Practical ways brands are using AI to drive growth & profit

By now, most senior marketers have heard the same AI story repeated: content at scale, faster production, smarter personalization. Those things matter, but they’re no longer differentiators. Many brands are already there or working to get there.

What’s more interesting is what’s happening around marketing: the quieter, less-discussed ways AI is being applied to remove friction, sharpen decision-making, and unlock profit in places humans simply can’t operate at speed or scale.

Across our work with retailers, platforms, and global brands, AI’s remit is broadening from a creative accelerator to a decision engine for growth.

Below are a few practical examples of how that’s showing up today.

Retail media networks are a great example of where growth is often constrained by process, not demand. In theory, brands want to spend more. In practice, approvals, compliance, and governance slow everything down.

Working with Sainsbury’s, a major UK grocery retailer, we helped apply AI agents to automate large parts of the retail media ad approval process. Instead of manual reviews that took days or even weeks, ads could be checked, approved, and deployed in 90 seconds at close to zero cost per audit. And with 95% accuracy.

Faster approvals have meant:

• More campaigns are going live
• More budget captured
• Less friction between brand, retailer, and platform

In other words, AI isn’t producing more content. It is clearing the operational choke points that were holding revenue back. And unlocking real savings from your retail media network.

Most media optimization still looks backwards: doubling down on what converted last week. That works until it doesn’t.

With Foot Locker, AI has been applied further upstream, helping decide which products deserve investment in the first place. It’s also about scoring audiences for longer-term growth. Not just the fastest conversion, but also predicting which audiences will return products, which increases costs and reduces profit. Instead of relying solely on platform signals, product-level decisioning incorporates factors like margin, return rates, and sell-through alongside performance data.

The result is a more balanced growth model:

• Products that drive volume but erode profit get deprioritized
• Products that support long-term profitability get scaled
• Media decisions start to reflect business reality, not just channel metrics

And most importantly, this has resulted 13% revenue uptake from product-scoring-based shopping campaigns.

This kind of AI is giving marketers a clearer view of trade-offs that are impossible to model manually at scale.

One of the most overlooked benefits of AI is its ability to say “no” faster.

At Rituals, AI-powered audience prediction and personalization are used alongside a consolidated global SEO and platform strategy. The goal is to reach the right people, in the right moments, without inflating costs.

By unifying data, reducing duplication, and applying AI to audience and channel decisions, the brand has been able to:

• Improve return on ad spend
• Reduce media waste
• Drive growth without increasing complexity
  

Here, AI acts less like an accelerator and more like a governor, helping the business scale sustainably instead of chasing short-term gains.

For many CMOs, the job has quietly changed. The remit now extends well beyond brand health and demand generation into areas that directly affect revenue, margin, and efficiency.

That shift is forcing marketing leaders closer to commercial decisions that historically sat elsewhere in the organization. And AI is often the bridge.

For a global technology and consumer electronics leader, for example, AI decision engines are being used to determine whether second-hand devices should be resold, refurbished, or stripped for parts, based on which option will be most profitable at a given moment.

While this may not look like “marketing” on the surface, the implications are very real:

• Pricing and promotional strategies can be informed by real-time margin logic
• Inventory decisions shape which products marketing should prioritize—or avoid
• Demand generation becomes aligned with what the business can profitably sell, not just what it can promote

For CMOs under pressure to drive measurable business outcomes, this kind of AI-driven decisioning helps connect marketing activity directly to commercial reality, where growth is shaped as much by what shouldn’t be pushed as by what should.

Finally, there’s a growing class of AI applications focused on insight rather than execution.

In our exploratory work with a global sports organization, AI has been used to:

• Analyze audience and media patterns to inform smarter commercial partnerships
• Allow teams to query vast amounts of customer and research data using natural language, dramatically reducing time and cost

These applications are still a bit fresh, so the real results will come later in 2026. But they point to a future where AI helps organizations see opportunities they couldn’t previously articulate, let alone act on.

The common thread across all of these examples is simple: AI is becoming less about doing more and more about deciding better.

The brands getting the most value today aren’t chasing novelty. They’re applying AI to:

• Remove friction from revenue-generating processes
• Make investment decisions that reflect real business outcomes
• Scale growth without scaling waste
• Connect marketing activity more tightly to profit

Content at scale will continue to matter. But the next wave of advantage is being built in places that don’t always show up in campaign recaps or creative decks.

That’s where AI is quietly doing its most valuable work.