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Tech & Innovation Blog
ADP Brazil Labs in Porto Alegre: Home to Global Innovation
Impact, Innovation, Brazil Labs
We look forward to South Summit Brazil 2023, where top speakers worldwide share their expertise and leaders look for business opportunities.
Porto Alegre: Home to ADP Brazil Labs
Porto Alegre, the capital of Rio Grande do Sul, is home to one of ADP’s Technology and Innovation labs in the South of Brazil. The city has an estimated population of 1,492,530 (about the population of West Virginia in the U.S.). Known for offering tourism and leisure time, Porto Alegre features several urban parks with green areas that attract those looking to enjoy nature and history.
In 2022, Porto Alegre celebrated its 250th birthday with enthusiasm and vitality. The city is constantly developing and becoming a hub for generating new technology-based businesses and attracting and retaining talent. Join our community to follow our events and what we are developing at the Labs.
May 2022 – South Summit Brazil
The first South Summit Brazil took place in Porto Alegre as the world continued to recover from the global pandemic. The global entrepreneurship and innovation event started in Spain ten years ago and is now an international conference.
South Summit in Madrid, Spain’19
The public and private sectors, academics, and other institutions collaborated to make it happen, all contributing to positioning Porto Alegre as a global innovation player. The numbers were awe-inspiring, with 20,000+ visitors representing more than 50 countries, 500 presenters, and 1,000 submissions to the startup competition.
We look forward to South Summit Brazil 2023, where top speakers worldwide share their expertise and leaders look for business opportunities. The summit will take place from March 29 to 31 next year. Learn more about the event here.
South Summit 2023 will take place in Brazil.
Introducing Instituto Caldeira
Instituto Caldeira, also known as the “Boiler Institute,” is a non-profit organization for creativity and communication. The hub provides an opportunity for people to network together to improve the new economy and innovative ecosystem of Porto Alegre and the state of Rio Grande do Sul.
It was founded by forty-two major companies in 2017 that refurbished the old industrial complex for innovation activities and the new economy. The complex still houses the boilers imported from Europe back when prominent businessman AJ Renner started it over 100 years ago!
With only a little over a year of operation, it has already hosted an impressive number of activities. The Boiler comprises more than 22K sqm of space, 42 corporate founders, 330 affiliated companies, 700 startups in the ecosystem, and 15 national and international associated hubs.
ADP Brazil Labs Offsite
In early June, Julio Hartmann, VP of ADP Brazil Labs, and his Senior Leadership Team (SLT) team met for a strategy meeting at Instituto Caldeira. It was an excellent opportunity for the team to get together in person after two years of working remotely. Everyone was impressed with Instituto Caldeira’s structure and the initiatives, looking forward to expanding collaboration in the future.
ADP Brazil Labs Offsite
Julio began the leadership offsite by discussing the content from the Global Product & Technology (GP&T) Leadership Summit. Julio proposed a transformational strategy for the Labs to connect better and leverage the external ecosystem. The plan included presentations from some development leaders about their groups, from Workforce Now (WFN), DataCloud, NextGen (core platforms and Centers of Excellence), and myCareerConnect, to ADP Ventures. There were also presentations about cross-organizational areas and initiatives, such as Product, UX, Agility, Innovation, and the Machine Learning CoE.
Thinking Forward
Data Science Guild
The Data Scientists from the Brazil Labs worked together in the Data Science Guild, an internal group created in 2018. They meet biweekly to discuss recent papers on machine learning (ML) and artificial intelligence (AI). The meeting helped them share knowledge and work together more effectively, including Data Analysts and Data Engineers from various ADP product teams like Roll, DataCloud, myCareerConnect, WFN, and Marketplace.
Innovation Time
Innovation time was a moment for the leaders to think about how the companies stay relevant in the future, reinforcing the innovative culture throughout the labs. The leaders plan to accelerate new opportunities from various sources, including employee ideas, discoveries from client needs, and technological advances.
UX at Brazil Labs
ADP Brazil Labs Offsite
The leaders from ADP Brazil Labs met with the User Experience (UX) team to understand the balance between UX and other areas, including the development concept guided by Triads that gave our digital product development a more organized structure.
The team talked about how the area has been growing quickly in recent years and an overview of team size and the project distribution. The leaders and the team ensured the UX team had the resources for product decision-making.
