Fashion companies have invested heavily in digital transformation over the past decade, yet many merchandising, planning, and inventory allocation decisions are still managed through spreadsheets and manual processes.

In this interview, Digital Fashion Academy speaks with Pini Usha, CEO and Founder of Buffers.ai, about how artificial intelligence is helping retailers and brands improve forecasting accuracy, optimize inventory, and empower planning teams to make better decisions.

Q. Pini, let’s start with the problem. What are the biggest challenges retailers and brands face today when it comes to inventory planning?

A. The biggest challenge is forecasting demand accurately.

Whether you’re a retailer or a manufacturer, you need to predict future sales and make purchasing decisions accordingly. If the forecast is too high, you end up with excess inventory. If it’s too low, you face stockouts and miss sales opportunities.

Many companies still compare planned sales versus actual sales and discover significant gaps. Some products sell out much faster than expected, while others remain unsold in warehouses. This creates both financial and operational challenges.

Fashion companies know these problems very well. Excess inventory often leads to markdowns, while stockouts mean lost revenue and disappointed customers.

How does Buffers.ai help solve this challenge?

Our platform helps companies make better forecasting decisions using a combination of historical sales data, business events, and human expertise.

We have two main modules:

• Purchasing Planning, which helps companies decide what quantities to buy or manufacture.
• Replenishment Planning, which helps distribute inventory efficiently from warehouses to stores.

These are connected processes. First, you determine how much inventory you need. Then you determine where that inventory should go.

Q. Let’s talk about forecasting. How does the system generate predictions?

A. We use three main sources of information.

The first is historical sales data. We analyze previous consumption patterns to identify trends and seasonality.

The second is business events. The system automatically identifies recurring events such as holidays and understands their impact on sales. For example, if sales increase every year during a specific holiday period, the system learns that pattern and incorporates it into future forecasts.

The third source is human input. Sales teams and planners often have information that doesn’t exist in historical data. They may know about a large upcoming project, a major customer order, or a planned promotion.

Our platform combines these inputs to create a final forecast.

Q. I found the event management functionality particularly interesting. Fashion companies frequently plan around Black Friday, seasonal promotions, marketing campaigns, or store openings. Can these events be included in the forecast?

A. Absolutely.

Users can create future events and specify their expected impact. For example, if a retailer plans a stronger Black Friday promotion than last year, they can adjust the expected uplift accordingly.

Similarly, companies can identify unusual events from the past.

Imagine sales dropped significantly because of a temporary store closure or a geopolitical event. If the system doesn’t understand the reason behind that drop, it may incorrectly assume the same pattern will repeat next year.

By adding context, planners help the AI make more accurate decisions.

Q. One thing that stood out during the demonstration was that the platform combines multiple forecasting models rather than relying on a single algorithm.

A. That’s correct. Different products behave differently.

For one SKU, a forecasting model developed by Google may provide the most accurate prediction. For another product, a model from Meta or Amazon may perform better.

We continuously test multiple forecasting models using back-testing techniques. The system evaluates how accurately each model would have predicted actual sales and automatically selects the best-performing approach.

This allows us to optimize forecasting accuracy product by product.

Q. Forecasting is only one part of the process. Once inventory has been purchased or manufactured, retailers still need to allocate products across stores.

Why is replenishment such a complex challenge?

A. Because it happens every day.

Forecasting may be performed once per season or once per month, but replenishment decisions are continuous.

Imagine a retailer with 150 stores and thousands of SKUs.

Every day, planners need to decide how much inventory should be sent to each location. Doing this manually is extremely time-consuming and often impossible to optimize using spreadsheets.

Our replenishment module automatically calculates inventory distribution at the store level, ensuring products are available where demand exists while minimizing excess stock.

Q. What about new products that have no sales history?

This is a common challenge in fashion, where collections change every season.

A. We call this the “first allocation” challenge.

For new products, we offer several approaches.

Some retailers prefer rule-based allocation, where percentages are assigned to stores based on category performance.

Others use fixed allocation ranges.

