Storytelling About WorkFusion With Adam Famularo
WorkFusion’s Work AI platform was developed out of MIT Labs over a decade ago, and has been sold to various companies over the past eight years. By focusing on specific use cases that customers had found valuable, WorkFusion was able to tailor its technology to their needs and create more accessible products. Its customers had spent years training the models, so by creating an “Uber product”, WorkFusion enabled multiple firms to benefit from the network effect of using them.
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Show length: 24 minutes
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Adam Famularo is a visionary business leader who is dedicated to customer and partner success, building world-class teams, and delivering great returns for customers, shareholders, and investors.
Since joining WorkFusion, Adam recast the company’s vision with a focus on digital workers that are transforming the future of the global workforce. In addition, Adam has enhanced the company’s go-to-market functions and prioritized customer success as key to WorkFusion’s mission.
Prior to joining WorkFusion, Adam served as chief executive officer at erwin Inc., acquired by Quest Software. As a co-founder of erwin, where he partnered with Parallax Capital Partners, Adam built an extraordinary company that helped the world’s largest organizations discover, manage, protect, and leverage enterprise data to drive successful digital transformations.Follow Follow
Storytelling About WorkFusion With Adam Famularo
William Tincup: [00:00:00] This is William Tincup and you are listening to the Use Case podcast. Today we have Adam on from Work Fusion and we’ll be learning about the business case or use case that his prospects and customers use to purchase Work Fusion. So Adam, lead you do us a favor and introduce yourself and
Adam Famularo: work Fusion.
Sure William, and first, thanks for having me on your podcast. Sure. So, uh, I’m Adam fro, I’m the c e O of Work Fusion. Uh, work Fusion is a AI software company focused on bringing digital workers into the
William Tincup: workforce. Oh, [00:01:00] that’s fantastic. All right, let’s, so digital is defined by what, at this point?
Adam Famularo: So what we have today is, uh, AI software.
Um, so it is all based upon, you know, machine learning models that do specific work jobs, right? So, um, we have our own platform. We, we call it work ai, and we have designed six digital workers. Specifically for financial services and insurance companies, so specific job roles, right? With, with years of working with those companies in, in creating intelligent models for doing specific job roles, and those job roles are around like financial crimes, so, We got Tara and Evelyn that do sanctions and sanction screening, and we got Darryl, Casey, and Kendrick that focus on K y C.
Uh, so know your customer. And and the last one is Alana, who does insurance underwriting.
William Tincup: And so well, we’re gonna pick all these, [00:02:00] uh, and talk about all of them. So let’s go character by character or worker by worker in, in this case. And how did we, how do we seed them with the knowledge to start? I mean, obviously they’re gonna learn.
Through machine learning and ai, they’re gonna learn as they go. Uh, and it’ll get tighter, you know, five years from now they’ll be, they’ll be, they’ll be, you know, crazy. Excellent. Um, but how, how do we see them with just their knowledge of, you know, we’ll start with any, any one of the workers. Yeah. But how, how did they get their initial knowledge?
Adam Famularo: The best way to get there, William, is to start the beginning, right? And the, the beginning was us building our own, what we call work AI platform. And this platform was derivative work out of m i t labs for like, literally, I think it started 10 years ago. Um, building, developing, you know, the, the software behind where we are today.
That work AI platform, um, has been in use and sold to all different types of companies over the last seven [00:03:00] to eight years or so. And what, what I found in joining about, uh, a year and a half ago now was that their specific use cases that our customers, specifically financial services companies, have created the most value around and that.
Was these specific use cases that we brought to market and we named them, gave them a face, um, and, and, and really a persona, a job role and a job function to make it easier for new customers to adopt our technology. That was the challenge that we saw, is that this is so cutting edge software. That a lot of companies, you know, really had to dive in and spend a lot of time and effort to build and develop out use cases that were specific to their companies.
But they even asked for it. They said, listen, Adam, if you could take and productize these, sell them to multiple companies, where we get that network learning effect where they get better over time together. We all stand to benefit. So I wish I could say I did it on my own. We didn’t. We actually worked with the customers that we [00:04:00] had and took the training that they spent years on training these models to, in essence create an Uber product.
And then the other thing that we did by giving it a name and a face, you know, this was the feedback that we received from a lot of our customers, was that. You know, these digital workers are augmenting the workforce. Right. And they felt like it was important to give them a persona so that they felt like they were part of the company.
