Video: Will SaaS Sprawl Cost Your AI Transformation? | Duration: 2740s | Summary: Will SaaS Sprawl Cost Your AI Transformation?

Transcript for "Will SaaS Sprawl Cost Your AI Transformation?": Welcome. We're just getting things set up here and giving folks a few minutes to walk in. We'll officially get started in just a moment. Thanks for your patience. K. Great job, Charlie. It would be great to know where everybody's from, business, state, country. Love to hear from everybody, see where we're talking see who we're talking to today. Wow. Everywhere. Alright. West Coast representation for everybody. Alright. Well, I think I'm gonna get up on it. Hello, everyone, and welcome to today's webinar, Will SaaS Sprawl Cost Your AI Transformation. I'm Alice Rogers, chief marketing officer at Quickbase, and I'm gonna be your host for today's communication. We're really excited you've joined us today to dive in to some eye opening findings from Quickbase's third annual gray work research. So before we begin, a few quick housekeeping notes. This session is being recorded, and it's gonna be available on the Quickbase website. We also have some time for q and a at the end. So use the chat function to submit your questions throughout our presentation, and we're just gonna do our our very best to answer as many of those questions that pass as possible depending on how long our conversation goes today. Today, we're exploring this really interesting paradox at the heart of this this year's gray work survey. And, and that is that even with the increased investment everybody's making in productivity and SAS tools, which is intended to make work a lot easier, it still feels harder than ever to get work done. So we surveyed nearly 2,200 workers across 10 industries to see just how productive they feel in their daily work, and the results weren't that great. Workers are feeling more overwhelmed by tools and processes and less productive than ever, revealing some of the uncomfortable truths about this current state of AI and digital transformation that many organizations are going through today. Not only are we gonna take a look at some of this research, but by the end of this webinar, you're also gonna have some really tangible steps on how to use AI to enable your technology transformation. I'm delighted to be joined today by two experts on topics of productivity, digital transformation, and improving the way work gets done. And they're gonna help us really unpack these findings and discuss practical solutions. So first, let me introduce Isaac Sacalick. He is the founder and president of Star CIO, a technology learning and advisory company that guides leaders on digital transformation. He's been a CIO, a best selling author, and an industry speaker for two decades, and he has more than 1,100 published articles. Isaac Isaac, we're really, we're really excited to have you here today. So thank you so much. I'm so glad to be here, Alice, and thank you. Yeah. Great. And we also have Megan Milam with us today. She is our senior product marketing manager at Quickbase. Her excellence comes from her customer relationships and the research she's done for us here. And from a clear understanding of the particular pain so many of our users and buyers face. And while Megan's been with Quickbase for three years, we're delighted about that, she has spent the past fifteen years understanding the technology buyer and users through extensive qualitative and quantitative research. So we're really happy she can join us today and walk us through some of the most recent research on the investment and inefficiency paradox we just let, just outlined in the beginning. So so let's get started. And, Isaac, I think I'll turn to you first. You are frontline with so many CIOs. And, what have they shared with you about their experience with productivity tools and just closing this productivity gap? Yeah. I mean, we we talked to CIOs, not just CIOs, CIOs, IT leaders, business leaders. The common point is is is that it's, you know, it's really easy to throw technology at a problem. The average enterprise has over a 120 SaaS platforms that they're using across different departments, and that's a common theme. Right? You see departments, they have a problem they need to solve for, there's a SaaS solution that's easy to go procure, they go and procure it, and what people often forget is that when you start looking at, you know, how do I how do I enable efficiency across departments? How do I go after customers in a new innovative way? How do I take advantage of all the AI that's coming out there? And you start looking at the SaaS sprawl across, you realize it's not really a technology problem when you're trying to outfit an organization. It's a people problem. It's a change management problem. And so every time we start putting more technologies in place, we're going back to our existing people and our departments and our workflows and giving them more screens to go work with, giving them more tools to learn, and figuring out, should I use tool a or tool b to do a job. And when a tool a and tool b tool b can't do the job, you know, how do I pull the data out of it and put it into another tool and send that data off somewhere else in an email so that we can connect point a to point b. And you see this across all kinds of