategies and confidently execute winning assortments, reordering and promotional decisions—down to style, color, size—at the store level. The new AI advancement debuts by powering three functionalities: Like Style Planning, New Item Reorder and Promotional Forecasting. 

“7thSense reshapes how 7thonline is approaching AI. It’s multi-dimensional and layered—emulating the retailer’s ‘sixth sense’ for merchandising. Most retailers simply don’t have the time or resources to drill down into the level of detail that today’s market demands, and that’s where 7thSense steps in,” said Max Ma, CEO of 7thonline. “7thSense helps brands finally get ahead of chronic inventory challenges by analyzing product performance with granular insights, empowering nuanced, data-backed decisions by SKU by channel.”

Continuously evaluating hundreds of product and location attributes, retailers can rank new and seasonal products against past winners—across silhouette, fabric, color, size, region and more—to repeat successful patterns and make confident decisions with limited sales history. 

7thSense in Action:

Using advanced AI capabilities, 7thSense automatically generates a similarity matrix of various product and location attributes by selecting high-performing items to reveal connections between products and recommend strategies that align planning and execution. This new AI engine enhances assortment planning and in-season open-to-buy reordering and promotional decisions—elevating them from tactical features to strategic decision systems.

LIKE STYLE PLANNING

7thonline’s updated Like Styles feature uses product similarity insights to predict the performance of new and seasonal items at individual locations—down to style, color and size. Rather than selecting Like Styles from a structured hierarchy, 7thSense analyzes all potential styles from different categories. With detailed insights, assortment planning teams can replicate winning strategies, localize their mix effectively and sharply reduce the risk of markdowns or lost sales for products with limited data.

NEW ITEM REORDER 

7thonline’s New Item Reorder uses real-time sales velocity and 7thSense’s similarity index to project order quantities for new items at various store locations. 7thSense’s AI monitors early performance and automatically recommends reorder quantities that are adjusted for seasonality, lead times and individual store demand. The results are smarter reorders, fewer errors and optimized inventory without the manual scramble.

PROMOTIONAL FORECASTING 

7thonline’s latest Promotional Forecasting enhancement determines an ideal cadence for new and seasonal items based on past promotional lifts of similar styles throughout their lifecycle. By analyzing historical lift and current inventory, 7thSense predicts unit movement and sales impact for various promotional activities before a promotion goes live—empowering planners to understand the potential sales life of promotions and make dynamic promotional decisions with confidence, maximizing profit impact and minimizing margin risk.

The new release utilizes predictive analytics, insight generation and pattern recognition to enable new sales trend discoveries and improve efficiency by quickly and accurately analyzing data at the most granular level. 

To learn more about 7thSense, stop by NRF Booth 6823 or book a demo to chat with the team. 

The Best Use Cases for AI in Retail & What to Shop for at NRF 2026

Retailers today are drowning in choice on the tech side. From flashy in-store experiences to cutting-edge AI forecasting, the hype around retail tech has never been louder. But the real question—as we head toward NRF 2026—is this: which AI tools are worth investing in?

Debuting a new AI stage, artificial intelligence will once again be dominating the NRF floor; attendees will be swarmed with talk about how their brands can benefit from AI adoption. The biggest opportunities for retailers don’t just lie in consumer-facing tools, they also lie in invisible “back-of-house” improvements, analytics and hybrid systems that tie everything together.

Where AI Already Delivers

As NRF 2026 approaches, retailers face a sea of AI-focused vendors on the exhibition floor peddling everything from gen AI customer service chatbots to enterprise-scale forecasting platforms. Here’s a rundown of what AI really offers retail (not because of hype, but because it solves real-world problems).

In short: AI isn’t a one-trick pony. Its value spans from supply-chain to marketing to customer experience.

AI Shopping on the NRF 2026 Floor

At NRF, there will be a temptation to go for what looks cool. But the pressure is real: budgets are limited, margins are squeezed, competition is intense. 

So the question remains: which AI tools are worth investing in?

“Retail margins are notoriously thin, and solutions that can address the margin challenge and drive financial impact are the ones that reign supreme,” commented Max Ma for the Retail Technology Review

Brands that win will be the ones who treat AI as serious infrastructure, not a shiny accessory—approaching AI not as a checkbox or a trend, but as part of a broader strategy. Blending front-end and back-end AI tools drives higher margins, reduces waste, improves product availability consistency and fosters customer satisfaction.

Find the 7thonline team at NRF Booth 6823, and let’s discuss how our unified planning platform may be a fit for your 2026 inventory goals. Schedule a time with the team here: https://www.7thonline.com/contact-us/

Why Retail Can’t Afford to Not Adopt Retail-Specific AI

Three years after generative AI first captivated global audiences, nearly 9 out of 10 organizations now say they use AI in some capacity. And yet, according to the latest McKinsey Global Survey on the State of AI, many of them are only in the dabbling, testing and evaluating phase. Only a minority have fully scaled AI across their workflows to drive enterprise-wide impact.

