Enterprise AI isn't just about automation — it's about helping people make better decisions, faster. In this episode of The Dialogue Architects, host Lauren Goerz, Staff Product Marketing Manager at Rasa, welcomes Eugenia Zeibig, Strategy & Innovation Lead — Marketing AI & Commercial Analytics at Pfizer, for a discussion on designing AI-powered decision systems for enterprise pharma marketing teams.
In this episode of The Dialogue Architects, host Lauren Goerz sits down with Eugenia Zeibig, Strategy & Innovation Lead — Marketing AI & Commercial Analytics at Pfizer, to explore how AI is reshaping decision-making in pharma marketing.
The conversation focuses on what it takes to bring AI into the daily workflow of pharma marketers — combining visual insights with natural-language dialogue to move from weeks of stitched-together analysis to faster, more informed strategic decisions.
Lauren and Eugenia discuss the challenge of designing AI systems that marketers can actually trust, why quality matters more than quantity when delivering recommendations, and how AI behavior needs to adapt across different therapeutic categories in pharma.
The episode also explores:
A practical conversation for enterprise AI leaders, marketers, product teams, and conversational AI practitioners interested in how natural language interfaces are reshaping data analysis and strategic decision-making in regulated industries.
Views shared by Eugenia Zeibig are her own and do not reflect Pfizer corporate guidance.
Guest Bio
Eugenia Zeibig is the Strategy & Innovation Lead — Marketing AI & Commercial Analytics at Pfizer. With nearly two decades of experience connecting analytics to commercial outcomes across healthcare and retail, Eugenia leads initiatives focused on building AI-forward, data-driven solutions that empower enterprise marketing teams with conversational AI, strategic insights, and faster decision-making workflows.
Key Topics Discussed
Episode Timestamps
(00:00) Welcome and guest introduction
(01:01) Eugenia Zeibig's role at Pfizer
(02:50) Understanding pharma marketing audiences
(04:55) The marketer workflow before AI
(08:00) Designing AI for pharma marketers
(10:24) Balancing push and pull interactions
(13:07) Building trust through quality
(15:23) Big-loop vs small-loop decision-making
(18:46) Scaling AI across brands and therapeutic areas
(22:59) Measuring adoption inside a marketing organization
(27:07) Natural-language dialogue for marketers
(29:24) Empowering marketers and data science teams
(31:22) The future of AI tools and orchestration
(33:51) Closing thoughts and farewell
Links & Resources
About The Dialogue Architects
The Dialogue Architects is a podcast from Rasa, hosted by Lauren Goerz, exploring the craft and strategy behind designing conversational AI in the enterprise. Each episode brings together technologists, designers, and product leaders to unpack how dialogue is built, scaled, and governed in real-world systems.
[00:00:00] Lauren Goerz: Welcome to The Dialogue Architects, where we explore how enterprises can thoughtfully design, scale, and govern conversations between humans and machines. Today's guest is currently the marketing analytics and strategy and innovation lead at Pfizer. She spent 18 years actually connecting data to commercial outcomes.
[00:00:16] Lauren Goerz: She's led transformations across luxury retail and global healthcare, and right now, she's deep inside one of the most fascinating AI deployment challenges in the enterprise world, reinventing how pharma marketers make decisions better, faster, and more informed than before. Eugenia Zybig, welcome to the show.
[00:00:48] Eugenia Zeibig: Thank you. Thank you so much, Lauren. So excited to be here. Um, you, you can call me Easy. That's what they do to me at work. Um, but really excited to spend this time with you.
[00:00:58] Lauren Goerz: Great. Easy is easy. Let's kick it off. All right. So usually to start these segments, usually we kinda wanna ground it. Okay, what's your job role?
[00:01:05] Lauren Goerz: Who do you serve? What are you doing? So let's kind of kick off with the s- tr- you know, the first part of, you know, what, what are you currently doing at Pfizer? What are you building? What are you working on? And how would you describe it to someone who's maybe not a marketer as well?
[00:01:17] Eugenia Zeibig: Sounds good. Um, so the title is a mouthful as you, as you just practiced yourself.
[00:01:23] Eugenia Zeibig: Uh, but I lead a team that is responsible for bringing, um, strategy and innovation to our marketing community through data-driven, um, AI-forward, and tech-centric solutions. Um, uh, you asked who we serve. Well, really the, the primary customer for us is our marketing organization. Um, for those of you who don't work, uh, in pharma, you will be surprised of how massive commercial and marketing organizations, uh, in pharma are and how diverse they are, right?