Right, right. Um, so that’s why they, we took it to another level. My marketing team took it and ran. And, uh, we hired actors and actresses to represent the, the personas. We gave them job descriptions, specific job roles that they perform. Um, and, and here we are today with the, you know, we’re just at one year now in of the launch of our digital workers and it’s, it’s really picking up some great steam and momentum in the market.
William Tincup: So how do, um, what mediums do they do they interact with? Uh, in the know, uh, in the know your customer part, let’s just, Just use one, but I definitely wanna cover all of [00:05:00] ’em. How do they interact with humans, both on, let’s say, on the customer side? How do they do that? Is that over chat or email or phone, et cetera?
And then how does that information get back to the humans on the business side? So let,
Adam Famularo: let me start with, uh, the easiest ones to understand Tara and Evelyn who are our sanctions experts. You know, Tara doing, you know, transaction sanctions and Evelyn looking at entities and entity structures and adverse media.
Um, so what they’re doing is they are literally looking at all the individual transactions that are going on and, and deeming that this is a good or bad. Bad character. Right? Right. And if they catch something that looks like it’s a bad character, they will then reach out. We have human in the loop technology, right?
To their peers, right? So say, uh, John or Jane, and they’ll, they’ll reach out to them and say, look, I’m stuck. I believe this person is a bad actor. Here’s why I believe [00:06:00] that. Can you confirm or deny that? And, and then they will then respond and say, yep, we confirm it is. Then, you know, Tara will learn from that experience.
We’ll log it, um, and then move on to her next task.
William Tincup: Oh, wow. What’s great about that is it, it, it’s takes some of the, I say low value, it’s, none of that stuff is low value, but it speeds it up because a human can only consume so much data in a, in a, in an hour, you know? Well that.
Adam Famularo: That’s what we’re hearing from a lot of our success stories are like, look, guys, we’re the, the job role that they, that was being done by humans before, so tedious looking at all this data and it’s very easy to make a mistake and miss something where it’s much easier to hire Tara to do that job and work.
But hire her to still work alongside of somebody else so that when she gets stuck, she has a person that she can reference and work with through these, these challenges. Um, but, but we find that the speed and processing goes up exponentially. It protects people against, you know, the regulations that are out there.
And [00:07:00] when auditors come in, you have a, you can actually showcase, here’s all the work that Tara did, here’s why she made these decisions. Um, and it’s not, you know, put on the individuals in the company. Right.
William Tincup: Right. And, and they don’t have technically, uh, ai, they don’t have breaks, they don’t have unions.
They don’t have time off. So they can be working yet,
Adam Famularo: right? Yes, William, you’re right on the money. Right? So they work, they work 24 hours a day, you know, you don’t have to give them time off for breaks. And, and, and the other most important part that we are realizing now with our new customers that are onboarding them, um, is that the time to value so much faster.
So if you go to hire an employee to do these jobs today, it takes you three months to find the person, right? It takes you another six months to train the person, and then you have an entry level person at nine months. At hours, you can buy Tara. Today. In a couple of weeks you’ll have her trained and up and running within your system.
And then from there, she’s a three year trained employee. [00:08:00] She already has the knowledge set of a person that’s been there doing the job for over three years. And she’s getting exponentially smarter every day. Yeah.
William Tincup: Uh, unlike human beings, uh, we, we do get smarter. It’s just not at, not at the right. Um, maybe depends on how much reality TV we consume.
Oh, so true. So what industries are we in right now? You said it, you said it at the beginning, but I wanna make sure the audience gets it.
Adam Famularo: Well, specifically the digital workers that we have today, right? Um, five of our six are focused on financial services, right? And the other one is on insurance. Now that’s Alana.
Now we do have our work AI platform that is in other industries, right? So, you know, but that’s all custom de designed and developed, right? Digital workers that, that run on our
William Tincup: platform. And, and so both insurance and financial service, one of the things they have in common is compliance. Correct. Um, do you see kind of the expand, if we have [00:09:00] this call in a year from now, what are some of the industries that are, that you feel are, are ripe for digital workers?
Adam Famularo: Right now the easiest for us to traverse into is anything that, that does deal with regulations, um, that does deal with, uh, large amounts of, of data that needs to be read and deciphered, and then an action created thereafter, and that that’s really where we fit in really well. You’ll see like we’re a leader in intelligent document processing.
That’s kind of the cornerstone of our AI platform, which really just means that we can read and decipher data than it better than anybody else, and then we can drive intelligent actions from that data. So
William Tincup: that, that, that leaves us open for, you know, healthcare. There could be some things in there. Cause I, I re I remember, uh, especially on the billing side of healthcare, that look pawing through, uh, coding and billing to make sure it was accurate or, or not.