It was exciting to see the Brazil team’s participation grow within our global UX equation. We grew approximately 150% in the number of projects we participated with even more planned for the future. For example, we launched a local talent strategy and workstreams initiative, aligning with the GPT talent vision and Objectives and key results (OKRs). The positive result has led us to work hard on structuring and supporting our strategies.
Learn more about working at Brazil Labs and visit Who We Hire.
Porto Alegre, Instituto Caldeira, Data Science, South Summit, South Summit Porto Alegre
Podcast: ADP’s UX Leader on Data-Driven Design
Podcast: ADP’s UX Leader on Data-Driven Design
Transcript
Mark:
Welcome to PeopleTech, the podcast of the HCM Technology Report. I’m Mark Feffer. My guest today is Joe Kleinwaechter, the vice president of Global UX for ADP. Among other things, it’s his job to make data accessible and useful. So he’ll tell us about those efforts, about how you make use of tens of millions of records and whether analytics and HR deserves all the attention it gets, on this edition of People Tech.
Mark:
Hey Joe, it’s good to see you again. Could you tell me what you’re working on right now? ADP’s a big company, deal with a lot of data. You are basically in charge of helping people get access to the information. So what’s that translate to on the ground right now?
Joe:
On the ground, my job is a lot of questions. Asking lots of questions and trying to really understand. One of the greatest challenges with us as human beings is that we think we have a really good understanding of others and we only understand it through our lens. And so trying to dismiss that and constantly realize that people do things a lot differently than I do on a daily basis. So my job is to figure out when they need data, when they need access to something, why do they need it? What are they ultimately trying to do? Not necessarily, yeah, maybe they’re trying to get their pay slip, but why are they doing that? What’s the bigger picture?
Joe:
Because it’s in that understanding of what they’re actually trying to do and those emotional states they have, that I can maybe get them there quicker to the end, rather than through a series of steps such as this is the way you always get your pay slip. So I really focus a lot on trying to listen for things that don’t make sense to me or are cognitive dissonance to the way we think about the world.
Mark:
Do you have an example of that cognitive dissonance?
Joe:
Yeah. It’s funny. You think that, listen, if I wanted to pay in the old days, if I wanted to pay somebody, I would have to go to my wallet, give them money because that’s where the money was, in my wallet. And it was only until you realize later that the money was just there because that’s all we had. People didn’t want to have a wallet. People didn’t want to have money. They wanted to ultimately give something in exchange for something else. They didn’t even want to spend money. They wanted to go get a cup of coffee at Starbucks.
Joe:
And then Starbucks figures out guess what? If I have a card for you, I can keep on file for you, or I can know about you. I can maybe help you get there better, not just in paying for the money, but maybe there’s something else. Your favorites, your history. How you operate. Things that make you happier as a customer that maybe you didn’t think about when you pull out your wallet with your Starbucks case, $20 for my coffee. But at the same time, what were the other things along the way that maybe could have been easier for you? So in my job, it’s not just about how do I go and look at my pay. I got to figure out what are they really trying to do? Are they trying to figure out if they have enough money to pay something? Or better yet, maybe they have some ambitious goals to try to accomplish and I can help them along that way. And that’s exactly what we’re doing in wisely right now, in our wisely product line.
Mark:
Now, obviously ADP has a ton of data and that’s kind of factored into your work, I would think. How does it factor in? How do you approach making all of this data digestible and useful?
Joe:
By ignoring most of it. I know that sounds kind of contrarian, but you could get absolutely awash in all of the data. Data’s a really fascinating thing. They say from a mathematical sense, data never lies, no, but, reading it does, right? Somebody could say something perfectly legitimate, but you can interpret it a lot of different ways. So the danger you have with lots of data is that the more you read, the more you make it confused. And what you have to do is take the data and figure out, okay, what can I start with as a hypothesis? Does the data support this? Does it not? And if it’s not, how do I change and pivot on my hypothesis? Those pivots often come by taking that hypothesis and trying it out with people. Seeing does it resonate?
Joe:
Okay, this says, this says this about the great resignation. This is what we know about it. Is that really what’s happening down there? And that’s where UX comes into play. Because we then go out and say, okay, we have this hypothesis, the data says this, what really is it? Is it really true or not? Maybe there’s other ways to interpret that data. And that’s probably one of our biggest challenges, there’s many ways to look at data and you can make data loo, however you want, right? The old statistics line, right? You’ve got to figure out a way that it’s unequivocally true for the people that you serve and localized to their needs. That’s the hard part about data.