Different Industries, Different AI Needs

In sectors like healthcare, financial services and education, AI is already being deployed to deliver personalized customer experiences. In manufacturing and retail, it’s driving agile production ecosystems and improving predictive maintenance and resource usage. 

According to Workday’s 2025 report, five industries are already feeling tangible impacts of AI integration:

  • Financial Services: Automating risk analysis and compliance
  • Healthcare & Life Sciences: Supporting diagnostics and patient care planning
  • Manufacturing & Retail: Supply chain optimizations, demand forecasting and inventory planning
  • Media & Communications: Revolutionizing content personalization and ad targeting
  • Public Sector: Citizen services and back-office automation

While AI tools may now be commonplace, material results only come when they are deeply embedded in decision-making processes and daily workflows. That’s especially true in retail, where AI is enabling precision at every step of the workflow, reducing costly inefficiencies in demand forecasting and inventory allocation and empowering brands to keep pace with quickly evolving consumer demands. 

Retail’s Critical AI Moment

Retail has always been about one promise: getting the right product to the right place at the right time. With advanced AI-powered solutions tailored for the retail space, businesses can make faster, smarter decisions grounded in real-time data. But what does AI precision actually deliver?

  • Fewer stockouts and overstocks
  • Lower markdowns and reduced waste
  • Happier customers and more loyal shoppers
  • Lean inventory with optimized working capital
  • Improved margins and profitability

In an industry increasingly defined by selective consumers and turbulent spending patterns, forecasting is a strategic pillar. Getting it right means fewer surprises, faster reactions and better bottom-line results.

The Lag Between Hype and Impact

Despite the hype, many organizations have been slow to embrace AI for their everyday workflows. Shifting from legacy systems and traditional decision-making models to AI-driven processes requires significant change management—and securing buy-in is difficult as teams juggle fear of displacement, skepticism about AI’s promise and limited bandwidths for implementation. This transition demands a willingness to rethink workflows and enough confidence in the system to apply insights.

These hurdles are particularly challenging in industries like retail, where success depends on managing complex supply chains, navigating volatile demand and preserving tight margins. Without deep integration, AI can’t fulfill its promise.

What’s clear is this: AI is here, but how it’s applied—where, when and to what extent—still depends heavily on the unique challenges and goals of each industry. Business leaders are in a critical phase of review and education, determining what kinds of AI will actually move the needle for their organizations.

Read the full article here: https://aijourn.com/why-retail-cant-afford-to-wait-for-the-right-ai/

To learn more about why retail-specific AI is important to actualize full returns, talk to the team.

The Death of “One-Size-Fits-All” Retail in a Data Driven Retail Industry

Retail is at an inflection point. The reign of the “one-size-fits-all” retail strategy is almost over. After decades of top-down merchandising and uniform planning cycles, consumer expectations and data capabilities have outgrown many planning, inventory and allocation models. 

For too long, the industry has operated with an approach that relies on generalized data, blanket decisions backed by gut-instinct, cumbersome Excel spreadsheets and blind hope that past mistakes won’t repeat. This model isn’t sustainable in a world of hyper-fragmented consumer choices, volatile demand and expensive misallocation. Retailers are experiencing huge markdowns and wasted inventory, but that’s all changing.

person shopping for clothes

The Broken Centralized Approach

The conventional retail buying cycle is fundamentally broken. Oftentimes, the exact bulky winter coats offered in blizzard-expecting Boston are available in sunny Los Angeles. This is the industry’s most critical blind spot. 

Old school reporting tools can show you big picture trends, but they often miss regional patterns. While algorithms and AI models are setting a new standard for data accuracy, the real success in fashion merchandising comes from combining creativity and industry expertise with nuanced AI insights. When retailers understand local markets and customers, they are able to anticipate trends, adjust and connect with shoppers wherever they are. AI enhances this art by analyzing data at scale and providing performance-based recommendations tailored to each SKU, store and channel.

Shifting Away from a One-Size-Fits All Approach with AI Precision at Scale

Artificial intelligence is reshaping retail by enabling a level of precision and personalization that was previously unattainable with purely manual efforts. By analyzing a wide range of real-time internal and external signals from performance metrics and weather, AI is enabling retailers to operate more like thousands of micro-businesses—scaling precision. This level of insight enables retailers to anticipate shifts in customer behavior at a micro level, empowering planners to fine tune their strategies to reflect the specific preferences, behaviors and seasonal trends of local consumers. 

By leveraging real-time data and predictive analytics, retailers can shift from broad strategies to highly targeted, demand-driven operations that optimize turnover, minimize markdowns and protect profit margins. This tailored strategy ensures that retailers are responding effectively to specific consumer demand and each market’s unique trends and seasonality with dynamic open-to-buy budgets and agile allocation strategies. Shift from static forecasting models to adaptive approaches that ensure relevance—the future of retail inventory management lies in precision mixed with human instinct.

