What does it really take to build an AI-powered customer experience from the ground up?Lacey Peace sits down with Katie Bianchi, Chief Customer Officer at Palo Alto Networks, to unpack how her team rebuilt support systems for the AI era — eliminating handoffs, reducing resolution time by over 40%, and transforming how their teams work.Katie walks through their phased AI implementation strategy, from data unification and process re-architecture to training internal copilots capable of diagnosing the most complex cases. She shares why tight alignment between product, IT, and customer success is now a non-negotiable, and how empowering frontline employees to lead experimentation created real transformation.Plus, Katie gets candid about what she would’ve done differently, why NPS might be obsolete, and how AI is becoming the operating system for work itself. Whether you're navigating digital transformation or trying to unite siloed teams, this episode offers a clear, tactical, and deeply human roadmap to scaling customer experience in the age of AI. Key Moments:00:00 Meet AI-Expert Katie Bianchi, CCO at Palo Alto Networks01:31 Why Dirty Data Is Killing Your AI Strategy02:46 AI Adoption Starts with Empowered Employees06:49 Joining a Cybersecurity Giant During Peak Disruption08:41 Step-by-Step: How Palo Alto Networks Rolled Out AI14:17 Building a Culture of Rapid Experimentation21:50 What AI Agents Can Do in Tech Support Right Now23:12 What We’d Do Differently: Lessons from the Frontlines of AI25:47 End-to-End Automation: Connecting Pre- and Post-Sales27:38 Smarter CX: Turning Data Into Actionable Insights36:41 Rethinking Metrics: How to Measure AI’s Real Impact39:17 From Reactive to Proactive: Real AI Customer Wins41:26 AI as Your New Operating System: What’s Next –Are your teams facing growing demands? Join CX leaders transforming their AI strategy with Agentforce. Start achieving your ambitious goals. Visit salesforce.com/agentforce Mission.org is a media studio producing content alongside world-class clients. Learn more at mission.org
Chapter 1: What is the main topic of this episode?
AI is a shield and AI can be a sword. And we've got to be the best, strongest, fastest shield there is. Our processes were built for case resolution pre-AI, not post-AI. No shortcuts in innovation. It's that notion of forgetting everything you know, acknowledging you know nothing, and starting from the beginning. So humbling. My goodness. You're eliminating handoffs. You're reducing friction.
Chapter 2: How can dirty data affect your AI strategy?
We're using our AI right now to reduce our time to resolve by over 40%. It's the only tool that can actually help you keep up with the pace of innovation. It's not about replacing people with AI. It's about elevating the role of people and increasing their impact in a world that does not stand still in cyber.
Chapter 3: Why is employee empowerment crucial for AI adoption?
Welcome back to Experts of Experience. I'm your host Lacey Peace and joining me as always is our producer Rose. Hey Rose. Hello. We just got off the mic with Katie Bianchi, the Chief Customer Officer at Palo Alto Networks. Now this one was a fun one. Katie actually gave us, and I'm not joking here, she gave us a step-by-step process for AI implementation.
So for all of you guys out there that are kind of not sure about what to do next or are unclear on how to implement this in your organization, Katie has given us basically a mastermind in this. I found it so fascinating.
It was so fascinating to hear her talk about like high quality data versus not. Before this interview, I would have thought, well, I'm sure all data, I mean, all data in a company is probably pretty important, right?
There's an insane amount of data that you have the potential to collect and then deciding what data to collect, what's useful. And how to store it. Like, is it kept in a place that's like we can actually use it? Because there's no point in collecting it if it's so messy that I can't even use it.
Chapter 4: What were the key steps in Palo Alto Networks' AI rollout?
What's super interesting to me is how she's bridging the gaps between departments by making it so their data collection means are consistent. Over here in sales, we're tracking this one thing. In marketing, we're tracking this other thing. Instead of everyone being in silos, she's taking those data points across the board and connecting them all together. She mentioned an insane stat that...
about how quickly their customer case times are improving versus what they were like two and a half years ago. I mean, it's just bonkers.
Yeah. I feel like this is a great episode for anybody that's like struggling to get buy-in, the leaders that are like struggling to get everybody in the C-suite on the same page or anybody that has a team right now that's maybe a little apprehensive, maybe a little bit scared of what AI agents really mean for them.
What she mentioned that I found very profound was how her leadership basically came in and they were like, we know nothing. This thing is so new, this beast, this AI beast is so new. We know nothing. And we're going to turn to the people on the ground, the people doing the work every day, and ask them to help us, to help guide us through this AI process.
And she talks a lot about how her leadership has empowered their employees to test, iterate, experiment, to figure out what can work within their organization to actually augment their team so they can effectively stay ahead of others. bad actors. She talks about how to implement AI step by step.
She shares her biggest mistakes, their biggest wins, the things that they're planning for and looking forward to in the future, and ultimately why this is something that gets to be integrated in every step of the organization to improve not only the customer experience, but also the employee experience. So with that, let's get into it. Katie, welcome to Experts of Experience.