departments, from marketing and sales, inside IT. Many of you are in IT, if you see it between development groups and IT groups, you see it between finance and operations, and this clouds our ability to get work done. So even though we're putting a lot more money and emphasis in putting technology to automate or to use AI, we have this backlog of stuff that's out there that's in front of every single one of our users. And so when you ask them, are you more productive? They're like, no. You just keep giving me more tools to work with. Yeah. I appreciate that insight and for the marketing call out. I mean, certainly, we feel that as we work with, sales tools and marketing tools and BI tools and website tools and and connecting all of that data. So that was a great insight. Megan, how do Isaac's observations compare to some of the research that your team has done? Yeah. Honestly, what Isaac said is is absolutely at his perspective is spot on. It's what we're seeing in the data. I think he said throwing technology at the problem. That that's very common. We actually find that 80% of our respondents to our survey said that the organizations that they work for have invested more in these tools this past year, and that's up from 66% last year, which is a pretty big jump year over year. But despite all the investment, we're only seeing that 18% of those people have actually seen a reduction in the manual work that we're doing. So the productivity gap isn't closing by these, additional investments. It's actually widening widening, and that's kind of at the heart of the challenge. More tools doesn't necessarily mean better. And when those tools don't integrate, when data lives in silos or when teams have to adjust how they work to fit the rigid software, it causes increasing problems. And I think that it keeps coming up for a very specific reason. I think Isaac said it's not a technology problem. It's a it's a people problem. The technology isn't broken. It's just misaligned with how people are actually doing their jobs on a day to day basis. So you end up with this digital overload instead of digital transfer transformation. People have those more systems to log in to, more workflows to patch together manually, and more gray work just to make things function. So we do talk a lot about productivity tools, but they only work when they're built around those real workflows and real users. Wow. That's great insight. Like, this was a really strong body of work that's now longitudinal, which is incredibly valuable. We can see it changing over the years. Can you tell us a a little bit more about the twenty twenty five research survey respondents? Like, what industries do they represent? What did they report about their ROI from productivity tools? Any any anything you wanna call out in the front of this before you go through more research? Yeah. Absolutely. So we surveyed, over 2,000, nearly 2,200 operations IT and business leaders across a variety of industries, tech, construction, manufacturing, health care, finance, all industries that really rely on complex cross functional project work. And we spoke to people who are deeply involved in deploying productivity tech within their organizations. Now across the board, we've know that they've all made major bets on productivity software, but those bets haven't really paid off in the way that they've hoped. For example, as we see here, 73% of respondents said that using multiple project management tools actually make it harder to share information. That's up 4% from last year. And 75% said that they can't see all their data in one place. So even with more software, they're still struggling to get that clear connected view of what's happening. Also, when we asked about some of the top challenges in managing complex projects, the responses paint a clear picture. The number one reason is data is scattered across systems, then, you know, too many manual tasks, data silos, etcetera, etcetera. So what we see here when we take a look at this chart is that the frustration isn't with technology itself. We're not seeing a lack of functionality or, user access or something like that. It's more about the disconnection between all of these tools. So the ROI problems that they're experienced, it doesn't have to do with the software necessarily itself. It's that it's all not working together to make people more productive. Makes sense. So so I think as as you look at this data, maybe you can make this more tangible for us. And in in all of your client work and all of your influence work, do you have any examples where a company maybe increased their software budget but still struggled with all this manual work that you've been outlining? And and if you do, was there, like, something that really made the difference when they turned the corner? Yeah. I'll share two, Alice. I'll start in the construction industry, which over the last decade has put enormous, technology in place to do things like, you know, go use BIM models, to do the modeling and architecture to put scheduling software, put project management software and put, you know, data in the fields of mobile applications. But if you look at the heart of what's happening in the back office, there's a a standard tool called a work in progress report, and it's pulling data from all these