Integrative Intelligence: A Framework for Finance | Bryan Lapidus

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This is a podcast episode titled, Integrative Intelligence: A Framework for Finance | Bryan Lapidus. The summary for this episode is: <p>We strive to be better, together by connecting our data with our systems, planning, and people. But that won’t be enough to get a compelling return on our investment if we continue operating with yesterday’s processes and technologies. Join Bryan Lapidus, Director, FP&amp;A Practice at the Association for Financial Professionals (AFP), as he explains integrative intelligence and how it defines work, distributes it across a fluid and disparate team, and pulls it back together to drive decisions. It’s a concept with profound implications, and Bryan will explore how, when information and expertise are plentiful, creating the right work and asking the right questions can deliver exceptional value. He’ll also present a framework for integrative intelligence and tactics you can use to manage the information and people around you.</p>
Create the right work - more data, less clarity
00:56 MIN
We are more connected and less connected - define a shared value with your team
01:08 MIN
Create more automation to add value beyond having data
00:42 MIN
Build with modularity and optionality
01:12 MIN
How to optimize your team
00:58 MIN

Trish Randolph: I'm Trish Randolph. I lead our revenue marketing team here at Planful and all I have to do today is introduce our speaker. We are going to go ahead and get started. We'll have a few minutes at the end for questions, but I'd like to introduce Bryan Lapidas and welcome him to the stage. He is the director of FP& A Practice at AFP and he's going to explain the integrative intelligence and how it defines work, distributes it across a fluid and disparate team, and pulls it back together to drive decisions. Bryan, yes, welcome.

Bryan Lapidas: Thank you very much. As she said, hopefully everybody's caffeinated as always the trouble of having the challenge of being right after lunch, so hopefully you guys are all here for that. When I listened to Grant talk this morning, I'm a scribbler by nature, and so one of the things that I noted was that he said there was$ 15 billion is expected to be spent this year by finance and accounting in their space, in upgrading their systems and upgrading everything that they do in order to be more data- centric, right? You guys are here, you're at Planful, you're at a data conference. What I want to talk about today is really less about the data itself, but more, what is it that you're going to do with it? Because if we, as corporate America, are going to spend$ 15 billion to do the exact same thing that we're doing now, just maybe a little bit faster, we're really missing the opportunity. I was thinking about this actually last night at the reception, when I was talking with a friend of mine and I asked why she came to the conference, she said," Well, we have software. It does some things, but it's not quite as flexible as we want it to be, and we really want it to fit what we're doing." That idea that your software has to fit what you're doing, I just want to plus that up to one other thing, which is you need to be doing something different, right? You've got new tools, or you're here because you're on the cusp of getting new tools. A lot of what I talk about at AFP is how new tools lead to new capabilities and a new operating model. Finance needs a new operating model for what we deliver and you need a new operating model for the work that you do and so that's really what we're going to talk about today. Just real quickly about me, as Trisha said, I am Bryan Lapidas. I'm the director of the FP& A Practice. That means that I used to be in FP&A. I spent 20 years in finance, actually. I've been in treasury, I've been in audit, I've been in risk, I've run F& A at some big companies, you've heard of, some private equity back companies you haven't heard of, and now, my role at AFP, having had your job, my role is now to help you be as best as good as you can in your finance roles. For those who may not know about AFP, we are a not- for- profit association, and our whole mission is to advance the success of treasury and finance professionals. We do this through certifications. We have a treasury and FP& A certification. We host events like our upcoming half- day conference in June. It's a virtual conference about, it's called Getting Your Data Right, and then we have an annual conference. Honestly, the easiest way, just go to our website. You could learn about the guides, the research that we do, the survey work, the training, and all of that we have available for the finance profession. All right. Anybody know how do you see a black hole? Show of hands. One. By the light coming into it, right? You don't necessarily see the black hole, you see all the light around it, because what happens in a black hole is you have this incredibly dense mass in the middle, and the gravitational pull is so strong that the light and the debris that's swirling, almost at the speed of light, about to fall in, it glows. This is a picture from April 10th, I'm sorry, 2019. This is the first image ever of a black hole that was captured. Black holes were theorized about 100 years ago. Einstein famously was a little bit nervous that his own theory of relativity produced this idea of a black hole. He couldn't escape the idea. He kept coming back to it in his computations, his calculations, but he said that" Matter, space, and time come to an end and vanish like a dream." He didn't know what that would look like. Over the past hundred years, there have been little ways that we've gotten closer to it. There have been incremental things that have proven, or indirect ways of proving that it's there, but it wasn't until the picture that was taken in 2019. To see this black hole, this is 55 million light- years away, and in order to see it, what you need is a telescope, but not a regular telescope, you need a big