Mark:
Okay. Can you tell me a little bit about the technology that’s behind all this? What’s going on under the hood?
Joe:
You mean gathering the data?
Mark:
Not just gathering the data, but putting it together and presenting it in a way that’s usable.
Joe:
Yeah, I think the biggest thing we have to focus on really is what are people actually doing versus what does the data? So the data gives us a starting spot, but really the really good data comes from what they’re actually doing as they’re using your software, for instance. How are they using it? What are they doing? So the best data is the one that actually follows them doing what they’re trying to do, rather than maybe some larger data set that gives you great demographics and breakdowns, but doesn’t really get personal enough. So what we typically do, I’ll give you a great example. In one of our latest products here called Intelligent Self-Service, we actually go back and look at all of our calls that come into the service center. And we find out which ones are the most plentiful, because those are the ones that are probably, our hypothesis is, nobody wants to call into a service center. Nobody wants to call cam Comcast, right? Or call Google. They don’t want to do that. So therefore, how can we subvert those calls ahead of time?
Joe:
Well, okay, we go and look at the top 10, and this is what they’re calling in for. It doesn’t really tell me why or what their circumstance was, or other characteristics like do they really need a human, maybe they need some confidence. We then take that data and apply it in, let’s say hypothesis. We say, listen, people want to know, for instance, who their HR benefits person is whenever they do this. We then watch the way they behave using our software and say, okay, at this time we think they want this. And that combination will help them not call. So it’s a series of hypothesis driven design along the way that takes the data that we see in the call center, combined with the demographics of what we know from our products and how they use our products. Combined with what the user did at that moment, that triggers us wonderful little in, we use the AIML phrase, this black magic that happens with AIML that causes us to say, oh, these things when together have a high degree of confidence that what he’s trying to do is this. Give him this.
Joe:
Now 20 years ago, I mean, we’ve been try AI for a real long time, right? For a very long time. And what makes it really good today is that the models have gotten so good that we’re right far more than we are wrong. Remember the old days of Clippy trying to figure out what you were trying to do? Hey, it looks like you’re trying to write a resume. Irritating as all get out. But now we know what you’re actually trying to do with some high degree of confidence, because we have so much data that built that model so great that we actually have a good idea that maybe not only can we tell you what you need, but maybe we can actually do it on your behalf if you want us to.
Joe:
And that’s really where the state of experience is going to, can we be predictive? Can we be insightful? Can we be intuitive to what they’re trying to do and then be bold enough to offer to do it. And then when we find out that we’ve got really high degree of confidence, that we can do it every time, maybe recommend doing it on their behalf, without them knowing about it, if that’s what they want. That’s the model that the experience is going to.
Mark:
Well, how does this all fit into ADP’s efforts overall?
Joe:
In which respect? In terms of the UX, the experience model, this intuitive model?
Mark:
Yeah.
Joe:
So I would say right now we recognize that the big position that ADP has different than a lot of others in the industry and competitors, is that data, is the wealth of data. It would not be wise for us to ignore the fact that’s a competitive differentiator. So we use that data all over the place. So what’s really key? Our data sciences inside of ADP are pretty, pretty high level. And I say that with the great degree of confidence, because I’ve seen it operate on myself. Our AIML models that we have out there for telling where you’re going to go separates from everybody else. Now, since we have all that data, now the question is what’s the right thing to do with that data? What is the proper thing to do with the data?
Joe:
And our view is really simple. If it helps our clients, our customers, our users out there to do something that they wanted to do or to make them aware of something that they want they should know, then that’s good. Right? So it’s the alignment of that big data through a good model to get into the data at the right time. That’s across the whole product line. That’s across everything ADP is trying to do. We’re trying to become, a little bit like a barista at Starbucks where we know you enough that maybe we have your coffee ready for you because you always do that. You come and say the usual. Okay, good. Here’s the thing that took you half an hour to spout out before, now happens as you get in line. And that’s what ADP is really trying to do, is to be there before even you are there.
Mark:
I mean, obviously there’s a lot of technology behind this and that makes me wonder, how has the technology evolved over the last 10 years say. Which as the technology was evolving, it seemed also that the use of data was spreading. And I’m curious about, first how the technology became more of a foundation. But also how did the growing demand for it influence the technology and vice versa?