places, and it's a key tool for making decisions around finances, around, orchestrating work across, different trades, and, you know, because of all that integration work that's required, you go talk to different project managers in mid sized construction industry companies, and they each have their own version of the WIP report. They're each each one of them is looking at a different set of data, a different way of managing it. Every time a new project comes up, they're duplicating that effort. So, yes, construction's put a lot more money into technology over the last decade, but when the CFO is asking a question about which of our projects are at most risk, which of our projects are we running a health health and profit margin on that you you should go bid more on, that CFO doesn't have that data because they're all locked in different spreadsheets with different data formats. And another example, you know, this was a government agency. This I find very typical when organizations are looking to do more automation, and so what they do is they go buy an RPA automation tool, they put that out as a service, they start automating on top of the existing user interfaces. It goes live and people are happy that they have automation in place. They made a six, sometimes seven bigger investment on that platform, only to find that four to six weeks later, that automation is breaking, or they've changed their process. They've gotta rebuild that automation, or it's not capturing a lot of the exceptions that come up month two and month three, and they have to go back and reinvent it. And so what's missing in all of this is coming up with a platform standard by which you are trying to interconnect work across multiple different areas in a way that allows, using tools for what they're really good at doing, but then provides a horizontal level of connectivity, that, management can go out and look at. It's a good example. Thank you. Thank you. That just sort of really brings it home. Maybe what I'll do now is I'll jump back into your data, Megan, so that I can, then ask, Isaac some questions against that. So Sure. Maybe there are some trends in, in slides you've pulled together to the today that that this audience should be aware of and maybe what titles were represented and other key findings that you think are of of, like, really worth a good call out here. Absolutely. I've got a few slides teed up. So, first, our respondents oh, can you take that back one slide, please? Over, over half of our respondents, to the survey itself work in IT. IT. So they have titles like CIO or IT systems engineer. Nearly 30% of the people who responded worked in operations, like COO or a director of supply chain, and then the remaining were in functional roles or consultant roles, like, CFO or director of demand gen. But some of the things that we found regardless of their functional role, what industry they're in, we saw big trends. As I mentioned earlier, you know, over 80% of organizations increase their investment in productivity tools, Only 18% saw a reduction of manual work. So you can kinda see that broken out here on this slide a little bit by our different or across our different audiences. This productivity challenge is across all industries. And when you take a look at especially maybe manufacturing and construction, to Isaac's point earlier, where you kind of expect automation to to dominate a little bit more. Over 50% of our respondents said that manual work had actually increased, which is, you know, kind of a productivity paradox that we're talking about. We think we're solving problems with more tools, but it's really creating more fragmented text decks and disconnected data and all that nonsense. Let's look at some more data. If you go to the next slide, we found that c level leaders are more likely to report increased manual work. 68% of them said that it's gone up. If we take a look at the next slide, what's really interesting is that they also, that they're more likely to say that they're very satisfied with their current tools. 65% compared to just, like, 41% of managers. So when we see these two charts together, we see something really interesting. C levels report more manual work and more difficulty sharing information, but more satisfaction with their tools, and that sort of disconnect is kind of a blind spot. So it's it's like they see the problem and they feel the increase in gray work, but it's more like they're still giving, like, their tech stack a thumbs up. That that's kind of a disconnect. So when you take a little step further into the details of the operational impact, you kind of see it a little more clearly. On the next slide, we see that, 81% of c levels say that it's hard to share project information with others. And 82% of c levels say they struggle to see all their data in one place. So these are higher pain points than what managers are reporting. So what this all kind of tells us together when we take a look at it is that leaders might be focusing on the promise of the tools in their tech stack, on their dashboards or budgets or cool widgets or whatever it happens to be while the people doing the day to day work are are kind of navigating those fragmented workflows. And the risk here is huge. And leaders think about the tech that it's working. They think that