The Art and Science: Turning AI Insight into Localized Assortment

As retailers plan for 2026, the focus isn’t simply on managing inventory through a more targeted and accurate assortment—the technology exists to provide the precision needed to match products to demand at the store level. The impact is measurable. 

Real success isn’t about replacing the human planner’s creative instinct, because it’s that intangible that creates success, creativity and an exciting shopping experience. Better results come from enhancing that expertise with data. AI manages the complexity, giving planners and regional managers prescriptive, actionable insights. The data tells them what they should do next. Merchandisers, the experts who hold the brand’s DNA and deeply understand their community, apply their intuition to fine-tune assortments, ensuring they connect not just with a data profile, but with the specific needs of the local shopper.

Embracing the Data-Focused Future

As 2025 comes to a close, there’s a unique opportunity to move from a generic, centralized strategy to an efficient, demand-driven and data-focused way of working. This new strategy thrives by managing complexity at scale, seamlessly combining algorithmic speed with the nuanced interpretation of human planners.

Retailers need to stop viewing technology as a niche project for the IT team. It is now the essential tool for operational efficiency and survival in a volatile market. The future belongs to those who embrace the market’s complexity and manage their entire enterprise as a finely tuned network of thousands of responsive, demand-centric micro-businesses. It’s time to invest in the next generation of AI planning tools. That’s how we ensure every decision is made intelligently and profitably.

Find the full article in Fashion Mannuscript here: https://issuu.com/mannpublicationsmagazines/docs/fm_november_december_full/77

Talk to the team to find out how you can join the end of “one-size-fits-all” retail at info@7thonline.com or book a demo.

Capture & Predict Sales Trends: AI Modelling for Retail Forecasting and Intuitive Reporting

In today’s fast-paced retail environment, staying ahead of emerging trends isn’t just a competitive advantage—it’s essential. Executives need fast answers. Supply chain teams need deep insights. And IT needs breathing room. You shouldn’t need a data science degree or IT experience to understand what’s happening in your business or extract actionable insights. With 7thonline’s advanced, AI-powered forecasting and reporting capabilities, retail businesses can turn data into decisions faster than ever before, identify trends early and align planning and execution with real-time performance.

This isn’t just about retail forecasting or reporting. It’s about unlocking meaningful insights that help you plan smarter, react faster and stay ahead of shifting consumer behavior.

Act with Confidence: Retail Forecasting with Precision

From better demand forecasting to smarter inventory planning, 7thonline empowers your business to proactively respond to market trends quickly and with accuracy. Shift from reactive to proactive and lead with confidence. Backed by proprietary, vertical-specific models and proven industry best practices, 7thonline’s forecasting system equips you to predict sales trends with precision and anticipate demand shifts across channels.

At the core is our adaptive forecasting engine, powered by cutting-edge machine learning. Whether you’re planning for peak seasons or managing mid-season shifts, the embedded system forecasts allow for seamless, on-screen analysis that helps you optimize every step of the merchandising cycle and improve inventory productivity. 

Improved Forecasting Is the Foundation for Growing Sales

By leveraging AI, retailers can more accurately forecast sales trends and respond to changing consumer behaviors—making it a core part of retail strategy in today’s turbulent market. With brand loyalty challenges and selective shopping behaviors heightened by purchasing power fears, improved forecasting is the key to reversing the trend of declining sales, and here’s why. When a retailer is spot-on with their forecasting, they enjoy:

  • Minimized stockouts, overstock and waste
  • Improved inventory and supply chain management
  • Enhanced customer satisfaction and brand loyalty
  • Reduced operating costs and inventory costs
  • Better operational efficiency and resource allocation
  • A competitive edge

All of these benefits lead to greater revenue and net income growth because they all impact sales and expenses in various ways.

Analytics Without the IT Bottleneck

Gain powerful insights into sales performance by comparing historical data with current in-season trends through flexible reporting capabilities. 7thonline’s built-in reporting engine highlights real-time visibility, right out of the box. Today’s supply chains must be more than efficient—they must be intelligent. 

7thonline’s built-in reporting engine is intuitive, yet robust, automatically generating reports that highlight key operational indicators in real time—from top-line performance metrics to in-depth operational KPIs, you get the data you need.

With 7thonline’s intuitive, yet robust, reporting engine, getting to the heart of your business performance is fast and easy. The platform delivers real-time reports that cover everything from high-level sales metrics to deep-dive operational KPIs—so you always have a clear picture of what’s working, what’s not and where to focus next. Users are able to automatically generate reports that highlight key operational indicators in real-time, or customize reports tailored to business needs. The drag-and-drop Report Builder empowers retailers of all skill levels to customize data visualization in minutes—no technical skills needed—and set permissions for reports.

Reach out to the 7thonline team to learn more about our embedded forecasting and reporting capabilities at info@7thonline.com, or book a demo to see it for yourself.