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Enterprise AI at Scale: Eli Lilly's Field Assistant with Databricks and Genie

Summary

  • Eli Lilly deployed an AI field assistant across diverse sales and medical personas — including territory managers, medical liaisons, payers, and reimbursement specialists — to consolidate fragmented performance data from multiple systems into a single interface.
  • The technical architecture uses a flexible LLM selection model, Genie spaces for tailored structured data queries, Unity Catalog for role-based governance, and RAG for unstructured data, all on the Databricks Data and AI platform.
  • Lilly's success relied equally on people and process: co-creating the tool with field users from day one, applying ADCAR change management for adoption, and maintaining a three-persona collaboration model between tech, operations, and business teams.

Enterprise AI at Scale: Eli Lilly's Field Assistant with Databricks and Genie

Watch: Enterprise AI at Scale: Eli Lilly's Field Assistant with Databricks and Genie
Field teams face a common challenge: fragmented data spread across multiple systems, forcing sales professionals to toggle between tools instead of focusing on customer interactions. Eli Lilly's solution combines Databricks and Genie to consolidate field performance data into a single AI-powered assistant that surfaces insights without the busywork.
Discover how Eli Lilly deployed an AI field assistant across diverse personas (territory managers, medical liaisons, payers, reimbursement specialists) using flexible LLM selection, Genie spaces for tailored queries, Unity Catalog for role-based governance, and RAG for unstructured data. Learn the operational playbook: establishing cadence, enabling team members to speak up, co-creating with field users from day one, and using ADCAR change management for adoption. Explore three tech lessons on right-sizing solutions for different personas, rapid pivoting every three months as the business evolves, and achieving the vision of agentic workflows embedded directly in user tools.
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Chapters

FAQs

What problem did Eli Lilly's AI field assistant solve?

Eli Lilly's field sales representatives faced significant administrative burden from toggling between multiple systems to gather performance data before and after customer interactions. The AI field assistant consolidates information from these fragmented sources into a single tool, letting reps focus on the brief window of time they have with healthcare providers.

How does Eli Lilly use Databricks Genie in its field assistant?

Eli Lilly uses Genie spaces within the Databricks Data and AI platform to enable tailored structured data queries for different field personas, including territory managers, medical liaisons, payers, and reimbursement specialists. Each Genie space is configured to match the specific data needs of each persona while Unity Catalog enforces role-based governance.

What is ADCAR change management and how did Lilly use it?

ADCAR is a change management framework referenced in this video as Lilly's approach to driving AI assistant adoption among field teams. Lilly applied it alongside co-creation practices, involving field users as testing partners from day one to ensure the tool addressed real needs rather than assumed ones — a practice the presenters identify as a key reason for successful adoption.

How does Eli Lilly approach governance for its AI field assistant?

Unity Catalog provides the role-based access controls that ensure each field persona sees only the data they are authorized to access, maintaining compliance with pharmaceutical industry requirements. The governance layer is described as a collaboration driver rather than a barrier, enabling Lilly to expand the assistant's capabilities while maintaining appropriate data controls.