it's working, but it's not actually fixing the root problems for their teams that are doing the work. The productivity stalls and burnout grows and, you know, digital transformation efforts kind of lose a little bit of credibility. Oh, thanks, Megan. There's a lot of nuance in that. That's really interesting. I'm gonna throw throw something at you, Isaac, and that is like look look at that. 65% of c suite are satisfied, and then 41% of the people actually using the tools are not that happy. So, clearly, there's a perception gap between the leadership and the teams doing the work, and feels like a little bit of a blind spot. And, you've been at this for a while. What are the biggest risks in that scenario? Well, it it it's more than a blind spot. I mean, what you're seeing here is a, you know, different viewpoints of a of a problem. You know, what the c levels are saying is, I don't wanna throw more money at tools. Right? I know how hard it is to roll tools out. But, you know, I'm I'm still focused on the business outcomes that I'm trying to achieve. I'm still trying to grow. And what managers are are saying is perhaps when you chose the tools that you have, they work for the at the time when they were required. Right? We put a new tool in as a layer on top of something that existed. We weren't gonna go change our ERP, so we go put some new tools in place to work around that. We needed a a specific tool to do contracts. We put a specific tool around that. We needed another tool to collect data and have collaboration between the field and the office. We put another tool around that. And so now the managers three, four years are looking at this and saying, jeez, like, every day, I'm using a different tool with a different user experience, with a different way of navigating around. And when I actually have to do something that requires a decision, I gotta pull data from all of these different tools to be able to do that. And the data is messy, the, you know, the comms and rows don't line up. I've gotta do a lot of work around this, and the person who's saying that the most is the CFO. And so what do we do then? We start putting even more tools around that to do the analytics, to do dashboards around this, to do data cleansing around this. And so very soon, you you know, you bring someone like me and say, oh, look at this and say, it's a house of cards. Right? I got too much tool out there that's not connected. And it it you know, the problem is, you know, if you're a big company with a lot of resources, you can have all these different tools, and you can put some fancy technologies in place to do integrations, to do data fabrics, to bring all this data in, to have people responsible for, you know, doing all the data cleansing. Most of the companies I work for do not have all the technology prowess and investment to bring all this stuff together. And so what we start doing is saying, okay, let's leave the existing world behind a little bit, and let's let it live for a little bit. Let's start reimagining. And the reason that becomes so important now is is that, you know, our world changes so quickly nowadays. You know, the process that we wanted to build something for got whacked during the pandemic, and we had to adjust things very rapidly. And we started figuring out how do we do this quicker. We have opportunities that we wanna go after, that require us to reengage our sales force a little bit differently than we did six months or a year ago. We have tariffs coming in that's changing our supply chains, and we have to, you know, adjust for things like that. What we're finding is we don't need lots of tools. What we need is a platform that's agile enough that allows us to change and adapt to the way we're working today. Oh, that's interesting. And Jay Cohen totally agrees with you on the tech debt. He's, written in the comments. So okay. Let's pursue the scenario you slightly brought up. We're just bringing a new CIO. What okay. How do you recommend CIOs bridge this gap in a way that surfaces I mean, what you talked about is a messy situation, but surfaces the real friction without causing what can happen in organizations, which is a lot of defensiveness, a lot of blame, like, what's the most productive way to for our audience to move through these kinds of challenges? Yeah. So, I mean, the very first thing I try to do with groups is really apply some blue sky thinking. Right? Leave the jargon behind. Leave the the existing problems behind. Leave all the exceptions that we're dealing with that contribute to gray work behind, and let's leave what departments we live in, and let's think like business users and look at the end to end problem, and solve the end to end problem for what value it needs to actually solve for. I'll I'll share an example from '10 is it's gotta be ten years ago now. It's a use case that I continue to use. I built it on Quickbase. I wrote it by it in my books, but, I had this problem. Alright? I have lots of project management software out there. I couldn't solve that problem right off the bat. There were different reasons that different teams in my group and in other groups were using different project management methodologies and different tools around it, and but I still needed to bring the data into one place. And so I said, well, what data