Joe:
Yeah, there’s a couple of things. It’s funny having been in many industries that relied on data. There’s a good natural checks and balances with the using of data as we know. There’s good ways to use data. There’s bad ways to use data. And it’s different for every person. I used to, and I still do, refer to something called the creep factor. Something is creepy. Back in 2002, if somebody told you that you need to get in your car because your flight is going to leave in a half an hour and the roads are blocked, you’d be kind of like, well, that’s kind of creepy. How did it know all this stuff, right? And you go, well, that’s creepy. But there’s a point at which you say but that’s useful. Okay.
Joe:
In the early days, we didn’t expect people to have all of that data. Now we’ve come to the point where we are growing up with societies where our kids and all others just assume you have that data, just assume that data is out there. It’s a different world about what we assume the data. Right or wrong, or whether you have that data, they make an assumption that data is there. Therefore, why wouldn’t you use it for me? How dare you not use it to help me become better? And that’s a far cry from where we were in the early 2000s, where how dare you use that data, to the point we said that data’s actually pretty useful. I kind of like the fact that you can do this for me. And then you start allowing a little bit more data, a little bit more data. And next thing we have data fields all over the place that are being mined for lots of different reasons.
Joe:
First, it was just concrete data, physical data. Now it’s behavioral data. How you operate, where you move, where you go. And to the point that it’s useful, great. But there’s always this paranoia that it’s not being used in the right way. And that’s something that I think is really healthy. I think that’s a really healthy check on making sure that we are good ambassadors of that data.
Mark:
What do you mean by paranoia around the data?
Joe:
Well, I think anytime somebody knows something about you that you either A, didn’t want them to know or didn’t know that they know, there’s a natural paranoia in us that asks how are you going to use that? What are you going to do with that, right? And knowing that if this were a benevolent world where everybody was going to use it, right, we’d have no problem with it probably. Not everyone, but a higher majority. But now we’re in the place where we have to be very careful about those that want to use the data to harm us or to use it in a way that annoys us at the very least, right? The scam calls that you get all the time, all the phishing techniques that are being used, things like that. There’s a whole black science of UX out there to trick you to go do things because they have some data, right?
Joe:
There’s reason HIPAA was set up, right? There’s a very valid reason why HIPAA was set up and needs to be needs to be respected and done because of the bad that you could do with that data they aren’t governed correctly. So we treat governance with data incredibly, incredibly important. It’s at the top of what we do in all of that governance. We know we have an ethics board. We have our chief data officers constantly making sure that we are using data in an ethical way. And that it really truly not only is just ethical, it’s got to be valuable. It’s got to be something valuable for our clients and our customers. Otherwise, it’s just data.
Mark:
I’d like to shift gears a little bit for the last few questions. Delivering data in the flow of work, the whole notion of in the flow of work is gaining a lot of traction. A lot more vendors are exploring ways to present their products that way. Does that pose any particular challenges for a data service or is it better? What’s your response to it?
Joe:
Yeah. There’s a fascinating thing that I learned, again back in the early 2000s, I worked at a company that we decided at the time Google had come up with Appliance, right? That you could put inside your internet and all of sudden you could use as a search engine localized to your internet. We put the Appliance up there and it didn’t perform well at all. We let it run. We let it run for a couple of months and it kept getting data. It could never, the finds were just not good. They weren’t even close to what you would get on the internet. And what we learned from the Google data scientist at the time was the reason that the internet is so valuable as a search tool and so accurate, is because it has so much heterogeneous dat. Data that doesn’t appear related but in a way is, and that heterogeneous data gives us a much greater chance of finding that needle in the haystack that you’re looking for.
Joe:
Whereas inside of a company, it all looks like the same thing, give me the latest dev report, give me the latest financial report. It’s more of a monocosm of stuff, and therefore you couldn’t find things. As we start meeting people on the go, where they are, we now have the chances for other types of data to improve that. Now depending upon where you land on the privacy of knowing where you are, geofencing and things like that, there’s a lot that can be done by knowing where you are. The question is by knowing where you are could you also use that for nefarious means? Yes, I guess so. Sure.
Joe:
So you’ve got, I think the real challenge is, as we learn all this new data, what’s right to keep and what’s right not? And that’s not necessarily our choice, right? That needs to be our client’s choice of what’s valuable because again, going back to the creep line, if I know where you are and I can offer you this new service, it should be your choice, whether you want to exchange that data for that service. Not we’re going to take this all from you.
