December 14, 2017



AI in Business: New Decision-Makers on the Rise

   (c) Trusted Counsel (Ashley) LLC. All Rights Reserved.

(c) Trusted Counsel (Ashley) LLC. All Rights Reserved.


Speaker 2:           It's time for 'In Process,' conversations about business in the 21st century, with Evelyn Ashley and John Monahon. Presented by Trusted Council, a corporate and intellectual property law firm. For more information, visit Trust-Council.com. Now, with 'In Process,' here are Evelyn Ashley and John Monahon.

John:                  Welcome to 'In Process.' I'm John Monahon.

Evelyn:               I'm Evelyn Ashley.

John:                  We are Trusted Counsel, a corporate and intellectual property law firm.

Evelyn:               Really excited about our topic for today, 'Making way for the robo-bosses.'

John:                 Absolutely, yeah. I think this goes into a lot of stuff that's been in the news lately about artificial intelligence and the ramifications of that, and how it's going to play in the workplace.

Evelyn:              Well, and I think the more surprising part of that potentially is even though we read a lot and hear a lot about artificial intelligence, I don't know that anyone really thinks about AI moving into the professions; and how ... who we think of as being really educated and needed roles in business particularly, can actually be replaced by robots.

John:                Right. I think as we've learned recently, those things can start pretty small. Yet, these robots are getting better.

Evelyn:             They are. We should all be scared. Hal from 'Space Odyssey,' he will be here soon.

John:               Absolutely.

Evelyn:            Or maybe he's here already.

John:              Right.

Evelyn:            I think it's interesting, and we'll talk to our guest a little bit about this, too. At the World Economic Forum this year at Davos, basically it was predicted that over the next five years, about seven million jobs worldwide will be replaced by robots.

John:              Geez.

Evelyn:           While I think people understand that that can be in a fairly, ... what we think of manufacturing roles and such, to increase efficiencies, I'm not so sure that anyone of us have actually thought about managerial roles that could actually be replaced. I think as part of our various discussions on innovation, disruption, and better innovate or be destroyed. This will be a great topic for us.

John:              I agree.

Evelyn:          Our guest is Dan O'Hara. He has a deep knowledge of the rise of robo-bosses from a variety of perspectives. Most recently, presenting in various venues on the coming robo-boss, and the benefits to businesses that implement and embrace this technology. As part of his research, Dan decided he needed to know what it was like to have a robo-boss. He signed up to be an Uber driver for a few months. Today, we're going to talk about [Avanade 00:03:03], who Dan works with, and robotics, and their benefits to companies; and what the Uber driver experience was like. Dan O'Hara leads Avanade's digital workplace market unit, and is a member of the Microsoft mobility partner engagement board, and also serves as part of the [Accenture 00:03:20] digital leadership team. Dan created the Avanade experience design group, which now has design studios in over 20 countries. Dan's global role has him working with clients implementing digital solutions all around the world. Prior to joining Avanade, Dan spent ten years leading the dot com and startup companies; and was also previously with Anderson Consulting in the TIS organization in Chicago, Stockholm, Nice, and Buenos Aires, and Atlanta. Dan received a Bachelor of Science degree from Purdue University and lives in Atlanta with his family. Welcome to the show, Dan.

Dan O'Hara:        Thank you very much.

Evelyn:                We're so delighted that we have you here today. I'm not sure that everyone is all that familiar with Avanade. Can you tell us a little bit about its business?

Dan O'Hara:        Sure. Avanade is actually a very large joint venture between Microsoft and Accenture. We're focused on business solutions with a technology bent on Microsoft for large enterprises. We have about 28,000 people around the world, and operate in 20 countries. We are a very large global player, but if you're not in enterprise IT, you're probably not as familiar with us as our parents, Microsoft and Accenture; who I'm sure most people are familiar with.

Evelyn:                 Are familiar with, absolutely. Tell us a little bit about your role for Avanade, what do you do there?

Dan O'Hara:        A big part of our business is digital transformation. As we work with customers, we look at how they change the relationship with customers and equally importantly, how they change the relationship with employees. I lead in a practice area called 'digital workplace.' That is all things from how do we reward employees, how do we give them the tools they need to do their jobs, and how do we integrate them with new technologies like artificial intelligence and internet of things, and other things that will change the way they actually work in the future.

