Agility requires more than just speed—it demands relevance and empathy, especially when AI is stepping in to play a bigger role in the customer experience.
What if the problem isn’t that AI moves too slowly—but that it moves without context, without empathy, and without earning trust?
Today we’re going to talk about how Agentic AI is changing that—offering a way to transform experience management from reactive to proactive, and from transactional to genuinely helpful. To help me discuss this topic, I’d like to welcome Manisha Powar, VP, Head of Product, Customer Experience Suite at Qualtrics.
About Manisha Powar
Manisha Powar is a product and business leader specialized in building B2B Enterprise SaaS Products. Passionate about incubating new products and expanding mature products into new markets/use cases. 15+ years of experience in building, growing and scaling teams of high performers to drive innovation and deliver high quality results through data-driven decision making and scrappy execution. Strong leadership track record in delivering strategies to enter new markets that have led to a billion-dollar acquisition and building out global teams to win.
Manisha Powar on LinkedIn: https://www.linkedin.com/in/pmanisha/
Resources
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Transcript
Greg Kihlstrom (00:00)
Agility requires more than just speed. It demands relevance and empathy, especially when AI is stepping in to play a bigger role in the customer experience. What if the problem isn’t that AI moves too slowly, but that it moves without context, without empathy, and without earning trust? Today, we’re going to talk about how agentic AI is changing that, offering a way to transform experience management from reactive to proactive, and from transactional to genuinely helpful.
To help me discuss this topic, I’d like to welcome Manisha Powar VP Head of Product Customer Experience Suite at Qualtrics. Manisha, welcome to the show.
Manisha Powar (00:35)
Thank you, Greg. Thanks for having me on the show.
Greg Kihlstrom (00:37)
Yeah, looking forward to talking about this with you. Definitely, you know, agentic, definitely top of mind. But I think the connection here with really, you know, how does it impact and improve the customer experience is key here. Before we dive into all of that, though, why don’t you start with giving a little background on yourself and your role at Qualtrics?
Manisha Powar (00:56)
Sure. I started in the software industry 25 years ago, and it’s really incredible what even in the last 25 years we’ve come to see and the differences and the step change that we’ve seen in our industry over the last, just last five years even. I started as a software engineer deep, deep down in the system area of Windows and then moved into product management.
At Qualtrics, I lead our product management team for customer experience suite. And I really love the bridge that CX product creates between human experiences and technology.
Greg Kihlstrom (01:35)
Love it. Well, yeah, let’s got quite a few things to cover here. But I want to start with kind of the zoomed out view of things and then then we’ll kind of work our way in a little bit. So, you you’ve you’ve talked about how organizations are overwhelmed by signals, but still miss the mark in delivering great experiences. Certainly capturing data isn’t the isn’t the challenge so much. But, you know, why do you think that capturing data you know, it’s gotten not only so much easier, but while acting on it in meaningful ways has gotten harder.
Manisha Powar (02:08)
That is the central paradox of modern businesses, isn’t it? We actually have a saying in the team, organizations are now data rich, but insight and action poor. We’re swimming in so much data, but we are failing to make human connections at scale, that that data is actually supposed to enable for us. The capturing of the data has gotten easier because we have so much digital footprint now. Everybody leaves this thing we call signals.
We know what’s going on. You can leave a thumbs up. You can leave a comment. You can talk about the brand on X or other social media channels. You are spending time on your web page or app, like frustrated tone in a voice in a call center. Signals are everywhere. But really, I think the challenge has been threefold. First is just a silo effect. All of this data is fundamentally disconnected website signals and your call center is completely not connected in most cases. Like how many times do you feel go to the website or the app you try to do something and then you go call the call center and you have to explain everything again. I mean, you’re head, you’re going like, don’t you, don’t you talk to each other? Don’t you see like, why do I have to explain it to you when I just did it on your website?
Greg Kihlstrom (03:25)
That was literally my afternoon, by the way. But no, no joke. Yeah.