telescope, and not just a big telescope. You need a telescope that literally is the size of the Earth, so what the team did was they created the Event Horizon Telescope. There are actually nine different telescopes at six different locations, the radio telescopes, and the longest stretch from Spain to the South Pole is actually the diameter of the Earth. Starting in 2017, over 10 days, all the telescopes were turned on, and they tracked the movement of the sky at night. They captured the data from all of these. They had to actually stitch together the data, which took two years. When they did this, they said," Trying to find this black hole out there with a telescope of this size is equivalent to trying to find an orange on the moon with your naked eye," that's the kind of relative analogy of what they were able to accomplish. I find this amazing for a lot of reasons. One is just how they went about it. When the results were announced, they had to recognize the fact that there were more than 200 people working on this, and so they had a press conference simultaneously at six locations because there was no just one leader, right? Everybody had a part in this. There was this guy, Dole Shepman got a lot of press. The caption here says he photographs the black hole, but he never said that. He said that he always let a team that photographed the black hole, and that team consisted of all different kinds of backgrounds, of researchers. Katie Bowman became famous. This picture went out soon as the picture the resolution came through. She was actually a post- doc fellow at MIT and she wrote the algorithm that stitched together the data that processed it for two years. The algorithm hadn't been written to handle the amount of data that had to be analyzed. I found this amazing. In order to sort through the data, the amount that was needed was too much to actually just put it in an email as an attachment and hit send and it was too much actually to send over any fiber- optic network, even to MIT's research labs, so they had to ship the data on a half- ton, it was a half- ton's worth of hard drives shipped to MIT that she then had to load and process on a program that had never been written before. She said," None of us could have done this alone. It came together because of lots of different people working together." I really love this as an example of what we mean by integrative intelligence. It's pulling together widely disparate information, using a team that is widely distributed. You may not even know them. You may not have had a chance to meet them, but you're working with them altogether. You've got to sort through this. What they had was a shared goal to answer a question. It wasn't that they had the data and said," What is the data telling us?" They said," We have a question. Let's go find the data. Let's sort through this data. Let's find an answer that works for all of us." That's really what motivated everybody on this team, this team that was spread literally across the Earth. That gets us to integrative intelligence. It is creating value from disparate people and data. It's literally pulling information around you into a value- added insight. The way that we do this now, the what's different is that it's about two things. It's about creating the right work to be done, right? We're spending$ 15 billion. Let's ask different questions, let's go about this differently. Then it's about optimizing the team that's going to get that work done. What we're going to do is we're going to talk through each one of those and then I'm going to give you a couple ways to think about this, to challenge yourself and to challenge your team on each of these. Does that sound good. Guys with me? All right, excellent. All right. Does anybody ever feel that they're drowning in data? Just quick show of hands? Does anyone feel like they've got a lot of data. For all that extra data that you have, do you feel like you have more or less visibility? I see a couple smiles, a couple nods knowing we have this, but what are we going to do with it? The trend that's leading up to this is we have more data, but we have less clarity around that data because data is plentiful, it's varied. Maybe it's dirty, maybe we have data from different sources that look, or that's incompatible, but we definitely have a lot, and we have to figure out what to do with it. I saw a stat recently that if you look at all the data that was created from 2010 to 2019, all that data, we've already surpassed that amount of data created in the 2020s already, and we're only halfway through the year. I don't know what a zetabyte is exactly, but we're going to create 100 of those in 2022, and so what that's doing is it's changing the way that we work, and it's changing the way that we see the data, the way to navigate. Old finance would say," We have value in the data. You want an answer, you come to us. You want the numbers that are blessed and approved and everyone can agree with, you come to us." But because we had certain controls, because it was in finance, people wanted their own data, so you had proliferation of data. Now, obviously, the controls are important, but we're in a world wherein with so much data, the value is in trying to navigate your way through, and just like in the Event Horizon Telescope, and seeing the black hole, what helps us is creating a question and a hypothesis to know what to do with the data, to ask the questions, the business- related questions, in order to see things that we didn't. In order to see our way through this much data, what we need are two things. We need automation and we need imagination, and we'll talk about that in a few slides, but automation, you can think about that as the tools that are going to help you to find the needle in the haystack. That's what machines do great, crunch mass amounts of data, find the needle in the haystack. What you need is a person to tell what needle to look for, and that's important, so we need to manage the automation. But we also need the imagination. This is where you have the