Change Management For Customer Experience

Want your team to actually adopt AI? Pay them first, then train them. That's the counterintuitive approach Lauren Franklin, VP of Customer Experience at Zapier, took with her support team – and it's working. Rollouts of AI in customer service are often done outside the team: turn off the flow that goes to the humans, then lay people off. Not at Zapier. Lauren made clear to her team that they had to adapt, learn new skills, and hit new performance standards. She also: 🔸 Raised base pay for her entire customer support team 🔸 Invested heavily in hands-on training, experimentation time and cross-functional support 🔸 Rolled up her sleeves -- she was in the queue every Monday morning The results: Her team is now resolving significantly more customer issues while maintaining quality scores and customer happiness. Here's what I've learned from talking with Lauren and Brandon Sammut: ✅ Be specific about performance expectations. Franklin didn’t speak in generalities about “embracing change.” She set concrete standards for customer satisfaction and resolution rates, as well as efficiency metrics. ✅ Lead from the front lines. Working alongside employees using new tools provides insights about what's real, what's hype, and where teams are stuck. ✅ Invest in people before demanding results. Whether it's pay, training time, or both, demonstrate commitment upfront. Franklin's approach recognizes a fundamental truth: people won't fully embrace technology that feels like a threat to their livelihood. How are you helping your teams see AI as an opportunity rather than a risk? 🔗 Read the full story about Zapier's approach in TIME: https://lnkd.in/gyk2TpVb Huge thanks Jacob Clemente and Kevin Delaney at Charter -- if you're a Charter Pro subscriber, you saw this article already! #GenAI #ChangeManagement

AI has changed customer service forever. Looking at past trends won’t cut it anymore: We’ve entered a completely new AI-first era. To reflect this shift, Intercom has replaced its annual “Customer Service Trends Report” with the “Customer Service Transformation Report.” They surveyed 2,000+ customer service professionals to understand how they’re adapting - and what’s changing fast. Intercom also created a snapshot of how this transformation is impacting support teams in finance and fintech in particular. Here are a few key highlights: 1. Investments: 91% of support teams in finance and fintech invested in AI in 2024, and 93% plan to invest in 2025. AI adoption is clearly accelerating. 2. Customer Attitudes: 94% say customer attitudes toward AI have shifted in the past year. AI is no longer a novelty - it’s an expectation. 3. Service Economics: 92% agree AI is transforming customer service economics. Faster, smarter, and more cost-effective support is now possible. 4. Lots of Work Ahead: Only 25% of teams say their current tech fully supports their needs. Outdated systems need to be transformed. The future of customer service is here. And this is just the beginning. Check out the full finance and fintech snapshot: https://lnkd.in/eezP8sSD

As the Head of Capgemini Business Services's, I’ve had the privilege of working with numerous industry leaders to navigate the complexities of customer service. Our latest research from the Capgemini Research Institute highlights a critical insight: while nearly 60% of consumers view customer service as crucial to their brand perception, less than half (45%) are satisfied with their experiences. This gap presents a significant opportunity for transformation. In the GBS industry, we often face operational challenges that hinder our ability to deliver seamless customer experiences: Interdepartmental Coordination: A staggering 74% of executives say this is a major barrier, leading to fragmented and inconsistent experiences. Underutilized Customer Insights: Only 50% of organizations use customer service data in decision-making processes, missing key opportunities to enhance CX and operations across departments. High Agent Churn: Just 16% of customer service agents are satisfied in their roles, highlighting the need for better support and engagement. To drive satisfaction and loyalty, organizations need to improve collaboration between departments and make better use of customer insights across the business. AI is a game-changer here, and those that have implemented Gen AI are already seeing the benefits. AI can improve response times and cut operating costs. Generative and agentic AI can also enhance the experience for agents by providing real-time customer data from across departments like sales and marketing. Virtual agents are taking over repetitive tasks, and most agents (70%) are seeing a lighter workload, allowing them to focus on more valuable interactions. I believe that by leveraging AI and building a connected enterprise, businesses can transform customer service from a support function into a key strategic driver of value and growth. Read more in the comments below: https://lnkd.in/e6RhY2bN