Um, there’s, [00:10:00] there’s been several attempts at that, uh, from both, you know, doing it with home employees and also doing it somewhat digitally, but not like this.
Adam Famularo: Well, Humana is one of our largest customers. Ah, and, uh, they, they’ve built dozens of their own digital workers, um, on our platform.
William Tincup: So take us through that process of building a digital worker.
So we, once you’re in with, let’s say a Humana, um, they kind of get it and then, and then they start looking at another task that needs to be done that’s rudimentary, but, you know, has a lot of data, et cetera. How do they,
Adam Famularo: what’s their process? So, so companies that are outside of the, the financial services and insurance that, that we’re focused on building them for them right.
To, to create that repeatable model. Um, the custom side, um, usually like, like a Humana or we have, uh, like Ika and Disney and others. Um, they will purchase the platform. Um, they will get tons of training to go along with that. They will [00:11:00] usually have teams that are responsible for automation or ai. Mm-hmm.
Um, and then we’ll train them on, on how to train our models. Cuz that’s really what you’re doing right? If you think about it, right? We’re giving you the shell of a being, but you have to train the model on the, on the material that you want them to perform against. Um, and then they will package ’em up internally as their own.
Product offering that that, that they’re using as part of their tech stack. Um, so that’s, that, that’s very, very, uh, common for, um, larger companies that have, you know, deep technology stacks of people in their company. Um, very harder for a mid-size organization that, you know, doesn’t have that expertise in house.
Right. Um, and that’s what what we’re trying to solve for from our side was, Look, by launching our own six digital workers, we substantiate here’s how you can build on the work AI platform. And then, you know, we, we also make it as a more repeatable model where multiple companies can use the same [00:12:00] digital worker versus look, the digital workers that Humana built for themself only Humana is using.
Right. So I ha I’m not packaging up those digital workers and selling them to other companies.
William Tincup: Right, right, right, right, right. They, they’re truly gonna be unique to that particular company and it’s, and what they need the digital worker to be doing. You got it. So in sales, they have this concept of fud, fear, uncertainty, and doubt, right?
What? No, that all what is what? When you’re talking to a prospects or when your team’s talking to prospects, what, what, what’s the no, like what is the why would, why would someone, especially if they have this problem or I could see talking to a retailer and the maybe there’s not a great fit. Okay, that’s fine.
But. You’re talking to somebody that, where you already have digital workers in place, they’re doing something very similar, what? Why would they say no? Well,
Adam Famularo: usually, um, it’s speaking to your HR audience. It’s the inertia of introducing digital workers to a workforce. [00:13:00] That is, that is more of the conversation that we have, um, than anything else.
I. Which is, you know, that’s, that’s kind of a change, right? Right. It’s a change in, in how we do business. It’s a ch sometimes it’s a change in culture. Um, how do we adopt to bringing digital workers into a workforce where we used to have a human doing this job and it’s now being done by software, um, that’s working alongside of humans.
And, and what, what training do we need to do for the individuals that remain in, in those roles? What are, how do we up-level the people that are there to take on, you know, more complex jobs, right? Um, that add more value to the firm. Um, those are usually the conversations that we’re having, William. Um, so it’s more of a, a training enablement and, and getting them up and running on how welcoming digital workers to the workforce.
William Tincup: What’s interesting in the consumer market? Um, everyone’s familiar with Siri or Alexa or something like that? Well, there’s just. [00:14:00] Going down and getting answers for us. So, you know, they’re, they’re not a digital worker, but in a sense they’re, you know, like Google Maps. What is, what is Google Maps? Like you put in an address and it tells you five different ways you can get there.
Like, I can, I can’t, I can see. I can see some of the redness. I get that. But I also, like if you’re interacting with technologies in in the world, most of ’em have some form of machine learning or AI in them. Like my washer dryer have AI in ’em, so yeah.
Adam Famularo: Look, I think people are starting to get it right. You know, I, I thank OpenAI and Chat G p t right?
And, and apps like that, because I think that’s really helping consumers understand the consumer applications to ai. And the more consumers understand it, the, the consumers work, right? So right. They’ll, they’ll then understand where work AI and work fusion come in, which is we’re bringing digital workers and AI to the workforce.