hypothesis that says," Maybe the problem is not the needle in the haystack, maybe it's the storm on the horizon that's going to come and blow my haystack over," because machine learning trains itself on historical data, it doesn't have the imagination to look out, and that's what you guys do. That's why you have to have the hypothesis in the questions. Some things to ask yourself to help kind of train yourself and train your teams on this," No matter what it is you're doing, is there a better way to do it? Is there a different way to do it? Is there a trend?" All the software is great at finding trends, right? They'll run various regressions, it'll give you great trend lines, so ask yourself," What makes the trend, but more importantly, what's going to break the trend?" That's the imagination part that we need to get to. You might have heard of the three whats that finance would have to ask itself when it's trying to, say, explain a variance report," What happened, what does it mean, and what do we do next?" There are three more whats in a integrative thinking world, what if, what else, and what may happen? Train yourself and your teams to think a little bit broader. While you're working with your teams, this is especially true for managers, to navigate your way through, define the outcome that you want, not necessarily the path. In the same way that with the black hole, they didn't know how to get there, but they were able to distribute the work, the tools, and the freedom to experiment out to the people who are closest to it, people who had their expertise, so if there's many ways through the data, allow your team to surprise you. This is one of the nice parts about working in FP&A that has a little different from accounting, right? There are fewer rules, there's fewer expertise. You also, as a manager in FP& A, you don't have to know everything that your people know. You can hire people from different backgrounds to bring things to you. The goal is to create a culture that prizes creativity and you have to have a little bit of ambiguity, a little bit of that exploration. There's a great quote from Tom Friedman. He says that" Innovation that happens from the top- down tends to be orderly, but dumb. Innovation that happens from the bottom- up tends to be chaotic, but smart." One of the great things that we have in finance is the ability to shape innovation around the company. How are investments viewed? How are contracts viewed? What's the role of finance in joint ventures, or in customer deals, and in pricing, right? Our role is to create a process by which our people who are closest to the business can come up with different ways, come up with different solutions, and we maintain that top- down control of here's the process, here's how we're going to look at it. That tension is a healthy tension that you need to have and that's really part of the new way that we work today. Did I go back? One other thing about that top- down, bottom- up that I forgot to say is what makes it work is a shared value. Going back to that idea, it only works if everybody who is putting the process in place is giving the right guidance to the people who are creating the work. When we get to that, we have to talk about what it means to work with those people, and to think about that team. We have more ways to connect to everybody, to everybody in this room, to everybody outside the room. I have a member of my advisory council. He says he just can't get to all of his emails in a single day. It's just impossible. He's getting more than 300 emails, plus he's on Teams, plus he communicates with people by LinkedIn, plus he texts. Sometimes he actually gets a phone call or a Zoom call, right, and then he's got meetings from sun up to sundown because one thing that's happened since the pandemic is the number of meetings have exploded. Part of creating the right work is recognizing that we are more connected to everybody, but at the same time, we're less connected. What that means is we're having more transactional kinds of meetings, especially if we're remote, we're not having the water cooler conversation, right, the ability to connect with each other. As Barbara Corcoran said, what was her quote?" Fun is fun is good for business," right? That ability to be with people to have that deeper connection, again, it goes back to this idea of what it means to have shared values with everybody. One of the things, kind of a trite comment, but it's trite because it's true, we say it a lot, is that finance wants to have a seat at the table. Well, in order to have the seat at the table, you have to have something in common with everybody else at the table, so if finance is coming and bringing metrics and KPIs that don't matter to that business, right, they're not really going to call and invite us. We did some research last summer where we actually interviewed the CFO's primary partners. The CFO had sponsored this project we had, a whole series of questions, and one of the things that came out for several business units in this probably Fortune 100 company was what finance was bringing was reports that hadn't changed in 10 years. 10 years ago, that's what the business needed, but the business had changed, so in order to make sure that you have shared value, make sure that you have metric alignment, that you're reporting what matters to them. Context. There's a lot of chatter out there about storytelling, explaining finance in a way that is not full of jargon, in order that the other person can do something with it, right? Finance spends a lot of time talking to other people in finance, so when we go out to the business, they're not always accustomed to the language that we use, so we have to give context to what we're saying, context to what we're doing. Goal alignment. I won't ask you to raise your hand, but just think about it. If I were to say," Do you know your business partner's top three goals for 2022?", do you? Does everybody in this room? Does everybody on your team know? Finance, FP& A especially doesn't exist for the sake