Our most advanced approach uses similarity analysis. The system identifies comparable products from the past and learns how they performed in different stores. It then uses those insights to recommend initial inventory allocation for the new product.

Q. Many people assume AI is primarily about reducing headcount. What are you seeing among your clients?

A. Most clients are not buying our solution to eliminate jobs.

They’re buying it because they want better decisions.

Today, many companies have teams of planners spending countless hours collecting data, updating spreadsheets, and trying to create forecasts manually.

AI automates much of this work, but people are still essential.

Instead of spending time manipulating data, planners can focus on higher-value activities such as strategy, assortment planning, and business analysis.

Q. That’s a perspective I strongly agree with. The goal isn’t necessarily fewer people. The goal is allowing talented professionals to focus on more valuable work.

Who are typically involved in the decision to adopt a solution like Buffers.ai?

A. The decision-makers are usually executives such as Chief Operating Officers, Chief Technology Officers, Chief Supply Chain Officers, or CEOs.

However, the people who use the system every day are usually merchandisers, planners, and supply chain teams.

It’s interesting because executives are often excited about innovation and AI, while operational teams want practical solutions that are easy to understand and trust.

Successful adoption requires addressing both perspectives.

Q. Finally, how do companies measure success after implementing a solution like Buffers.ai?

A. For forecasting, the primary KPI is prediction accuracy.

We compare forecasts against actual results and measure forecasting error over time.

For replenishment, companies typically focus on three key metrics:

• Stockout reduction
• Excess inventory reduction
• User productivity and decision-making efficiency

When these metrics improve simultaneously, the financial impact can be substantial.

The Future of Fashion Planning

As fashion supply chains become more complex and consumer demand becomes increasingly unpredictable, the ability to combine AI-driven forecasting with human expertise is emerging as a critical competitive advantage.

Rather than replacing planners and merchandisers, platforms like Buffers.ai are helping them make faster, smarter, and more accurate decisions, reducing inventory risk while improving product availability.

For fashion retailers, brands, and manufacturers, the future of planning may not be about choosing between human intuition and artificial intelligence, but learning how to combine both effectively.

Enrico Roselli – Co-founder Digital Fashion Academy. Former CEO of La Martina

Gianluigi Zarantonello – Former Innovation Manager at Valentino

Enrico Fantaguzzi – Co-founder Digital Fashion Academy. Former Manager at Gucci, Tod’s, Yoox

How is AI changing fashion retail?

To create a seamless shopping experience, AI must facilitate a frictionless journey where the customer can move between e-commerce, physical stores, and mobile platforms without being conscious of the channel they are using.

The goal is to make the technology “invisible,” operating in the background to enable users to do whatever comes to mind without technical barriers,.

How can AI help to create a seamless customer experience in Retail

Unified Customer Identity and Recognition

A major hurdle to seamless shopping is the disconnection between online and offline Customer Identities. A customer might be recognised in their local shop but treated as a complete stranger online, or vice versa. AI can bridge this gap by analysing cross-channel data. For instance, AI can leverage a customer’s online browsing history, where they may spend time without buying, to enhance their experience and opportunities when they eventually enter a physical retail store,. This requires building technical capabilities to recognise the customer at various touchpoints, creating a holistic view of their journey.

Hyper-Personalisation via Generative AI

AI is moving personalisation beyond traditional customer segmentation. Previously, retailers might identify a segment but still show the same content to everyone within it due to production limitations. Through generative AI, retailers can now unlock data to produce unique content tailored to specific individuals based on their interactions and intent. This allows for a highly personalised experience that still adheres to the brand’s overall creative vision,.

Frictionless Payments and Loyalty

AI enhances the final stage of shopping, the transaction, by integrating advanced payment systems. Machine learning algorithms contribute to a seamless experience by:

• Scoring transaction reliability to instantly approve valid purchases, thereby improving e-commerce conversion rates.
• Utilising tokenisation and blockchain to create safe connections that integrate directly with loyalty programmes, managing client information securely to further customise the experience.

