When tech saves your customers time and hassle, it\u2019s a win-win. But when customers have to put in more work to get the same results (or worse), they\u2019re not going to be happy \u2014 especially when they\u2019re already hangry.<\/p>\n
AI and the Context Switching Problem <\/p>\n
While we\u2019ve seen significant progress recently, many of today\u2019s AI-powered CX solutions<\/a> still require context switching, which adds friction and results in a poor customer experience<\/a>. Ask yourself\u2014 when your customers use your chatbot, can they do everything they need in the same window, or do they have to check their texts for a link, navigate a separate payment system or open a new tab for more info? As Microsoft noted, \u201cif interacting with AI feels unnatural, adoption suffers.<\/a>\u201d<\/p>\n Context switching hurts customer retention. The online restaurant menu frees up employees (at least theoretically), but at what price? How many of your customers would have given up after five minutes of trying to order a burger and fries? Journey fragmentation will be \u201cthe greatest barrier to CX excellence<\/a>,\u201d according to a Capgemini retailer survey. Employees often pay the price. AI platforms that don\u2019t deliver a seamless experience put more stress on your frontline employees when they have to do what AI should have done \u2014 all while dealing with frustrated customers. <\/p>\n Of course, that\u2019s not even accounting for the risks of AI-washing, which can further erode customer trust<\/a>.<\/p>\n Your goal for agentic AI should be to make flow portable, allowing work and context \u201cto move together across systems without breaking the customer story.<\/a>\u201d When it\u2019s done right, you get success stories like Lenovo improving customer service efficiency by 50%, and<\/a> IBM supporting an AI-powered assistant with a 94% customer satisfaction rate<\/a>.<\/p>\n Related Article: Where Agentic CX Pays Off First (And Why It’s Not Customer-Facing)<\/a><\/p>\n Why You Need to Hold AI Agents to the Same Standards as Humans <\/p>\n Is a 2x time savings for the server worth making the customer\u2019s experience 10x worse?<\/p>\n Even if it were the other way around \u2014 a 10x savings for the waitress for a 2x worse customer experience, many CX leaders would say it\u2019s not a smart trade-off.<\/p>\n Humans are still more efficient at many CX tasks, at least from the customer\u2019s standpoint. The server at the restaurant didn\u2019t ask me to scroll through pages of appetizers and entrees \u2014 she just took my order. And even if she messed up, customers are willing to accept more mistakes from humans than from AI<\/a>.<\/p>\n Overall, people still prefer human support vs. AI<\/a>, according to Capgemini research. That said, the desire for human-driven CX is stronger in high-risk industries like banking and pharmaceuticals, while customers actually preferred conversational AI <\/a>support in retail and travel \u2014 all the more reason to improve your AI platforms.<\/p>\n If you\u2019re thinking about firing a human and replacing them with AI, the standard shouldn\u2019t be \u201cdoes the AI function from a technical standpoint?\u201d Instead, look at it from the customer\u2019s perspective, and ask if the AI is at least as good as the human. If not, you\u2019re just disrespecting your customers.<\/p>\n View All<\/a> <\/p>\n Ultimately, AI-to-AI commerce \u2014 when your customers\u2019 agents talk directly to your company\u2019s agents \u2014 may remove the friction. But until then, it\u2019s your job to watch out for any solution that makes your customers do more work.<\/p>\n Reducing AI Friction in Customer Experience: Key Shifts and Actions <\/p>\n Editor’s note: The following table highlights the most important lessons, actions and strategic considerations emerging from how to deliver a more frictionless AI-driven customer experience.<\/p>\n Key AreaWhat HappenedWhy It MattersRecommended ActionArchitectureHeadless CX decouples the front-end presentation layer from back-end dataEnables a consistent experience across any channel or deviceEvaluate headless architecture<\/a> to minimize context switchingInterface DesignDrivers completed tasks roughly twice as fast with physical knobs than touchscreens<\/a>Human-centered design can outperform flashier AI-driven interfacesPrioritize speed and ease of use over interface noveltyAI OrchestrationOrchestration layers route requests to the agents and data that can act on themPoor orchestration causes failures on out-of-the-box requestsAsk vendors to demonstrate exactly how orchestration connects LLMs, chatbots and dataService-Level ObjectivesSLOs define the outcomes that actually matter to the business, like a three-minute booking flowAI adoption without SLOs risks optimizing for the wrong thingEstablish SLOs first, then evaluate whether AI helps meet themContextual DataReal-time context (like weather) can shift what’s promoted to customersContextual relevance drives more authentic, timely interactions that deepen customer loyalty<\/a>, per IBMBuild contextual data into AI-driven recommendationsOutcome-Driven CXNearly 80% of executives are shifting CX from query-based interactions to outcome-driven experiences<\/a>, per CapgeminiCustomers increasingly expect nuanced, goal-based requests rather than simple queriesDesign AI to handle outcome-based requests, not just direct questionsDevice UptimeKiosk, POS and endpoint outages undermine AI regardless of its qualityAI is only as reliable as the hardware delivering itInvest in endpoint monitoring to proactively catch and fix device issues <\/p>\n Right now, you\u2019re caught between two worlds \u2014 your CFO wants the cost savings of AI, but your customers want their burger and fries without tapping through five different screens. But your customers shouldn\u2019t be paying the price for your shiny new AI. Because even if your customers don\u2019t have a plane to catch, their patience \u2014 and their loyalty \u2014 will only go so far.<\/p>\n
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