of finance, we exist as part of this larger business. If we don't know the goals, then we shouldn't be at the table because we had nothing to offer. One of the other things that came out of that same research was someone talked about the difference between a good finance partner and a rigid finance partner. They said," A rigid finance partner is the one who, when you go to them and say,'Can I do this? What is the accounting on this? How does the numbers shape up?' they just give you an answer,'Yes, no, it works, it doesn't.' The finance partner, the true partner says,'This is a business problem. I recognize that I have to live within certain rules and the confines of ethical work and accounting rules and principles. Let's see how we can solve that problem.'" That's the mindset is finance as a business person who brings the finance expertise to the table. That's the shared value that we have and that's the value that we bring. Next, I want to give an analogy that I borrowed from Professor Avi Goldfarb, who spoke at one of our conferences. He's an economics professor at the University of Toronto. It's a thought experiment, so hopefully everybody had their coffee before coming here. Imagine a world where coffee was free. Every Starbucks is free. Every Dunkin' Donuts is free, right, every Pete's Coffee, I don't want to upset anybody depending on your coffee preference. But if coffee were free, the price goes to zero. The other thing you would expect in a rational economic world, people would drink more coffee, right? Goods come down to price, demand goes up. The other thing you would expect is that substitutes like tea would suffer. Who's going to pay for tea when you have coffee for free? At the same time, demand for tea goes down, what's increasing is demand for cream and sugar, the compliments, the ways that go along with is good that's now ubiquitous. This is what's happening with automation. This is what's happening with data. Data is becoming cheap, right? 100 zetabytes this year, all the data, all the stats that Grant showed this morning about how much more data Planful is processing, right, 4X in three years. If data's cheap, if reporting and automation and that kind of rudimentary analysis is cheap, you can't compete with that. Maybe not today, maybe not tomorrow, but at some point you can't compete with free. Your job then is not to be the financial dump department, right, where you just take data and you pass it off to somebody else, where you prepare reports and just push it across the table, or hit send because that's going to be automated away. Our research shows that the number one, the lowest- hanging fruit is automation, deep- dive, self- service, all of that. For you, if you're not going to add value, competing with something free and cheap, free and plentiful, then what you have to do is be a good human, right? You can't be a robot. You can't be the algorithm. You'll be a bad algorithm, so be the good human. What that means is you have to add value and put that" A" in" FP& A." What does that look like? It means you have to be a strategic thinker. You have to think about what the business strategy is and how everything ties into that and how do you connect the dots and see both the forest, the overall picture of the business and the trees, whatever it is, whatever business, project, contract you're evaluating. You have to be the human who gives context and tells the story and makes the data come alive. Give people a reason to listen to what you're saying. Don't be answer- driven. The answer is out there. The answer is cheap. The answer is in the data. Don't be answer- driven, be analysis- driven. Add value. Every time that you touch something, be agile, change your mind. If the data changes, then you have to change your mind. If the decision was set and the decision looks bad, you have to change the decision, and be a collaborator. You have to talk to people. You have to make the connections with people. You have to understand what they need in order to deliver the value that they need. Sorry, I forgot to forward on that. Part of creating the right work is recognizing that the speed that people are asking for things from us is faster. Our survey research shows that from the start of the pandemic, till now the number of forecasts and reports and cycles are up about 57%. Anybody feel like you've been asked for more? Everybody feel like you're delivering more, right? You have to deliver more. But at the same time, the data, as you told me before, is big, it's overwhelming, it's varied. I don't know if everybody knows the VUCA acronym, but it's a way of describing some of the challenge of information and data that's out there. The problem is that the data is volatile, it is uncertain, it is complex, cause and effect is hard to know, and it's ambiguous. You can't always see where you're going, so we have this combination of driving faster, but with less view, right? It's almost like we're wearing blinders while we're going 80 miles down the highway. That's great as long as there's no bumps in the road, there's no deer, and the road doesn't change. The response to this is to build in modularity and optionality. That means that when you think about your processes, think about everything in terms of sprints and experiments, right? We'll give you a little bit of capital. We'll see how it goes. We will try something. We may give you some budget money at the beginning of the year, but your budget can't be rigid. You've got to allow the fact that the world changes. As soon as the ink on the budget is dry. In your planning, think about range estimates, right? A single point of view, a single NPV, right, the fact that the probability of you hitting that single NPV is probably pretty small. There's a range estimate, there's probability scenario, planning. You have to build all of that. You have to hold in your mind multiple points of view, multiple outcomes, simultaneously. Technology. This is where technology is critical. You have to get around that decision wheel of question to data to analysis