[00:01:56] Eugenia Zeibig: We talk to patients, we talk to physicians, we talk to payers, we talk to, uh, medical accounts, et cetera. So we have a lot of different layers in the organization. And our mandate is to make sure that our commercial organization, our marketing organization is, uh, as tech-forward as, uh, it can be, that we're setting up our, uh, organization to kind of win the digital and the AI race, uh, in pharma.
[00:02:27] Eugenia Zeibig: And now it is w- digital and AI race everywhere, right? Not just in pharma. And to essentially really bring the solutions to our, uh, patients, to our, uh, physicians, to our, uh, medical partners as quickly as possible to, uh, to really bring those health outcomes, uh, in the, in the society. Ultimately.
[00:02:50] Lauren Goerz: Got it. So you're ultimately, you're serving, you know, as a marketer, you're serving the other marketers within your organization, and the customer of your customer, so the customer of the marketers is, am I understanding this correctly, then it's actually the people that are buying the different pharmaceuticals, and this would be then doctors, or would this be hospitals?
[00:03:08] Lauren Goerz: What would be, what would be kind of the customer of your customer there?
[00:03:11] Eugenia Zeibig: It's everything, right? Everything. Okay. We, as a, as a marketer, we do have a relationship with a, with a patient. We want to make sure that they are advocates of their health. As a marketer, we do have a relationship with a physician to make sure they have the latest and greatest information about the therapies, the vaccines, uh, the medications that are coming to market, and know exactly, uh, what they're bringing, uh, to their patients.
[00:03:35] Eugenia Zeibig: To the hospitals, we want to make sure it's as, as, uh, easy as possible to work with us, to make sure that they, you know, they have their stocks of the vaccines, uh, et cetera, i- in place. And even with the payers, we want to make sure they understand how, how to really navigate the therapeutic space that, that we operate in.
[00:03:55] Eugenia Zeibig: Uh, so a marketing organization pharma is very diverse, and we do have different kind of priorities as we roll out AI solutions or technical solutions or data-driven marketing solutions, which of our marketing segments will be prioritized. So right now, you asked me what we are working on. Uh, we're kind of bringing an AI workbench to our HCP marketing community.
[00:04:19] Eugenia Zeibig: That's the- Mm-hmm ... the community of our marketers who are primarily focused on creating, uh, uh, content, channel, and engaging with our HCP, which is healthcare professionals, physicians. Mm-hmm. Uh, and, uh, that's kind of right now what, what I'm focusing on, but it varies, right? Uh, we want to make sure that the entirety of the marketing organization as Pfizer is set up for success in the kind of tech-forward AI future.
[00:04:47] Lauren Goerz: No, I think one of the best ways to understand what you do, especially in your kind of role, as you mentioned, there's lots of different areas, lots of different things you could be doing. But I think the best way to kind of ground it is, you know, look at your customer, the Pfizer marketer. Paint me a picture of their day kind of before AI capabilities and what it looks like now after you've started working on this.
[00:05:08] Eugenia Zeibig: Sure. If you think about the AI marketer, uh, i- if you think about the HTP marketer, um, in a pharma company, the first thing they do is they analyze a ton of data, right? Think about it, they wake up, there's 50 tabs open on their browser or, uh, Excel, email from their data science partner, um, potentially some, some, um, newsletters that they read to understand what the market is up to, what are the infection rates, if that's what their, their space is, uh, a bunch of, uh, spreadsheets, dashboards, emails, analysis that they have to synthesize in their head to be able to form a point of view.
[00:05:52] Eugenia Zeibig: This is where the market is. This is where my, uh, uh, drug is and my brand is at. These are the opportunities I think, uh, should exist. That triggers a couple other questions, right? Well, let me see how, you know, the East Coast is doing versus the West Coast. Let me see how this physician specialty versus that physician specialty is reacting to this message.
[00:06:14] Eugenia Zeibig: Uh, let me try and understand who's adopting the new, um, uh, message and therapy versus not. All of that creates additional questions, additional meetings, additional emails, additional requests, that then take three, five, maybe two weeks, um, to, uh, to analyze and to come back, and again, clog their, their inbox.
[00:06:36] Eugenia Zeibig: What we're trying to solve for is to really speed up that process. In order for that marketer to formulate their point of view of where they are as a brand, where the... what the customer perceptions are, what the opportunities are, and how to effectuate those, we are trying to really streamline that from weeks to hours, right?