Manisha Powar (03:29)
So I think that data silos is the first one. The second one is, as we exploded our data signals and our channels, and it’s a good thing in terms of the amount of data available, it also created so much signal overwhelm because now you are in this place of too much data. And by the time somebody creates a report, like today, if you’re not doing this at scale and humans are doing this work.
by the time somebody looks at an insight and act, like decides to act on that insight, life has probably moved on much further. So just the amount of signal and the expectation of how fast you need to act on it has just dramatically changed.
Greg Kihlstrom (04:09)
Yeah, yeah. And, to take to take that to the next step, then even, you know, talking about analysis, I mean, so, OK, we’re you know, we’re flooded with data more more than we can possibly do anything with. Then we take that and distill that and start analyzing it. And then we have all this analysis. But there’s only so much analysis that you can do, you know, without action. Right. And so, you know, I think.
Even the organizations that get past that that first hurdle are then stuck with a million dashboards and reports and everything like that, which means we need to get to to action. So I think, you know, agentic and I think you’ve talked about this as well as, you know, agentic is a path forward here. And, you know, so first for those a little less familiar, you know, how do you define agentic AI and, you know, how how does it kind of fundamentally change the way that some of these challenges can be overcome and to manage these customer experiences.
Manisha Powar (05:05)
Yeah, that’s a great question. I completely agree. Action is, think, where the next big frontier is at. The way I look at I’m sure you will get many, different definitions if you ask different leaders. The way I look at agentic AI is it’s a system that you can give a complex goal to, and it can autonomously reason and understand and then execute on a multi-step plan to complete task. It can make decisions, it knows which tools to use or which systems to reach out to get the job done, and it does all of that somewhat autonomously. It fundamentally changes the way action can happen at scale.
Greg Kihlstrom (05:47)
Yeah, yeah. And I think this is this is so important because there’s a lot that can be done with, you know, if this, then that and the, you know, the rules based bots and the reactive systems. But we’ve kind of reached the limits when we’re talking about, you know, Omni Channel. We’re talking about the segment of one. There’s so many like buzzwords and terms, but they’re all meaningful because the consumer is, you know, they want to they want to access
the brand, where they are on whatever device and so on and so forth. you know, what separates an AI agent from that chat bot that is very reactive and very if this than that kind of programming, you know, maybe share an example of how an agentic system might differ than than one of those previous types of methods.
Manisha Powar (06:37)
Yes, for sure. So before I jump into a specific example, the analogy I think of is the difference between the yesterday’s chatbot and what we think now as agentic AI is that between a scripted call center operator and an expert problem solver. yesterday’s chatbot is reactive, it’s scripted, it follows a very static decision tree. If you ask it
Anything that goes outside of its decision tree, can’t actually, it doesn’t understand, it doesn’t reason, it doesn’t really know anything. It just follows a path. Whereas the AI agent, like a Qualtrics Experience agent, is going to be more proactive and goal-oriented. It’s going to know the context, it’s going to have memory, it’s going to understand who you are. And that’s really where the biggest difference is between a scripted
a scripted or path-based agent versus an AI agent.
Greg Kihlstrom (07:34)
Yeah, yeah, I that analogy there. And, for those brands that I’m sure all of this sounds great to everyone listening here and they want to do that. But it’s one thing to be, you know, to be proactive versus thinking about being proactive. lot of a lot of companies are still, you know, in that reactive kind of like firefighting mode. You know, what does it take for a company to shift from
you know, reacting to anticipating customer needs with, you know, using AI.
Manisha Powar (08:07)
that’s a core challenge for so many organizations, Greg. The need to shift from firefighting to proactive problem solving isn’t just a technology challenge. It’s actually a strategic and a cultural challenge as well. So when I think about, or when we’ve talked to the forward thinking brands and brands that have taken this action or this step, I see kind of three patterns. First one is they look…
to build a future ready tech and data foundation. Now, you can’t be proactive if you’re blind. So the first thing that has to happen is you have to know how to break down data silos within your organization. You need to know how to unify the data from all channels. You need to have a single source of truth that your agents can actually learn from and leverage. The second one is understanding and setting clear risk.
and governance policies. With AI and with autonomy, we are going to have a great responsibility to ensure that we have trust, we build trust with our customers. This means setting clear policies around responsible AI use, bias mitigation, privacy protection.
