There is no shortage of commentary right now on AI shopping, agentic commerce, and the future of discovery. What is harder to find is a useful answer to a simpler question: what should a brand actually do next?
After attending Azoma’s conference, my view is that the answer is less complicated than many teams think.
The conversation across the event kept coming back to the same point. This is not mainly a story about chasing the newest tool. It is a story about fixing fundamentals that now matter more because AI systems are part of the path to purchase.
For brands trying to decide where to start, here is the short version.
1. Audit your product and brand data before you do anything else
This was probably the least flashy point raised all afternoon, which is exactly why it matters. If your product data is incomplete, inconsistent, or weak across systems, AI tools will not trust it. Multiple speakers described clean data as the prerequisite for everything else.
That means checking the basics with real discipline:
- product titles
- attributes
- descriptions
- packaging details
- taxonomy
- claims
- content consistency across retailers and brand-owned channels
If your data is sloppy, no amount of AI hype is going to save you.
2. Stop thinking only in keywords and start thinking in questions
One of the clearest shifts discussed at the event was the move from keyword optimization to contextual relevance. AI systems are trying to answer layered shopper questions. That means your content has to explain who the product is for, what problem it solves, and how it fits a real use case.
This applies to PDPs, FAQ content, support materials, brand pages, and educational content. If your copy only mirrors a retailer taxonomy, it is probably too thin for where shopping behavior is going.
3. Treat brand.com like a performance asset again
This came up repeatedly, and it should get more attention than it does. Brand websites are becoming a key source of truth for AI systems. That creates a real opening for brands that are willing to publish clear, useful, crawlable content on owned properties.
For many brands, that likely means:
- adding stronger FAQ content
- tightening product education pages
- publishing use-case content
- expanding pages that explain ingredients, claims, comparisons, or category context
If AI agents are scraping your brand site, then your brand site needs to do more than look polished. It needs to answer questions well.
4. Build a citation strategy, not just a content strategy
One of the more useful ideas from the conference was the emphasis on citations. AI systems do not rely only on what brands say about themselves. They look at earned media, reviews, forums, listicles, blogs, and other third-party references to validate credibility.
That creates a broader content challenge, but also a bigger opportunity. PR, communications, social, ecommerce, and content teams should not be working in parallel on this. They should be working together.
If your brand has strong product pages but weak third-party validation, that is now a visibility problem.
5. Put a cross-functional team in place now
This may be the most important operational lesson from the event. The brands that sounded the most prepared were not treating this as an ecommerce-only initiative. They were building task forces and rapid test teams across functions.
That usually means some version of:
- ecommerce
- brand
- PR and communications
- legal
- analytics
- IT or data
- product or category leads
The reason is simple. The work touches all of them.
6. Measure what is changing, even if your framework is imperfect
The conference did not pretend measurement is solved. It is not. But that is not a reason to wait. Share of voice, AI visibility, rankings, conversion shifts, and content performance all came up as early indicators that brands can track now while better measurement frameworks mature.
A messy but active measurement framework is more useful than perfect reporting that never gets built.
The real takeaway
The strongest message I took from the event was that agentic commerce is not rewarding novelty first. It is rewarding readiness. The brands best positioned for this shift are the ones that get their data right, make their content more useful, strengthen their citation footprint, and move faster across internal silos.
That is not glamorous work. It is also probably the highest-leverage work on the board right now.
If brands spend the next 90 days doing that well, they will be in a far better position than the ones still waiting for the category to settle before they act.
The AI-First Agency
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