Evelyn:                 Then, tell us the size of your usual client. It must be fairly large, right?

Dan O'Hara:        Fairly large. A public one is Delta in Atlanta. They're a very good client of ours, but we also get into much smaller customers as well. Depending on the market; but we're generally dealing with people with probably 1,000 employees or more, because that's where the digital workplace solutions that we offer have probably the most differentiation.

Evelyn:                 Are those solutions generally consulting services, or are they actual implementation of technologies, too?

Dan O'Hara:        A lot of it is implementation of technology. Digital first comes, just like we're talking here, is what is it? How do I use it? What does it mean for my business? We do a lot of digital advisory services, but then it gets into what does the solution look like? What is it going to look like in the hands of my employees? What is my customer going to see? That gets more into a solution implementation with technology, change management, and other aspects of the change required for a business.

Evelyn:               Then, ... artificial intelligence must be ... is a large of what you do for clients, is that right, or?

Dan O'Hara:        It's becoming a bigger part. What we've realized with digital ... In my view, digital is all about the data. We have been doing things with operations for quite a while. We've been doing things with employees for quite a while, but now that we're actually looking at the data and making new decisions about the data, that really is what defines digital to me. It's not an existing CRM system. We've had those for many, many years. Once you start to say, "I'm going to use that data to figure out who the best customers are and what's the best next offer, and which employee is the right one to go talk to them," that to me is more digital. Data then leads to analytics, which then leads to artificial intelligence; which I think leads to robo-boss.

Evelyn:               Here we go. Okay, but before we ... go into robo-boss, let's talk a little bit more about the data. Essentially, a company comes to you and says, 'We have all of this data. We want to use it to our advantage. Perhaps for hiring purposes, because we know that we can tell from this data who our best profile employee is,' or something along those lines. Are there typically software programs that are available already for that? Is that new development that gets done?

Dan O'Hara:        There is software that will pull together the data. We think of it as systems of records. We know who employees are. We know who apply. Very interested in the Microsoft acquisition of LinkedIn that brings your employee data that is Office 365, and things like that, with the network of professionals that are in LinkedIn; and how do you look at combining that data? Then, the next step is really to go, 'what am I looking for, and how can I use data to start determine these are my most successful employees. Where did they come from? What jobs did they have before? What interactions did they have?' That's how we start to use data to make better decisions on hiring, development tools they should use really throughout the whole business process.

John:                  What's the difference between data analytics, and then the jump to artificial intelligence? It seems like it's a natural evolution, but there must be some jump.

Dan O'Hara:        Yeah, I think the jump is decisions. Data analytics is also often thought of as 'I'm bringing data together, and I'm going to look at a dashboard. I'm going to look at a report. I'm going to have it make suggestions.' As you start to get into intelligent automation, or artificial intelligence, that's where a machine is actually making the decision. Who's the next customer that I want you to call if you're in a call center? Where is the next place I want to send you if you've just come to a web page? All of those type of things is when artificial intelligence is actually making the decision, rather than analytics, which is saying, 'Here's the best place to go. Now, I'm going to send them there.'

John:                  Are people actually asking for, let's say, AI, or are they ... do they have a need which they're asking you to solve, and it's just gradually evolving into artificial intelligence?

Dan O'Hara:        I think the common business problems are the same. How do I attract the best customers? How do I get the most productivity out of my employees? How do I use my assets most effectively? All those basis business problems, but now you've got new digital disrupters, and it says, 'I'm AirBNB, and I don't need to own a hotel.' How does that disrupt my business? I know you've had several discussions on disruptions. Uber is another one of disrupting a business. I think that disruption is actually causing people to go, 'These are my basic business problems, but now I've got a whole new set of competition and playing field that I need to use data, artificial intelligence, and other things to compete on.'

John:                  Welcome back to 'In Process.' We are with Dan O'Hara of Avanade. We are speaking about artificial intelligence. Dan, I guess the question we have is what is artificial intelligence capable of now, today?