Full transcript

[00:09] All right, good afternoon everybody. Thanks for uh coming on the end of the day and joining our session. Um let's see. My name is Thomas Rayborn. This is Todd Walker. Uh we both work for Eli Lillian Company. Um they make medicine. We're based out of Indianapolis, Indiana. Anybody from Indianapolis here?
[00:26] Yeah, there you go. Okay. Couple couple people. Anybody pharma in here too? There we go. Okay. Yeah. Yeah. More fun. Anybody on the operation side in here? Okay. You guys come to this side for me, please. Everybody else could go to that side for Todd.
[00:42] But uh I work on our US field operations team. Todd works on our US um tech team and he leads that group as well. So he and I have been partnering for many years uh building a lot of different capabilities um deploying new processes as well. Um
[01:00] we've got one recently we've been working on as kind of our um AI assistant. Um we've deployed it. We're in the middle of enhancing it um scaling it up right now. That's going to be the focus of our conversation today. So I'll start just make sure you know what you're getting into just a little bit of
[01:16] um why are we doing it? What are we trying to solve for? Todd's going to hit up on some of the platform, the technical components of it, and then we'll cover um a lot more about our collaboration, some of our lessons learned as well and our partnership and why we think that's a key to success for
[01:33] what we're doing for our field teams. So, starting um with kind of the why, if you're if you're not um if you don't know pharma, just give you a typical day in the life. We'll talk about our sales rat specifically. Their primary job is they spend their days out in the field
[01:50] to try to see as many customers as possible to make sure that our customers are the health care providers, their offices, make sure we share the most up-to-date information with them in their office so that they can make a good informed decision for their patients. Um, if you understand pharma
[02:08] or can just relate, um, offices are busy. If you ever been in a doctor's office, they're busy. They've got important work to do. They're kind enough to let us come in, but the challenge we have is time. So, on an average, what we might get just
[02:23] generally, you may get seconds, maybe a couple minutes with a provider to talk about your product. Um, we can't control that. So, that's something we can't control. It's just our environment and our nature. What we're focused on though and what we believe we can control is on
[02:40] the back end making sure our sales professionals are the most productive and effective they can be. That moment and the few that we get with them is so important. So we want them to have the information at their fingertips so that they can have an engaging and a very effective dialogue. Now the few things
[02:58] we've experienced over the years that get in the way, we all know data is fragmented. It's in so many different places. Our sales professionals have multiple tools they have to work in every day. The ones related to customer engagement, but even other ones related
[03:13] to the administrative portion of their job as well. Um, so what we've tried to do and we think about that that just adds the administrative burden and it takes away their focus of what we want them solely engaged with is how do I get an awesome customer interaction. So the
[03:31] visually how this looks right now is on the left side um you've got the territory manager in the middle and that's a pretty true representation of the number of tools our territory managers have to interact with whether it's um customer information, sample
[03:47] information, uh employee like expense reports, policies and procedures, um training, you name it. They've got so many different places they have to go to get information and process um for their job. What we've done is we've shifted and
[04:03] obviously put our um AI engine in the middle of it. A new user interface powered by AI as a field assistant. So what that does is it's going to consolidate all that information. So the territory manager doesn't have to manually go out find the information.
[04:20] It's right there for them. More importantly though, if you look at the left side, think about one territory manager. But if I give five territory managers the same set of customer information, I know for a fact they will interpret it differently and make
[04:36] different decisions off of it of how they should engage their customers off of it too. Just multiply that by hundreds and thousands, you've got a lot of variability and inconsistency in the information that they are processing and using in terms of insights and actions. using our AI engine, we're able to kind
[04:52] of use that to help drive the insights and the suggestions. So they don't have to come up with that themselves. But as we know, they are still in the middle of it. So it is still up to the territory manager to take that information and see if it applies for the situation they are
[05:09] walking into with their customer. So they at least have that at their fingertips. They are saving time. it is helping them be more productive and effective having this AI engine there with them as well. So Todd's going to now talk about the tech platform, how
[05:25] we've gotten to where we are, and then we'll come back and talk a little more about our collaboration. Thanks, Thomas. So, as Thomas mentioned, what the opportunity really was is we wanted a a unified view of field performance across all these various platforms.
[05:42] And every bit of time that one of our territory managers spends looking for information is time that they are not in front of the customers which is where we want them to be. We also wanted to not just have general knowledge but rather we wanted to have specific Lily data woven through that as
[05:59] well. And of course it had to be scalable because as we're going to talk about this was not just for territory managers and business directors and AVPs and senior VPs but rather some of our colleagues are here on the front row. MSLs, our medical community, uh,
