For industries that rely heavily on product imagery, such as retailers, manufacturers, wholesalers, real estate, media, and cultural heritage institutions, the promise of AI is dazzling. However, transitioning from the ‘shiny new toy’ phase to generating tangible business impact requires more than just enthusiasm – you need a strategic approach.
Drawing on insights from WoodWing's Product Development Manager, Paul Walker, and Content Operations and DAM specialist, Kristina Huddart, we examine how AI is evolving from a ‘wishlist item’ to a standard utility within the content engine.
Before diving into specific use cases, it is critical to understand the drivers behind this shift. Retailers are adopting AI to solve three specific challenges:
We asked the participants that were part of the live webinar about their current AI in DAM implementations. The results we received are quite interesting, and more than half joined us to learn about the potential use cases with around 30% of them not even being AI DAM users yet.
For those searching for potential uses of AI in DAM, the following applications are currently driving the most impact in the retail sector.
The foundation of a functional DAM is clean data. AI solves the ‘garbage in, garbage out’ problem by automating the tedious ingestion process.
Improved search is a consistently high-ranked wish-list item for DAM users. AI transforms discovery from keyword guessing to natural interaction.
This is a critical AI use case for retail, addressing the need to localize content at scale.
As generative AI scales content volume, maintaining brand integrity becomes harder.
Perhaps the most undervalued use case is using AI not just to make content, but to measure it.
To see some of these AI DAM implementations, watch the whole webinar below. Our two experts are presenting their insights and walk you through the dos and don'ts of successful implementations of artificial intelligence in digital asset management and content operations.
Successful implementation of AI DAM requires a strategic approach. Avoiding the ‘pilot purgatory’ of endless testing requires adhering to clear dos and don'ts. See below what the experts recommend, and see if you are also able to avoid the don’ts to really improve the success rate of AI implementations in the day-to-day management of your digital asset libraries.
Start with business objectives
Define the why. Are you trying to speed up time-to-market (55%) or improve data quality (41%)?
Fix foundations first
AI amplifies what you feed it. Scale content with a structured taxonomy and clean metadata – without those elements, scaling content leads to ‘scaled chaos’.
Operationalize governance
With 64% of consumers lacking confidence in AI privacy, establishing guardrails and ‘human in the loop’ validation is non-negotiable.
Create a ‘safe zone’ for experimentation
Don't unleash new AI tools directly into your production environment. Instead, run ‘safe experiments’ first. Use a sandbox or test environment to trial auto-tagging or generative cropping. This allows you to catch ‘hallucinations’ (like AI misidentifying a coat as trousers) without corrupting your live commercial data.
Build a feedback loop
AI implementation isn't a straight line; it's a cycle. You must ‘harvest the results’ of how assets are used and feed that performance data back into the system to improve future suggestions.
Don't just use AI to run the same race faster. Use it to change the game.
Kristina Huddart – Huddart Consulting
Don't treat DAM as an island
Your DAM must be a dynamic hub integrated with your PIM and CMS, not a static archive.
Don't skip measurement
Success isn't just efficiency; it is business impact. Track metrics like ‘optimal color by season’ or ‘best converting photo’.
Don't ‘over-stuff’ your metadata
Just because AI can do something doesn't mean it should. Experts warn against ‘overstuffing’ your assets with static metadata, such as hard-coding translations for every possible language. Instead of bloating your database with tags for five different territories, rely on AI to translate and interpret search queries on the fly. Keep your core data clean and let AI handle the complexity at the moment of discovery.
According to Kristina Huddart, the retail industry is currently focused on using AI for speed and cost reduction. However, as these efficiencies become ‘table stakes’, the competitive edge in 2026 will shift.
“Success is business impact, not just efficiency,” notes Huddart. “The goal is moving from doing things faster to doing things better and differently. Retailers should prepare for this shift by using AI to uncover deep customer insights and create immersive experiences, like 3D digital twins and personalized video, that were previously impossible to produce at scale.”
The transition to AI in digital asset management is not about replacing human creativity but removing the friction of manual administration. By automating ingestion, discovery, and adaptation, retailers can focus on the strategy that drives the bottom line.
Ready to optimize your content operations? Ensure your DAM foundation is solid before scaling with AI. Contact WoodWing to assess your content maturity and explore how these use cases can be applied to your specific workflow.