Dan O'Hara:        It's capable of quite a lot. You've got some very high profile cases. We are talking about autonomous cars and the Google car, and Tesla, and other things, being able to drive. We're talking about AlphaGo and Watson winning at very complicated games, 'Jeopardy' and 'Go.' A lot of them were saying, 'Oh, this is going to get beaten so badly.' A very interesting thing about artificial intelligence is it's learning. The ability to take very large sets of data and learn from it, and then be able to say, 'I've got a better answer to this question.' That's how AlphaGo won. That's how cars getting able to drive themselves, and all those things. There's a lot of high profile capabilities of what can it do starts to be the question.

Evelyn:               Then it could well be that predictive analytics are really what the whole world is made up of, is that right? Is that where we're going?

Dan O'Hara:        There will be a lot of predictions and then, with all of those technologies, there's humans which often screw up the predictions. We don't always act in rational ways, whether what we're buying or the fact that millions of us are playing Pokemon Go right now. We're not [crosstalk 00:12:22] always predictable as the analytics often is.

Evelyn:                 Where do you think that, ... I mean ultimately, do you think this is something that should we be worried about where this going?

Dan O'Hara:        I don't know that I'm worried about it. I think there's a lot of things that can be done better. There's the discussion on the autonomous car, and the classic trolley. Should you hit this group or save yourself? Things like that. There's 37,000 people that get killed on the roads every day because humans make mistake, or are tired, or doing other things. Maybe there's going to be two of them where the decision is left to computer that's potentially the wrong decision, or debatable; but how many of them can we save by making the right decisions when we're too tired to drive? Or, we aren't looking ahead of us and behind us at the same time; which an autonomous car can do.

John:                     Yeah. Elon Musk has sort of had some opinions on this where the things day-to-day, of course, are very helpful; sort of like his own Tesla, self-driving car. Then he theorizes that at one point, the help, the robots will take over. Have they thought about how that's going to be prevented? Is that a real concern?

Dan O'Hara:        I think it's a good thing to discuss at this stage. We don't have robots that can take over right now. They can't take over the grid and have terminators, and things like that. We do have to put in rules in place on how we're going to use them, what decisions that they can make, what decisions do we want a human to make? That boundary will continually change, so we need to expect that that boundary will change related to all kinds of things from genetics to driving cars to who is the first one that gets to have this product? That may be a decision that a computer makes some day, and picks one group over another for different reasons.

John:                  It seems like it could be a good new area of law.

Evelyn:               Yeah, absolutely.

John:                  If we're not replaced by then.

Evelyn:               The ethics of robotics. Get on that, John.

John:                 Yeah. Assuming we still have our jobs, I mean.

Evelyn:              Well yeah, exactly. Well, we might have a lot of free time, so we can actually create new areas like this.

Dan O'Hara:       Excellent.

Evelyn:              Dan, what is a robo-boss?

Dan O'Hara:       What is a robo-boss? I think one of the things we need to look at with robo-boss is not it's a robot that's going to come and sit at a desk, and drink coffee, and do all of the same things that a boss does. I think we need to break it down into what does a boss do, and then go, 'How does artificial intelligence assist that process, augment that process, or replace that process?' That's the way I think of it. One of the things I had looked at when this topic is we talk about it quite a bit. What does a boss do? Well one, they're involved in hiring decisions. Who should I hire, bring into my team, form it? The second, they give direction. Where should we go? What should we focus on? They also develop people in teams. How do I get more skills, make you more successful, et cetera? Then, they also reward us. Thank you, pat on the back, good job, here's your bonus, all of those things. If you start to break those individual pieces of what a boss does, I think you can start to get into a better definition of what a robo-boss should be doing.

John:                 Is there some instances or some jobs or industries where robo-boss is more appropriate than others?

Dan O'Hara:       Clearly we're going to see a move up of sort of the blue collar, white collar stack of things. Uber is a great example of who actually works for Uber, and who is the boss there, et cetera. We're going to see that in a lot of different cases. We work with construction companies that have to go manage very complex construction projects. How do you keep track of all that in one human brain's head, or is that some sort of system that is saying, 'The concrete gets poured at 2:00, and I need 25 people over here at 2:15,' and all those things. As you get bigger problems that involve directing machines and people, it starts to be a problem that maybe a robo-boss is better at, and better at coordinating, and things of that nature.

John:                 Right. It's interesting to hear you talk about the blue collar, and maybe white collar difference. It's funny because I have a feeling. I don't know whether you've seen any research, but that a lot of white collar professions may be in a bit of denial as to how much they could probably be replaced as well.