faster and faster and faster, right? You can't get stuck on what's happening and gathering the data because you have to make the decision sooner. What that looks like, there's a quote from one of our instructors, he says," One of the things that works best with data is iteration," again, getting around that wheel, right? Don't assume you're going to get it right the first time. It's about repeating the process. It's a lot easier to make a small adjustment and another small adjustment and more incremental adjustments than it is to make one big wholesale change. Question data analysis. Review your question. Does it still make sense? All of those experiments. Have to get around that and you have to build that into your thinking. All right, I think I'm going to have to go a little faster on the second part, and make sure I save time for questions. The second part is talking about the team who's with you on this journey, and not just who's with you, but how do you manage that team? We talked before about being more connected, but at the same time, less connected. You have to have an expanded view of who is on your team. Here I get, maybe it's a little bit small, if you think about the FP& A, the finance person in the middle, and who you talk to in order to get your work done, you have your business partner, right? That's the person that you're working with. You also have finance because in many ways you are the conduit from the business to all the other deep expertise within finance. If they have a question, you might want to go to treasury or accounting or to whatever other group, right, so they're on your team. You also have to think about maybe if you're in a larger company, you're offshore center of excellence, or maybe you have a combined back office, or you have an outsourced team somewhere, or maybe you have gig freelancers who are involved, or maybe bots are part of your team, there is a way to manage all of this. You have to think about all of these people as integrated, right, because the answer of who's on your team is anybody who adds value to what you do. Now, with this as the baseline, the question is, how do you get the most out of this team? The first part has to do with teaming as a skill and specifically creating transparency. So often what we do can be thought of as tribal knowledge, right? This is how we work here. You work with somebody, you work side by side with them, you stand up in your cube farm, and you seize them, and you say," Hey, this is what I'm working on," right? That doesn't work anymore, so what you have to do is you have to recognize that team members change, you're working with a broader set, and so you have to create transparency in terms of data. What is your data? Where is your data? What data is good? How do you make sure that the data all around the company is compatible, right? No one wants to get in front of the CEO and argue over whose calculation of whatever sales number is right or wrong. Your data has to be known and trusted throughout. Processes. How do you get work done? This is how we get work done here. We're going to set it up. We're going, whatever it is, may you have to manage your team in a way that it's clear for everybody who's coming on board. You have to name the controls. You have to assign roles. Some people use RACI or DACI to identify who's Responsible, Accountable, Consulted, and Informed. How we make decisions, right? Let's have some transparency on how decisions get made by the group. I just want to take a moment to talk about bots here, because the idea of how you manage transparency with bots and algorithms is going to be coming more and more important. You have to treat them like another member of the team. They need a periodic review. They need feedback to know whether what they're producing has moved from the tolerances or the guide rails that you've created, so they need training data, they need guides, they need to have explicitly written out what their function is, not just their job title, but what their deliverables are, and other people need to know that in order to build that into their work. When we talk about optimizing the team, there's certain things that you can do when you think about your extended team. Projects, teamwork is the best way to upskill your team. If you don't have a project for you or for members of your team create a project. This is what our members tell us every time, the ability to work with people from other departments to network through teams, to build something new projects are the best way to upskill everybody, and so think about your project and your team and design them as stretch goals, as stretch targets, and as learning experiences. If you're using consultants, or if you are a freelancer, or even if it's just somebody else from another department, specify that you want mentoring, you want training, you want improved methods. Create a program where who you're working with maybe is somebody, a group within your company that's more advanced. Build in there knowledge transfer. When you build a team, when you're trying to decide who's going to work on a project, evaluate people for how they communicate, because that's more important than the actual skill they bring. If they're lacking a skill, you could find it, you can ask for it, but if they don't tell you they're lacking it, if they don't communicate that, you'll never know. When you hire somebody on your team, especially an outsider, hire their best skill, and when you think about the team in general, just remember, this is also about networking. This is about finding other people that you want to work with in the company. Think about it outside the company as well. These are job opportunities. These are other people who can mentor you all the way, so how you think about your team is critical. Optimizing the team. A few years ago, there's a lot of news that came out about Google's Project Aristotle, which is they wanted to know,'cause they have lots of projects and project teams, so they looked at all the teams that they created, and