Joe:
Companies have gotten in trouble in the past. We’re going to take this data. We’re going to read where you are and not tell you, and we’re going to give you a great product. Even if it may benefit you, the fact that you took that without my knowledge makes me suspicious that you may take it to do something else. And I think we’ve got to be really, really careful that having an honest conversation, a full disclosure and a strong ethics policy behind your data is really going to make the difference. Now with that in place, now I can meet people where they are. I can see where they are. I can get a lot more information.
Joe:
A great example. One of our customers has a lot of field workers, right? And they have their phones on, they got GPS on their phones. If they want to transmit their GPS information, great, they’ll be great. They can do it. We can tell when they’re going to clock in, when they’re going to clock out and maybe even clock them in automatically. So we get rid of the single biggest call, to most HR departments, is I forgot to clock in. Can you clock me in? I forgot to clock out. Can you clock me out? Something as simple as that, just by turning on GPS location. Is that valuable or not? Well, that’s kind of a client thing, isn’t it? You tell me. Is it something you want to exchange for that? Then I have put governance about what I’m not going to do with that data. That’s just as important. And maybe I’d say is even more important. Because just because I have the data doesn’t mean I can use it however I want. I’ve got to use it in a prescriptive way,
Mark:
Joe. Thanks very much. Really appreciate it.
Joe:
My pleasure, Mark. Thank you.
Mark:
My guest today has been Joe Kleinwaechter, the vice president of global UX for ADP. And this has been PeopleTech, the podcast of the HCM Technology Report, a publication of RecruitingDaily. We’re also a part of Evergreen podcasts. To see all of their programs visit www.evergreenpodcasts.com. And to keep up with HR Technology, visit the HCM Technology Report every day. We’re the most trusted source of news in the HR tech industry. Find us at www.hcmtechnologyreport.com. I’m Mark Feffer.
Tech & Innovation Blog
Future of Conversational AI: Here’s What You Should Know
Voice of Our People, Innovation, Career Insights
The more we understand what drives our situational awareness, consciousness, and creativity, the more we will evolve conversational AI and sentiment analysis with more robust outcomes.
Future of Conversational AI: Here’s What You Should Know
By Azfar Rizvi, Conversational Designer
“I’ll be back.”
We first heard this iconic line in the 1984 Hollywood blockbuster The Terminator, and it’s become a part of our collective consciousness ever since. It was mainstream media’s first attempt in depicting a fictional artificially intelligent system (Skynet), thus catapulting the concept of Artificial Intelligence (AI).
Then the depiction of AI went downhill. At least for a while.
People started to fear AI taking over the world through sentient neural networks. There are entire television series dedicated to playing on our fears about how antagonistic AI can be. And rightly so – drama and destruction sell more headlines. That’s the realist ex-journalist in me, LOL! As AI continues to fascinate humanity, our understanding of its limits and potential is evolving, and within it, there lies hope.
Azfar Framing in Action
This year, we finally transitioned from fearing robot overlords to cheering for sentient non-playable characters (NPC). The most recent Hollywood movie, Free Guy with Ryan Renolds, is a step in this direction. The story starts when an NPC develops self-awareness and strays from its programming. The NPC interacts with elements around itself in the game – it starts to think and feel. While this is interesting to posit, NPCs can’t develop sentience and act beyond their programming without human interventions.
The juxtaposition of the extremes has challenged us to think about the boundaries in AI. Corollary, these strides have been a significant force behind the digital transformation of businesses and entrepreneurship. We managed to bootstrap humanity’s collective learning with these recent advancements in AI and deep learning, manifesting the true meaning of the term global village. We’re truly connected and have transitioned from merely if/then/else chatbots to contextual ‘Conversational AI.’
ADP: Leading Digital Transformation
We provide payroll solutions for over 38 million workers worldwide. That means one in six US workers interfaces directly or indirectly with our universe. From a chatbot/conversational AI perspective, it means even more people will potentially interact with A.V.A., ADP’s virtual assistant. That’s where someone like me comes in and introduces Conversational Design (CxD).
Azfar Rizvi, UX Designer
ADP’s Service Technology leadership makes enormous strides to invest in the right infrastructure and create the right teams, producing trustworthy conversational AI platforms. We’re reimagining AVA to ensure our CxD is inclusive. Our persona aims to be innovative and empathetic, allowing intelligent responses to meet user expectations. Unlike conventional chatbots, ADP’s conversational AI understands the context of conversations and answers scenario-specific questions for users.