Dan O'Hara:       Yeah. In the World Economic Forum, it was interesting. There was some research done on that. The least likely person to get replaced is someone like a physical therapist, or even a maid at a hotel, because picking up a paperclip and knowing how to fold towels, and all kinds of other things, as well as knowing where on your back needs the best massage, et cetera; all of those things are very hands-on, people sensitive tasks it will be a while before the robots do. If we look at functions like tax preparation, if we look at assembling managerial reports, if we look at sales allocations and sales management, there's a lot of opportunities for a machine, a robo-boss, whatever, to make different decisions or better decisions because they're looking at more data and being able to make some of the decisions without depending on instinct, or without [inaudible 00:17:58] on gut feel; which may be important for the business.

John:                 We were talking about discovery and about how ... Evelyn and I were on the drive over here, about discovery, and about how artificial intelligence is bleeding into that, picking up keywords, and going through the pages, and then making decisions based on that. I have to say from my own experience of doing discovery as a law clerk, I think a robot, or an artificial intelligence machine would be a lot better. It gets quite boring after you flip through thousands and thousands of pages. I could see it being much more precise.

Dan O'Hara:       Yeah.

Evelyn:              Well, and being able to maintain the ... I mean you go into ... In that discovery process as part of litigation, you go into it with the idea that I'm seeking very specific information. When you're looking at thousands of documents, it's hard to keep the focus of where I'm trying to go with that. It seems to me that from that quantitative perspective of being able to pull the right data out, that's a really interesting implementation.

Dan O'Hara:       Yeah. There's also a good balance between automation and human intelligence on that. Where a robot boss or artificial intelligence, it's starting to really well is in image recognition. What is a car? What is a dog? What is the sentiment? A lot of what they're doing there is having the engine look at all the data it can, and then start to say, 'Hey, that looks like the grill of a car; but I don't know what this is,' and then having a human go, 'Oh, yeah. That's the tail lights of a '57 Chevy.' It's a little different than you probably have seen in your thing. That combination of maybe the machine looks at all of the documentation, and then says, 'Here's four things that I don't completely understand. Evelyn, take a look at that.'

Evelyn:              Right. ... [crosstalk 00:19:54] Provide me with information ...

Dan O'Hara:       'Provide me with guidance so that I learn and make it better.'

Evelyn:              Right. It's interesting, though, because the ... particularly we don't do litigation, but both John and I have enough familiarity with that process there. Many lawyers that become litigators are actually trained by that discovery process. It's essentially the first thing that a law clerk will do. The displacement part of that could well be what ultimately does the litigator look like who is supposed to be experienced, and how do you actually modify the training process there that goes along with it?

Dan O'Hara:       Yeah.

Evelyn:               It makes it very challenging.

Dan O'Hara:        Yes. Training us and training the robots or machines at the same time is going to be that combination that we need to get right. Some companies will do a great job of it, and will do wonderful things. Other people won't. You'll be going, 'Why am I talking to this robo-boss? I just want a real person.'

Evelyn:               Yes.

Dan O'Hara:        Like we've done for quite a while with IBRs.

John:                 Well, we need to take a quick break. We will be back and speak more about robo-boss. ...

John:                 Welcome back to 'In Process.' We are here with Dan O'Hara of Avanade. We are speaking about robo-bosses and artificial intelligence. Dan, when we broke, we were talking about the workplace and how it's changing. One of the things that arose, or we were sort of thinking about on the break was there's the assumption that the robots will do all the boring work. We'll do all the exciting and creative work. Is that true and do you think that we can be really creative for that long?

Dan O'Hara:        Yeah, so it's a good debate right now of, 'Okay, let me give you my tax form filing, which is not something I enjoy doing anyways, and let me spend a lot more time thinking about how we're going to design something.' Clearly, the breakup of artificial and human has definitely the mundane, repeatable task that can be learned versus the creative, can a computer write a sonnet, or a song, and things like that? You even have a few attempts of that, which are entertaining. I'm not sure they're as entertaining as a human would do. How do you get into that creativity? If you take that song example, could Beethoven, Mozart, or anybody else have created what they did if they didn't spend so much time practicing at the piano?

Evelyn:               Right.