And you need a cross-functional governance framework that brings together multiple departments, your IT, your legal, your business leadership, as well as your product leadership. Building that trust is kind of the second core pillar of getting to this jump of proactive agents. And then the third one is, and this is very important, organizations shouldn’t try to do everything at the same time. Don’t boil the ocean.
The best way to build momentum is to identify small and focused pilot areas, pilot use cases that have extremely clear value that allow you to both validate your data silo problems or solve your data silo problems, but also show you value. And then once you have those wins, you can always scale much more quickly with your executives too, because you will get a much better sponsorship and cross team collaboration once you start showing the value.
Greg Kihlstrom (10:10)
Yeah, yeah. And so to to build on that and to keep kind of building towards moving from from theory to practice, know, how do you see, you know, how do you see companies doing this? And, know, in the real world with across customer journeys, like are there specific industries or maybe use cases where there could be some whether there’s those pilot projects or some first cases or, you know, industry is already showing showing promise.
Manisha Powar (10:36)
Yeah, the one where we are seeing immediate promise and immediate excitement are high volume industries or complex customer journey industries. Travel and hospitality, detail, financial services are probably some of the early front runners. For example, in retail, you can detect digital frustration during checkout and then an agent can proactively intervene to save your sales which again in the past you would have seen the sale drop in your post purchase or post cycle analytics and then you would go debug and you would go figure it out but in the meantime you’ve lost a ton of business whereas now the agent can detect these frustrations and course correct them in real time by reducing your cart abandonment. Imagine that an AI agent notices you return three pairs of shoes of the same size.
instead of just like helping you process another return, it could ask like, are you having trouble with the fit? So you can imagine this is also just an incredible product improvement opportunity. Similarly in travel, if your flight was canceled and there is a bunch of pressure that you’re going through, you could just quickly have an agent help you with a bunch of things that a human can also do, but many of these underlying tasks are pretty automatable, like book your car, set up your next travel booking, or set up a find a flight, and change your car reservation. So you can imagine an agent being able to fulfill that, an AI agent able to fulfill that really, really fast without having to wait in a queue for an hour for a pretty high volume use case.
Greg Kihlstrom (12:14)
You know, I think a lot of the use cases that may come to mind first for a lot of people, a lot of organizations and teams that are thinking about agentic are technical ones or efficiency gains, productivity. But, know, this idea of empathy at scale, you know, it’s a pretty interesting concept, especially when we’re talking about, you know, we’re talking about AI agents and and empathy. So, you know, what role does customer context, you know, whether that’s
survey data, reviews, call transcripts, even interactions on an app or a website. How do all of these help agents actually act with an understanding of customers?
Manisha Powar (12:54)
Yeah. I strongly believe that customer context is everything. Without it, empathy at scale is just a buzzword. You can’t empathize if you don’t understand. And the way you understand is by listening to what’s happening, what your customers, what your users are currently going through. So for me, these experience signals you talked about are really just that fundamental building block for a truly
empathetic AI agent, whether it’s a human agent or AI agent actually, but in the world of agentic AI, it becomes even more important because now you are relying on this AI agent to act on your brand’s behalf at scale. And this also goes back to the data silo problems we were talking about. If the AI agent is only seeing that you gave a low score on a survey, but it has no other context.
it’s not going to really be able to help your customer that much versus if it had everything that this customer has done on your website and your app and your call center. But now that agent, the AI agent has a lot more context and a lot more memory about who you are and what you, the human, what you, Greg, are going to need and how to personalize the actions just for you.