do I actually need to be able to look at top down and understand what the status was of all these different projects? And so we built a tool, right out of Quickbase, that was a tool everybody used to provide basic project status, red, yellow, green light, here's where here's the here's what's working, here's where the problems are. And so every week, we had a management team, we knew exactly where our projects were, and if there were questions, we knew which ones to go drill down into. So we focused on the problem. We weren't trying to cleanse all the data upfront or fix all the project management software. We said, we just need a voice of truth that we come in in Monday morning, we know what problems to solve on that currently. That's what I advise organizations to do. And once you start with that, and you get a layer of that, you start looking, okay, what's the next level of problem that we need to solve for? That very agile mindset is gonna lead you to, how do we connect departments in a holistic workflow that starts with the end in mind, and starts working backwards into how we're doing things today and starting to evolve it to a more agile, homogeneous approach to doing things in a more standardized way. I love that. Sort of big picture right down to pragmatic. Thank you. That was really helpful for our audience. So, Megan, I know your team has done, there was a lot more research in, in this particular, study. And I wonder, if you could walk us through some additional findings that I can sort of, pressure test against Isaac again. Yes. Love it. Love it. Go ahead and challenge us, Isaac. Mhmm. So, this part of the research that I'm gonna talk about really kind of highlights, how much time is being lost, in companies, not because people aren't working hard, please don't get me wrong, but because people are fighting against these fragmented systems. So let's start with, that first statistic on the top right there. 59% of respondents said that they spend eleven or more hours every week just chasing down information, emails, spreadsheets, platforms, people from a variety of different places. That's nearly a day and a half each week and not spent on the work that they were actually hired to do. For those that don't know, you know, Ali is my leader. And so, Ali, you know, if if I'm gonna be chasing down information, I'd much rather take an extra day and a half of vacation each week. You know? So, like, that's not how I wanna be spending my time. And when we asked our survey respondents, you know, how much of their week is spent on work that actually moves those key projects forward, only 53% said that half of their week fits that description. So we've got you know, we're hiring all these really talented skilled professionals, and they're spending more time finding answers than working on projects and delivering results, and that's sort of the basis for the hidden cost of SaaS sprawl. And if we take a look at the next slide, you know, we have to recognize that even with all this tech, spreadsheets still remain the duct tape that holds us all together. I thought, you know, if I could see show of hands, I thought everybody on this call is is has uses spreadsheet at least once this week. So, what we know is that nine out of 10 workers use them multiple times a week and over half, 56% of them use spreadsheets daily. So, you know, again, don't get me wrong. Spreadsheets have their place, but when they become kind of a default way to connect your disconnected systems, they're not helpful anymore, and there's sort of this crutch or this band aid, between all the systems that that you're paying to use that have so much power. But but it's not just about the cost, it's also the impact. The more times that, you know, teams are spending navigating around all of these tools, you know, the less time they have to move their projects forward. So, successful, it doesn't just slow people down, it slows the entire business down also. Yeah. That's a great insight. And, you know, as you pointed out on our team, I do feel like when you start to move into spreadsheets, you've lost your single source of truth, and there's a lot of human error that can happen in there. So for us, just trying to keep it in our main systems. But I said maybe based on what, Megan just said, maybe I'll throw two things your way. Like, you you experienced this firsthand. So first, what are the most common ways that Sassprawl is creating friction across teams? But the second one, since Megan jumped into spreadsheets, is, like, somebody might look at that data point and assume that spreadsheets or any specific tool are the real problem, but is that's the right way to approach this problem. So there's a there's a there's a lot baked in there, but, answer the way you'd like. Yeah. So I'll start with an example. This is again coming from the construction industry. And you look at, you know, what the things people are doing across a construction company. I've got people out in the field doing work. You know, safety is my first, you know, line of importance for them. I wanna make sure that they have access to the right level of information, when they're taking on a new job or using a new tool. I need feedback in terms of their questions and request for information. I need them to provide status back to what's going