[06:15] director of strategic accounts, our payer community, our health outcomes related people, our reimbursement people in the field, a lot of different personas that we were going after as well. And we know that we also needed to have a platform that grew with the organization there. I don't need to tell
[06:31] all of you things changed so quickly on the business side and on the tech side. We had to have something that could adapt to that as well. So, with being from Indianapolis and I saw some hands in here that from Indianapolis, we thought we would use this platform discussion to compare it
[06:47] to the journey of a race car. If anybody watched the Indianapolis 500 this past year or were there in person, it was fantastic. It was the closest race in the history of the 500. 23 milliseconds is what decided the winner. I mean, we talked about milliseconds all through
[07:03] the keynote this morning. You think about that. 23 milliseconds is what the winner won by. It was set up by a a yellow that happened I think on lap 197 out of 200 and then they restarted at 199. Armstrong was in the lead and Malucas and Rosenquist quickly caught
[07:21] him and then 23 23 milliseconds ahead of Malucas Rosenquist came across the line the yard of bricks and won. So we thought what a perfect way to talk about here what we're talking about with the platform
[07:36] for the AI field assistant. What the user sees is really the cockpit. What does that front end look like? What the where the power comes from is really the model. The engine behind it all and then as well how do you hang it all together with the chassis? Those are three things we want to talk through.
[07:54] So starting with the cockput, we wanted this to look like any other AI tool. What that means is whether it's clawed or whether it's chatgpt or something, we wanted there to be suggested prompts. We didn't want it to be where a field user would show up with just a blank chat and
[08:10] they would have to start from scratch, but instead we have suggested prompts. We also wanted them just to be able to type natural language. It's pretty obvious in today's world. We keep their chat history so they can see and revisit those things. Some data we don't even want them to look for at all. We say
[08:26] let's surface that in dashboards right as soon as you come in. So that way you can see that data without having to go query anything. We know it's highly soughta data. Not just the data, but rather how do we surface insights for them that tells them what to do with it.
[08:42] So for example, instead of just showing prescription data, be able to have data that shows them this account is going down. Here's some insights about why we think that could be and what actions you should take to try to remedy that. And then the last piece around unity catalog that's super important for us. If you
[08:58] think about all the various brands of medicine that Lily has, you think about all the field personas that I talked about. You think about all the geographies that are spread across the US. The mix of all those things are very important for us to make sure it's the right data for the right person at the
[09:13] right time. So behind that then what's powering all that? We love the fact that data bricks allows us to have a flexible large language model choice. And if you pair that with the second point around genie spaces, let me tell you how that played
[09:29] out for us is we started with a very deterministic start in this. We had pred prevalidated queries tools that we set up inside of data bricks to say, hey, we don't want you to answer things that are not in these pre-validated queries. We think precision is super important. Only go
[09:45] after this. and we used llama because it was more deterministic. We have now switched. We're using sonnet and we're using genie to develop those queries for us in real time because people have become much more comfortable with you
[10:00] don't have to be we're still trying to be of course 100% precise but depending on what you're servicing there can be more tolerance for things that maybe aren't exactly precise but are very directional in nature. So we are very much in that journey now um and working through those things.
[10:15] Not all of our data is structured, some is unstructured, especially for our non-sales roles. And that's where vector search and rag is very important for us to be able to surface that data back as well. And then agentic workflows. We're going to talk a little bit about this on some coming slides, but in this case,
[10:32] think of this more of like supervisor agent calling other data related agents to go grab what you need to do. And then thirdly, then we've got the chassis. So what holds all this together? I mentioned Unity catalog earlier. Again, very important for us because of all the different field personas, brands, geographies, all the
[10:50] different pieces to be able to make sure that we can have that role-based access. We have about 50 different types of data that we're bringing together. Some of that's purchased external data like claims data, something like that. Some of it's alignment data, master HCP data,
[11:05] it's activity data. All these different data pieces have to come together in the semantic layer to be able to help us with this. And then of course when you're bringing in all that various data, you need to make sure that you're monitoring it. Make sure it's high quality. Does it make sense to bring it in if it's not high quality? And you're
[11:22] monitoring that and uh having all the appropriate data checks on it. So if we talk about collaboration really between Thomas's team and my team, I'm going to start here in the middle. So Tech Lily, that's just what we call our internal IT group. So in tech at Lily
[11:39] we're really responsible for all the data bricks platform architecture all the AI large language model engineering pieces all the data foundation I was speaking about security is super important in our world scale is really important in our world have to make sure
