Learning from Losing with AI
Context
AI is moving quickly from experimentation to expectation inside creative and marketing teams. But behind the headlines, many of the most useful lessons are coming not from flawless success stories, but from the pilots that stalled, the tools that under-delivered, the workflows that proved harder to automate than expected, and the moments where human judgement remained essential.
This Panel
Takes a practical and honest look at what Creative Operations teams are learning from real AI adoption:
- Where AI is genuinely improving speed, scale, consistency and production efficiency
- Where it still creates risk, rework or false confidence
- How leaders can make better decisions about which use cases to pursue, pause or stop
The Focus
- Not hype or theoretical futures
- Rather what happens when AI meets the realities of creative quality, brand governance, approvals, rights, team capability, change fatigue and operational accountability
- Frank lessons from practitioners who have tested AI in real creative, marketing and production environments. Including what failed, what surprised them, what they would do differently, and how those lessons are shaping more mature approaches to AI-enabled Creative Operations
Covering
- Where AI is delivering real value in Creative Operations today, and where expectations are ahead of capability
- Common reasons AI pilots fail, stall or fail to scale
- How to choose the right use cases across briefing, ideation, versioning, localisation, QA, workflow automation and production
- The operational, legal, brand and cultural guardrails needed before scaling AI
- How Creative Operations leaders can balance efficiency gains with creative quality and meaningful human work
- What “good” looks like when moving from experimentation to governed adoption
Delivering
- A clearer view of what AI can and cannot currently do for Creative Operations
- Practical lessons from real-world AI experiments, including failures and false starts
- A sharper framework for selecting, governing and scaling AI use cases
- Confidence to have more honest internal conversations about AI investment, risk, capability and change