William Tincup: You know, what I, what I see is, [00:15:00] you know, the folks that were doing the job before, you know the argument of, well, we were displacing jobs. Um, you know, that’s a little weak. I think what we could do with the folks that were doing the job before is elevate them to then be able to, to, to then look at those cases that come in and have a little bit more discerning eye on those cases.
Like they don’t have to go through all that data. They, they just have to have those cases brought to ’em, and I think it actually makes them better. Um, then than, uh, than doing the task itself. If that sounds right,
Adam Famularo: you’re, you’re, you’re right on the money. And, and that’s that, that learning, right? Right.
That’s that, that’s what we’re bridging that gap. And, you know, from a, an HR standpoint, when you think about training and enabling, uh, employees, that’s a component of it is. Teaching them how, you know, this, this changing times of bringing digital workers in the workforce. How is this gonna actually make their jobs better?
Right? How is this going to augment the work that they used to do and, and enable them to do things better and grow, most [00:16:00] importantly, grow in their careers?
William Tincup: So I’m, I’m assuming this is sas.
Adam Famularo: Yes. Yeah, it’s sas. But you know, since we sold the financial services, uh, they could also, you know, install it OnPrem if they Oh yeah, yeah,
William Tincup: yeah, yeah.
Adam Famularo: we. The, the power of of it being sass is like we have, um, Another very important ingredient, which is network learning. Right? Right. As long as our, our customers opt in. Right? So, you know, Tara, you know, that’s working at one company and Tara that’s working at another company as Tara does these model improvements by working with their humans and the human in the loop concept, right?
So Tara’s getting smarter, she can share those model changes with other terrorists so that the string of terrors that are out there working, they all get smarter together.
William Tincup: I, I can’t imagine why you wouldn’t do that. I know some are industries, especially with compliance and, and, uh, and oversight. So I can see some of them not wanting to do it now, co-mingle data, even if it’s gonna make them [00:17:00] smarter.
Adam Famularo: honestly see that once they figure out, William, that they’re not, we’re not actually sharing the data, but we’re sharing model improvements. Right. And once they see it, then they, they just gotta get comfortable with it. Right. Any, any change is, isn’t, isn’t, uh, something that usually people take lightly, right?
So, uh, but uh, that the concept behind it, if, if they open it up and like I mentioned, it’s an opt-in model, if they open that up to have that network learning feature plugged in, um, it’ll only drive the enhancements that much faster Now. What we are doing from our side, for those that decide they don’t want to opt in, is that we are gathering those model improvements and then we’ll just do updates.
But those updates will come, you know, more sporadically like once every six months or so.
William Tincup: So let’s, let’s talk about a little bit of the buy side. When, when you show this, okay, or when your sales team shows this to somebody for the first time, you, you’re obviously, you know, you get, you get people on a call, you’re, you, you to walk ’em through kind of a demo or you walk ’em through, kind of, [00:18:00] here’s what we do, here’s the, here’s the bit.
What’s the, what’s the aha moment for them? Like, what do they fall in love with? Like you do, you go through, I mean, you go through the whole bit. There’s probably a thousand different things you talk about, but there’s one thing that they’re like, Uh, yeah, I like that.
Adam Famularo: That it, it usually doesn’t take much.
It, it’s usually within the, the first five minutes of the demo when they see like reams of data going through and, and Tara just making decisions, literally like boom, boom, boom, boom, boom, boom. They’re like, oh, okay. Wow. That is, that is amazing. And then usually the second part is when I u usually demo the human in the loop component where, okay, Tara just got stuck and she just reached out to Jane, and then Jane just logged in and responded back.
Once that happens, they’re like, okay, we got this. Okay, now I, I can get it. I can s I can visually see this working within, within my, my environment. Yeah,
William Tincup: it’s, it’s almost like you’d love to see a video of a human doing the exact same [00:19:00] thing side by side and just see the amount of data that the, the digital worker can consume and, and make decisions on, as opposed to the best worker in that space.
They could just, their brain can only consume so much data. You
Adam Famularo: know, it’s funny you say that we are working on a video that I’m, I’m actually going in a slight other direction, which is I want to, I wanna show people what it’s like to work with them in the workforce. Right? Right. I wanna make it so that there’s less anxiousness about onboarding a digital worker.
So I am kind of trying to work on that video that showcases, okay. We just hired Tara. Here’s Tara day one. Well, now what does that mean for my job and how do I work with Tara? So I’m trying, I want to try to visualize that concept of, right, Tara working with John and Jane, right. Um, and setting that kind of environment up.
