This article was based on the interview with From CRMC: Phaedon’s Jaclyn Wands on humanizing loyalty in an AI-driven world by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
The current sprint to integrate artificial intelligence (AI) into every facet of the customer experience feels less like a marathon and more like a land rush. Every vendor, every platform, and every internal roadmap is screaming “AI-powered personalization.” The promise, as we all know, is a seamless, predictive, and hyper-relevant journey for every customer. We’re told AI will anticipate needs, optimize offers, and build unbreakable bonds at a scale previously unimaginable. And yet, a nagging question persists as we deploy these sophisticated algorithms: are we simply building more efficient transactional engines? In our quest for algorithmic perfection, we risk sanding down the very human imperfections, nuances, and emotional triggers that create genuine, lasting loyalty.
This isn’t a Luddite’s argument against technology. Far from it. This is a strategist’s appeal for wisdom. The challenge for today’s marketing leaders is not whether to adopt AI, but how to wield it with precision and purpose. It’s about understanding that loyalty isn’t just a series of optimized transactions, but a complex tapestry of emotions, trust, and shared values. To explore this, I recently spoke with Jaclyn Wands, the VP of Product and AI at Phaedon. With a background as a data scientist specializing in causal mathematics, she brings a uniquely grounded and refreshingly pragmatic perspective to the often-hyped world of AI in marketing. Our conversation cut through the noise to focus on the critical misconceptions leaders hold about AI, the necessity of evolving our metrics beyond the transactional, and how to build loyalty programs that use technology to amplify, rather than replace, human connection.
The Foundational Misconception: AI is Not Infallible
Before we can build a more human-centric loyalty strategy, we must first dismantle the biggest myth surrounding its technological foundation: that the AI will be right. In a world of generative content and predictive analytics, there’s a dangerous tendency to view AI output as gospel. Leaders, eager for data-driven certainty, can fall into the trap of outsourcing critical thinking to the machine. Wands argues that the most effective AI strategy begins with a healthy dose of skepticism and an acceptance of the technology’s inherent limitations. This isn’t about distrusting the tool, but about understanding its nature.
“As a leader in marketing, you need to have at least a conceptual understanding of just how frequently these models can be wrong. And I quote my favorite statistician, E.P. Box, ‘All models are wrong, but some are useful.’ And when you approach your AI technologies and you approach your AI strategy with this fundamental understanding, you are more likely going to implement fail-safes and the ability to do human in the loop checking that allows for a more powerful integration of AI, keeping it safely implemented within the parameters that it’s made to be used.”
This is the cornerstone of a mature MarTech organization. Viewing AI not as an oracle but as a powerful pattern-recognition engine—one that is only as good as the data it’s trained on and is incapable of true comprehension—is a strategic imperative. It forces us to build processes with human oversight, to question outputs that seem counterintuitive, and to design systems that can gracefully handle the inevitable errors. Without this foundational understanding, we risk creating automated systems that compound biases and alienate customers at scale, all while the dashboard glows with misleadingly positive engagement metrics.
Beyond the Points Economy: Using AI to Foster Emotional Loyalty
For decades, the bedrock of loyalty has been the transactional “points-for-stuff” economy. It’s simple, measurable, and effective at driving repeat purchases. It is, as Wands notes, the fundamental requirement of any program. But it’s also table stakes. True brand affinity—the kind that withstands a competitor’s discount or a rare service failure—is built on an emotional connection. The irony is that many brands are now using AI to simply optimize the transactional layer, delivering points and offers with greater efficiency but failing to address the deeper human drivers of loyalty. The real opportunity lies in using AI to listen for signals that exist outside the checkout flow.
“If you are building on top of your transactional point economic systems with understanding your consumer, understanding their experiences with your brand, understanding what uniqueness you bring to their kind of life… you can start to learn the emotional loyalty drivers around your brand and then you can build loyalty programs outside of mere transactional… And then if you implement AI to enhance this, it will enhance it in a way that is more of an emotional loyalty driver than a transactional loyalty driver.”
Wands points to compelling examples already in the wild. Nike using AI to scan a customer’s foot for the perfect fit isn’t about points; it’s about enhancing the core product experience in a deeply personal way. Chewy analyzing search inputs to understand a pet’s life stage—moving from puppy food to senior care—demonstrates empathy and awareness that transcends a simple purchase history. These brands are using AI not just to ask “What did you buy?” but to understand “What do you need right now?” and “How can we be genuinely useful in this moment?” This requires a shift in data strategy, moving beyond CRM and purchase data to incorporate search intent, sentiment analysis, and even service interaction logs to build a richer, more human picture of the customer.
Redefining Success: Measuring What Truly Matters
If our goal is to build emotional loyalty, then our metrics must evolve to reflect that. Obsessing over open rates, click-throughs, and redemption velocity only reinforces a transactional mindset within our teams. While these KPIs remain important, they don’t capture the resilience of a customer relationship. A truly loyal customer isn’t just one who buys frequently; it’s one who forgives you when you make a mistake. Wands suggests that some of the most powerful indicators of emotional loyalty can be found precisely at these moments of friction.
“What about how quickly did your loyalty member reengage with you after a documented error? So now you’re able to determine whether or not they’re a habitual transactional loyalist or an emotional transactional loyalist, ’cause now you’re talking about what was that distance of time for them to reengage you and trust you again. And this is the way measuring around error and around product, like product issue or a service issue, this is how you start to paint a picture of how loyal your customers are, are to you on an emotional level versus transactional level.”
This is a profound shift in measurement. It moves from tracking success to analyzing recovery. How a customer behaves after a flight is canceled, an order is lost, or a product fails is a far more telling indicator of their underlying affinity for your brand than their last ten purchases. Another powerful, yet underutilized, area is monitoring non-tagged brand sentiment. Wands highlights how a brand like Hilton might track general travel disruption chatter to anticipate a guest’s stressful arrival, allowing staff to proactively turn a negative experience into a positive one. This is proactive, empathetic, and powered by AI at scale. It requires us, as leaders, to equip our teams with the tools and the mandate to look for these signals and to create KPIs that reward relationship-building, not just revenue extraction.
The path forward is not a retreat from technology, but a more sophisticated and human-centric application of it. The commoditization of AI is inevitable. In a few years, AI-powered offer optimization will be as standard as email marketing. The brands that win will be those that have used this time to build the cultural and technical infrastructure to move beyond the transaction. They will have trained their teams and their algorithms to listen for the subtle, non-transactional cues that signal a customer’s true state of mind. They will have built systems with the wisdom to know when an automated response is sufficient and when a human touch is required.
Ultimately, the most agile brands will be those that maintain a high “curiosity lever,” as Wands puts it. This means encouraging our teams to step outside their roles as marketers and to critically observe their own experiences as consumers. It means fostering a culture that questions the output of the black box and constantly seeks to understand the “why” behind the data. AI is an incredibly powerful tool for pattern recognition, but it is up to us—the human leaders—to recognize the patterns that truly matter and to build brands that don’t just transact, but connect.
