The discussions around digital product creation (DPC), artificial intelligence (AI) accountability, and digital transformation reinforced a critical point: Fashion does not lack innovation; it struggles to translate innovation into connected decisions, scalable processes, and measurable business value. The perspectives shared onstage and throughout the event shaped this point of view on what must change for the industry to move forward with greater speed, confidence, and accountability.
The apparel, footwear, and fashion industry has invested heavily in DPC, AI, automation, and cloud platforms. Yet many organizations still operate through disconnected decisions, sequential processes, functional silos, and business models built for a slower, more predictable market.
The main question to ask is: Are we using technology to make a broken operating model move faster, or to create a fundamentally better way of working?
That question framed many of the most useful conversations at the show. Across DPC, AI, supply chain transformation, and organizational culture, one conclusion held up: The industry's next breakthrough won't come from another isolated tool. It will come from closing the behavioral, process, and accountability gaps that keep technology from delivering its full value.
DPC must become digital decision-making
DPC has transformed how fashion products are designed, visualized, sampled, and developed. 3D design, digital materials, virtual sampling, and AI-assisted creation can shorten development cycles, reduce physical samples, improve collaboration, and enable teams to explore more creative possibilities.
But digitizing the product is not enough.
Many companies have implemented DPC tools without redesigning the decisions surrounding them. A product may be created digitally, yet approvals still happen through email. Merchandising plans may remain in spreadsheets. Costing, sourcing, demand, sustainability, and production feasibility may be evaluated too late. Physical samples are sometimes reintroduced because teams do not fully trust the digital representation or because downstream partners have not adopted the same capabilities.
The result is a digital process layered onto an analog operating model.
The next stage of DPC must connect creative development with commercial and operational intelligence. Designers and product developers should be able to understand cost, margin, material availability, supplier capacity, demand signals, compliance requirements, and environmental impact while the product is still being created, not after decisions have already been locked in.
The goal is not simply to design faster. It is to make better product decisions earlier, with greater confidence and less waste.
AI is accelerating decisions, but who owns the outcome?
Quicker and more accurate product decisions depend on trustworthy information, and increasingly, that information comes with the help of AI. AI is rapidly changing how fashion companies forecast demand, develop assortments, create product concepts, optimize inventory, select suppliers, manage exceptions, and serve customers.
That creates real opportunity, but it also raises a fundamental accountability question: When AI influences the decision, who owns the result?
AI may recommend a style, forecast demand, prioritize an order, adjust inventory, flag a supplier, or generate a product specification. But when the recommendation is wrong or carries unintended financial, operational, ethical, or reputational consequences, the algorithm cannot be accountable.
The business has to be.
This is the accountability gap. Companies are moving quickly to adopt AI, but many have not clearly defined decision rights, escalation thresholds, validation requirements, or ownership of AI-assisted outcomes.
The answer is not to slow AI adoption. It is to build the governance and operating discipline that allow organizations to move quickly with confidence.
Every AI-enabled process should answer four questions:
- What decision is AI supporting or making?
- What information and assumptions are shaping the recommendation?
- When is human judgment required?
- Who remains accountable for the business outcome?
Human oversight should not mean approving every AI recommendation manually. That would simply replace automation with another bottleneck. Instead, companies need intelligent accountability: clear guardrails, transparent reasoning, confidence thresholds, exception-based intervention, and named ownership.
AI can accelerate the decision. It cannot replace responsibility.
Supply chain transparency must move upstream
Fashion supply chains were historically built around controlled information and closely protected vendor relationships. Today, transparency, traceability, and partner responsiveness are becoming competitive requirements.
Regulation, geopolitical uncertainty, raw material price fluctuations, sustainability expectations, and customer demand for product provenance are forcing companies to know far more about how, where, and under what conditions their products are made.
But transparency cannot begin after production. It must start during product conception.
Material selection, sourcing strategy, product composition, supplier decisions, compliance evidence, cost, and environmental impact must become part of the product record from the beginning. If traceability is treated as a downstream reporting exercise, companies will continuously struggle to reconstruct information that should have been captured at its source.
This also changes the definition of a strategic supplier. Price and capacity remain important, but partners must also provide reliable data, collaborate digitally, respond quickly, and support increasingly transparent operating models.
The question is no longer simply whether a supplier can produce the product. It is whether that supplier can participate in the future operating model of the business.
Culture is the real critical path
None of these efforts—DPC, AI accountability, and upstream transparency—can take hold without the right behaviors to support them. Technology can enable transformation, but culture determines whether it actually happens.
Apparel, footwear, and fashion companies often say they want greater agility while continuing to reward scale, certainty, and adherence to traditional calendars. They invest in advanced forecasting while relying on historical assumptions. They introduce digital samples but hesitate to approve products without physical confirmation. They encourage experimentation but penalize teams when an experiment fails.
Organizations cannot become agile while preserving every behavior designed for stability.