Agentic AI for Natural Interaction

The emergence of “Agentic AI” changes how users interact with complex software. Rather than forcing customers or employees to navigate rigid interfaces and rules, Agentic AI allows them to use natural language to ask for exactly what they need,. This shifts the paradigm from the user adapting to the software to the software adapting to the user, making the interaction intuitive and removing the complexity of the underlying technology,.

Empowering Physical Retail

Since a significant majority of transactions (around 80%) still occur in brick-and-mortar stores, AI is crucial for enhancing the physical environment. This includes using data to improve store efficiency and potentially integrating robotics for deeper interactions, ensuring the physical space is as responsive and data-informed as the digital one.

How can generative AI produce unique content without losing brand vision?

Generative AI allows companies to produce unique content at scale by unlocking data and production capabilities that were previously limited. Historically, even when retailers identified specific customer segments or “personas,” they were forced to show the same content to everyone within that group due to the difficulty of producing numerous variations. Generative AI solves this by using customer data to create individualised content dynamically.

To ensure this unique content does not compromise the brand vision, the process relies on the following principles:

• Adherence to Core Creativity: The brand vision remains firmly in the hands of the creative director and the company. The content produced by AI serves as variants of this central vision rather than independent creations, ensuring that the foundational aesthetic and message provided to the customer remain consistent.
• Strict Guidelines: Businesses can “teach” the AI to provide outputs that adhere strictly to the exact guidelines of the brand, ensuring that automated content stays within the established identity.
• Human-Centric Finalisation: While AI is excellent for brainstorming and providing “divergent,” out-of-the-box ideas to spark inspiration, human creatives are still considered “much better than AI” at creating the final, polished output. The AI acts as a tool to expand possibilities, not a replacement for the creative mind that defines the brand.

This approach allows brands to move beyond generic segmentation to true personalisation without the fear that the technology will distort the brand image.

How can AI identify customers across online and physical stores?

AI identifies customers across online and physical stores by integrating specific “technicalities” into the customer journey that link data points, rather than relying on “magic” or passive observation.

According to the sources, AI achieves this cross-channel identification through the following methods:

Payment and Loyalty Integration
One of the most effective ways to identify customers is through advanced payment solutions. By applying AI to payments, retailers can use tokenisation and blockchain to create safe, secure connections that link transactions directly to loyalty programmes. This allows the system to instantly recognise the client via their payment method, unlocking their history and preferences to customise the experience without requiring complex manual logins.

Leveraging Digital Intent in Physical Spaces
AI bridges the gap between the two worlds by analysing a customer’s online browsing history to enhance their in-store experience. For example, in the luxury sector, e-commerce conversion rates can be as low as 1%, meaning 99% of visitors are merely browsing. AI can capture this “intent to buy” or interest shown online and make it accessible to store staff when the customer eventually visits a physical location, transforming a “stranger” into a known prospect with specific preferences.

Designing Identification Touchpoints
The sources emphasise that technology alone cannot identify people without a strategy. Retailers must engineer specific “moments in the journey” where the customer is prompted to reveal their identity. Whether through a mobile app interaction, a loyalty scan, or a tokenised payment, these intentional touchpoints provide the data AI needs to connect the online user with the physical shopper.

What are the hidden costs of implementing AI in fashion?

While the surface narrative of AI often suggests a “plug and play” solution, there are several hidden costs and challenges associated with its implementation in the fashion industry.

Unpredictable Financial Costs A significant hidden cost lies in the pricing models of corporate AI tools. While individuals may be accustomed to free versions of tools like ChatGPT, corporate environments face costs based on consumption. This variable pricing makes financial planning highly volatile; while CFOs and boards may request three-year forecasts, it can be difficult for technical leaders to predict consumption costs even one week ahead. Without proper knowledge and management, companies risk wasting money rather than investing it effectively.

