Building a Customer-Centric Culture With AI

CX and EX teams that say “we’re using AI” often mean they’ve added a tool, not changed how they operate. When AI shifts how decisions are made, how teams prioritize, and how outcomes are measured, then you're onto something. Remember: It’s not about more automation. That's been done for decades. It’s now about better orchestration, with intelligence guiding every move. Let AI interpret what’s driving effort, churn, or confusion, then prioritize opportunities that tie directly to revenue or cost. Act fast, track outcomes, and adapt. Speed up wins in environments where customer expectations change regulalry. And while there is power in action, the real juice comes in energizing the business around the outcome. When product, ops, and frontline teams all know what metric they’re moving and how, you create alignment that no dashboard alone can deliver. Share the results, celebrate progress, and make customer-led thinking contagious. Real-world example: one firm unified chat, survey, and usage data to spot a single feature driving support calls. AI flagged the issue in real time. Teams pushed an in-app guide live in hours, dropping calls by 25% in a week. That’s not magic. That’s modern CX, done right. #customerexperience #employeeexperience #ai #cxleaders #outcomesoveraction

In a world where AI announcements seem to drop every 15 minutes (seriously, it’s so hard to keep up), I've been reflecting on what actually matters beyond the hype. As a people leader navigating this landscape, I've learned that the challenge isn't just adopting AI tools quickly—it's adopting them thoughtfully. This is especially important at HubSpot, where helping our employees move faster helps our customers win faster. I'm seeing AI reshape not just what we do, but how we make decisions and prioritize our people. Here are some approaches that have worked well for us as we continue to test and learn: 1. Expedite access to AI tools and encourage experimentation. We're experimenting with the latest versions of Claude, Gemini, ChatGPT, and more—providing teams access within hours of new releases, not weeks. This creates a culture of experimentation and keeps us ahead of the curve. 2. Foster knowledge-sharing. We've created dedicated channels where employees share their AI wins and habits. Our People team sends a weekly "MondAI" digest featuring different employee use cases that inspire others across the organization. 3. Prioritize leader enablement. We've built AI-first resources, starting with People Leaders who then cascade knowledge to their teams. This isn't just about tools—it's about developing judgment for when AI enhances human work and when human expertise should lead. 4. Seek external expertise. We regularly bring in experts from companies like Anthropic and Google to share insights with our teams. We've cultivated a culture of learn-it-alls, not know-it-alls. 5. Integrate AI into existing workflows. We're incorporating AI tools directly into team processes, focusing on high-impact, repetitive tasks first. Our AI support bot now handles over 35% of tickets while maintaining high customer satisfaction. The most exciting part? Watching our teams develop the discernment to make AI work harder for them, not the other way around. When people and technology make each other stronger—that's the sweet spot. Fellow people leaders: How are you balancing rapid AI adoption with thoughtful implementation that truly empowers your people? Other insights we can learn from?