[00:06:58] Eugenia Zeibig: Mm-hmm. Uh, and we're also trying to really to reduce that cognitive load and clutter, and multiple locations, multiple dashboards, mul- multiple sources of information into a one-stop shop where you can actually look at all the information in front of you and query it vis-a-vis each other, right? Um, so that was the day of the marketer or maybe, like, the multi-week circle- Mm-hmm
[00:07:20] Eugenia Zeibig: of the marketer, um, as they, um, absorbed the information, made decisions, uh, created kind of like scenarios of what those actions could be, tried to simulate those scenarios, then acted on them, and then learned about it. That loop took, you know, weeks, maybe months. We want that learning loop to be really rapid, right?
[00:07:44] Eugenia Zeibig: We want our marketers to become, uh, active experimenters with all the information that they have, ti- ti- uh, try to take some bets, right? That's what marketing is all about. Uh, but ultimately learn about it, le- learn the outcomes of those bets as quickly as possible.
[00:08:00] Lauren Goerz: So high level, it sounds a little bit like you've got- a data challenge.
[00:08:04] Lauren Goerz: You need to find a way to get all of this data together, and then you also have maybe a querying challenge. I'm assuming that your marketers are also not data scientists and need to find a way to ask maybe in natural language, dare I say, how to get this information. Talk me through what the solution actually looks like for them.
[00:08:21] Lauren Goerz: Is it a dashboard? Is it, you know, a chat box? It... How, how do they actually go about getting all this information served up to them, or how do they go about querying the information?
[00:08:31] Eugenia Zeibig: What we're trying to s- to strike is the balance of push and pull here, right? Um, because very often the marketer, or any of us frankly- Mm-hmm
[00:08:40] Eugenia Zeibig: react to information as it comes in, right? Uh- Mm-hmm ... we hear the news, we read the newsletter, we talk to others, the information comes in, but then it triggers a ton of follow-up questions, right? Mm-hmm. So the same idea is how we're trying to design the system for, for, for it to be a, a balance of push and pull.
[00:09:00] Eugenia Zeibig: So yes, there- Mm ... will be that visual cue, the one that, that pushes information to you. Think of it like where they, y- it used to be 15 different dashboards, 15 different sources of information, uh, that sometimes didn't, by the way, jive, right? Now it's all in front of you, and it's, it's really meant to trigger some of those follow-up questions.
[00:09:21] Eugenia Zeibig: Mm-hmm. And now those follow-up questions is where the chat experience kicks in, right? Like, so it, the, the path is from visual to verbal, right? Where you start, uh, querying the information, you start interrogating it. Uh, AI is actually going to, even with that visual cue, give you some, some prompts, uh, maybe give you some recommendations that are a little bit, uh, maybe theoretical, right?
[00:09:45] Eugenia Zeibig: Uh, for example- Mm ... your market is down on the East Coast, maybe you need to deploy some, some more resources to the East Coast. Mm. That's where as a marketer you should be then engaging with a chatbot and asking why would, what, what assumptions are you making in terms of the information you're getting- Yeah
[00:10:02] Eugenia Zeibig: right? Um, what if I did not, um, uh, deploy my resources to the East Coast? What would that do, do to the West Coast if I had to redeploy that? So that's where, like, the path is from visual to verbal, and- Mm-hmm ... for us to kind of like get, uh, the push and the pull information kind of together into one, uh, workflow.
[00:10:24] Lauren Goerz: I think that's so interesting, 'cause one of the things I struggle with as a fellow marketer ultimately is, I would say recency bias is the fact exactly what you were describing as well, is you've seen, you know, the most recent thing that you've seen is the thing that stays in your head when you're thinking about the next step.
[00:10:35] Lauren Goerz: So I, I like this kind of conglomeration of information being served up with kind of, you know, looking at all the options before just thinking about the problems that you've had most recently. So that's really interesting. Also, this push piece. I'd, I'd love to explore this a little more, getting to the right balance of push and pull, because I think push, that element, is something that I very rarely experience as a marketer from the tools that I'm using, and I think that's quite powerful.
[00:11:00] Lauren Goerz: Is that something that's, that's been super, you know, w- why does there need to be such a balance? Is, is it more about making sure that the pushes are accurate? What's the balance there?
[00:11:10] Eugenia Zeibig: The, uh, the beauty of a good push is for it to be well thought through, uh, is to be, is to be relevant, and it is really about quality over quantity, right?
