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(see the * 'adobe' handle in functions.php — no async, no defer), so a page-load * Target offer has already set `data-hdr` by the time this runs. * * An answer that lands after this point is inert rather than acted on: * `apply()` finds the prune settled and does nothing, so the visitor * keeps the nav they were served instead of watching it change. That is * the deliberate trade-off — a late activity collects no data, but no * visitor ever sees the wrong nav or waits for the right one. */ (function () { var VARIANTS = ["a","b","c"]; var DEFAULT = "a"; /** * QA-only. When the on-page switcher is drawn we keep every variant in * the DOM so it can toggle between them live and you can see that the * chrome never repaints. Gated by the same constant as the switcher, so * it is false everywhere the switcher is absent — including production. */ var QA_KEEP_ALL = false; var root = document.documentElement; var settled = false; function valid(v) { return VARIANTS.indexOf(v) !== -1; } /** Publish the decision and let the nav JS bind. */ function finish(active) { root.setAttribute('data-hdr', active); // Read by the Adobe Analytics client to attribute the test. root.setAttribute('data-hdr-active', active); if (typeof window.initHeaderNav === 'function') window.initHeaderNav(); /** * `variant` is set before the event so late listeners can tell * "already done" from "not yet". main.js is enqueued in the footer * and therefore always misses the event itself. */ window.medalliaNav.variant = active; document.dispatchEvent(new CustomEvent('medallia:nav-ready', { detail: { variant: active } })); } function prune(active) { if (settled) return; settled = true; if (QA_KEEP_ALL) { finish(active); return; } /** * Only menu items carry data-nav-variant, so this can never touch * the shared chrome. CSS has already hidden the losers; removing * them keeps document.querySelector-based nav JS unambiguous. */ var list = document.querySelectorAll('#site-header [data-nav-variant]'); for (var i = 0; i < list.length; i++) { if (list[i].getAttribute('data-nav-variant') !== active) { list[i].parentNode.removeChild(list[i]); } } finish(active); } window.medalliaNav = { variant: null, apply: function (v) { if (valid(v)) prune(v); } }; // `?hv=` sets this server-side; a Target offer in the blocking // embed sets it before the body is parsed. var pre = root.getAttribute('data-hdr'); if (valid(pre)) { prune(pre); return; } /** * Carried forward from an earlier pageview. Read client-side, never in * PHP: WP Engine does not vary its page-cache key on arbitrary cookies, * so server-side personalisation here would serve the first anonymous * visitor's variant to everyone. */ var m = document.cookie.match(/(?:^|; )medallia_nav_variant=([^;]*)/); if (m && valid(m[1])) { prune(m[1]); return; } prune(DEFAULT); })();
Customer Experience

What Every Customer Experience Leader Needs to Know About Innovation with AI

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Many organizations say they “champion innovation.” But what does innovation mean for your customer experience, exactly?

(Don’t worry, there are no wrong answers!)

Innovation continually improves how your team works and how you show up for customers. You might fix an internal process, create helpful how-to videos, or make real-time customer data accessible for personalization.? Every idea is an innovation worth exploring.

Today, generative AI in particular redefines what’s possible by handling countless hours of work and delivering rich customer intelligence in seconds… but learning how to position AI effectively within your workflows is an innovation in itself!

It’s okay if you’re struggling to balance humans, AI, and innovation. (A lot of teams are.)

The best way forward is to start with a strategy that asks the right questions: Where does AI make your people faster and more informed, without adding friction? Where do data silos still stand between your team and the customer? And who stays accountable for what AI produces?

Here’s how to build an innovation strategy that keeps AI purposeful, and your people at the center of the experience.

The AI Divide Holding Teams Back From Scaling CX

Most customers haven’t felt a major change in their experience, despite the widespread adoption of AI. This means there’s a gap between how teams use AI and its actual benefit.

