Creating A Culture Of Innovation With AI In Service

If you’re serious about growth, you can’t afford to treat AI like a sideshow in your CX playbook. Start with one metric that matters, like revenue, cost to serve, or culture. Then pick a process that drags: onboarding that takes days, service queues that never shrink, customer insights that die in PowerPoint. Now think about how you can leverage AI to help you. Not to automate for the sake of efficiency, but to see what happens when you cut the busywork and give people time to solve real problems. Test fast, measure faster. Track how many tickets close without escalation, how much quicker you move new clients through the door, how often front-line teams can fix issues before a customer even asks. Make the results visible to leaders in your company: post them on your intranet, celebrate them, obsess over them until the new normal is smarter, faster, more customer-led. Don’t expect everyone to cheer at first. Change never starts with applause. But when the early wins land, the skeptics get quiet. Culture shifts one experiment at a time, and suddenly your team starts seeing “what if” instead of “we can’t” or “I won’t.” This is the moment to take a hard look at every CX process and ask, “Is AI helping us grow, serve, and lead? Or is AI just making more dashboards?” Choose to be bold. A little bold is okay too, but you have to move, and the best teams and companies move first. #customerexperience #leadership #ai

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?

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.

I've spent a lot of time recently thinking about how to ensure Zebra is truly integrating AI into our company, not just faking the use of AI. It has been a significant wake-up call for me and a feeling of immense pressure to get this right. The biggest thing I've learned is the the crucial role of change management. Building an AI-ready culture isn’t just an IT task (I believe IT should take the lead); it requires the synergy of EVERY SINGLE PERSON in the company. It crosses people, processes, technology, and a supportive corporate culture as part of a broader digital transformation. An article by Joe McKendrick in Forbes prompted me to reflect on our AI strategy. Have I effectively outlined it? Have we fostered a truly innovative and collaborative culture around AI? Are we providing adequate education and training? How fast do we want to push? Top down or bottoms up adoption? And last, but definitely not least, how are we measuring its impact? My key takeaways from this article: ➡️ Set clear AI goals tied to strategy. Are you planning to do more with the same, the same with less, or something in the middle? Then define work items like automating tasks or enhancing the efficiency of frontline work, to better realize ROI. ➡️ Communicate AI’s role clearly, to reduce resistance and build trust. Make sure what you communicate aligns to your goals. A mismatch of goals and communication is a recipe for disaster. ➡️ Ensure AI is accessible to everyone, fostering a culture of experimentation and teamwork. Remember that AI is not perfect, and neither are people. Accept it for what it can do well, and provide feedback on what it doesn't. ➡️ Provide ongoing training so employees understand AI’s impact on their roles. With the pace of change right now, AI training is not a one-and-done or even annual exercise. It needs to filter into team members learning rhythm weekly or monthly. As Zebra continues on our journey, I'm focused on these areas to help guide our progress. I invite you to share your thoughts and experiences on building an AI-ready culture. How are you approaching this challenge within your own organization?