[00:11:24] Eugenia Zeibig: Mm-hmm. 'Cause the last thing you want is you, um, you know, you enter the portal, your AI companion or your AI work- uh, workbench, and its first recommendation is something completely off, right? That creates a, an experience that does not really lend itself to nice kinda like, uh, stickiness, frequency of you using, or ultimately trust- Mm-hmm
[00:11:48] Eugenia Zeibig: into AI, right? Right. So, uh, it was an, a major design journey for us to think through on the quality over quantity axis, and to really interrogate the, uh, the, the agentic system, right? To make sure that it is honing in what the right recommendation, what's the right kind of like logic, right? That you want to be pushing to, to the, um, to the end user, to our marketer, to make sure that it's relevant and it is really kind of like is the start of that journey.
[00:12:22] Eugenia Zeibig: What's interesting- Mm ... is that if you think about it, um, Pfizer, right? You have vaccines, you have oncology, you have rare diseases. The logic in each of them is m- massively different, right? Mm-hmm. So the amount of design thinking that we had to put in in that upfront piece is critical to make sure that the, the logic that applies to oncologists', uh, marketing versus the logic that applies to pediatricians, uh, is not the same, right?
[00:12:55] Eugenia Zeibig: Mm. And that it is relevant and really speaks to the, to the, to our customer, to the marketer, uh, to, um, to be relevant and, uh, is a good thought starter for their journey.
[00:13:07] Lauren Goerz: Well, I think this is kind of bringing us also to an interesting segment that we wanted to talk about anyways, is kind of rolling things out, and I think you've touched on, on something that's important that has a parallel in architecting dialogue as well.
[00:13:18] Lauren Goerz: Mm-hmm. Foundation models generally lend themselves to generalist tasks. So I think when I see people go to implement, um, language models in dialogue systems, so A- AI agents, one of the things is often, um, a very wide use case, which is challenging because yes, of course it, it can do lots of things. But I think historically when we've, when we've looked at building these systems, we've always said, "Go narrow, go deep," so that when you actually roll these things out, people find value in it.
[00:13:45] Lauren Goerz: Because the amount of trust that you can lose from your first, um, pilot can be devastating for the success of, of the product later on. And while I am a big, I would say, proponent of learn from live, I think there's also a certain level you have to achieve in order to get that trust. It sounds like maybe that might be something that you had an experience with as well with the marketers on your team.
[00:14:06] Eugenia Zeibig: I do think the, the design choice of going, uh, for quality over quantity, uh, lends ourselves, uh, lends itself nicely to this, um, process of widening, right? And going- Mm-hmm ... broader, right? And then maybe releasing some of the constraints that we maybe put in upfront, because the first part was the trust-building exercise.
[00:14:28] Eugenia Zeibig: Um, yes, I do think that the... Like, think about it this way. We are right now not necessarily teaching marketers to use AI. We're teaching AI to, to speak marketing, right? To speak pharma marketing, to speak oncology marketing versus vaccines marketing versus- Yeah ... immunology marketing. And those AIs are actually becoming very distinct, uh, and, uh, it's really building the expertise in a, in a certain space to be relevant, trustworthy, and, and useful.
[00:15:01] Eugenia Zeibig: Over time, as, as this core foundation is built, yes, we will be adding more data, uh, more use cases, and that, yes, it will be actually broadening a little bit. Uh, but the design choice of going for quality, uh, was, was very intentional for us- Mm ... uh, to, to really build that foundation of trust.
[00:15:23] Lauren Goerz: And maybe tell me, uh, 'cause I think this is a piece we didn't touch on as well as a part of the solution.
[00:15:27] Lauren Goerz: So you talked a lot about the, the push and pull of data analysis, getting information, gathering information, making decisions. What are some of the decisions that need to be made? I know you mentioned, "So East Coast, I might need to deploy more resources." Is your system also talking to your marketers about the type of campaigns they should be running?
[00:15:46] Lauren Goerz: Is it also talking about, I, I'm, I'm not an expert in pharma marketing but, you know, events that should be run, but all of the different marketing activities that could roll off as a part of, of that data analysis?
[00:15:57] Eugenia Zeibig: If you think- Mm-hmm ... about the marketer, I talked about the loop, right? We're sending them on a loop.