A recent Gartner survey dropped a staggering stat, in that 91% of customer service leaders say they are under pressure from executives to implement AI this year. That pressure is not coming from the need for just a POC. It’s coming from budgets, headcount, and leaders asking a simple question, “Why are we still paying for work that AI can now handle?” And players like Decagon, Sierra, Cresta, Parloa, and big incumbents like NiCE have a fast-growing book of business around this demand. Here’s what’s happening in five parallel paths: First, AI is being treated like a cost program. The contact center ask is “do more with less,” as fast as possible. But this can lead to shortcuts and sloppy rollouts if not managed carefully. “Digital Transformation 2.0” anyone? Second, customers are definitely feeling it. More self-service. More automation. More “try this first” before a human ever shows up. Taking stock of where a human needs to be in the conversation depending on the journey is key before you begin. Automate everything is a recipe for failure. Third, the work that does stay with humans gets heavier. Harder cases for agents, more emotion for customers, and more judgment calls by supervisors. Interestingly, Gartner also says many organizations plan to expand human responsibilities, even as routine work gets automated. I’m encouraged by this per my point above. Fourth, the gap between good and bad service is widening. Teams that invest in clear rules and safe handoffs will get better. Teams that bolt on a bot will burn trust, and at scale now. Lastly and fifth, governance is quickly becoming a real product. It’s no longer a committee. Simple rules like what the system is allowed to do, what it must never do, how it proves it did the right thing in the first place, are all key questions to answer before deployment. Oh, and someone needs to own it (not the machine). When the pressure hits your team this year, how will you optimize for speed AND for trust that lasts? #customerservice #contactcenter #customerexperience #ai

In today’s competitive European market, customer service must go beyond simply resolving issues and become a true driver of brand loyalty and revenue. New research from the Capgemini Research Institute reveals fewer than half of consumers are satisfied with their customer service resolution experiences. This presents a significant opportunity for organizations to transform their customer service functions. Generative and agentic AI are already driving impressive results - delivering faster resolutions, improving efficiency, and shifting customer service from a support function to a strategic value driver. Across Europe, we’re witnessing this transformation take shape, according to the report: ✅ The Netherlands leads in Gen AI adoption, with over half of organizations implementing it. ✅ Italy is close behind, with 46% integrating Gen AI to enhance customer interactions. ✅ Across the region, AI-driven solutions are reducing service costs, improving resolution times, and making customer service a strategic value driver. For European business leaders and executives, the message is clear: by harnessing a blend of human and virtual agents, augmented by AI, businesses can redefine and elevate customer service. This shift not only enhances customer satisfaction but also unlocks new revenue opportunities, positioning customer service as a pivotal element of strategic growth. Let's embrace this opportunity to lead the way in customer service innovation. Read the full report to explore the trends shaping the future: https://lnkd.in/gVsxe-iC

AI that saves money but frustrates customers is a liability For years, companies rushed into AI with one goal: automate, cut costs, do more with less. But something was missing. That's, the customer. The new wave of AI adoption is shifting focus: from operational gains to customer-centric AI that humanizes interactions, builds trust, and creates loyalty. 📌 Personalization at scale is becoming the new baseline. 📌 Hybrid approaches: AI + human touch are driving better service outcomes. 📌 Metrics are evolving from time saved to trust, integrity, and emotional engagement. 𝐖𝐡𝐚𝐭'𝐬 𝐭𝐡𝐞 𝐥𝐞𝐬𝐬𝐨𝐧 𝐡𝐞𝐫𝐞? Efficiency might get you short-term ROI, but it’s experience that creates long-term value. In Issue #12 of Meaningful AI, I explore how forward-thinking organizations are moving beyond optimization toward transformation, designing AI that enhances, rather than diminishes, the customer journey. 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐲𝐨𝐮: When you think about AI in your organization, are you measuring efficiency, or are you measuring experience? Because in the end, customers don’t remember your automation. They remember how you made them feel.

