Max Yankelevich, CEO and Chief Architect of WorkFusion explains everything about automation solutions

Max Yankelevich, CEO and Chief Architect of WorkFusion explains everything about automation solutions

We cut out all the intros and other small talks from our podcasts but I should begin by thanking Max Yankelevich for his time. Max is the founder, CEO and Chief Architect of WorkFusion and one of the pioneers in applying Artificial Intelligence to enterprise processes. Though I had a very high-level idea of cognitive robots at the beginning of the talk, Max explained everything a knowledge company executive needs to know about automation: the concept, the industry, automation potential for enterprises, pricing, ROI and how enterprises should implement automation solutions. We also had a quick discussion on how this all affects future of work. Below you can find our podcast edited for clarity and brevity.

We started off with the founding story of WorkFusion:

Max: I was part of the research team at MIT CSAIL Lab that was studying Artificial Intelligence and we came up with machine learning technology that could watch people perform knowledge work and mimic that work and over time learn it and become much better than humans. This is sort of like how self-driving cars work.

Then I took a sabbatical from the research and I went out to India, just to travel, and I found there are many firms in India that offer outsourcing services for knowledge processes. They call them Business Process Outsourcers (BPO) and I went to visit them like Wipro and Infosys and Cognizant. I went to visit them and I saw that these were pretty laborious operations. A lot of people working in almost like on sweatshop conditions I would say, working 24/7 doing mortgage processes, back office processing for these companies and all of them were smart people but they were wasting their talents doing this repetitive labor.

And that’s how we came up with the idea to take the research out of MIT and apply it to automating repetitive knowledge labor. So instead of outsourcing to humans across the globe and sort of taking away their humanity if you will, you can outsource it to AI robots. That’s the genesis and we started the company in 2011, so I was in research from 2009 to 2011.

Cem: How is the current landscape different from this beginning state you described and what sets WorkFusion apart?

Max: The space that we occupy is called Intelligent Automation, which is also sometimes known as Robotic Process Automation. These are software robots that primarily take over human repetitive work in knowledge industries.

What are knowledge industries? These are banking, financial services, insurance, healthcare, telco, anywhere people sit in the office and do work behind computers with documents and data. I mentioned all of these different verticals and we are active in all of them but if you think about banking you know the use cases are all across different parts of the bank. For example, account opening.

We have a customer in South Africa, Standard Bank of South Africa, it’s a $170B enterprise, almost 200 years old. It took them 20 days to open an account. They deployed the robots, it actually took about 5 minutes. Obviously, all the compliance checks and document processing are done by robots. Back office, compliance, operations, loan processing, mortgage processing are other major areas benefiting from our robots in banking.

In insurance, it’s claims processing. Processing a car damage or house insurance claim are all things that people usually do. It’s fairly repetitive and boring and the robots after learning can do these jobs much better.

In terms of the market, we are differentiated by providing two types of robots. One we call Robotic Robots which primarily do cut and paste operations. If you think about a company there are people who take information from one system and paste it into another. There are many systems that enterprises have and sometimes the information needs to be copied from one to another. A good example is when you go to a bank and you open an account usually information will be entered in several different systems. It’s the same information. So we called these copy-paste robots Robotic Robots. There’s not much judgment going on, so it’s robots just doing this mind-numbing work just over and over again.

We also have robots that we call Cognitive Robots. Essentially they are robots powered by machine learning or artificial intelligence. Those robots watch people apply judgment to their knowledge work which is should I grant this claim, what should I do with these documents, things like reconciliation. Let’s say I am getting insurance claims that claim a certain amount of items were damaged, we need to check if that correlates to the insurance policy you signed up for. Or data entry. When you look at documents like loan documents, that come as faxes or emails and then enter that information into the system. That requires human judgment and this is where WorkFusion is the strongest. MIT research that I mentioned really focused on this part which is watching humans perform that work and actually learning that over time and becoming much better. Sort of like the Go player, AI playing Go first is not as good as humans but over time becomes much better than humans, so that’s really the differentiated technology that we invented, patented and over the last seven to eight years developed with sort of $75M worth of VC funding.

Cem: You said that robots are essentially teaching themselves by watching people but there is also an interface for people to explicitly program the robots, right?