William Tincup: Well, it’s, it’s, it’s the connectivity, right? So if they think that the digital workers over here off to the side and doing their bit, that’s great, but it’s bringing [00:20:00] those in, in some cases, those cases over for review, that. It’s, that’s that interaction, that glue, that kind of, uh, that holds the, the two together.
I, I love that. And I think, I think that is gonna be a great video actually showing ’em how that works. Um, what is your, what’s your take? And, and again, we can take it any direction you like, but what’s your take on kind of auditing the ai, how either the companies do that with their, their digital workers or how you do it with kind of looking at all of what you’ve done H.
How do we kind of make sure that we’re, you know, every once in a while that we’re, we’re still on path of what we should be doing by auditing, uh, ai.
Adam Famularo: Yeah. So it’s, look, it’s a, it’s a big part of, of who we are and, and what we do, especially since we have regulators that come in to, they wanna actually see the work, William, right?
So we actually do document everything. They actually, the auditors like it better because most humans don’t document all their work, right? So we actually [00:21:00] do, we actually do capture all the work, all the decisions that we’ve made. And then this way, when the auditors come in, they can run through this and very easily see, oh.
Oh, you made this decision and, and here’s, here’s why you made that. Um, so from our standpoint that audited work is a very, very important ingredient. Um, and then the other side from our standpoint is that’s how you also make the models better, right? So the fact is that you’d be able to go in and see the decisions that you made and be able to fine tune the models to make sure that, you know, you’re, you’re, you’re actually running at the, the right rates that, that you require as a company.
William Tincup: love that is when auditors show up, In any form. People get nervous, even if they’ve done everything well, they just get nervous. My wife, um, excuse me, my mom was, uh, IRS worked for the IRS for 35 years in the liens department. So like whatever auditors of any kind, uh, you know, show up, just the tension gets crazy.
Uh, and even if everyone’s doing the right thing again, [00:22:00] this is one of those things, you can ease that tension because you know it’s a digital worker. Yeah, and so there’s no, it is not personal. We’re just gonna go and make sure that this is being done correctly. Well
Adam Famularo: said.
William Tincup: So, well said. Last question is, uh, questions.
If they’ve never worked with digital workers, maybe their AI literacy isn’t as high as we want it to be. What questions should they be? Should buyers and prospects, should they be asking work fusion?
Adam Famularo: You know, I would say the most amount of time that I spend with customers is about, um, onboarding digital workers.
Mm-hmm. So, so, so now that you’ve decide to hire, we use that term and we, we use it right strategically because you are, you’re hiring these digital workers to come to work for you. Now that you’ve hired them. How do you start getting value out quickly, right? Because at the end of the day, you made a technology decision.
You made a bet on us. You’ve invested money, time, and effort. You know what? You gotta be able to [00:23:00] show a return to your bosses, right? At the end of the day, I want all of my customers promoted, right? That’s my ultimate success, is the people that buy us, they get promoted. So at the end of the day, it’s about that is how do I get my, my value back as fast as possible and spending the right amount of time on the onboarding process, uh, training, you know, both the people that you know are being replaced as well as the people that are taking on new jobs, roles and functions.
And, and, and including them as part of the team member from, from day one, um, is the way that you will get the most value out at the quickest period of time and really start to shine within your company and start to grow with the concept of digital workers
William Tincup: drops, Mike walks off stage. Adam, thank you so much for your time.
This has been wonderful and I, I love what you’re building, what you’ve built already, but also just the future of what you’ve done. Uh, it’s just, it’s exponential and I, I’m really excited to kind of see where,
Adam Famularo: where you take it. Thanks, William. I I really enjoyed the conversation, um, and I, I appreciate you and [00:24:00] your audience learning more about us.
Um, you could go to work fusion.com and you could literally click on a two minute video of any one of our digital workers, and they’ll teach you exactly what they do, and any one of, uh, the members of our team will help take you through any of the material. I love it. I’d
William Tincup: love it. Thank you so much.
Absolutely. And thanks for everyone listening. Until next time.
William is the President & Editor-at-Large of RecruitingDaily. At the intersection of HR and technology, he’s a writer, speaker, advisor, consultant, investor, storyteller & teacher. He's been writing about HR and Recruiting related issues for longer than he cares to disclose. William serves on the Board of Advisors / Board of Directors for 20+ HR technology startups. William is a graduate of the University of Alabama at Birmingham with a BA in Art History. He also earned an MA in American Indian Studies from the University of Arizona and an MBA from Case Western Reserve University.