ADP provides our clients with the best payroll and HR experience, reflecting our processes and outcomes. Our teams work with internal and external stakeholders to ensure the AVA experience has enough context and intelligence to solve our customers’ problems and help us learn for future iterations of our products. As a CxD and Persona evangelist, I relish the opportunity to collaborate with colleagues and industry leaders, envisioning what AVA could represent to the workforce. For many employees, AVA is their first touchpoint with ADP. I write, design, advocate, and build an empathetic experience for this reason. We want to set the tone right for a great experience from the beginning.
The Future of Conversational AI
In one of his letters, Ernest Hemingway wrote: “A man’s got to take a lot of punishment to write a really funny book.”
That quote summarizes the journey in conversational AI – with chatbots starting ambitiously and, as time passed, aligning more with market expectations. The CxD universe that was first created by telling jokes during the formative years of chatbots has now segued into more transactional experiences. We are iterating rapidly at ADP, and the learning allows us to create better, more empathetic conversational AI experience with higher engagement levels. While I may have transitioned from narrative film production and journalism, not a day goes by when I don’t think about the quintessential role storytelling plays in creating holistic CxD.
The chatbot market is projected to grow from USD 2.6 billion in 2019 to USD 9.4 billion by 2024 – with an overwhelming 80% of businesses expected to have some chatbot automation by the end of 2021. According to insights on MarketWatch, “The chatbot market is driven by factors, such as advancement in technology coupled with rising customer demands for self-service and 24*7 customer assistance at lower operational costs. However, lack of awareness about the outcomes of the use of chatbot technology with various applications to restrict the growth of the chatbot market.”
Azfar Visiting School
Good news: ADP is ready for the challenge! We’re working to humanize AVA, our conversational AI. We will continue to create more empathetic, accessible experiences as we build from the number of experiential and transactional use cases every year. Whether enhancing value around payroll or helping to create workforce management automation through AVA, we are determined to harness AI as a tool to boost productivity and enable even better support to our clients!
A significant part of these #ADPTech enhancements depends on our ability to incorporate sentiment analysis and predictive analytics to intelligently understand our users’ conversations and the intents behind those queries. These enhancements allow us to deliver a more robust solution to standard enterprise functions such as employee onboarding, HR-related questions, and global help desk.
All this gives me hope for the future of AI AVA’s global footprint allows us to continue innovating and designing more holistic experiences. As one of the pioneers in Conversational AI, ADP is constantly evolving at a pace limited only by our understanding of how the human brain works. The more we understand what drives our situational awareness, consciousness, and creativity, the more we will evolve conversational AI and sentiment analysis with more robust outcomes. As a storyteller who fell in love with AI, I remain enamored by the possibilities of our collective AI future.
In the weeks to come, let’s talk more about the opportunities around AI storytelling, leadership, and mentorship at ADP. I’ll be back! 🙂
Interested in a Conversation Design career at ADP?
We are hiring! Click here to see what we have available.
Tech & Innovation Blog
Roll Forward: How breakthrough products are redefining ADP as a tech innovator
Senior Leaders, Innovation, Future of Work
For ADP as a tech innovator, this is just the beginning of the journey.
Roll Forward: How breakthrough products are redefining ADP as a tech innovator
Roberto Masiero, SVP Innovation Labs
From my long tenure at ADP, I’ve learned that when a company gives you the latitude to move around—either within the technology space or from technology to business or sales—you get plenty of chances to reinvent yourself. And reinvention on the individual level influences the reinvention of the company as a whole, which I think we really see now with Roll™.
Roll™ is a mobile chatbot platform that uses AI and natural language processing technologies to anticipate users’ payroll needs intelligently. It’s the first-ever DIY payroll technology, and it’s so intuitive that our clients just download it and go; a lot of them never even talk to a customer service rep. But while we designed Roll™ to seem effortless, it’s the product of years of creative work with a unified team. The idea for Roll™ was to simplify payroll and HR using a novel UX and platform. I run the Innovation Labs at ADP, where we develop new products as quickly as possible. We’re a relatively small team, around 30 people from diverse backgrounds and with no hierarchy, allowing us to pull together tightly as a group. It’s important to me to have a flat organization because the moment you create hierarchies, you create ways to point fingers. In the way we work, everyone shares responsibility.
