AI Moves From Possibility to Practice: Key Takeaways from The Interline’s AI Application Era Session

AI Moves From Possibility to Practice

Recently, The Interline invited leaders from the digital landscape around fashion to an insightful session on the role of AI in product creation. Hosted by The Interline Editor-in-Chief Ben Hanson, the panel featured Browzwear's Craig Planson, CTO, and Ugnius Rimsa, Senior Manager of AI Engineering, in addition to Centric Software CTO Ravi Rangan.

A clear shift emerged: brands are no longer exploring AI out of curiosity. They want real, immediate results — and they want the tools that plug seamlessly into actual product workflows.

Key signals shaping the conversation included:

• AI is now tied to KPIs, board-level goals, and digital transformation roadmaps

• Real value comes from connecting AI to production-ready workflows — not adding more disconnected tools

If your team is interested in how AI-guided workflows can minimize repetitive tasks, increase accuracy, or deliver trusted fit, we can map where these workflows will provide value first. Request a demo to see how AI-guided workflows fit into your product pipeline. 

From Inspiration to Production

A recurring point was the importance of bridging creative ideas and production-ready assets. AI can generate concepts quickly, but turning them into digital twins, patterns, and reusable samples requires expertise and precision. Ugnius emphasized that the goal isn’t just faster ideation—it’s creating assets that can be used across e-commerce, B2B, and internal workflows while preserving intellectual property.

The Right AI for the Right Task

Not all AI is the same. Generative AI does a great job on broad ideation, but specialized models are good at high-precision tasks, e.g., a pattern conversion or a garment classification. Selecting the right tool for the job means that AI works meaningfully, efficiently, free from needlessly complex or costly solutions. 

Humans in the Loop

AI is a teammate, not a substitute. All panelists agreed that human oversight is essential to keep quality and trust up. Through validation of AI outputs against enterprise rules and domain expertise, teams can validate that insights are actionable and correct. This also minimizes the “work slop” effect, where outputs need additional validation before being applicable. 

Driving Adoption Through Value

AI may provoke some resistance against introducing it. Some fear it will displace jobs or devalue experience. The panel emphasized that effective adoption relies on creating augmentation rather than replacement. AI clears teams to do more work where they can make even bigger contributions, improves design quality, and accelerates time-to-market. Pointing to specific, tangible benefits — reduced sample costs, improved trend alignment — helps teams view AI as an enabler, not a threat. 

Integration and Partnership Matter

AI is most effective when it integrates with other systems. AI integrates seamlessly with PLM and digital workflows through open platforms and strategic partnerships, including Browzwear and Centric Software. The closer we get with vendors and brands, the faster we adopt and increase long-term success. 

Conclusion

AI for fashion is no longer a prospect — it’s an actionable tool in creativity, precision, and productivity in product development. Brands will safeguard their own intellectual property, get tangible results and elevate the product process by marrying generative and specialized AI with human knowledge and integrated workflows.

If your team wants to see AI-guided workflows in action, request a demo to explore how it can transform your product development pipeline.