Thanks so much, Lacey. I'm thrilled to be here today. So you lead customer experience at one of the most important cybersecurity companies, Palo Alto Networks. And I'm wondering when you're at like a dinner party or in the elevator and someone asks, what do you do? How do you answer them?
Very simply, I say I'm responsible for making sure that customers stay ahead of threats, with our AI-powered platform that secures users, networks, and clouds at scale.
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Chapter 5: How can organizations build a culture of rapid experimentation?
Awesome. And before you got into this work that you're doing now, what was your background like and your experience like before this? What brought you to this role?
Yeah, I've always had, I've actually always really had a passion around customers and service. So as I started earlier in my career, I was very focused on driving a process and driving automation that improved customers' ability to get value out of whatever solution it was that they were purchasing from us.
And I really built on that passion over an almost 25-year career where I've done different roles in leading strategy and execution for large businesses focused on what I like to believe are great missions. So I was, during my time at GE, really focused on hardware, software, services, development for driving... Technology that inspects critical industrial assets.
So ensuring that airplanes are safe to fly and power equipment is safe to run as sort of the digital industrial era was starting to take hold. This notion of making customers successful with technology. software and analytics became something I really, really gravitated to. And that really started my journey into what I call the full, the full tech space experience.
So for the past eight or nine years, I've been really focused on how to drive that experience and how to drive the acceleration of value realization in security.
That's awesome. And what I like about what you shared is that you're choosing really complex companies to work within and really complex customer experiences to solve. Have you always been someone drawn to these like big problems? Yes, absolutely.
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Chapter 6: What AI capabilities exist in tech support today?
I think the application of technology to solve some of the hardest problems in industry is something that's always really, really important. really, really compelled me.
The harder the problem and the harder the solution, the more I gravitate to it because really at the core of it, what I know is that that problem that's being solved for a customer has such an impact on the mission that they're actually serving, right? Whether it is
healthcare, financial services, social services, energy, or transportation, those are all really compelling things to help, you know, build move and empower the world.
Chapter 7: What lessons have been learned from AI implementation?
That's so cool. So I pointed this out in our prep call, and I want to reiterate it for our audience. You've been at Palo Alto Networks for two and a half years. So you came right in at the beginning of when generative AI started to take the stage. And right after COVID, so what was that like for you?
You kind of came into this company and it's probably just a lot to take in, not only being at a new company, but with all these big changes and big trends happening at once.
Yeah. Yeah. Well, look, I came in at an incredibly exciting time for the company. I think what it does so well is just continually disrupts itself and innovates in the security space.
So for me to have the opportunity to come somewhere and a company that's trying to disrupt itself in the network security space, build a new category in cloud security for customers and also transform the way that SOCs are run and The power that a post-sales organization or a customer experience organization has in helping customers execute on that mission is too good for me to pass up.
Chapter 8: How can organizations improve data utilization for better CX?
And, you know, I did end up joining, right, I think it was in December of 2022. So right at the time that Generative AI was hitting the scene. And honestly, it felt like jumping off. onto a rocket. It's exciting. It's intense, but it's also impossible to ignore. What was great about the timing and the way our leadership team approached it was it forced actually urgency and clarity.
So we knew we couldn't sit back and observe that we had to lead. And I think to do that well, we had to return to the start in many ways, especially from a customer experience perspective. So- You know, we had to rethink everything we knew about how we did things through the lens of this incredibly powerful technology that was hitting the scene.
And it really forced us to go back to the start and learn about the way things were done so we could actually learn about how to do them better with AI.
So I want to get into this implementation process and all the lessons you guys have learned in doing this. But can you just set the table here and tell our audience kind of where you are in this AI journey? How long has it been? So it's been, it sounds like two and a half years since you started that you guys started implementing this. Where are your teams at in this process?
Just as a quick, quick overview, you know, at the end of the day, my role is all about how to get our customers deployed fully and reliably. how to get them to value and continue to deliver value, and how to ensure that whatever we've deployed for them stays technically healthy as they grow, change, and scale with us.
So as you think about that landscape of work, we're really in the execution and scaling phase. We continue to experiment because the technology changes so quickly that you have to continue to experiment at the rate the technology is changing to figure out how to apply it. But AI is already embedded in how we resolve every single technical support case.
And we focused first on technical support, given what we knew to be a really massive opportunity to completely change and delight our customers and respond cases much more quickly, even the more complex ones. So we've also started the journey to actually how we embed AI and the way that we deploy our products and drive value for our customers.
There was something you mentioned to me also was that you guys chose the most complex topics to begin with, not the easiest things to automate. Could you talk to me a little bit more about that mindset? Because that is, it's wild. I mean, I've heard the exact opposite from many other companies.
Yeah. It's interesting. We did. We focused first on areas where scale and complexity were slowing customers down. And for us, that was support and deployment. And if I think about
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