Dan O'Hara:        Or any of our other modern artists being able to do that same thing. How much is learned and created in that creative process by having to do something that may be boring.

Evelyn:               Right. In the process of the mundane comes the brilliant, right? I mean a lot of the places that we've gone with technology has been triggered by someone being frustrated by what they were doing, and therefore coming up with a better way, right?

Dan O'Hara:        Yup.

Evelyn:                 I don't know, maybe we all turn into the drones.

Dan O'Hara:        Yeah.

John:                 Yeah, I have a perhaps related question, which is a little bit different. Speaking of robots or artificial intelligence being creative, do you think that when they are creative, they receive the recognition, or that we would ever be able to give them the recognition for being creative that we would give to another human?

Evelyn:              Would they care?

John:                They wouldn't care, but I feel like I would not give them their due respect; because I'd say, 'Well, that was created by a computer.'

Dan O'Hara:       Yeah, and you'd probably give the designer or the engineer, or whoever's behind it; 'Look at the great machine they created,' right?

Evelyn:              Yeah.

Dan O'Hara:      Ferrari created great cars. It wasn't the car. We're going to have to look at that of what happens when a robot can make a design for something new and send it to a 3D printer, and actually have it created, and send it through [crosstalk 00:24:10] UPS service, for example, in Atlanta.

Evelyn:              ... Final delivery.

Dan O'Hara:       And have it delivered. There's a lot of things that it will be able [inaudible 00:24:16] make that next step, and then how much credit do we give them for that capability?

John:                I'm sure somewhere, somebody will be taking credit for it, right? Some human will step up.

Evelyn:             Absolutely.

John:               There's no shortage of that.

Evelyn:            As they always do.

Dan O'Hara:    You've got actually a real example right now, that discussion on Netflix and some of the Netflix shows are based on what we watch and when we turn them on and off. How much of the latest 'House of Cards' or any of the rest of that is based on human creativity versus just analyzing the data and go, 'Well, they like this, this, this and this. If we put it together in a different way, we've probably got a good format.' I think we're already seeing that somewhat of how do they apply creativity, but also understand what people like.

Evelyn:          Well, but then that does actually bring us back to that same issue of the ethics. Do we all become more ... Do we lose any of our humanity in the process of allowing what we do to be completely mechanized or driven by AI?

Dan O'Hara:        Yup. Short plug here, part of this research started with a paper we did on smart technology. That had two concepts in it, artificial intelligence and digital ethics. I think those go hand-in-hand. You need to understand what you're applying artificial intelligence, and then when do you make a human decision to say, 'Maybe this isn't the right time to put this offer forward, or make this decision, or maybe there's some downstream effects that the algorithm or machine doesn't understand all of the impacts of.' I think we need to keep artificial intelligence and ethics very much hand-in-hand as we look at these problems and opportunities.

Evelyn:            Well particularly as ... You had also mentioned to us that one of the implementation abilities for AI is in that process of mentoring an employee, and helping them with their training, their career future; that focusing on the analytics, the data, can actually tell us that perhaps there's not someone ... perhaps their supervisor or their manager is not the right person to actually mentor them through. If you're utilizing the data for the mentoring and ultimately, there are human elements that come out of, 'Well, no. I'm not interested in doing that,' but you need to be because the data says you are.

Dan O'Hara:     Yup. I know in Avanade and in Accenture, we are getting very big into instead of having career managers, having career advisors. The employee is responsible for their career and their decisions, and are they interested in going into artificial intelligence or specialize in financial services? All of those options are available, but it's now the employee that starts to make those decisions. Then, they need the tools to go, 'Well, if I want to be excellent at artificial intelligence, how do I get there? What's my development process?' In new areas like that, it may not be my boss that is the best one at giving that advice? How do I get that from different angles or different people, or different sources?'

Evelyn:            In doing some of the reading in preparation for this podcast, I actually came across a company called 'Valve Software.' I don't know if you're familiar with these guys, but it's a gaming company out in Bellevue, Washington. They have a very long process for hiring their employees. Then, when you get hired, you're basically told you don't actually have a boss. You're responsible for your own career direction. Essentially, in order to survive, they've developed about a 40 page handbook on how you can move your desk around the office, so if there's someone you actually want to work with; and then you have to decide what it is that you're going to work on. They've been around for about 20 years; very, very successful. Which, it's not ... They're not using all AI to actually do this, necessarily. It's kind of the same thought process, in a way.