Max: Yes, that’s the role of the Robotic Robots. You can program those robots and you can give them rules. That’s the simplest case and we have a product that’s free that’s called RP express that allows you to create simple robots and program them yourself. The complex part comes when the robots have to learn because those rules are not easy to program. That’s why artificial intelligence and machine learning have created a lot of noise in the industry. It’s because instead of programming computers you can make them learn now. This allows them more freedom of decisions and it’s a much easier thing. So, you can use simple robots and program them. But there are more complex operations for which you simply cannot program all the rules. They live in people’s heads, think about driving, it’s a complex operation. Then you need to apply what is called cognitive robots that actually watch by learning.

Cem: You have mentioned some really critical cases for the bank like opening an account, if you use a Cognitive Robot for such a use case, since there is no explicit programming there can be cases where robots sees an edge case so what happens then, someone needs to check the logs to make sure that those edge cases are dealt with or how does that work?

Max: Part of our invention is something that’s called human in the loop. The edge cases are always learned by robots through watching humans perform you know more and more of edge cases right. So if you don’t program a robot, the robot learns from examples. You eliminate edge cases because examples are really the work that humans are doing. Over time the robot learns most of the examples. If there is some example the robot cannot handle because it has never seen it, it will delegate it to a human and by human actually handling that edge case one more time the robot will learn. So it works like a human in the loop.

Again think about a self-driving car, it doesn’t start out driving autonomously on its own but it watches the driver and over time learns how to handle different situations until it becomes completely autonomous. So it’s the same concept where over time WorkFusion’s technology Cognitive Robots become completely self-sufficient because they’ve learned.

Just like you would teach your new employer, you will bring him on and you would show him the easy stuff, then hard and harder, in the end, you would rely on them to do most of the things and if they don’t know how to do it they would probably give it to their supervisor but in the end they will become very proficient. Same thing with robots they become more and more proficient until they’re able to handle what humans do and much more actually right, they become superhuman in a way.

Cem: And this is quite interesting indeed, what is the current limit? For example, how do you choose processes to use?

Max: Yeah, so it’s anything that’s repetitive and lives inside the computers so any type of data entry, reconciliation, routing, chatting, these are good use cases. We actually provide a framework with our consulting partners that grades different processes. We also issue heat maps for different industries that show best types of processes that are good for intelligent automation. I would estimate that usually about 70% of knowledge-based companies’ work can be automated using intelligent automation.

Cem: That’s quite impressive. What are the percentages that you have seen with your clients? Are people really pushing it that high or they are more cautious?

Max: Most big companies are on the way. They want to achieve and I don’t know if you’ve seen the recent announcements for instance from a CEO of Deutsche bank or ex-CEO of Citigroup and then P & C Bank, they’re all saying that within the next five years or even less, thirty to sixty percent of all the jobs in the bank will be done by robots. Most of these announcements are coming because these companies are customers of WorkFusion and they have a program that over a span of five years plans to achieve that level of automation within the bank. Or insurance companies, they start smaller but of course over time that’s where it’s going to get where most knowledge work will be done by robots in the enterprises.

Cem: That’s indeed ambitious and impressive and with so much work automated, the maintenance of the bots and potentially dealing with the edge cases becomes important. On the maintenance side do you rely on the company itself or is a work split between you and the company?

Max: So obviously we’re software a company so we provide the technology and the technology has a lot of capabilities for security, oversights making sure that robots are well managed, so the software provider itself has a responsibility to provide a software platform that treats robots just like employees from a perspective of security and oversight. Of course, you have implementation partners such as Deloitte, Cognizant, Cap Gemini and others that help companies to do the implementation and first level of support. Most companies themselves that have ambitious goals of automating 69-70% of the jobs build what is called Center of Excellence internally and then WorkFusion trains their employees. We provide online training, you can go to automationacademy.com and actually take training courses in WorkFusion’s technology and so they train their employees and form a Center of Excellence which looks after the software.

Cem: And what percentages of the cases in your experience companies use these outsourced service providers?

Max: I think it’s always a blend, I’ve seen most companies use partners and their own people as the center of excellence and blend those two together. Because if you think about the scale of automation in a large company it is just a lot. Think about 100K person enterprise where 70% of those jobs can be automated, so there are many many processes that can be automated. So it’s not enough just to have your own center of excellence, you need partners to be able to help you to drive the program. So, we see a blend of these just because of the scale of the implementation.

Cem: If you shed a bit more light on pricing and ROI that would be great.