[11:55] this thing performs well and it's got to be reliable operational excellence is what we start with if we don't have that then we know we won't be able to do anything else that's cool so Thomas why don't you go ahead and take the other two personas talk about those But back to the Indy500 for a second. Um, if you
[12:10] attend or however we sometimes what we do, you pull names for winners and I pulled the guy that came in second and it doesn't feel good losing by milliseconds. So, um, that was a little sore for me, but that's okay. It was a good race. Um, from the field operations
[12:25] standpoint, our role we we face the business. I mean, we all do, but our primary job is to partner with our um, different business partners. We help drive um understand their strategy, the use cases, make sure we understand like we talked about those important moments,
[12:40] but what else is it? What else are they trying to solve that we could bring solutions to to help them be more productive and effective? Um change management, we're going to talk about that in a little bit. That is a big part of my team working with our business partners as well. And then nothing hap,
[12:56] we know nothing happens in a silo. So between my team's leadership, the tech team's leadership and our uh business teams, their senior leaders, um we kind of take that role of like going across function uh bringing all those together to get to the decisions that are needed
[13:12] to move along our capabilities for our uh field teams and then now not on stage but introducing a core group to our team are our field users. So, we've very fortunate. I don't know how it works in a lot of places, but we've got a dedicated group of field team members
[13:28] that are our partners. So, like it's a role. It's above and beyond their day job, but they sign up for it. They're interested in it, and they want to be partners with us to help build. So, they uh validate a lot of the use cases. So, hey, what are we talking about? And is
[13:44] it really going to solve something that's happening on the ground? more importantly they do a lot of the um user acceptance testing for us and then also um they'll help drive adoption. So when when we talk about OCM and we go to launch they're kind of the forefront in
[13:59] front of their peers helping us drive adoption. But just having this on a slide for us is an example of collaboration because you got to know your lane and I think we know what each of us brings to this collaboration and we know what we have to do extremely
[14:16] well to make it successful across the board. So talking a little bit more about our field users, we engage them a number of different ways, but just like two examples uh we've done um this like a first look Friday. We got to have a
[14:32] catchy name, right? But we we've we're in the middle right now of a pretty big enhancement deliverable that's coming up in the next few weeks. So we actually had one of these Friday. I I was off but um my team Todd and team met with one of our u senior business partners and and
[14:49] some of her team and we were rolling out the updates that we've had and what progress we've made so far. So we're a able to keep them on track. Hey, is this meeting the needs of what you're looking for? Is this kind of getting the look and feel? So we kept them up to date and I think fortunately we are on track. So
[15:04] we're going to keep moving but we kind of keep that on like what we call that first look Friday. The other thing something another example we've done and this is more probably with our field users is like the testing Tuesday. Um look they're kind enough to take time
[15:19] out of their day job to partner with us. So we don't want to surprise them or throw stuff their way. So if we're in the middle of a big build getting into the UAT section, what we've done is, hey, if it's over one to four weeks, whatever, however long that takes, we'll kind of try to spread it out, get a
[15:35] little bit of time, and just get two hours of their time so that they could work around it. But just a couple ways we try to keep our users engaged. But I'll tell you, there's so many other examples. These were just two we thought we would bring on how our kind of field users um interact with us. And maybe
[15:52] surprisingly or not, I don't know, we have an opinion that governance is good. So I we believe governance is a driver of collaboration because it's a it's crossf functional efforts between the business field ops and the tech team. And if you don't have a governance, if
[16:08] you don't have a hey here's how we work, here's how we make decisions, our experience, you just swirl and spin and then nothing moves forward. Um, what we want and where we've seen success is when our team members, our core team members are enabled and know that they
[16:25] could drive and get the work done they need to do. And then they know, hey, we've got example on this project bi-weekly leadership meetings that we come together and it's like, hey, any status progress, anything happening, any
[16:40] barriers, any decisions, any stakeholders we need kind of need to work through and that's with our senior leadership. So we've seen great success in terms of driving governance through our build of these capabilities across ops and the tech team. So I guess in in
[16:57] summary um governance is good field users getting them like early on board is great and then how do you just make sure everybody knows their roles partner together um have a little fun along the way as well too from a um collaboration
[17:12] standpoint. Um, I'll start with a few of our lessons learned and then Todd will close it out on some of the tech lessons learned. I think pretty general, but when you talk about people and process over the
[17:28] years, what we have seen and what we've learned is we all want to be fast. We want to we want to deliver with speed and we want to be agile. You've got to have the process in place to do that. You can't do a square peg in a round hole and expect to get the results. We
[17:45] actually spend time creating the process for speed. We've redefined several processes. Say we were on like a monthly sprint. Like we would get the group together and say, "What does it take to get this down to two weeks, our teams would make adjustments, they'd figure out how we would need to adjust work so