Dan O'Hara:     Yes.

Evelyn:            Where it's ... I'm not going to be [inaudible 00:28:39] personally, because you work for me. I'm not really responsible for your success.

Dan O'Hara:    Yup.

Evelyn:           In this environment.

Dan O'Hara:    Yeah. I definitely think we're going to see more of that. Equally, we need to figure out who works well in that environment, and who doesn't. Some people need the task lists.

Evelyn:          [crosstalk 00:28:55] ... Yes.

Dan O'Hara:        And the to-dos, and things like that.

Evelyn:                 Yes.

Dan O'Hara:        One interesting thing of my wife is much more on the analytics side, but her company rewards people based on what I call 'playing well with others.' Every three months, the company gets peers to review each other. You never see it. You get your bonus based on your score on your reviews. Is the boss giving the direction, or is the peers that actually work with them all of the time the ones that should be giving the feedback and say 'Hey, you could have been a little better in this meeting. This training wasn't as clear,' all the rest of that. That continual feedback, which we are getting used to all over the place from Yelp to everything else.

Evelyn:                 Right.

Dan O'Hara:        We are rating and [crosstalk 00:29:40] recommendation.

Evelyn:                 Yup.

Dan O'Hara:        Society now. Why isn't that true of people as well?

John:                     Yeah.

Evelyn:                 That's a good point.

John:                     Yeah, it is very strange that ratings and peer reviews in the workplace, although quite often mandatory or expected, are not very welcome; but yet we do spend pretty much the rest of our time on the internet rating and reviewing.

Evelyn:                 True. Which, and I think raising that concept of rating is a great segway, if you will, to your Uber experience. As part of doing your research and compiling your thought leadership perspectives for Avanade, you decided to become an Uber driver to see what it was like to have a robo-boss.

Dan O'Hara:        Exactly.

Evelyn:                 Tell us about that; that experience and that process.

Dan O'Hara:        For everyone, I think if you're going to talk about something, you actually have to experience it. I had been talking about the Uber-ization of the workforce for a couple of months. I'd been talking about robo-boss for a couple of months. I put those together and go, 'If I actually want to experience a robo-boss, I should sign up for Uber and see how robo-boss that is, and see what interactions we could do. Back in April, I decided it's time to sign up. The amazing thing, the hiring process of a boss; I was hired or made an Uber driver in six hours. I applied at 11:00 a.m.. I did my license, my registration. I took the very short video training. Six hours later, they said, 'You're approved. Go forth and Uber.' All of us in corporate settings, if we know the person, they've worked for us before, maybe even at that same company, that is weeks long process.

Evelyn:               Right.

Dan O'Hara:        What did the computer know that we don't know in our existing systems that really allowed us to be able to do that? That was one example.

Evelyn:               Did they ask clearance type questions of who are you, and what do you do, and why do you think you should be an Uber driver? Or, did they just avoid that process and just say, 'Sign up, and you're in, baby, when you take a training.'

Dan O'Hara:        I think the assumption is you're there to apply, so now the question is, are you able? Are you licensed? How is your record? How is your vehicle? How new is your vehicle? All the rest of those things. All of that was done because I had taken the initiative to say 'I want to be an Uber driver.' Now, how can they make sure that I'm going to be a safe Uber driver, et cetera?

Evelyn:               We're going to take a short break and when we come back, we're going to continue this chat about being an Uber driver. ...

Evelyn:               Welcome back. We're here with Dan O'Hara of Avanade, talking about robo-bosses and most particularly right now, Dan actually became an Uber driver in order to find out what it was like to have a robo-boss. Dan, you've taken us through the process of signing up, which start to finish, six hours. You're ready to go as an Uber driver. You never dealt ... Did you ever deal with a human in that process?

Dan O'Hara:        I did not deal with a human through the whole thing, other than the passengers. What I enjoyed immensely about the Uber experience, both as an Uber passenger, as a driver, that there is a good community there. It was fun to talk to the passengers. Where are they going? How was their day? What are the interesting things like that. Then, how did I know where to go? Well, Uber sent me a text that says 'The Atlanta Braves are in town, there's going to be a need for more Uber drivers down here at that time,' 'Some of the festivals were downtown, so here's places that you might want to go.' Uber gave me some direction on 'Here are places you want to go.' It was my decision whether I wanted to go there, or someplace closer to home. I got to participate in that decision.