Most companies assume customers prefer digital self-service. But here’s what the data shows instead: customers prefer 𝐜𝐥𝐚𝐫𝐢𝐭𝐲. According to a new report from Liferay, 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐭𝐰𝐨-𝐭𝐡𝐢𝐫𝐝𝐬 𝐨𝐟 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬 𝐡𝐚𝐯𝐞 𝐚𝐛𝐚𝐧𝐝𝐨𝐧𝐞𝐝 𝐚 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐭𝐚𝐬𝐤 simply because the process was too annoying. Your AI may have counted those tickets as “resolved” or “deflected.” But they’re actually failures. Some of these customers ended up in your phone, email, or chat queue. Others ended up switching to your competitors. Customers' frustration is rarely about the technology itself. It’s about poor implementation—vague instructions, clunky flows, and no clear way to escalate. If your self-service journey leaves customers frustrated, it’s not a convenience. It’s a churn risk. Strong self-service doesn’t mean hiding your humans. It means structuring your systems around three fundamentals: 👍 Clear, updated knowledge bases on all the most relevant topics  👍 Built-in paths that let customers escalate to a human   👍 Feedback loops that treat the knowledge base as a living system According to Gartner’s Keith McIntosh, “The key characteristic that makes for a seamless digital self-service experience is guidance.” Not automation for automation’s sake—but systems that support customers all the way through. Peloton Interactive is a great example. When my bike randomly stopped working, my first instinct was to reach out to phone support. But the chat self-service was incredible. It included annotated photos and super-short videos (7 seconds) showing me exactly what to do each step of the way. Even more important, the troubleshooting steps actually worked. Within five minutes I was back on the bike.  As companies navigate tech transformation, this is the gap that will define winners and losers in CX. It’s not whether you implement automation & AI. It’s how well you do it.  

Why technology rarely fails but adoption often does? Every client I’ve worked with gets excited about new technology. Automation, AI, GenAI, platforms, dashboards - the energy is real. There’s leadership buy in, funding, roadmaps, pilots, and often a moment of celebration when the solution finally goes live. And yet, a few months later, the questions quietly begin to surface. Why isn’t this being used the way we expected? Why hasn’t the value landed? Do we need yet another tool? After an year of working across global customers as a Change and Adoption partner, I’ve come to a simple but uncomfortable truth: technology rarely fails. Adoption does. This is why Everett Rogers’ Diffusion of Innovation often referred to as the Adoption Curve - remains one of the most powerful lenses for understanding digital transformation, even decades after it was introduced. Not because it explains technology, but because it explains people. Most organisations design transformation for Innovators and Early Adopters. They enjoy experimentation, tolerate ambiguity, and often make pilots successful. But together, they represent only about 16% of the population and pilot success is not adoption, it’s validation. The real challenge begins with the Early Majority. This is where most initiatives stall. The gap between early success and scale isn’t technical; it’s emotional. Fear of change, fear of loss of control, and fear of relevance often cause even well-designed solutions to quietly fail at the frontline. The Early Majority doesn’t adopt because something is innovative. They adopt because it works consistently, fits into daily workflows, and has clear proof of value. Organisations that cross this chasm stop selling features and start demonstrating outcomes. They treat adoption as a capability, not a phase designing for trust, visibility, and relevance from the start. Across the customers I’ve worked with globally, I’ve seen that real transformation begins when people feel supported, confident, and genuinely included in the change - that’s when technology stops being introduced and starts being embraced. Let’s not just do more with technology, let’s do it better with people at the centre. 🦾 🧠 #TheHeartOfProgress #HumanPoweringProgress #TechnologyAdoption #ChangeLeadership #DigitalTransformation #HumanCenteredChange #InnovationInPractice #EnterpriseTransformation #LeadershipPerspective