on because in my mid office, I've I've got a project manager who's trying to keep the trains running on time, who's trying to look forward into what's scheduled coming up next and looking at what risks are coming up. Is there bad weather coming up? And that project manager is using another set of tools. And then even before that, you know, or after that, depending on how you look at that, is the is the person estimating and bidding on new projects. Right? Is looking at and asking questions, like, can I look at the history of how we've operated in the past, and say, can I improve my estimate around future projects? Do I wanna bid aggressively against a new project because we're more profitable at it? Do I wanna maybe not bid at all against the project because it's just really outside of our expertise? And look at all that data and equations in, you know, I just named at least six different tools across three different groups that have to find a way to integrate. So that's really at the heart of solving these problems. And what's happening in the middle of this, you know, what's surprising to me, Megan, is that we're still talking about the spreadsheet problem. I first I remember my first, keynote, I think it was back in 02/2015, where I talked about the problems with spreadsheets, and back then, it was also Microsoft Access databases. It was the only tool that everybody in the organization had to work with data. And so it became the defacto tool around it, and it's not necessarily that they're bad tools, it's that once you're getting multiple people using it over and over again, they're creating copies of it, and they're, you know, there's five or six people, you lose all the data around it, you lose all the governance around it, you lose a lot of efficiencies around it, and, you know, it's one of the things I look for when I go into organizations and say, look, let's treat this as a symptom. It's not the problem. It's not the problem that that people are using spreadsheets where they have lots of tools around there. It's that we need to discover what problem they're solving for in there. It's usually connecting data, or connecting a workflow, or a workflow just that just doesn't exist in tools today. I mean, many of you work in, enterprises where you have proprietary workflows. They're not traditional CRM. They're not traditional sales. They're not, you know, a traditional, medical record system workflow, and you need to build some tools around it. And so now we're trying to understand how to connect all these things together. So that's that's the message here, I think people have to say is when you look at all these different tools, tools, spreadsheets, lack of tools, all add up to what Quickbase calls great work. I think it's a really good term around this. It's really easy to find in the organization. And the problem the way to solve for it is start looking top down and saying, what are we trying to what are we trying to achieve here? What level of flexibility do we need, and how do we start connecting the dots from a to z in a more unified way. Thanks, Isaac. And, yes, you did point out that we have kind of coined and called this, dilemma, and this inefficiency gray work. So, Megan, you've done your team has done three years worth of longitudinal study on what we're calling gray work, but maybe it's worthwhile to just take a a step back here and talk about what gray work actually is. And over this three year period that you've been doing this work, how how have you, seen this sort of research evolve? Yeah. Absolutely. So, I I mean, I think I've mentioned great work a couple times. You all may have heard it some place else, but, really, we define great work as, this hidden manual effort that people use to bridge gaps between tools and systems. Things like, you know, chasing down updates or reconciling data, hunting through inboxes for versions of things, etcetera, etcetera. And then we used to not have a term for it, and it was more just this, like, felt hidden tax on on productivity. Like, you could just feel it. And then when we defined it and started talking to people about it, you see something really interesting happen where, we describe it and, like, everybody had this look of realization or, like, oh, yeah. Like, I know exactly what that is. Like, I feel that regularly. So, that's what gray work is. And what's really changed over time is kind of the urgency. This year, we saw that fifty six percent of respondents said that the amount of manual work on their job has increased, which is up from 40% last year. That's that's a really big jump. So people are recognizing it more and experiencing it more. And 59% also said that it feels harder to be more productive than ever before, which is up from fifty three percent last year. So the friction just isn't going away. It's intensifying, which may be in part to kind of the fastball that we've been talking about. Yeah. It's self identified pain. So I'm I'm it's it's, really interesting how that's evolved over the years in our research. So, Isaac, once an organization names and identifies this gray work and this inefficiency, like, let's make this practical. What are what are some steps, at least some first steps, to take in just addressing and reducing this? Yeah. So I think a top down and then a bottom up approach