In a landscape where economic anxieties are high and artificial intelligence is rapidly reshaping consumer interactions, we sit down with Aisha Amaira, a leading MarTech expert. With a deep background in CRM technology and customer data platforms, Aisha brings a sharp focus to how businesses can navigate these turbulent waters. Her work centers on leveraging innovation not just for efficiency, but to forge genuine connections and build lasting brand loyalty. We’ll explore the pivot from transactional relationships to trust-based partnerships, discussing how brands can provide certainty in uncertain times, master the delicate handoff between bots and humans, and redefine value in a world tired of generic perks. The conversation will also cover the new frontier of AI-driven product discovery and how to communicate with customers in a way that’s helpful, not haunting.
With consumers shifting to a recessionary mindset focused on self-protection, how can brands pivot their value proposition? Can you share a step-by-step example of how a company might position a product as a trustworthy “investment” to increase certainty for anxious customers?
That’s the core challenge right now. We’re moving past an inflationary mindset, where people were just trying to make their dollars go further, into a true recessionary one. This new mindset is driven by a deep-seated need for security and self-protection. It’s less about chasing deals and more about avoiding mistakes. What customers prioritize above all else is certainty. They don’t have the mental or financial bandwidth to deal with a poor purchase. So, for a brand, the key is to reframe their product as an investment. Imagine a company selling a high-end coffee machine. Step one is to shift the marketing language away from “It’s the coolest, sleekest machine” to messaging that emphasizes durability and long-term value. Step two would be to proactively highlight the 5-year warranty, the money saved per year by not going to a coffee shop, and the quality of the components. The final step is to package this all together as an “investment in your morning ritual,” something reliable that will bring you joy and save you money for years to come. This transforms a discretionary purchase into a smart, safe choice, which is exactly what an anxious consumer is looking for.
Given that many consumers find AI support unhelpful and get trapped in “self-service mazes,” what are the critical steps to designing a better handoff from a bot to a human agent? How can a business measure the success of this transition to avoid customer frustration?
The “self-service maze” is a perfect description of the frustration so many customers feel. The data is stark—nearly one in five consumers say they get zero benefit from AI support, which is a catastrophic failure. The most critical step in designing a better handoff is ensuring a seamless transfer of context. The customer should never, ever have to repeat themselves. The bot’s entire transcript, the customer’s account information, and the attempted solutions must be delivered to the human agent before they even say hello. This makes the customer feel heard and respected. To measure success, businesses need to look beyond traditional metrics like “average handle time.” Instead, they should focus on “customer effort score.” After an interaction, ask a simple question: “How easy was it to get your issue resolved today?” Success is a low-effort score. You can also track how often a handoff leads to a first-contact resolution. If the human agent can solve the problem without further transfers, the handoff was successful and you’ve avoided pushing that operational burden onto your customer.
As over a third of consumers use AI for product research, the customer journey now often begins outside a brand’s ecosystem. What practical steps should CX leaders take to redesign their journey maps and buyer personas to account for this new AI-driven discovery phase?
The first and most important step is a mental one: CX leaders must accept that their customer journey no longer starts on their website or social media page. It starts with a natural language query in a third-party AI tool. Acknowledging this reality is foundational. From there, the next practical step is to evolve the buyer persona. Instead of just demographics and pain points, you need to add a section called “AI Research Profile.” What kinds of questions would this persona ask an AI? What kind of concise, tailored advice are they seeking? Finally, you must physically redraw your customer journey map to include this “pre-discovery” phase. This means your website experience has to change. You have to assume visitors arriving from an AI engine are more informed than ever before. They don’t need the basic pitch; they need validation, deeper specifications, and social proof that confirms the AI’s recommendation.
Consumers are willing to share data for compelling rewards but are tired of generic discounts. Beyond coupons, what specific, persona-based benefits can companies offer to earn trust? Could you walk through an example of a value proposition that helps a customer achieve a personal goal?
Absolutely. The generic 10% off coupon in exchange for an email is dead. While more than two-thirds of consumers are willing to share data, it’s for a truly personalized and rewarding experience. Trust is earned by showing you can use that data to help them, not just sell to them. It’s about creating a value proposition that helps a customer accomplish a goal. For instance, consider a home improvement store. Instead of offering a coupon, they could say, “Share the details of your DIY bathroom remodel project with us—your budget, your style preferences, your skill level—and we’ll provide a step-by-step project plan, a customized shopping list, and access to a 15-minute video call with a project expert.” This is incredibly valuable. It saves the customer time, reduces their anxiety about the project, and positions the brand as a helpful partner. The brand gets rich data, and the customer gets tangible help toward a personal achievement, which builds far more loyalty than a simple discount ever could.
The need for websites to be readable by AI tools has been compared to the early days of SEO. What specific actions should CX, marketing, and tech teams collaborate on to ensure their brand appears in AI-powered search results? What are the biggest risks of ignoring this?
The comparison to the early days of SEO is spot-on; this is a fundamental paradigm shift that requires a unified front. It can’t be siloed. First, the tech team needs to ensure the website’s back-end is built with structured data that AI can easily ingest and understand. Think of it as creating a clear, legible blueprint for the bots. Second, the marketing team must shift its content strategy to focus on creating clear, concise answers to the natural language questions customers are asking. Finally, the CX team is the glue that holds it together. They must use their voice-of-the-customer data to inform marketing about what those crucial questions actually are. The biggest risk of ignoring this is simply becoming invisible. If a potential customer asks an AI for a recommendation and your competitors show up in that generated summary but you don’t, you have been eliminated from the consideration set before you even knew you were in the running. It’s a threat to your brand’s digital existence.
Loyalty programs must balance helpful communication with avoiding an “eerie” or annoying frequency. What are some best practices or metrics for finding that sweet spot? Can you provide an example of messaging that is helpful and transactional without feeling overly familiar to the customer?
Finding that balance is an art. Customers understand that brands are tracking them, but they don’t want it thrown in their face. The relationship is transactional, and the communication should reflect that. The best practice is to always frame messaging around utility and empowerment for the customer. For metrics, I’d look at the unsubscribe rate after a message is sent and the engagement rate on the offers themselves. A great example of helpful, non-eerie messaging would be a simple monthly or quarterly summary. Something like: “Hi Sarah, here’s your loyalty update. In the last three months, you’ve saved $62 with us and earned enough points for a free coffee. Click here to redeem.” It’s direct, it quantifies the value you’ve provided, and it’s helpful without being overly familiar or making assumptions. It respects the transactional nature of the relationship while still reinforcing the benefits of being loyal.
What is your forecast for customer experience in 2026?
My forecast for 2026 is a great divide in customer experience. We are going to see a clear bifurcation between brands that get it right and those that fall behind. The laggards will deliver fragmented, frustrating experiences. Their customers will feel trapped by clumsy AI, ignored by human agents, and spammed with irrelevant offers based on poorly used data. But the winners—the brands that will thrive—will master the human-AI partnership. They will use AI to make discovery effortless and then deliver what humans crave most: certainty, reliability, and genuine value. Their competitive advantage won’t come from just having the technology, but from using it to build trust, reduce customer effort, and provide a sense of security in an increasingly uncertain world.