[00:16:01] Eugenia Zeibig: Yeah. We're sending them on a... Well, there's two loops. There's the big loop, and then there's the little loop. Okay. Let me talk to you about the big loop. The big loop is your strategic decisions that you do not make frequently, but they're, uh, really kind of high-risk, high-impact decisions that really, uh, drive, you know, the brand strategy.
[00:16:23] Eugenia Zeibig: And then the little loop is those fast, rapid learning cycles where you deploy campaign to A versus B audience, or you tweak the creative- Mm ... uh, a tiny bit, uh, in terms of language in, in the claim that we use, et cetera, and then you t- really rapidly test it out. We are intentionally starting with a little bit of that larger loop, the bigger loop- Mm-hmm
[00:16:47] Eugenia Zeibig: um, to make sure that our marketers, um, start trusting AI On their journey of making those decisions in terms of, "Here's my market, here's my customer, here's how it, uh, the customer, uh, is, uh, adopting the drug. Here's the different claims or different messages, whether it's efficacy versus, uh, mechanism of action, uh, versus et cetera."
[00:17:15] Eugenia Zeibig: But right now we're trying to start with the decisions that are much more in- uh, impactful- Mm-hmm ... a little bit lower frequency, and, uh, will bring a ton of, uh, of upsides. So think about it, reallocating across your channels, right? Mm-hmm. Whether you should be doing mass versus personalized versus in-person, uh, you know, outreach versus, uh, with our representatives.
[00:17:41] Eugenia Zeibig: Uh, picking a journey A versus journey B, right? Like, what are the- Mm-hmm ... big kind of, uh, impactful decisions? Uh, what's my campaign, uh, strategy overall? Um, and it's over time, once we've established that, that we can actually start tackling the smaller loop, uh, as well, which is rapid, you know, within a campaign, different AB tests, et cetera.
[00:18:07] Eugenia Zeibig: Uh, but this is also- Mm-hmm ... where marketing is a little bit unique, and you being as a marketer, you probably ... You rely on your agency to, to do some of this, um, smaller loop. Mm-hmm. You will soon rely on some automation, automatic media bidding, right, to do some of these- Mm-hmm ... smaller loop decisions.
[00:18:22] Eugenia Zeibig: You're not necessarily involved because the, the risk of misstep on a decision that is, you know, smaller is, is, is low, right? So it could be automated, could be delegated, the agency could be- Mm-hmm ... working on it, a junior marketer could be working on it, whereas we're trying to tackle the decisions that really make a difference for the brand, for the patients, for the, uh, for the physicians, and for the health outcomes overall.
[00:18:46] Lauren Goerz: How do you, how do you actually ... When you're rolling this out, are these different projects individually? Is it one giant project together that informs itself? You know, how do you segment, um ... D- does each team have their own special dashboard? What does that actually look like?
[00:19:01] Eugenia Zeibig: Oh, that's an excellent question, and, and it has been a journey, um, that was, that was fun, and it is really like threading the needle, uh, for us.
[00:19:09] Eugenia Zeibig: Mm-hmm. Because we, we just talked about how each therapeutic area, each market, each brand is so unique, right? Mm-hmm. Uh, it's, it's really ... It ... When we started with this landscape, uh, landscaping process where we under- understood how do you make your decisions, how often do you make your decisions, et cetera, we had a realization of how different they are.
[00:19:33] Eugenia Zeibig: But then over time, as we started building, uh, we also very quickly understood that it's not just an AI- project or initiative or a tool that we're launching. We're actually launching a marketing excellence initiative, where we finally have an opportunity across, uh, different brands to bring some rigor of a modern data-driven marketing across the board.
[00:19:59] Eugenia Zeibig: Give you an example. Um, a good marketing team always thinks about kind of their funnel. Yes, the f- you know, the, the customer funnel in vaccines, or like let's have migraine, right? Which is a, a rapid, very evolving, very active funnel, versus oncology. It takes you five months to get diagnosed on average, right?
[00:20:19] Eugenia Zeibig: And then you have to rapidly move once you have been. These funnels are massively different. In some medications, you know, it is the patient who's driving the decisioning. In, in others it's, it's the, you know, the hospital versus the physician themselves. These funnels are very different. The drop-offs are very different.
[00:20:38] Eugenia Zeibig: But the idea of understanding of all the way from the top, all the way to the bottom, and how to get the customer to not only prescribe, for example, but to become a, an expert, a, uh, a key opinion leader, for example, uh, in the certain therapy, is, is really, like, something that all brands will be tackling, right?