惭别诲补濒濒颈补’蝉 reveals where things stand:

  • Only 17% of customers say their typical experience interacting with companies has improved over the past year. (That means 83% don’t think it’s getting better!)
  • 58% (that’s a solid majority!) say they choose a company if it provides a better experience than its competitors.
  • 39% prefer whichever option resolves their issue fastest, humans or AI — and a similar percentage will pay more for human service when it counts.

CX remains highly influential in buying decisions. Customers accept AI if it’s used intentionally, but it can’t replace humans entirely.

Customers said organizations can improve CX through the following:

  • Deliver more personalized experiences
  • Keep humans involved in customer service
  • Provide faster service
  • Act on their feedback

These insights suggest a clear way to bridge the AI divide: Focus on frontline enablement.

AI Empowers Humans to Innovate with Purposeful Tools

AI is extremely powerful, but humans are the heart of your CX success.

Customers want fast, effective service and personalized experiences across a constantly changing mix of channels. Frontline employees need holistic, trusted customer data to achieve this personalization and continually meet customers where they are.

Organizations have adopted myriad tools, including AI, to balance workflow requirements and the employee experience with customer experience results, but data silos stop many from achieving true personalization and optimal team efficiency.

Connected toolsets are non-negotiable for driving innovation. You can see this in 惭别诲补濒濒颈补’蝉 Frontline-Ready AI?, which activates AI assistants that answer complex questions about your customer data and explain what’s happening, and what you can do next. It’s one of several future-looking solutions that can replace many standalone tools and complement an experience tech stack.

AI enables the agility and data-driven confidence required to meet modern scale and continually innovate. But AI alone can’t guarantee your success.

Intentionality wins in the AI age. An AI strategy is essential to identify where AI works best, how it makes humans smarter and faster, and whether your efforts are paying off.

Key Considerations for Developing an AI Strategy for CX

AI removes countless hurdles to save time, avoid errors, and deliver insights your team needs to make informed decisions. Your team can gradually augment high-frequency or time-intensive tasks, such as analyzing customer feedback trends, unifying customer profiles, or coordinating post-purchase nurture campaigns, with AI to achieve substantial time savings.

AI also extends your team’s mental capacity to innovate on seemingly small moments that build toward remarkable experiences. This is the exact type of innovation your team wants to unlock.

An AI strategy establishes clear guardrails around AI and enables purposeful growth. Revisit your existing strategy or start one by exploring the following areas with your team:

  • Workflow challenges. What are the most time-intensive, high-frequency, or error-prone tasks that AI can help with? These offer valuable starting points that can deliver a clear before-and-after time saving.
  • Personalization gaps. Where do you see an opportunity to better personalize your customer experience? Is data access, technical capability, or something else limiting personalization? Speak with your team to drill into specifics.
  • Customer intelligence. Do you have a trusted source of truth on individual customers? Where do data siloes exist, and how can you eliminate them to ensure every customer interaction fuels ongoing improvement? AI performs best when it acts on your complete dataset, and teams need accessible data through every tool they use.
  • AI governance. How will you retain control over AI systems and ensure human oversight of all outputs? Your technical leadership and legal team will provide valuable guidance here.
  • Team structures. How does your team’s day-to-day reality change with AI? Can revamped roles, responsibilities, or reporting structures better position everyone for success?

There is no single ‘right’ way to start with AI. Start with use cases that make your humans faster, more informed, and less involved with manual or repetitive work. These early use cases will reveal new opportunities and areas to refine your strategy, which makes innovation a daily habit.

Humans Design Fulfilling Customer Experiences

AI is an innovation catalyst, but not a standalone solution. Your frontline workers need a conscious mix of tools powered by your complete customer data to make every experience personalized, fast, and consistent.

An AI strategy is the foundational element many teams need to turn scattered implementations into a clear, cohesive path forward. With purposeful AI, humans can focus their attention on identifying emerging issues or opportunities, measuring activities to make consistent strategy improvements, and introducing new ways to nurture customer loyalty that lasts.

All your team’s efforts have led to this. With an AI strategy to guide you, there’s no limit to what you as humans can achieve next.

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