Max: You know we set out to provide a very clear and commercial model for our customers to achieve ROI. They expect at least sixty percent savings if not more so we charge what we call expert process right not robots because I see a lot of companies they think about charging for a robot which doesn’t really make sense because you actually don’t know if one robot is going to replace one person because it doesn’t work exactly that way. Maybe you need more robots because you want to do more work, maybe two robots will end up doing one person’s job because of the technical limitations. So the way we charge is, we charge per process, the process could be what I describe it could be the account opening or compliance process or invoice processing, order to cash whatever you want the process to be and we charge $25K per process per year. And usually, the process is at least work of 10 people. So it’s like 5 , 10 or 100 people so if you calculate their salaries $25K per year is probably less than one full-time person doing process that usually ten people or maybe even 100 people would be doing it, so we don’t differentiate between a process that 100 people do or 10 people do we just charge a flat fee for process.

Cem: Then of course companies start with the largest processes, I mean financially it makes much more sense that way, right?

Max: It makes sense almost at any scale if you are spending more than $25K a year on that person because you know the other benefits are not only the savings right, but if you think about it, the robots will do it at better quality than humans. Humans get tired of this repetitive work so their average quality is 85%, so they do miss keying and mistakes, robots quality is 98+% and robots work 24/7 they work every day, don’t take breaks, they don’t look at Facebook so they can process a lot more information and also you don’t have to spend money on real estate, software licenses and you can save on computers and so forth. So, if you think about it, it’s a much bigger benefit than just you know eliminating somebody who is doing this work, you’re getting better quality, a lot more work so it’s really a bargain.

Cem: It also becomes a leaner, easiest to manage, a more responsive organization as well. We have been talking about the current state of RPA so what’s in the pipeline, how do you think the industry will evolve and what will be the next phases of automation?

Max: We spend a lot of time thinking about the future. I think directionally, the robots will get smarter and smarter. Because I always say WorkFusion’s robots start from the mail room and work their way up. That means you know as they work in the enterprise just like any apprentice if you think about how humans promote their people, you start at a lower level of organization and then one day you might be CEO you never know right because you get smarter and smarter. So we see the same progression where robots are now able to take on more and more complex tasks. So you know the progression of that is I would say is theoretically infinite right, where robots will gain more and more knowledge and will be able to do more and more complex tasks so the initial estimate of sixty percent of current banking jobs being done by robots will go much higher from there and of course at that point and enterprises can become not only a lot agiler because they can make decisions faster but also much smarter, so it will only increase the capital growth of the economy.

Cem: And it also creates huge potential payout for the RPA industry as well. Do you want to talk a bit about the competition?

Max: Yes, sure so the competition is really when you think about other companies in RPA space, they focus only on robotic bots which is what you describe like that.

Cem: Not the cognitive bots you are saying? I understand

Max: Yes, because our roots are in MIT as AI company first and you know RPA is second.

Cem: Clear, but aren’t they throwing now millions into the cognitive space as well to build smarter bots?

Max: I think when WorkFusion entered RPA space a few years ago cognitive and AI was very far on the horizon, now every vendor talks about AI. But if you think about the space in general, there’s AI washing that’s going on in every part of being industry, you hear companies every day talking about AI this and AI that, in actuality it’s a very you know me being a scientist, an applied scientist at that, it is a very difficult problem to solve to be able to learn any process using AI to do learning of an arbitrary thing so it’s the problem that takes many years to solve not only on theoretical level but also on a practical level because it’s just not only money but there is time because it takes a long time to be able to train these things, so there is a time gap of several years that will need to be closed for anybody outside of us. We spent $75M not only creating the intellectual property in research but also building this. So it takes time and as you know we continue to invest more so we’re going to stay ahead of the competition but we’re happy that most vendors now woke up to the fact that you need both cognitive and robotic capabilities to be able to help enterprises automate their work.

Cem: Definitely. And a final question about automation because you have been seeing so many enterprises essentially automate a significant portion of the businesses, have you seen any successful patterns and companies that are able to upskill the personnel and manage this change in the company?

Max: Yeah, funny enough outside of like outsourcing companies, we see that most big enterprises are struggling with, doing more with less. It’s not that they’re saying we’re going to try to fire people that are doing those work, they are saying I have these people that I would rather deploy on more revenue-producing work that requires a higher level of human intelligence. Let’s robots do sort of this grunt work that’s non-differentiating for me. Most of the activities that we are seeing within our customers when they automate processes are around reskilling people and moving them on to smarter work because they’re very important to the enterprise in terms of the revenue production.

Cem: Yes, definitely. I was also thinking along the same lines but as you said outsourcing companies that’s a different situation.


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