[00:21:01] Eugenia Zeibig: Another idea of having a good, uh, segmentation that looks at how, how much upside or headroom you have versus how much you're persuadable, right? These principles now become the abstraction that we're starting to see across every single product, every single team, and it is now, which is so exciting for me, uh, as somebody who's, like, a, a marketing geek, to see that it is this AI tool, just an initiative from AI, is starting to drive cohesive marketing excellence across the organization, and is bringing, you know, the, the, the best marketing thinking into, into their team.
[00:21:44] Eugenia Zeibig: So it's supercharging the humans- Yeah ... uh, as much as it is, uh,
[00:21:49] Lauren Goerz: supercharging the agents. Actually, that's, that's a, that's such a good point 'cause I think no one's actually talked about that yet on the podcast, but I think that's, that's so true, and I've seen it in projects that we've run with customers as well, is that working on either a dialogue system or as you have, you know, a dialogue system plus many more elements as well, it's, it's really like a self-improvement exercise.
[00:22:10] Lauren Goerz: Y- you, you start to activate all the systems thinking minds in the organization, and actually work on the, the key problems across, across the business. And it really surfaces where you have problems as well. 100%. And I think that's, that's such a powerful thing, and I think that's an important mindset, and we can talk about mindset as well and kind of, you know, in, in the next segment.
[00:22:29] Lauren Goerz: It's an important mindset as a business to have, is that you have to be ready not only to do the thing, you have to build the AI, but also be ready to update, optimize, and change the way that things work at your cust- uh, company. And if you don't give the team the power to do that, it's gonna be really hard to make good change.
[00:22:45] Lauren Goerz: So I think that's exciting, and it sounds like you're in a good position to do this because you have both the innovation rolling up into your organization, and then also some of the processes rolling up into your organization. And that, that's- Mm-hmm ... kind of a, I think, a well-positioned place to actually drive change.
[00:22:59] Lauren Goerz: And a really big, you know, thing behind the importance of quality is being able to form habits and being able to get your marketers to actually use the system that you're building. How did you go about doing that? How's it been tracking so far? And what are you tracking in terms of success? The way we
[00:23:14] Eugenia Zeibig: think about the metrics is actually very similar to the way that we think about the customer funnel- Mm-hmm
[00:23:19] Eugenia Zeibig: believe it or not. Uh, if we just talked about customer acquisition, right, like, they would become aware, considering adopting, uh, frequency of, of their prescribing behavior, advocacy, et cetera. It's kind of the same thing. Once you launch a tool, the very first kind of signal from the market is just adoption, right?
[00:23:47] Eugenia Zeibig: Um, is, are the, uh, are, are users coming into the system? How long are they spending on... This is your basic, you know- Mm-hmm ... Adobe Analytics kind of stack. The, what you, you know, you've been tracking this on your website I'm sure for, for ages by now, right? But then you absolutely need to quickly start pivoting to the next, like down and down and down the funnel.
[00:24:08] Eugenia Zeibig: Um, so from adoption, what is the repeat rate? What is the, um, suc- uh, uh, satisfaction, right? And then you start s- start understanding, are we making the decisions? Are we making those impactful decisions, uh, r- that we were trying to tackle with this, uh, capability? Are we reallocating budgets using this AI, uh, supported- Mm-hmm
[00:24:32] Eugenia Zeibig: decisioning? Are we changing our creative strategy or customer segmentation strategy given this? And really, the litmus test is going to be- We s- we want to start seeing this AI being the co-decision maker in those quarterly business reviews that the brands have Mm. And this is where we start hitting the, kind of towards the bottom of the funnel, which I call the, the business impact layer, right?
[00:24:58] Eugenia Zeibig: Mm-hmm. After that, we're trying to tackle the advocacy layer. We want our co-brands co-presenting with us and advertising to others, uh, and to make sure that they, they join the, uh, the program as well, because a lot of it is actually kind of- Mm ... like opt-in. Um- Yeah ... so it's, it's the same rigor, right? It's the customer, uh, funnel rigor that we're, uh, we're going after, and it's just in those sequential ways, right?
[00:25:24] Eugenia Zeibig: Uh, from did you, did you go into the system, to what decisions are you making, and will you recommend it to others?
[00:25:31] Lauren Goerz: Yeah. And have you had any, and it's also okay if you can't tell me, but have you had any early signs of success? I'm sure you have adoption rates, but, uh, have you had any feedback, subjective or otherwise, you know, that, that this is helping your team?
