As loyalty leaders look toward 2026, the conversation is shifting away from feature sets and toward fundamentals: trust, relevance, and human connection. While artificial intelligence is accelerating what brands can do with data and personalization, it is also forcing a reassessment of what customers actually value in their relationships with brands.
In a recent conversation with Denise Holt, Senior Vice President and Head of Strategy, Experience, Research, and Insights at Phaedon, and Emily Merkle, Senior Vice President of Analytics and Data Science, they shared their thoughts about how brands are recalibrating loyalty strategies after a year of rapid experimentation, and what that means for the future of customer engagement.
Rather than viewing loyalty as a standalone program, both leaders argue the next phase of loyalty will be defined by systems that adapt to individual preferences, empower human interaction, and deliver value quickly and transparently.
From AI Predictions to Practical Application
In 2025, AI dominated loyalty discussions, often framed as a technology that would fundamentally replace human roles across engagement and service. According to Merkle, that expectation was overly simplistic.
“Yes, AI is here,” she said. “But organizations haven’t been able to fully productionize it at the scale people predicted, especially in consumer-facing environments.”
Instead of replacing people, AI has clarified where humans matter most. As automation increases, customers are becoming more intentional about the moments when they want real interaction. Holt emphasized that the real opportunity lies in using AI to remove friction from internal processes, allowing employees to focus on higher-value, emotionally meaningful interactions.
“AI should be amplifying human service, not eliminating it,” she said.
In that sense, the brands pulling ahead are the ones using AI to make loyalty experiences feel more personal, responsive, and human.
Why Many Brands Still Aren’t AI-Ready
Despite widespread interest, AI adoption remains uneven. Merkle pointed to foundational challenges that continue to slow progress, starting with data architecture.
“Gen AI and agentic AI require different data structures than traditional predictive models,” she said. “Organizations need time to capture, integrate, and operationalize that data correctly.”
Talent gaps are compounding the issue. While use cases are evolving rapidly, there are relatively few professionals with experience deploying AI responsibly at scale. Holt added that many brands spent 2025 focused inward, using AI primarily as an efficiency tool.Now, the focus is shifting outward.
“The next step is figuring out how AI enhances the customer experience, how it helps brands act faster, personalize better, and deliver value sooner,” Holt said.
Importantly, consumers are increasingly ready for that shift. As AI becomes part of everyday life, expectations for intelligent, context-aware brand interactions continue to rise.
Data Trust Is Built Through Action, Not Just Policy
Early hesitation around AI adoption was often rooted in governance concerns, particularly around customer data. Holt noted that many organizations delayed adoption until clear guardrails were in place.
Merkle stressed that governance is not just about protection, it’s about purpose. AI systems must be designed, so data is used only in ways that are relevant, transparent, and beneficial to the customer.
That benefit must be visible. “Customers are still willing to share data if they see value come back quickly,” Holt said.
Rather than collecting data broadly, brands are being pushed to ask more intentional questions and act on insights quickly, whether that’s through personalization, convenience, or choice. Transparency, Merkle added, is critical. Customers don’t need technical explanations, but they do need clarity around how their data improves their experience.
Loyalty Structures Are Getting More Complex and Fragile
As brands experiment with subscriptions, memberships, and paid tiers, loyalty structures are becoming more layered. Holt warned that complexity without clarity quickly becomes a barrier.
“When benefits overlap or tiers conflict, customers get confused. That confusion leads to disengagement,” she said.
Merkle emphasized that data and research should determine whether blended models make sense at all. Not every customer base supports a paid loyalty offering, and not every benefit justifies a price point.
The strongest strategies take a holistic view, ensuring that each layer of loyalty, whether free or paid, delivers distinct, understandable value. When done well, blended models can drive predictable revenue and deeper engagement.
Community Is Becoming a Loyalty Signal
One of the most notable shifts Holt and Merkle highlighted is the growing role of community and shared experience in loyalty design. As digital interactions increase, many consumers are seeking connection elsewhere, and loyalty programs are emerging as one place to find it.
“Community and belonging help restore human connection,” Holt said. “Loyalty programs can play a meaningful role there when it’s authentic.”
The key is alignment. Brands that succeed in this space build communities around shared values, not promotional mechanics. Merkle noted that customers can quickly sense when community efforts feel forced or performative.
AI supports these efforts by helping brands personalize how customers engage with community, such as identifying when someone is ready to participate, what kind of experience resonates, and how they prefer to be invited.
Partnerships Must Earn Their Place
Partnerships remain a major focus heading into 2026, but both leaders cautioned against volume-driven strategies. Holt noted that customers can only process so much choice before it becomes noise.
Merkle added that relevance matters more than reach. Partnerships must align with customer behavior, geography, and values to create real value. Otherwise, they add confusion rather than differentiation.
Looking ahead, brands are becoming more selective, and more intentional, about partnership strategy, using data and personalization to surface the right value to the right customer at the right time.
As brands head into 2026, the common thread across AI adoption, program design, community building, and partnerships is intentionality. The most effective loyalty strategies are not chasing complexity or novelty, but focusing on clarity, relevance, and value from the customer’s perspective. Technology will continue to expand what’s possible, but loyalty gains momentum when brands use those capabilities to strengthen relationships, respect choice, and show customers that their engagement is understood and valued.