around this, Alice. I mean, the very first thing is I start asking people some very simple questions. What can't we do well today? Or what can't we do fast enough? Or what can't we do with better enough quality? What's really slowing things down for us? And I start asking lots of people that question, and you get some common answers in different industries. You know, in construction, like I've said, you know, there's a lot of questions about, are we bidding on the right projects? Are we estimating them the right way? Where can we improve profitability? In health care, a lot of the attention, in my clients in health care were essentially responding to crisis, and and and being able to be very mobile around that. But now it's shifted quite a bit in saying questions like, how do we improve customer experience? How do we make the lives of our our nurses a little bit easier, and and and let them be in front of patients a little bit more than chasing down missing materials and things like that. In in manufacturing, there's a lot of questions I get from CIOs about aligning to tariffs and looking at their supply chain. So there's a lot of things that happen in the course of business that they just can't get to because their existing tools and process and way of working doesn't really align to changing things that quickly. And so that's the top down view. I do a bottom up view by doing some very classic things. Number one, I get my team to go sit in the shoes of how people are working today. I will sit behind a marketer and look at five or six different SaaS tools that they are using and trying to understand, well, using five or six different tools to run a campaign, how do you understand the overall effectiveness of that campaign when data is coming from all all these different places? I or I'll go out into the field. I'll go watch an engineer in on a plant floor, responding to a a an incident and trying to get machine up and running again, and understand what are their problems to resolve that problem a little bit faster. I'll also look this is actually really important today, is to look for handoffs, Where work of one department ends and another department begins is usually the gray work. Because, again, comp department one has one tool, department tool two has another tool, and sending in between them is probably an email, a spreadsheet, a meeting, Slack messages, and all kinds of ways to connect the dots, and that's where your inefficiency lies. So we'll look at that top down view and say, what do I really need to do better? And then bottom up view to start getting a sense of where the paint points are, and I'm gonna connect those two dots, Alice. Oh, wow. That's a really full picture. Thank you so much. With, like, eight minutes left to go, I I'm gonna transition a little bit to what I think is all of this is super interesting, but I I love some input, Isaac, on how this gray work is impacting everybody's AI transformation more specifically. Yeah. I mean, the very first question I ask about AI is it's sitting on top of your existing process. It's sitting on top of your existing data. And when you start talking about agents, right, an agent is playing out a role in your organization and being a partner to that person to make them more efficient, make them see data and all you know, in in ways that they couldn't see before, provide guidance on options for them to make decisions. But, you know, when you have AI sitting on top of great work, AI doesn't see any great work. The AI sees what's, you know, what's immediately obvious in your different tools and in your different systems. It's missing all that context. It's missing all that connectivity, that's required to make a real contextual decision. And so for me, I wanna, you know, I wanna start solving for that first. I wanna start bringing the holistic end to end process together, let that happen within a single workflow, and then start looking at where AI can be a a tool for augmenting people, either in decision making or being able to enact an automation a little bit faster. Thank you so much. Megan, I I am going to, jump a little bit more into the AI adoption, that you studied in the research. Before I do, just because Jay Cohen's brought it up, and I think it might be really interesting to continue your conversation here, Isaac. What happens to an AI initiative when gray work isn't addressed first, and how are CIOs identifying when gray work is holding up this digital transformation? And, like, more specifically around Jay's question, can AI help mitigate some of the gray work or the gap that's existing between the tools and the solutions? It's a part of it. I mean, where I start from here is, again, a top 10 and a bottom up approach. Right? There are specific areas that I look for in organizations where we're actually gonna look for and seek where AI can be a real differentiator. Right? And usually, that's tied to a more significant business process where I'm trying to scale something that I can't do very well. And when you start looking at that, you're gonna start finding the grade work underneath that. So I'm gonna take a top down view and saying, I wanna apply AI in marketing because I know I'm gonna be running campaigns ad nauseum in 02/1926. I wanna do it in parts of, understanding profitability and