[00:25:45] Lauren Goerz: Uh,
[00:25:46] Eugenia Zeibig: yes, actually. Uh, but the, the, the beauty comes not from those stats. They're, they're empty, right? They're just numbers. Uh, the exciting, uh, stuff comes in when you hear, uh, from in a meeting or somewhere that, "Oh, you know, our senior leader walked into a meeting and pulled it up and actually went and, and prompted it, and had the answer right in front of us all, and we all stared at, and we all did agree that this is, you know, this is what the market is, is telling us."
[00:26:18] Eugenia Zeibig: Uh, it's already happened. It's already happened at pretty senior levels, so I'm really excited where it's going. Now, because we put the pressure on ourselves to look at it as a funnel, we cannot stop at th- at the product as, as it is, right? In order for us- Mm ... to move from adoption to satisfaction to business value to advocacy, we're gonna keep, uh, have to keep improving it.
[00:26:38] Eugenia Zeibig: Mm. And, uh, the good or the bad news is that all of, you know, your f- your foundational models and your, uh, AI, uh, uh, industry is, is kind of pushing us to get better and better because their products are better and better. Mm. So we will not stop, right? It's not just, uh, about, oh, broadening the reach of, of capabilities like that.
[00:27:03] Eugenia Zeibig: It's constantly investing in them getting better.
[00:27:05] Lauren Goerz: So it, it's an exciting moment. I guess I would be curious, you know, just kind of tying it back to the central theme of the podcast. At the end of the day here at Rasa, we architect dialogue. We help our customers serve their customers, and in the same way that you're serving your customer, the, you know, Pfizer marketer.
[00:27:20] Lauren Goerz: And I'd be curious, uh, just, uh, is the solution that you have, are your marketers able to ask questions in natural language? Is that kind of, uh, part of the system? Or is that the core part of the system?
[00:27:33] Eugenia Zeibig: Yes, absolutely. The, the design is- Mm ... for natural language, um, dialogue. I like how you, you phrase it.
[00:27:39] Eugenia Zeibig: It's, it's really about the dialogue, and, uh, and that's where it gets tricky, right? Because- Mm ... if you think about the marketer, what is a, what are they after? They're after how am I doing? Imagine- Mm ... asking this to AI, how am I doing? What kinds of ways can AI answer that?
[00:27:54] Lauren Goerz: 101 at least. Infinite, yeah. Right?
[00:27:58] Lauren Goerz: Um,
[00:27:59] Eugenia Zeibig: so training that, uh, that, uh- Mm-hmm ... AI to be able to speak marketing and interpret how am I doing in the marketing talk is, is really where those design sessions were spent for weeks and weeks, um, because it is about designing the dialogue that understands that how am I doing as a marketer is very different than how am I doing as- Mm
[00:28:20] Eugenia Zeibig: easy on a Friday morning. Yep.
[00:28:23] Lauren Goerz: Absolutely. Yeah. Mm-hmm. And I think, I think that's, again, at the end of the day, why do people use a tool like Rasa? Uh, because ChatGPT can only get you so far, and it's the same in your scenario as well. Why do you build something in-house? Why do you, why do you use a framework like Rasa?
[00:28:37] Lauren Goerz: You need to inform the data. You need to inform the data about the type of questions it needs to answer, and I think that's a key thing. But I think this is ... What, what you're building is really interesting because it has such a clear overlap with the broader agentic AI, you know, conversational agentic AI space, because I think natural language as the medium for data analysis becomes more and more real every day, and you're kind of, you know, on the, on the cutting edge of it.
[00:29:02] Lauren Goerz: But I think that's so exciting because as a marketer, right, in the past, you used to have to ask your data scientist, "Hey, you know, I don't know how to do a, a query. Can you find this, this, this, and this?" And now you can just ask for it, and that's so powerful. Not that we wanna remove the data scientists, but it does remove the hurdle in between.
[00:29:21] Lauren Goerz: We still need the data scientists. Right. And I think that's, that's really powerful. Do, do your, does your team have the same feeling of empowerment with, with that kind of tool?
[00:29:29] Eugenia Zeibig: 100%, and it's, it's really almost like you have the, the data scientist right by you wherever you go. Mm-hmm. Right? In whatever meeting you end, uh, end up, uh, you, you feel kind of, like, more secure.
[00:29:44] Eugenia Zeibig: I remember the times when I was actually kind of working in the analytics community, and I remember the times where some of the folks in my team had to be brought into the business meetings just because the marketer was nervous being there without them, right? Mm-hmm. Um, and now they, they become empowered.