Organizational and Cultural Reinvention Implementing AI is not merely a technical update but requires a complete “reinvention of the organization”. The costs involve:

• Cultural Shift: There is a need for a deep change in mindset and culture to adopt these new instruments, as they cannot simply be used from scratch without preparation.
• Education and Literacy: Companies must invest in educating their workforce. There is a risk that without “literacy” (understanding) and “fluency” (ability to use), employees will struggle to adopt the technology.
• Underutilization: A historical hidden cost of technology is that companies often pay for tools with huge potential but only utilize 20% to 25% of their capabilities because they are too complex for staff to use effectively.

Data and Process Preparation Before AI can be effective, significant resources must be spent on the “boring” background work.

• Data Quality: AI operates on the rule of “garbage in, garbage out.” High-quality output requires high-quality structured and unstructured data, necessitating substantial work to clean and organize existing databases.
• Process Mapping: Companies must invest time in mapping their actual processes to understand what they want to achieve, rather than applying AI to obsolete workflows which would result in little more than a faster version of a broken process.

What strategies help manage the consumption-based costs of corporate AI?

To manage the unpredictable consumption-based costs of corporate AI, businesses must shift from a fixed-budget mindset to a value-driven strategy rooted in education and process efficiency. Unlike consumer tools like the free version of ChatGPT, corporate AI vendors typically charge based on consumption, making financial forecasting difficult for CFOs and boards.

Based on the sources, the following strategies help manage these costs:

Invest in “Literacy” and Preparation
The most critical cost-management strategy is education. Leaders must possess “literacy” (understanding) and “fluency” (ability to use) regarding AI technologies. Without deep knowledge, it is impossible to determine whether the organisation is “wasting money or investing money”. Thorough preparation allows companies to unlock actual value, whereas jumping in unprepared leads to financial loss and reduced competitiveness.

Optimise Processes Before Automation
Implementing AI requires a “reinvention of the organization” rather than a simple software installation. A major hidden cost arises from applying AI to obsolete processes; this merely results in a faster version of a broken workflow without generating real value. To control costs, companies must perform rigorous process mapping to ensure the AI is solving a valid problem efficiently.

Focus on Efficiency and Sustainability
Strategies should aim for maximum efficiency across the value chain, from sourcing to logistics and marketing. This approach aligns cost management with sustainability: by using technology to identify and eliminate waste (whether materials or energy), companies can simultaneously reduce costs and meet environmental goals.

Avoid “FOMO” Driven Adoption
Entrepreneurs are advised to be “bold but not a fool.” Rushing to adopt AI due to the “fear of missing out” (FOMO) often leads to wasteful spending on tools that are not fully understood or utilized. Taking the time to understand the specific value proposition for the business ensures that the consumption costs incurred actually drive return on investment.

How can AI identify waste and improve sustainability in fashion?

Based on the sources, AI identifies waste and improves sustainability in the fashion industry by reframing the concept of sustainability from a regulatory “burden” to an exercise in maximum efficiency.

Here is how AI achieves this across the value chain:

Redefining Waste as Inefficiency
AI helps retailers understand that waste—whether it is energy, materials, or products—is simply something that has not been utilised effectively. Instead of viewing sustainability solely as compliance with regulations, AI allows companies to view it as the elimination of inefficiency. If a resource is “put apart because it’s not useful to anyone else,” it represents a financial loss as well as an environmental one.

Granular Visibility and Root Cause Analysis
To improve sustainability, AI acts as a diagnostic tool that illuminates the “road ahead”. It provides access to data across the entire value chain—from sourcing and logistics to sales and distribution. By analysing this data, AI can:

• Pinpoint Origins: Identify exactly where waste is coming from in the production or supply process.
• Analyse Consumption: Monitor and reduce the usage of materials and energy.
• Forecast Accuracy: Help forecast the future to prevent overproduction, ensuring that what is produced is actually needed.

Maximizing Asset Utility
Once waste or potential waste is identified, AI helps determine how to maximise the value of every item produced. If a product fails in its primary channel, AI can calculate the best alternative path, such as:

• Selling the item in a different market.
• Repurposing it for a different use.
• putting it back into circulation rather than discarding it.