The AI-infused workforce is here. You can't just implement a chatbot, reduce your team size, and expect to keep meeting customer expectations. You have to reimagine the whole customer experience team. You need to think about both humans + technology and how they work together. What does this mean? ⭐ 𝐕𝐨𝐢𝐜𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐜𝐚𝐧 𝐫𝐚𝐭𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐀𝐍𝐃 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐧 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞. These platforms can monitor 100% of tickets in real time - far superior than the current method of combining CSAT & QA. But they have to be managed properly. You can't just set it and expect the insights to flow in. Which leads us to.... ⭐ 𝐍𝐞𝐰 𝐫𝐨𝐥𝐞𝐬 𝐚𝐫𝐞 𝐞𝐦𝐞𝐫𝐠𝐢𝐧𝐠. These include: 𝘊𝘰𝘯𝘷𝘦𝘳𝘴𝘢𝘵𝘪𝘰𝘯 𝘔𝘢𝘯𝘢𝘨𝘦𝘳. This is a role that Intercom has created on their own internal support team. The focus is managing the whole customer experience journey, as customers flow from the bot to human agents. 𝘒𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘔𝘢𝘯𝘢𝘨𝘦𝘳. Another role pioneered by Intercom, this person is responsible for keeping the knowledge base up to date and making sure it's written not just for humans to understand, but for computers to understand. 𝘝𝘰𝘪𝘤𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘊𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘈𝘯𝘢𝘭𝘺𝘴𝘵. VOC analysts will replace QA analysts. Instead of completing audits they'll be monitoring VOC systems for trends and insights that will be used to drive trainings and process improvements. 𝘈𝘐 𝘊𝘢𝘭𝘪𝘣𝘳𝘢𝘵𝘪𝘰𝘯 𝘚𝘱𝘦𝘤𝘪𝘢𝘭𝘪𝘴𝘵. Someone who will be in charge of continually monitoring performance of the AI bot, as well as other automations, to ensure they are doing their job. ⭐ 𝐏𝐨𝐝𝐬 𝐨𝐟 𝐬𝐮𝐛𝐣𝐞𝐜𝐭 𝐦𝐚𝐭𝐭𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐭𝐬 𝐰𝐢𝐥𝐥 𝐫𝐞𝐩𝐥𝐚𝐜𝐞 𝐭𝐞𝐚𝐦𝐬 𝐨𝐟 𝐠𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐬𝐭𝐬. AI will take on the simpler tickets, and what's left will be increasingly complex. According to Declan I., the VP of Support at Intercom, the best way to manage this complexity is to upskill agents so they have particular areas of expertise, which is what Intercom has done. ⭐ 𝐘𝐨𝐮 𝐧𝐞𝐞𝐝 𝐧𝐞𝐰 𝐊𝐏𝐈𝐬 Your AI and automation tools need their own KPIs, like AI Resolution Rate. More on this in a future post. ⭐ ... 𝐀𝐧𝐝 𝐦𝐮𝐜𝐡 𝐦𝐨𝐫𝐞 Agents will need different schedules and breaks to avoid burnout. More and better training is needed for agents. And so on. ⭐ ⭐ ⭐ ⭐ 𝐈𝐍 𝐒𝐔𝐌𝐌𝐀𝐑𝐘 ⭐ ⭐ ⭐ ⭐ AI tools aren't "set it and forget it." You need to reshape your team, and then you need multiple people on your team who can monitor your AI KPIs and recalibrate the technology when KPIs start to slip. You can do this internally, or partner with a #bpo who can do it all for you. But not all BPOs can or will. If you're already working with a BPO partner, ask them about it. We have an upcoming podcast episode with Declan I., all about the lessons Intercom has learned deploying its AI technology on its internal support team. Stay tuned!  

How should you think about building a culture of AI in your organization? Using AI seems to be a fundamental expectation now. But is this an “associate” problem or a “workforce design” problem? As we are building Lenity Health, it is increasingly dawning on us that AI is a workforce design problem. So what do we mean by that? Almost every organization I have worked with has a plan. Where do they want to get to by this time next year? “What are the key headlines?” “What metric should we move the meter on most, and by how much?” “How do we break down OKRs across the organization?” But the onus of execution? Completely passed over to people in different departments and roles. This is where companies are made or broken. The best companies proactively assess their workforce and determine if they are setting themselves and their associates up for success: “Do we have the right people in the right roles?” “Are they equipped to address the needs of their role in the upcoming year?” “How do we bridge potential gaps?” You could bridge the potential gaps by sending out memos to emphasize the importance of AI. You could provide your associates with paid licenses to AI tools that they should probably use, so costs aren't a barrier for people to try out. But if it were that easy, everybody would have just flicked their magic wands. So how are we designing AI into our workforce at Lenity Health? We set up a few broad guiding principles on where we will use AI and where we won’t: - Improving quality of our service - Improving responsiveness to our customer requests - Eliminating routine, boring tasks for our care coordinators that would rather automate themselves. We then thought about the broad areas where we would want our associates to “Think AI first”. For us, it was clearly in three areas - - Voice: Opportunities to simplify interactions with a voice-first approach - Analytics: Opportunities to use AI to interpret subjective text and provide near real-time feedback - Workflow agents: Opportunities where we could eliminate repeatable, manual tasks This has allowed us to clearly define our internal AI roadmap for our workforce. Product teams ship nimble AI capabilities every week from this roadmap.  Care coordinators work closely alongside to help write the requirements, the evals and co-build these capabilities. Together: - They test these solutions and make notes on where AI is falling short. - They tweak prompts to help ensure the AI output is as close to the human output (or better!). In the process, they have learned about the strengths and weaknesses of AI.  This has helped them internalize how much, or how little, of it to use in their day-to-day.