Shift the AI conversation from cost cutting to revenue

AI isn’t a cost-cutting tool. It’s a revenue multiplier. Yet too many companies are stuck asking how AI can help them run leaner with fewer people, faster processes, lower costs. That question won’t unlock exponential growth. The better one is: How can AI help us grow faster, sell more, and drive new revenue streams?

Yes, cost savings will deliver marginal gains. But accelerated and/or new revenue unlocks step-change impact. If your AI doesn’t show up in your P&L as higher conversion, more long-term value, and stronger monetization, then it’s not a strategy. It’s just automation.

THE REVENUE UNLOCK IS HIDING IN PLAIN SIGHT

AI’s real power lies in how it transforms commercial outcomes. The highest-leverage applications aren’t about doing the same thing with fewer people. They’re about doing new things better, faster, and more intelligently.

Here are three high-impact areas where AI is already delivering commercial lift:

Real-time relevance

This application is where AI shines brightest, not just in showing the right product, but in reshaping the entire customer journey to determine what matters most to each individual consumer. By analyzing live signals around intent, recency, device, geography, and behavioral patterns, AI models can decide which action, message, or product is most relevant at any given moment. Instead of relying on static customer profiles, AI is powering dynamic prioritization based on signal density, predicted value, and likelihood to act.

Checkout monetization

Checkout has always been a critical moment of truth and AI turns it into a revenue engine. Instead of offering a static “buy now,” AI can dynamically surface relevant add-ons, bundles, warranties, or services tailored to that exact customer in that exact moment. Because this happens when intent is already high, even small improvements yield disproportionate gains. For many businesses, checkout is the single best opportunity to transform a transaction into a marketplace.

Dynamic decisioning

Unlike one-off campaigns or basic customer journeys, AI-driven decisioning runs continuously in the background, recalibrating in real time. It can adjust promotions, product recommendations, and retention strategies in response to evolving signals: a shift in behavior, market trends, or even an external event. Done right, dynamic decisioning maximizes lifetime value by ensuring that every customer interaction nudges someone toward deeper engagement and higher spend not once, but over time.

Saks Global, Abound, and HelloFresh are just a few companies utilizing these applications in the real world with compelling results:

• Luxury retailer Saks Global’s AI-curated homepages maximizes personalization to deliver a 7% lift in revenue per visitor and a nearly 10% boost in conversions.

• Abound, one of London’s fastest growing fintechs, sets itself apart from competitors by using AI-driven dynamic decisioning. It harnesses open banking insights instead of outdated credit scores and statistical averages. They have lent about $1 billion in the five years since it was founded in 2020. AI insights allow Abound to understand each borrower’s unique financial profile with real-time financial data. This use of AI minimizes the company’s default rates while allowing it to offer lower borrowing rates to consumers.
• Meal kit delivery company HelloFresh has been using AI and machine learning extensively across its business. One way it’s been driving revenue in the U.S. is with machine learning-powered personalization preferences, optimizing meal selection in real time based on behavior. In August 2025, the company announced a $70 million investment, partly to supercharge AI-driven personalized meal planning across its expanded menu to help customers navigate choices more intuitively.

AI is so much more than an add-on or standalone feature. It should be thought of as a commercial operating system that is baked into every enterprise’s go-to-market strategy. However, to fully maximize a revenue-focused AI strategy, brands must undertake continuous testing, feedback loops, and optimization of customer touchpoints.

The C-suite has a responsibility to reframe the way we think about AI. Boosting productivity is important, but AI strategies shouldn’t just be about doing things cheaper. It should also be used to turbocharge growth and sell in entirely new ways. Using futuristic technology to do the same old same old, like reducing headcount to boost profits, is just a road to stagnation.

Efficiency is expected. It’s relevance that drives revenue. Saving money isn’t a strategy. Creating value is. The companies that define the next decade won’t be the leanest. They’ll be the most revenue-intelligent.

Elizabeth Buchanan is chief commercial officer of Rokt.

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