Evelyn:               Your timing, so the time period that you were working for them, you could just choose when you were on or off.

Dan O'Hara:        Yes. I still have a day job, so I did try and keep it a little bit more to the weekends; so that all the rest of it ... I literally could have said my day job's taking to me the airport. Do I want to see if there's any lifts going in that direction?

Evelyn:               To go along.

Dan O'Hara:        Et cetera, to do that. You make your own decisions, but you're in the Uber system that says we need to be great at customer service. One of the things is wherever the passenger wants to go, you are intended to take them. If I'm at the airport and they want to go to Peach Tree City, that may not be the direction I'm trying to go; but it is part of the service, and it's very clear in the training that that's part of what we're doing.

Evelyn:              That's where you go, right.

John:                How much of an effect did the messages that they were sending you have on you? Sometimes I imagine that they were somewhat of suggestions, other times it was command center information, or something like that.

Dan O'Hara:      The suggestions were good about where do you want to be and where do you not want to be. The more precise one was surge pricing. I did get into a surge pricing and go, 'Where do I want to go?' Quite frankly, the Buckhead neighborhood was one of the surges, so I can go up there and make two times or three times, or 1.5 times. You're taking a bat, is surge going to go up as you get there, or is surge going to go away as you move around. They are very specifically directing you in those situations, say, 'We want you here. We've got more passengers than drivers, and the passengers are willing to pay more.' In a very simple reward thing, as we think about the bosses. 'Well, if you do well, I'm going to give you the promotion next year,' or, 'Give you a better raise,' and all the rest of that. This was instantaneous. 'If you are here, you will make more money today than you would if you are here.' Some of them were nice recommendations, and other ones were more insistent with a specific reward around them.

John:                If you had to do the job ... Obviously you said that there was human interaction involved here, you're taking people to and from places; which is very nice. If you had to take away that human interaction, do you think it would have worked as well?

Dan O'Hara:      I don't think for me it would have been as fun. There's going to be plenty of jobs, and there are already plenty of jobs, long haul truckers, et cetera, where that's part of the job. You're supposed to do this for a long period of time on a very straight road, et cetera. That may be part of people's jobs today, hopefully that people interaction is actually where humans are better in most cases, than artificial intelligence. Maybe we become better at interacting and the humanity, and the creativity, and cars get better at driving, and things of that nature.

Evelyn:             When you were signed up, I gather that all of your information was through a downloaded app that came from Uber, is that right?

Dan O'Hara:     Yeah, yup.

Evelyn:            You just had that on your telephone.

Dan O'Hara:     I think that's an important thing to say. The Uber app, there's a driver version of it that told me where to go, things like that. I'm assuming most Uber drivers also have the passenger one up, because that tells you where all the Uber drivers are.

John:                     [crosstalk 00:37:19] Right.

Dan O'Hara:      If there's six cars already at that place, maybe I want to go someplace else. If there's ... I check by my house, and there weren't many cars at some places, so I drove down to places where I had regularly seen Uber cars, assuming that their system was smarter than I was, in terms of guessing where people like to get picked up, and close locations, et cetera. There is some of that direction going on, and all on the app. I'm going to do one side on it. When does this come to other professionals? Well, during the break, one of the personal assistants notified you of something, right? A lot of us are already carrying an app like that, that is reminding us of when our next meeting is, or telling us what the travel is, or the weather is, or all those other things. We're already starting to get an Uber or robo assistants in our pocket. When do they start directing us to ... 'Evelyn, you really need to be here on time.'

Evelyn:             That's right.

Dan O'Hara:      It's like that.

Evelyn:             What's wrong with you? ... Tell us about the reward system, though, Dan. Did you feel like ... Did the concept of being rewarded through a review, did it make you more chatty with the passengers, more friendly, any of those things?