[18:01] that we could hit more of a two week sprint versus a monthly sprint." And then ad hocs, we always get ad hocs that come in. Hey, if there we've got a way to prioritize, they're high priority. It's extra work. So, we've just got to move with that and try to meet those needs. In terms of establishing a
[18:19] cadence, um I I love having a good ways of working and how to get work done. um in our in our role and like with this project a a typical week what it might look like is hey Mondays we get together the core team here's the priorities we
[18:35] need to accomplish for the week um we reserve meeting time whether it's a Tuesday or Wednesday say a two to three hour block and then that Monday decides what goes into that meeting um you can't just slam calendars every day so we like hey these are the groups we've got the
[18:51] core team out of this core team who needs who to move something forward It goes into that Tuesday or Wednesday meeting. Friday, Todd and I meet with the team in the morning, give us some quick updates and then usually typically like that Friday afternoon, we'll send
[19:06] out an update to our leaders. One thing we've learned and don't tell our bosses, we just shifted and we do that on Monday mornings because we were getting peppered with questions over the weekend and our team felt compelled to answer them even though we did not want them to answer anything over the weekend. So,
[19:22] let's go out Monday morning now and they haven't even noticed. I don't think so. It works out pretty well. But that's the cadence in addition to the governance meeting. So we try to keep everybody coming along, keep everybody informed, keep everybody aware. Here's our progress, here's our barriers, here's
[19:37] how we need to work through things. The next thing, everybody has to speak up. We've got some high performers on our team and we do not have time for people sitting in the corner with what we are being asked to deliver and accomplish. So we count on people. our core team, it
[19:54] is up to them to bring recommendations back to the group and it's a collective group of recommendations. Um, examples on mine and Todd's teams, we have we have a number of individuals that work with us, our associates. They're demoing to the senior VP of a business unit.
[20:12] They're presenting to our senior VPs. They're getting pinged by this. They they are fully engaged across every level and speaking up and giving their ideas and thoughts. Um, another example how we feel. And I was off on Friday. Um, had a afternoon off with my daughter
[20:28] and then so Todd after we had that first look Friday with our senior uh VP, he was on with my team and his team. So they were going through everything kind of getting everything put together. What do we need to get ready to send out a summary? How do we get ready for next Monday with what we came up with maybe
[20:44] four big items we had to keep moving forward from that call that we had to drive focus on. So he was filling in for me on that one. I've talked about like the lesson of co-creating in this project we've we've brought our our team has been on from day one. I
[21:00] think the first workshop we had in Indie was the week before a Christmas and you know you start winding down then they still came. It was cold it was frigid the week before Christmas and we had our kickoff workshop with our field users in Indie. So they've been there from day
[21:16] one on this journey. We've had other projects though uh where you had to move with speed or maybe our field users in the middle of a product launch so hey don't really have time with them that we've not had a chance to co-create we've launched some other smaller capabilities they're still involved the
[21:33] only thing is it just shifts instead of getting it right up front you're just going to have to fix it and do enhancements on the back end so it just kind of shifts where the work is but they were still involved uh with that as well um but these are the a lot of the learnings from um people in process process. I don't know if other companies
[21:50] what you use, but from a change management perspective, we use ADCAR. It's kind of how we use from OCM. See a head nod over there. Our team leads the my field ops team leads the change management with the business. What I say is one and three are pretty easy. So
[22:08] what we'll do working with the uh field teams awareness knowledge you know if you need it training courses videos resource guides communications meeting with different lead teams like we could do all that and typically we could build
[22:24] up a pretty good buzz with something new coming out with a general sense of excitement but that's it. it really starts and the rubber meets the road for the racing metaphors on launch day. So when you actually roll out something new
[22:40] and now the users are expected to oh now I need to start using this incorporating this into my workflow. That's where OCM really starts and for us that's really like with number two and four that desire and ability there's only so much we could do sitting in Indianapolis. So
[22:56] that's why we count on two groups. Our field users as I mentioned to help drive the adoption and the use and then the local sales managers as well. They're obviously with their teams every day. They see the opportunities. They see the challenges and they know how to coach
[23:12] and position this to show them. It's all about showing them how they get value out of these tools. That's the goal and that's what we want to drive from an OCM perspective. And then reinforcement's pretty easy. But this is a model we've used for many years on drive and OCM
[23:28] pretty successfully across several big capability updates. Um my last slide for now is just over the years again uh our my opinion or our opinion like in our field teams there's kind of like three groups of users. You've got the early
[23:45] adopters. They're going to do anything new. They're excited. Let's get after it. Uh probably the biggest group we've got those that are just a little curious. Um they want to they've probably tried something. They've tested it. They probably just need like a what does good look like or show me an idea