We came up with the product idea for Roll™ about four years ago when we were finishing up ADP Marketplace and wondering what to do next. At the time, most of our lab projects were satellite projects, adjacent offerings to our existing core services. I thought, “What if we reinvented the core?” We saw an opportunity to improve multiple facets of our payroll platform—the architecture, the design, the user experience. We had a chance to envision a whole new system.
We fixated on this idea of events—that everything done as an action within the system should be recorded as an event. In fact, we initially named the product “E” for “events.” For example, if you hire someone, pay someone, or terminate someone, we record each action as an event. This way, we know who did what, where they did it, what time of day, and from what device. All that information taken together feeds a machine learning engine where the system gets better the more it gets used. Instead of a system with a bunch of menus, forms, and reports, we imagined a vector of events where events cause other events. We basically built the software as a workflow.
But we didn’t stop there. We also wanted to transform the UI into something much simpler and more direct. People tend to design user experiences with a sense of engagement in mind, but that’s not what we needed here. We didn’t want people engaged; we wanted them to get the job done and exit the software. So with Roll™, the user goes straight to chat and tells the system what they need, and the software understands. If it’s to hire someone, change someone’s W-4, change a payroll schedule, the user asks, and the software guides them through the process using conversational UI.
We also built Roll™ to function 100% on mobile. We decided the UX would use a simple chronological timeline, similar to Facebook or Twitter. Clients love having one place to go to see their activity: “Yes, I ran payroll yesterday evening,” or, “Great, that new W-4 went through.” In addition to optimizing for mobile, we also wanted a strong desktop presence. We noticed our desktop users liked to grab info from the system and transfer it to Excel spreadsheets, so we decided to give them an Excel-like UX.
We finished Roll™ in July 2019 and got a pilot client in August. That fall, we presented the software to ADP’s executive leadership team. We got the feedback that we were sitting on something big that works for small to large corporations. But they encouraged us to focus on the smaller markets, those with one to ten employees. So we spent a couple of months designing an additional layer of software to cater to small businesses. In March 2020, we piloted Roll™ with about 50 smaller companies who all liked what we were offering, and then the executive committee told us to put Roll™ on the market and sell it as soon as possible. So we went from pilot program to full rollout in under a year, and today we’re getting dozens of new clients a day signing up for Roll.
A big part of what makes Roll™ stand out is integrating natural language processing with machine learning. We designed Roll™ to understand the mental model of our user’s meaning. We wanted the chatbot AI to talk the way people talk.
We brought in ADP’s business anthropologist, Martha Bird, and copywriters to advocate for the user, helping us to shape the Roll™ voice. We didn’t simply want AI to predict what our clients needed for payroll purposes––though that ability was definitely important. We wanted the voice of Roll™ to demonstrate human understanding. For example, Roll™ learned to respond more positively when addressing a new hire or giving someone a raise in pay, whereas it is more subdued when discussing termination. It’s that empathetic understanding that gives Roll™ an edge in human interaction.
On the backend, we decided that we didn’t want to run servers, or even containers, like Docker or Kubernetes. Instead, we made every event a function. The beauty of functions is that they only exist while that function is running. So our cost of running Roll™ is extremely low. Using cloud services and this idea of functions is another way Roll™ sets itself apart.
Of course, Roll™ didn’t come without its challenges during the development process. Fraud is something we have to consider whenever we engineer or develop a new product. But this is what I love about the Lab: We think of our challenges as opportunities to make our products better. How can we improve? How can we automate? How can we reduce the amount of burden on the system from someone trying to commit fraud? And when we meet a challenge, everyone jumps in to help. We either fail as a team, or we succeed as a team.
I’d say we’re succeeding right now, and the beautiful thing about Roll™ is that it’s always running. We change our models to pick up on new ways clients ask for things, and every new question pulls into Roll’s knowledge and experience. So the more clients we have, the better the software becomes. It’s an unprecedented level of automation.
A program like Roll™ can help further ADP’s digital transformation from merely a payroll company into a competitive tech company. What makes Roll™ exciting is that it almost creates its own category; it’s a technological solution no one else has. We can dominate this market and apply some of the same breakthroughs—machine learning, using functions—with other ADP products. For ADP as a tech innovator, this is just the beginning of the journey.
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