Commerce leaders don’t need convincing that AI matters. They’re already seeing its impact in how consumers discover products, how platforms prioritize content, and how quickly teams are expected to respond.
The real challenge is focus. With decisions spread across teams, channels, and systems, value comes from applying AI in the right places, where it accelerates decisions, supports execution, and protects revenue.
The brands seeing results aren’t treating AI as an abstract capability. They’re using it intentionally, tying intelligence directly to how commerce actually works.
Across recent conversations on the BRAVE COMMERCE podcast, we’ve identified clear patterns in how leading brands are strategically using AI today, and the impact those choices are having on performance across the commerce ecosystem.
Read on to learn how your team can apply these patterns to connect intelligence directly to real-world commerce execution.
Use AI to Make Faster Commerce Decisions That Drive Revenue
One theme consistently surfaced throughout these conversations is that brands aren’t using AI in commerce to make decisions for them. They’re using it to mitigate uncertainty and move faster on the decisions that already matter.
Commerce leaders described AI as a way to:
- Surface patterns faster across fragmented data
- Simplify complex signals into actionable insights
- Help teams decide where to act first, not everywhere at once
As Andrea Steele, Area Vice President (AVP) eCommerce & Customer Marketing at Kraft Heinz, shared in her BRAVE COMMERCE episode, AI is most effective when it brings risk to the surface early and gets teams aligned faster:
Just as telling is where brands are choosing not to apply AI. Leaders are cautious of tools that generate insights without context, recommendations without constraints, or outputs that simply add another layer of interpretation.
Instead, AI is being applied selectively, in moments where delayed decisions have real revenue consequences, where teams struggle to align around a shared view of performance, and where manual analysis slows execution.
That focus is why many brands are investing in platforms that use real-time commerce signals to predict impact and guide faster shoppable media and sales decisions, ensuring AI-driven investment directly serves the shopper experience.
Start With AI Use Cases That Prove Value, Then Scale
Another clear pattern that emerged is that brands are resisting sweeping, top-down AI transformations and instead treating AI like any other performance lever: something that must prove value quickly.
Today’s approach looks like this:
- Running contained pilots tied to specific outcomes
- Measuring success in weeks, not quarters
- Expanding only what demonstrates clear return
When discussing how Mars approaches AI adoption, Global Chief Customer & Digital Commerce Officer Neil Reynolds emphasized the importance of discipline and speed, proving value before scaling:
This model reflects a broader reality. AI tools are abundant, but organizational focus is limited. Brands move faster when experimentation doesn’t require rebuilding infrastructure every time.
Testing works best when teams can launch quickly without recreating links, feeds, or retailer connections from scratch. When that friction is removed, learning accelerates and momentum builds.
Protect Conversion at the Point of Purchase
AI can’t fix broken commerce fundamentals. When products aren’t available where shoppers expect them, when content doesn’t meet retailer requirements, or when links break at the moment of intent, the impact is immediate. Conversion drops. Revenue is lost. And no amount of upstream intelligence can recover the sale.
These instances are where AI is already delivering value, not as a planning tool, but as a way to protect execution at scale. Leading brands are using AI to identify and resolve digital shelf issues before they affect conversion, prioritizing fixes based on real-time availability and performance signals.
Instead of reacting after revenue declines, teams are using AI to surface risk early, focus attention on the breakdowns that matter most, and keep shoppable media experiences working when shoppers are ready to buy.
As commerce media becomes increasingly transactional, protecting conversion is no longer a downstream concern. AI plays a critical role in ensuring that attention turns into action—and that intent doesn’t disappear at the final step of the journey.
Cut Through the Noise: Fewer Dashboards, Clearer Signals
Looking ahead, commerce leaders aren’t asking for more data. They’re asking for less noise and clearer signals. What they’re moving toward is a future state with fewer disconnected tools, less manual interpretation, and real-time signals that point directly to action.
As a result, AI in commerce is shifting from insight generation to decision enablement. The goal isn’t another dashboard to review, but a clear indication of where to intervene and why.
This need matters even more as media becomes increasingly shoppable, inventory shifts faster across retailers, and global teams look for shared context without added complexity. To support these efforts, many brands are investing in centralized commerce layers that bring media, retailers, inventory, and performance signals together in one place, surfacing what matters now, not everything that’s possible.
What Brand Leaders Should Do Next
The brands making progress with AI aren’t debating its potential. They’re putting structure around how and where it gets applied, focusing on execution, not experimentation for its own sake.
- Start slow to move fast.
Map where uncertainty or misalignment delays action today, whether that’s budget shifts, assortment changes, media optimization, or retail readiness. Apply AI in those moments first so teams can move faster without waiting for perfect information. - Prove value in focused use cases before scaling.
Launch small tests tied to clear outcomes. Define success upfront, measure impact quickly, and scale only what delivers. If a use case doesn’t move the needle, retire it and move on—without rebuilding systems each time. - Fix the breaks between intent and purchase.
Audit where shoppers drop off today: availability gaps, broken links, or incomplete digital shelf execution. Use AI to surface these risks early and prioritize fixes before intent turns into lost revenue.
The brands pulling ahead aren’t using AI to simply do more, they are using it to do more better. For these brands, AI is removing friction, clarifying priorities, and enabling accelerated progress with confidence, turning intelligence into action, not more analysis.
👉 See how MikMak helps brands connect AI-powered insights directly to real-time commerce execution, so intelligence leads to action, not delay.
