Is AI replacing fashion’s key roles? Should you be vibe coding? Is AI even profitable? Find out how AI is really reshaping fashion buying, planning, forecasting, and operations.
Explore the hot takes, quick answers, and definitive lessons from the webinar AI is the New Black, featuring Style Arcade CEO and Co-Founder Michaela Wessels, CTO Tristan Hoy, and 12th Tribe's Senior Director of Buying and Product, Patricia Infante.
Watch the Q&A Hot Takes discussion in detail and save the answers below for when colleagues, partnerships, and leadership teams wonder how AI is impacting the fashion retail landscape.
1. Will AI agents replace buyers and planners?
Q: Will AI agents fully replace buyers and planners?
A: The consensus is no, at least not in their current form.
AI agents were described as useful for scaling tasks, but risky when used to fully replace human decision-makers. The main concern is that agents optimize for volume, not judgment, leading to “supervised high-speed mediocrity.”
Instead, the stronger model is AI as an assistant, where humans remain “hands on the steering wheel,” delegating smaller tasks while maintaining control over decisions.
A key limitation is that current AI systems:
- Do not truly learn from working with users in real time
- Do not update their underlying model behavior dynamically
- Cannot yet be held accountable in the way humans can
So the likely evolution is:
AI assistance → assisted decision-making → (eventual) partial autonomy
But not full replacement in the near term.
2. Why is “AI-assisted” preferred over full automation?
Q: Why not just fully automate buyers and planners?
A: Because ownership, accountability, and brand intuition matter.
Buyers and planners don’t just process data, they interpret:
- Brand direction and aesthetic
- Market timing and trend signals
- Strategic growth opportunities
AI can surface patterns, but it does not yet understand business intent or brand identity. As a result, it lacks the context needed for high-quality discretionary decisions.
3. What is “vibe coding,” and is it useful?
Q: What is vibe coding?
A: Vibe coding is when engineers give high-level prompts like “build me a system that does X,” then iteratively refine outputs without deep involvement in implementation.
It is useful for:
- Internal tools
- Low-risk experimentation
- Quick prototypes
But it is not recommended for:
- Core production systems
- Revenue-critical workflows
- Operational systems involving real orders
The key rule is risk-based usage:
- Low-risk → vibe coding is acceptable
- High-risk / core systems → traditional engineering required
4. Is AI actually profitable for fashion brands?
Q: Are fashion brands seeing real ROI from AI?
A: Yes. Especially in operational and decision-support areas.
Reported benefits include:
- Higher conversion rates
- Improved operational efficiency
- Better inventory decision-making
- Stronger demand signal accuracy through integrations (e.g., analytics platforms)
However, the biggest gains come when AI is used to support decisions, not replace them.
5. What are common pitfalls when implementing AI?
Q: What mistakes are companies making with AI?
A: Several recurring “bear traps” were identified:
- Overbuilding in isolation (building too long without feedback)
- Lack of short, iterative MVP cycles
- No clear definition of “good” before launch
- Underestimating cost control and scaling risks
- Expectation that AI outputs must be perfect before release
A key lesson: Build small, measurable deliverables and iterate quickly with users.
6. What should AI forecasting actually focus on?
Q: Can AI solve forecasting in fashion?
A: Not fully, but it can significantly improve it.
The discussion emphasized that forecasting is not just prediction, it’s also:
- Scenario planning
- Risk management
- Decision simulation across business conditions
AI is most useful when it:
- Builds a strong “true demand” baseline
- Accounts for supply constraints and historical distortions
- Supports multiple scenario planning, not just a single forecast
The goal is not perfect prediction, but better decision resilience.
7. What AI tools are most valuable in day-to-day fashion workflows?
Q: Where is AI most useful today in fashion operations?
A: Several high-impact areas were highlighted:
- Inventory decision support
- Allocation planning (still largely unsolved but high-value)
- SOP generation and operational workflows
- Product attribution and enrichment
- Assortment planning support (emerging opportunity)
There is strong interest in expanding into:
- AI-assisted design ideation
- Visual changes before tech pack creation
- Automated allocation optimization
8. Should companies rely on AI for core operations?
Q: Is it safe to use AI for critical workflows?
A: Not without caution.
AI is best used for:
- Supporting decisions
- Reducing manual workload
- Speeding up analysis
But not yet for:
- Fully autonomous ordering systems
- High-stakes financial decision-making
- End-to-end operational control
The guiding principle is: Keep humans accountable; use AI to extend capability, not replace responsibility.
9. What’s next for AI in fashion planning and buying?
Q: Where is AI heading next in this space?
A: Key future directions include:
- Scenario-based forecasting tools
- AI-driven assortment planning
- Smarter allocation systems
- Deeper integration into demand modeling
- Workflow templates layered on top of enterprise data systems
The approach will likely remain gradual. Slow, controlled expansion into higher-impact decision areas, not rapid automation.
Final takeaway
Across all discussions, the central theme is clear: AI is not replacing buyers and planners; it’s reshaping how they work.
The winning model is augmented decision-making, where humans retain strategy, accountability, and brand judgment, while AI handles scale, speed, and data processing.