[00:30:02] Eugenia Zeibig: Um, they can have ... The way I like to call it is the first two of the five whys, right? Mm-hmm. Uh, why, why am I ... Why is my brand down? Why is it down in New York? Why is, is the pediatrician, is the, uh, a certain, you know, specialty that's down. Those first two, three whys in the journey of whys is you already have a companion to be able to answer that.
[00:30:31] Eugenia Zeibig: Now, exploring it in terms of building a new algorithm of segmentation, rethinking, you know, how to maybe revamp your creative strategy given that segmentation, is where you want to engage with your data scientist, right? Mm-hmm. Is really building new tools that this, this companion can start using, rather than dragging your data scientist into a meeting in order for them to be there on the, on the SQL query to quickly understand how is New York- Yeah
[00:30:59] Eugenia Zeibig: doing with pediatricians, et cetera. So it's, it's a, it's a empowerment for everybody really, and the idea is for, uh, our data scientists to become the, uh, strategic partners that are thinking proactively, uh- Mm-hmm ... and for our marketers to become, uh, much more empowered and, uh, self-sufficient, uh, when they need to make decisions rapidly.
[00:31:22] Lauren Goerz: Um, is there a question that we didn't go through that you'd be really keen to talk, talk through?
[00:31:29] Eugenia Zeibig: Uh, not a question, but something that just keeps us all, uh, uh, up at night, is, uh, for w- what is the future, right? Uh, every time you create a, a tool, every time you invest weeks in those design choices that we talked about, and those discovery meetings that we just talked about, in creating intake forms, et cetera, and then you see the industry moving at a massively high speed, and your customer base actually, uh, becoming more and more fluent with AI, how will these, um, enterprise AI capabilities coexist with the, you know, just the foundation models out there, and what is the right user experience we're trying to de- design for our users to make them as fluent and as well-equipped as possible?
[00:32:19] Eugenia Zeibig: I think we're gonna be revisiting this question every week, and tweaking, because the information comes every week, net new. But this is just, like, an exciting journey that I don't have an answer for, um, but I'm spending a ton of my mental energy on, uh, these days.
[00:32:36] Lauren Goerz: Yeah. Am I understanding you correctly?
[00:32:38] Lauren Goerz: Basically, the, the question is, how far t- in which direction technology continues to advance? And are you kind of evaluating, "Okay, these, this is things I'm going to have to continue to build, and these are things I'm, I might buy one day"? Is that kind of ultimately the question that you're trying to get to?
[00:32:52] Lauren Goerz: It's the
[00:32:52] Eugenia Zeibig: buy build and also the, uh, what is the full workbench, uh, AI workbench- Mm-hmm ... for a, uh, for a marketer, right? Where do they source which information? What, like, is it just a co-pilot tool- Mm-hmm ... versus the, uh, the custom-made marketing tool versus the custom-made Pfizer tool, right? You will have different layers of tools then for you, uh, and how do you integrate them all into your work, um, workflow?
[00:33:22] Lauren Goerz: Absolutely, and I think- Good question ... that's, that's a topic here at Rasa that we're so deeply focused on, is orchestration, which is kind of the- Mm-hmm ... terminology we're using to think about, okay, you have lots of different tooling, different teams working on these different tools. How can we unify it and kind of, for us, that'd be one continuous conversation across an organization.
[00:33:38] Lauren Goerz: For you, it would be probably one unified marketer experience across your different lines- Right ... of business. Right. Um, so I think that orchestration, I would also agree, is not a solved problem, but, um, certainly on the rise. Well, Izzy, I just want to thank- It's a journey, not a
[00:33:52] Eugenia Zeibig: destination, right? That's
[00:33:54] Lauren Goerz: true.
[00:33:54] Lauren Goerz: That's true. Right. Absolutely a journey. Um, but a really exciting one. I feel like it's, it's an exciting time to be in this line of work. And Izzy, I just wanted to thank you so much for joining us today. Learned a lot. I think, uh, there's so much more to the pharma marketing world than I would say even just, you know, standard marketing.
[00:34:09] Lauren Goerz: So I think, uh, I love the data-driven approach, and, uh, it's been exciting to hear how natural language is changing the way that your marketers work.
[00:34:17] Eugenia Zeibig: Thank you, Lauren, for all these questions. It was fun. Likewise.