construction. I wanna do it in patient experience in health care. That's that top down approach. I'm also gonna do another bottom up approach. There's a lot of tools out there. I don't wanna ignore what AI brings in these tools. I wanna be able to experiment with them, and then I want people who are experimenting in the tools that I'm using, to come back and provide feedback. Where is it working? That's a great use case again. I use Quickbase for this, as an example. We talk about prompting. Right? And all these people in the organization are doing prompting out there. Where do you sent centralized a knowledge base for people to understand what prompts people are using, what impact it's having, where there should be reuse against this. It's a great use case for a tool like Quickbase to build a knowledge base around this. So I'm doing that top down approach. Let's look for some strategic areas where we're actively going to look where AI is gonna help. Gray work is gonna be underneath that as a debt that needs to be operationalized. I'm gonna do a bottom up approach, learn from the organization, and use knowledge management as a way to share that information across the organization. Thanks, Isaac. Megan, we don't have a lot of time left, but I know you also in your research, had some respondents share concerns around AI adoption and how that linked to gray work. Do you wanna share some of that? Yeah. Absolutely. So really when it comes to AI, we're seeing, like, optimism meeting reality, where we see 72% of business leaders. So they expect to increase their AI budgets, which is a is a big jump from 51% last year. But we're also seeing on the flip side that 89% of respondents have concerns about, you know, data security and governance, which is only a small drop from 93% last year. But I think that, like, the other thing that we saw in the data, that almost kind of in response to to Jay's question is that AI can't really fix the broken processes. It can't necessarily make silent systems work together or fix things, so that's where the gray work will still come into play. Kind of like any other system, garbage in, garbage out, and AI is not necessarily different in that way. So, AI is just kind of another layer on top of chaos if you don't aren't able to sort out that chaos ahead of time. Thank you. Alright. I think we should end in a really practical way. So, Isaac, you've helped teams rethink their processes before bringing AI, and maybe you can walk us through some of the most impactful, quick, and early wins that, CIOs can look for. Like, what is good cocreation actually look like in the middle of this tech transformation? You know, Al, I'm thinking of you a really fundamentally simple answer. What I find inside organizations is a lot of people doing things without truly understanding the objective. And when it comes to areas like changing workflow and addressing great work, big change management area, When it comes to where are we really applying AI strategically, big opportunity, big disruption factor. You know, there's a lot of experimenting going on. I mean, the data point that I would add next to you when you do this story is that data point of what you're experimenting with versus what's actually making it into production is about a 40 to 60% gap there that's typically reported by other studies. And so what I find in closing that gap is making sure people are aligned with what the vision is, and that vision may have a six month target. It may also have a three year target. And what are some of the things that it's replacing? When I ask people to fill out a vision statement, I ask them, okay, you know, what you know, I don't ask it in terms of what is your gray work. I'm asking, what are some of the things that we need to do better or differently in the future that's gonna change your operating model? That's probably the most important thing around this, Alice, around transformation, is that we're trying to change our business models. What we're doing today isn't gonna be the same thing that we're doing three, five, and ten years down the road. And whether that a healthy way of looking at our platforms, our standards, and unifying ways of doing things, our weight of using tools that are no code in nature, like Quickbase to connect a horizontal layer without creating some standards, it's really hard to take advantage of the next data analytics prediction AI tools that we need to have in place. I really appreciate it. Thank you for that view. We are coming up to time here. So, I think we've answered some questions along the way. I really, I really just wanna thank you both very much for your incredibly helpful insights from Isaac, your frontline experience, and in your in your roles and in your own, interviewing and research and writing. And, Megan, certainly, from your research and, the work you've done with, the gray work longitudinal study over the last three years. It was really some terrific insights there. I think we've run out of time for questions, but I'm delighted that we were able to answer some of Jay's along the way. And, we look forward to, talking to you guys on future webinars, and I hope that you will download, this if you'd like to reflect back on any of our data. Thank you so much for being a part of today's webinar. Thanks so much. Thank you.