[24:01] or give me a prompt that works, right? Just kind of you got to give them a little nudge. And then you've got those that are slow to never adopt probably. And I that's okay, right? Because I mean they've been doing their job a certain way and they probably still can like they could probably still do their job.
[24:17] But I do know and believe because the tools and like this AI assistant, we've proven it to have add value. Like we can show how it can save you time. We can show how it gives you better insights. And remember our field users being there from day one, they've told us it's going
[24:32] to add value, too. So we feel pretty confident in the value that it's adding. And I think over time, those that just don't jump on board, you're going to start to see a pretty clear separation in performance and people that are driving things forward. It'll be pretty evident. But the the takeaway is move
[24:48] with the movers. Don't worry too much about the people that aren't ready to move. Just go with the movers. And I love I'm probably a curious one on we're all going through an AI journey. Like if you show me a way and it clicks with me of okay, now I got it. Like help me get it. Those are the people we really want
[25:03] to work with and get them moving with some of our resources like this AI assistant. Todd here's got a few key learnings from the technical side as well and then he'll wrap us up. Over the course of time, we've had these huge systems we build out that are one
[25:19] size for everybody. And I think those days are gone. We are now trying to get more in this idea of a right-sized approach. As I mentioned earlier, we have very different personas, different needs of what people have. And so the idea that they all see these different
[25:34] data, they may want to consume the data differently. They may want some sort of a clawed front end. They may want some sort of a React front end. But if we keep enough of the system the same, then we need to have the flexibility to be able to move with them wherever they want to go. We also know that if you make it too
[25:50] general, they don't like it. It's just underwhelming. If you make it too deep, it's overwhelming. It's too much. How do you really get the data and the insights that go with it for them? Uh I've talked about the one UX can't fit every workflow. The value pitch changes for
[26:07] the different audiences. So that's something for us to always keep in mind as well is that what might resonate with the associate VPs of a field group would be way different than what might resonate with some of our medical team or what might resonate with a territory manager that's out in a specific
[26:22] territory. And the tools and expectations shift monthly. We no longer think about things in terms of oh this is what we'll do for the next year or the next six months. We can see about three months out and that's about it. And so we are trying to make this to
[26:38] where we can pivot as quickly as we can. We've already pivoted. It's mid year. We've already pivoted a couple times this year about what we're going after and where we're what we're doing. So that's one key thing. Go from uh one size to right sized. And then this one's not as much as a
[26:54] learning other than just maybe a vision of where we're wanting to go. We're wanting to take this field assistant to be sometimes I think data bricks uses this term like the single pane of glass. That's what we want for this field assistant. We want them to be able to do all their actions that they need to do
[27:11] through this through other agentic things. Not just bring back data but complete tasks on their behalf embedded into the flow that they're already in the tools they're already using the places they already are. And of course it's got to be customized to them with
[27:26] their access. And you've got to have that sort of auditability and and approvals as needed on that. And then if for some reason those upstream systems aren't available, you want it to be graceful. You want it to say something like, "Oh, it's not available right now." versus just crashing on them
[27:42] because that's going to be a horrible experience. So really in summary, I would say for this whole thing, there's really three things that I think are super key. One is partnership. It's very intentional that Thomas and I are here on the stage
[27:58] together. It would have seemed really odd if him or his team would have done this without us and it would seem really odd if we would have done this without them. Partnership is very key in this thing. Second, I would say is speed. We get pushed to go faster than we've ever
[28:14] gotten pushed to go. We're uncomfortable every single day. But I also understand if we don't move at that pace of what the business wants us to move or what the tech is innovating at, then we become irrelevant. Even as quickly as we feel like we push ourselves and move,
[28:29] we've had a couple cases already where we very easily could have become irrelevant with what we were doing if we didn't have the ability to pivot and listen to what it is. So partnership, speed, and third is impact. If what you're doing doesn't really matter and you're not going to drive an impact, then don't do it. Really, you've
[28:46] got to have the value. So that's why we look at this and say, okay, we've kind of renamed MVP from minimum viable product to minimum valuable product. I mean, viability is just means it works. Value is really where you're making the impact. So it's kind of more like instead of just it working, can you get
[29:03] a spot in that person's day? That's where the value statement comes in. And in our world, that means you're bringing you're driving productivity and you're driving effectiveness for these different field personas. We are supposed to remind you to complete your surveys for this session
[29:20] and then Thomas and I will stick around afterwards for anybody that's got any additional questions. Thank you guys for your attention. Thank you.

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