Greg Kihlstrom (14:06)
Yeah, and so then you know tying those two things together so that you know that that empathy or even you know authenticity as well as that need to drive productivity and efficiency There’s a there’s maybe a little tension even there. You know that I know there’s always a lot of especially of late. There’s a lot of Emphasis on you know, how do we save dollars? How do we you know, how do we?
do more with less and all that. And yet, what you’re saying, the context and the empathy is what also really creates long-term, loyal, lasting relationships. How do organizations balance this? What should leaders be thinking about to balance these two things? And can agentic AI really deliver on both?
Manisha Powar (14:51)
Thanks for asking what I think is the most important question, This is where businesses really want to help customers, but this is where the tension happens because businesses want efficiency. They want to save money. They want to make money, but customers don’t want to be treated like a number. Nobody wants to be optimized. They all want to be understood. But at the same time, the business leaders
don’t wake up every morning and say, I’m going to treat, I’m excited to treat my customers as numbers today. They want to provide a personalized and really human experience to their customers. They just couldn’t do it at scale. And that’s really what gets me really excited about agentic AI, because when it’s designed correctly and it’s given the right data and the right autonomy, it can actually absolutely do both. It can give the business leaders the efficiency but at the same time gives the customers the experience that they deserve. It can augment your human agents and it can take away mundane and repetitive tasks that nobody really wants to be doing as a human agent in the first place. So AI can handle all of that scale and it frees up the human agent, your human workforce then to do truly high value work. And also gives them the superpowers of personalization.
Greg Kihlstrom (16:10)
Yeah, absolutely. I think, you know, the getting to the first step of, being able to, you know, get get past the the signals and moving towards action is is a great step. And then, you know, then there’s sort of beyond recreating what’s there. There’s so many new possibilities as well. Right. So I wonder
You know, what kind of skills or even mindsets are going to be critical for leaders that are building the next generation of, you know, not only experience platforms, but those on their teams that they’re going to come up with stuff that we haven’t even thought of yet, right, because they have tools to do that. You know, what’s what’s what’s going to be critical in the in the months to come?
Manisha Powar (16:52)
That’s a great question. And I think the technology with AI is evolving so quickly that a lot of this is going to change as we speak, even as we get through the next couple of years. But I do think the leadership mindset also has to evolve and stay open to this. I really see three critical parts as an executive. The first one is
shifting the organization from departmental thinking to a journey-oriented thinking. Going back to what we talked about, your customers don’t look at you as a department. They look at you as an organization. They look at you as a brand. So you should no longer be tolerant of this is marketing team’s data or this is the support team’s problem because your agentic AI needs to operate across all of that. So that’s the first one just shifting your organization from departmental to journey-oriented thinking. I would say the second big one is becoming ethical executive in the organization. As AI becomes autonomous, leaders must become the arbiters of ethical boundaries. This is not just about, can we do this? The business leaders also need to ask about, should we do this? And if yes, how?
And then the last one is what we started with, which is they have to embrace an agile and experimental mindset. We are in such early innings of this transformation that having a static mindset is not going to be helpful for our leaders. So start with high impact pilots, learn quickly from success, celebrate the failures, try the next experiment, and create that culture not just for yourself, but also for your company.
The future is not just about technology expertise, it’s also about creating that customer centricity and ethical governance and agility in the organization.
Greg Kihlstrom (18:41)
Yeah, yeah, love it. Well, Manisha, thanks so much for sharing all your ideas and insights today. One last question for you before we wrap up. What do you do to stay agile in your role and how do you find a way to do it consistently?
Manisha Powar (18:53)
Part of it is you and I met at a conference just not more than a day ago. Part of it is really staying open to things that are happening in the industry. I have a lot of conversations with customers. I have a lot of conversations with leaders in this area. have Gurdip who leads our, he’s a visionary AI leader. We have had conversations with a bunch of software vendors that are now going far beyond what originally anybody could thought possible. So just staying open, reading a lot, listening to a lot of podcasts, a lot of new things that are happening, and creating that same as what I talked to you about. I have the responsibility to do that in my team as well. So we experiment with AI in our team as a product manager. How are you going to be a better product manager because AI is now at your fingertips? So there is a lot of experimentation that we do within our organization as well. And we have fun with it.