HISTORY REPEATING: 𝐀𝐫𝐞 𝐖𝐞 𝐌𝐚𝐤𝐢𝐧𝐠 𝐭𝐡𝐞 𝐒𝐚𝐦𝐞 𝐌𝐢𝐬𝐭𝐚𝐤𝐞 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐞𝐫𝐯𝐢𝐜𝐞? 𝐓𝐡𝐞 𝐞𝐚𝐫𝐥𝐲 2000𝐬 𝐨𝐮𝐭𝐬𝐨𝐮𝐫𝐜𝐢𝐧𝐠 𝐰𝐚𝐯𝐞 𝐩𝐫𝐨𝐦𝐢𝐬𝐞𝐝 𝐦𝐚𝐬𝐬𝐢𝐯𝐞 𝐜𝐨𝐬𝐭 𝐬𝐚𝐯𝐢𝐧𝐠𝐬. 𝐒𝐨𝐮𝐧𝐝 𝐟𝐚𝐦𝐢𝐥𝐢𝐚𝐫? In the early 2000s, businesses rushed to outsource call centers, chasing 50% cost reductions on paper. By 2008, reality hit hard—hidden costs from rework, retraining, customer churn, and quality issues often tripled the expected savings. Yet companies kept chasing the headline numbers. 𝐅𝐚𝐬𝐭 𝐟𝐨𝐫𝐰𝐚𝐫𝐝 𝐭𝐨 2025: 𝐀𝐈 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐞𝐫𝐯𝐢𝐜𝐞 𝐢𝐬 𝐦𝐚𝐤𝐢𝐧𝐠 𝐢𝐝𝐞𝐧𝐭𝐢𝐜𝐚𝐥 𝐩𝐫𝐨𝐦𝐢𝐬𝐞𝐬. The parallels are striking: ↪️ 𝐓𝐇𝐄𝐍: "Outsourcing will cut labor costs in half!" 𝐍𝐎𝐖: "AI can reduce customer service costs by up to 30%!" ↪️ 𝐓𝐇𝐄𝐍: Hidden costs buried projected savings 𝐍𝐎𝐖: 44% of organizations have experienced negative consequences from AI implementation, mostly from rushing without proper planning ↪️ 𝐓𝐇𝐄𝐍: Customer satisfaction plummeted due to process gaps and lack of proper training 𝐍𝐎𝐖: 46% of consumers hate chatbots, and 44% of customers prefer human agents over AI ↪️ 𝐓𝐇𝐄𝐍: "Set it and forget it" mentality led to disasters 𝐍𝐎𝐖: 60-80% of AI initiatives fail before making it past proof of concept 𝐓𝐡𝐞 𝐬𝐚𝐦𝐞 𝐫𝐞𝐝 𝐟𝐥𝐚𝐠𝐬 𝐚𝐫𝐞 𝐰𝐚𝐯𝐢𝐧𝐠: 🚩 Projected vs. proven ROI: Only 25% of contact centers have successfully integrated AI automation 🚩 Hidden costs emerging: Integration complexity, ongoing supervision, manual intervention for failures, retraining teams 🚩 Customer experience risks: 57% of consumers dislike automated customer service for complex issues 🚩 Over-automation backlash: 45% cite maintaining personalized experience as the biggest AI challenge The lesson isn't "avoid AI"—it's "learn from outsourcing." The companies that succeeded with outsourcing eventually adopted hybrid models, invested in quality control, and kept customer experience at the center. The same applies to AI. 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐬: ✅ Realistic cost modeling (including hidden expenses) ✅ Gradual rollouts with constant CX monitoring ✅ Hybrid human-AI approaches, not wholesale replacement ✅ Long-term investment in quality and training 𝐁𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: With $47.82 billion projected for the AI customer service market by 2030, we're at a critical juncture. Let's not repeat the outsourcing mistakes—chase sustainable transformation, not just cost reduction headlines. 𝐓𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 𝐭𝐡𝐚𝐭 𝐠𝐞𝐭 𝐀𝐈 𝐫𝐢𝐠𝐡𝐭 𝐰𝐢𝐥𝐥 𝐝𝐨𝐦𝐢𝐧𝐚𝐭𝐞. Those chasing quick savings without considering customer impact? They'll face the same painful reality check that outsourcing taught us 20+ years ago. Your Thoughts 👇 #CustomerExperience #AI #CustomerService

Over the last 6 months, the conversation about AI in customer service has evolved into a really nuanced one. From talking about AI simply replacing entire customer service teams an year back, we're now looking at really thoughtful and impactful application of AI in customer service. I recently spoke to Christian Sokolowski from Rebuy Engine about how his team built AI for customer service that doesn’t just automate, but amplifies human judgment. They use daily tuning, empathy guardrails, and real-time sentiment analysis to keep the experience personal, even when a bot starts the conversation. The result? Faster resolutions, less burnout, and a team that feels more connected not less. Tune in to the full conversation here: https://lnkd.in/gs8Y5YJY [I didn't "sit down" with Christian - I just spoke to him :) I think 'sit down' is to speak is what peruse was to read.]