Dan O'Hara:      I think I used some human judgment on that. There were people that definitely wanted to chat. There were people that wanted to sit in their phone, and do whatever they were doing. You have to use that. I will say that I am five star rated on Uber, so I think I did a reasonably good job at judging the customers and determining what they want. Uber versus Lyft; Uber's not a very easy system to tip on. You don't get tips in Uber, because everybody's going, 'Well, the benefit of this is one swipe, I close the door, and I'm out.' Lyft actually has in it the ability to tip, which after having been an Uber driver, please tip your Uber driver if they do a good job. You don't make a lot of money as an Uber driver, and they do a lot of work.

Evelyn:             We will remember that. We'll be generous in the future. ...

John:               Well Dan, it sounds like Uber was a really interesting experience. How does that experience relate to other industries and companies?

Dan O'Hara:     I think there's a lot of learnings from that, that we do need to apply to the rest of businesses. A clear one for me, and a lot of the companies I deal with, the ability to hire quickly. If you know the person, or you've gone through the checks, why does it take so long to get them into the systems, to get them registered, to get them trained? We need to change that. This society is going to more gig economy, it's going to more varied experience in the employment. We cannot use old techniques that say, 'Okay, four weeks from now, you'll be able to join us.' People are going to need to interact on a much quicker basis. I think all of that applies. I think we also need to figure out what data is in our system to make better decisions? Uber's making very good decisions on where the passengers are. Uber's making decisions on what the priority is, and things like that. I think we need to take those back to our businesses and go, 'When is the system, the artificial intelligent agent better at making a decision, has a broader view of data, et cetera, so that that decision should be made by a machine and let people do what people do best.' ...

Evelyn:            Is artificial intelligence, though, just to bring us back, is this really for a large company? What if I am a 100 employee company, or 50, or 200? It seems like smaller businesses can actually maybe even utilize that kind of assistance more than the larger ones. Is it available?

Dan O'Hara:     It is available, and you're getting to see more examples of it. I think in a lot of cases, it will be built into software and systems we already use. We use Yelp and things like that, and it's got a pretty good recommendation engine. I think you'll see ... and you're actually already seeing that with systems like LinkedIn. LinkedIn is a great location where we hire a lot of people from. Being able to use that as a small business or a large business to see where the network is, and who you may already know that has those type of skills, things like that, is very easy to enter at a very inexpensive level for a small business. I think we'll see more of that appear in our human capital management systems. I think we'll see it more in our supplier management systems of how do I put artificial intelligence in the process so that it's making better or different decisions for us?

Evelyn:            Probably also for things like leads, sales leads, and probably utilizing that data to be able to replicate, 'Here's my best set of clients. Therefore, help me to actually find out who the others are.'

Dan O'Hara:     Yeah. I think one of the things we're going to see is who is best for task. If you have five sales people calling on the Atlanta area, and you've got a new customer, who knows them from previous? Who's been in the same industry? Who has demographics that may match them? I'm about the same age with two teenagers, or things like that. As I look at that, it may not be just, 'This is your territory,' and call on everybody in it; but, 'We found in looking at the data, that if there's a personal connection,' or 'If you have industry experience,' or any number of variables that we may not even guess at, but analytics engine can determine due to reuse ... These are good ways to start to say, 'A person should call on this person because of an analytics engine saying it's probably the best fit.'

John:               Yeah, that's awesome.

Evelyn:            Interesting. We want to thank you, Dan. This has been a really excellent conversation. We hope to continue it, because if you decide that you're going to move into some other robo-boss experiment, we certainly want to hear about it in the future. We look forward to your continued thought leadership in AI and robo-bosses.

Dan O'Hara:     It's always a pleasure. It's a very interesting topic that we're just at the forefront of.

Evelyn:            If you'd like to hear more about Avanade, please go to their website at A-V-A-N-A-D-E dot com. You can also find their thought leadership research link on the website, so we hope you'll go and take a look there. You'll provide us with any questions or if you'd like to reach out and perhaps talk to Dan, we're happy to actually facilitate that. If you'd like to hear more about Trusted Counsel, please go to our website at Trusted hyphen Counsel dot com.

Speaker 1:       [Singing 00:44:20]

Evelyn:            We'll see you next time.

Speaker 2:       This has been 'In Process,' conversations about business in the 21st century with Evelyn Ashley and John Monahon. Presented by Trusted Counsel, a corporate and intellectual property law firm. For more information, visit Trusted-Counsel.com.