they said," What's the most successful? Is it having everybody who's alike? Having everybody who's different? Is it having everybody who's the A team, the star players?" Not surprising, that team was clearly not the best, right? It wasn't about putting the best and the brightest on the team. What mattered in terms of the success of a team was collaboration and psychological safety. Psychological safety is the belief that you won't be punished, you won't be humiliated for speaking up with an idea, a question, a contrary point of view. This is how you define the culture of your team and of your company. Do it upfront and say something like this, from Google," We follow the best idea, not where it came from. We assume everybody's voice will voice their concerns and we welcome challenges to the work." This is how you integrate the intelligence from somebody who might be hesitant to say what they want to say. From Project Aristotle, they said," Behavior matters more than technology." How you act, don't put the blame on," Oh, it was just a text message. It was very terse," right? Behavior before technology, how you act is more important than the medium with which you communicate. Consider everybody else's incentive and goals. Remember, some people are in different departments, they have different goals, they have different decision- making processes, and while you may be running the team, you need to recognize what their background is and what their stressors and motivators are. Build collaboration into the work, and as a leader, know when to get out of the way, know when to just say," Here's the outcome, go find your own path." Project management. This is not sexy. News flash, project management is not flashy, but in this kind of decentralized world centralized world where you'll be running teams for people who don't report to you, you don't have the same influence, you don't have that command structure of me boss, you not, right? You have to run a project, you have to get everybody online. Do it with a project charter, do it with some way that you explain to everybody why you're doing it, what you're doing it, what the milestones are, the scope, all of that. It's good to pick up some project management skills. The last one is when we think about the team, we think about everybody else we're bringing on the team, I want you guys to remember your role on the team, right? Finance is the steward of company capital. What that means is that finance is responsible for reporting and control of where it was, moving money around, that's where it is, and helping make that decision for where the next dollar needs to go. This is a quote that I think really just explains it best," Finance has methods for thinking through an uncertain future and quantifying our thoughts and decisions. What we do is we bring analytical frameworks. This is what we bring to the table. We think through cashflows, we think through inflection points, we instill the discipline on process." This is what we need to make sure that we do, so in all the talk about being a good business partner and supporting the business, we need to remember that we work for the CFO. This is what the business is relying on us to do and part of integrating the intelligence is recognizing what it is that we're supposed to be doing and the part that we have to integrate with everybody else. Success is not guaranteed. There is such a thing as the opposite of integrated intelligence, and that is siloed people, siloed data, siloed work, right, underinvestment in any of these, because while some people are putting$ 15 billion into finance, not everybody is, not everybody's going to make that investment, not everybody's going to make the change to their process that goes with it. What does that look like for them? That's a limited finance role. What that means is that those CFOs... Let me say it this way. CFOs bring value to the company in different ways and compliance reporting, cash management, all of that is absolutely important, right? You can't run a business without it. That's also where a lot of the automation is. There are some CFOs who limit their view of the value of finance to that. There's other CFOs who see the opportunity to take finances, skills, talents, and abilities, and apply that to other parts of the business. That's a value expansion view to deliver that expertise. Not everybody's going to make the change, and the question is, what kind of finance group do you want to work for? What kind of finance group do you want to run? I guess to say it differently, finance can either help discover the black holes that are out there, or finance can fall into them. With that, this is just a summary of all the points that I had, but I'd like to open it up. I think we have probably just a minute or two for questions as I look at the time. Does anybody have any questions? All right, well, I do apologize for running right up to the end. I'll leave this up for another minute, but then I'll put my contact information because I'm happy to talk about this. I'd like to thank you all for your time and attention.


We strive to be better, together by connecting our data with our systems, planning, and people. But that won’t be enough to get a compelling return on our investment if we continue operating with yesterday’s processes and technologies. Join Bryan Lapidus, Director, FP&A Practice at the Association for Financial Professionals (AFP), as he explains integrative intelligence and how it defines work, distributes it across a fluid and disparate team, and pulls it back together to drive decisions. It’s a concept with profound implications, and Bryan will explore how, when information and expertise are plentiful, creating the right work and asking the right questions can deliver exceptional value. He’ll also present a framework for integrative intelligence and tactics you can use to manage the information and people around you.

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Bryan Lapidus

|FP&A Practice at the Association for Financial Professionals (AFP)