Aligning Profit with Planet
The sources highlight that cost reduction and sustainability are now intrinsically linked. With the costs of doing business in fashion (sourcing, advertising, logistics) skyrocketing, AI drives the efficiency needed to lower expenses. This creates a scenario where the “perfect company in terms of efficiency” is also a sustainable one, fulfilling both business objectives and legal requirements (such as those in the European Union) simultaneously.

Key takeaways from NRF 2026

Based on the discussion regarding the NRF (National Retail Federation) and the state of digital transformation leading up to 2026, the key takeaways focus on moving beyond the “hype” of AI to practical, structural, and “invisible” implementations. The sources suggest that by 2026, the industry focus will shift from simply adopting new tools to a fundamental reinvention of the organisation and the customer journey.

Here are the central themes discussed in the context of NRF 2026 and Fashion AI:

1. From Omnichannel to “Seamless” and “Invisible” Experiences The speakers argue that the industry is moving past the concept of “omnichannel,” which can feel disjointed, toward “seamless” experiences. In this model, the customer moves between e-commerce, physical stores, and mobile apps without consciously thinking about the channel.

• Invisible Technology: A major takeaway is that technology should be “invisible,” operating in the background to enable the user’s desires rather than being the focus itself.
• Unified Identity: A critical component of this seamlessness is using AI to bridge the online-offline gap. For instance, using payment tokenisation and blockchain to recognise a customer in a physical store and instantly access their online browsing history and preferences.

2. The Rise of Agentic AI “Agentic AI” is identified as a dominant trend at NRF, though the sources caution that much of it is currently marketing noise.

• Paradigm Shift: True Agentic AI shifts the paradigm from users adapting to software rules to software adapting to users. Instead of navigating complex interfaces, users (both employees and customers) can use natural language to command the software to perform complex tasks, such as creating a landing page by fetching data from disparate databases.
• Current State: While promising, the sources note that the industry is still “far away from actually adopting the real Agentic AI potential,” as it requires robust data structures that many companies currently lack.

3. The End of “Plug and Play” A recurring theme is the demystification of AI as a simple add-on. The sources emphasise that AI is “not plug and play”.

• Organisational Reinvention: Successful implementation requires a complete reinvention of the organisation, including culture shifts and extensive employee education.
• AI Literacy vs. Fluency: There is a distinction made between “literacy” (understanding the concepts) and “fluency” (the ability to use the tools). Leaders must possess both to avoid wasting money on technologies they do not understand.
• Process Mapping: Before applying AI, companies must map and optimise their processes. Applying AI to an obsolete process merely results in a faster version of a broken workflow.

4. Hyper-Personalisation via Generative AI The sources highlight that Generative AI is finally unlocking the true potential of personalisation, which was previously limited by production capacity.

• Scaling Content: Retailers can now use GenAI to produce unique content variants for specific individuals based on their data, solving the problem of having customer segments but only one piece of content to show them.
• Brand Integrity: This can be achieved without compromising the brand vision. The creative director sets the core vision, and AI generates variants that strictly adhere to these guidelines, ensuring consistency while increasing relevance.

5. Sustainability as Efficiency In the context of 2026, sustainability is reframed not as a regulatory burden but as an exercise in maximum efficiency.

• Waste Reduction: AI provides visibility across the value chain (sourcing, logistics, sales), allowing companies to identify “waste” as simply “inefficiency”—resources that are not being used effectively.
• Profit Alignment: By using AI to forecast accurately and maximise asset utility, companies can simultaneously reduce costs and meet environmental standards, such as those in the European Union.

6. The Financial Reality of AI Finally, a practical takeaway for the roadmap to 2026 is the shift in cost structures. Unlike consumer tools, corporate AI often operates on consumption-based pricing.

• Budget Volatility: This makes financial forecasting difficult for CFOs, as costs fluctuate with usage. The sources warn that without proper knowledge (“literacy”), companies risk “wasting money” rather than investing it, making education essential for financial control.

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