Our industry must become more comfortable making smaller decisions earlier, testing assumptions continuously, and changing direction when new information emerges. That means moving away from rigid seasonal processes, excessive approval layers, and markdown-dependent planning toward more responsive, learning-driven operating models.
The most successful organizations will not eliminate creativity in favor of data. They will use technology to protect and strengthen creativity, giving teams more time to create, better information to guide decisions, and greater confidence to act.
Transformation happens when technology changes not only what people use but also how the organization decides.
The questions fashion leaders should be asking now
DPC, AI accountability, supply chain transparency, and culture aren't four separate initiatives. They're four places where the same operating model gap shows up. Industry executives should look beyond technology adoption rates and investigate whether their investments are changing measurable business outcomes.
1. Digital confidence: Where do teams keep returning to physical samples, spreadsheets, email, or offline approvals, and why? These behaviors often point to gaps in data quality, system adoption, process integration, or trust.
2. Decision latency: How long do critical product, sourcing, inventory, and fulfillment decisions actually take—not just how quickly individual tasks get done? A faster design process doesn't help much if approvals and handoffs stay slow.
3. AI decision ownership: Map every significant AI-supported decision to a named business owner. Establish which decisions can be automated, which require validation, and which should always remain human-led.
4. Product-data continuity: Does product information flow from design and product lifecycle management through sourcing, ERP, supply chain, warehouse, commerce, and traceability, or does it get repeatedly recreated, rekeyed, and reconciled?
5. Upstream visibility: How early can teams see cost, margin, material risk, supplier capacity, compliance, traceability, and demand implications during product development?
6. Partner digital readiness: Are suppliers evaluated on data accuracy, transparency, and collaboration—not just cost, quality, and delivery?
7. Learning velocity: How quickly does the organization detect a change, evaluate its implications, make a decision, and apply the lesson to future processes?
What happens if these questions go unanswered?
Leaving these questions unanswered doesn't just stall progress. The risks compound over time. The bigger concern isn't falling behind on technology. It's creating the appearance of transformation while the underlying business stays the same.
Left unaddressed, these gaps tend to show up as:
- Faster execution of poor or outdated decisions
- More AI-generated recommendations without clear accountability
- Continued reliance on spreadsheets and manual reconciliation
- Digital product investments that never achieve enterprise scale
- Margin erosion from late costing, overproduction, and markdowns
- Compliance exposure caused by incomplete product and supplier data
- Growing distrust in digital outputs and a return to physical processes
- Fragmented customer, product, inventory, and supply chain decisions
- Inability to respond quickly to disruption, uncertainty, or changing demand
Technology debt is rapidly becoming decision debt, as fragmented data, delayed insights, and disconnected choices compound business costs and prevent organizations from acting with speed and confidence.
How Infor helps fashion companies get ahead of what’s next
Infor approaches transformation from an industry operating model perspective, rather than as a collection of disconnected technologies. This approach is supported by Infor CloudSuite™ Fashion, a suite of composable solutions.
Infor CloudSuite Fashion’s ERP provides an industry-specific digital foundation connecting financials, orders, inventory, manufacturing, supply chain, customers, and product operations.
Infor PLM for Fashion brings design, materials, specifications, costing, collaboration, and critical path management together earlier in the product lifecycle.
Infor Nexus extends visibility and collaboration across the multi-enterprise supply chain, while Infor WMS supports intelligent, complex fulfillment.
Through the Infor Industry Cloud Platform, organizations can connect applications, data, workflows, analytics, and extensibility across the enterprise.
Infor Process Mining helps expose how work is actually performed, where bottlenecks occur, and where value is being lost. Automation, generative AI (GenAI), Infor Value+ solutions, and intelligent agents can then help reduce manual effort, surface exceptions, recommend corrective actions, and continuously improve execution.
Technology alone is not the differentiator. Infor combines these capabilities with deep fashion industry knowledge of attribute-driven products, seasonal calendars, complex sourcing, multi-channel inventory, critical path management, supplier collaboration, traceability, and the commercial realities of fashion.
The objective is not simply to help companies respond more quickly to what has already happened. It is to connect product, operational, and external intelligence so they can identify risks earlier, make decisions with greater confidence, and anticipate what comes next.
The critical path forward begins by asking what must break first: the silos, the disconnected data, the outdated decision rights, or the belief that transformation can be achieved by technology alone.
The future of fashion will belong to companies that connect creativity with intelligence, speed with accountability, and digital transformation with genuine change in their operating model.
Learn more about Infor CloudSuite Fashion
Ana Friedlander
Industry & Solution Strategy Director, Senior, Infor
Ana has over 25 years of experience in the Apparel and Retail industry, both from the fashion and retail companies as well as software companies. Prior to joining Infor, she was the Chief Information Officer at the largest privately held outerwear company, leading all the IT, Supply Chain, and technology initiatives to drive growth. She also played an active role in the RFID and Blockchain projects sponsored by GS1 and Auburn University together with the larger retailers to test the feasibility of the technology before this became a mandatory requirement to the industry.