 Okay, we're back real live it's 10 o'clock in the morning on a given Monday, and this is a middle way very important show every single well every other Monday morning with Chang Wang he sets it up he's a contributor to this program and today we have Martin Hintman and Chang is now going to give us an introduction of Martin Hintman and also he's going to talk about the scope of the show Ray Chang. Yes. Good morning, Jay. Good morning, Martin. Thank you so much. And to be on the show back again. Appreciate Martin have the time to be on the show. The topic of the automation and our expert is Martin Hintman. Martin is a fine gentleman from London. And he received his bachelor and master degree from the higher education institutions from UK. I'm going to just spend 30 seconds to read his official bio bio. Martin had been working on the overlap between product and technology in the professional information industry over 30 years. During this time he has held senior roles in product strategy, research, design and the development. He has significant experience working with development in the teams with the waterfall, agile and hybrid processes. The current area of focus and expertise in defining the value that automation and AI can bring to data driven organization and the developing business cases that support investment in this fast moving and exciting industry. Now, please allow me to share his unofficial bio. His unofficial bio is Martin happened to be my first supervisor in corporate America, and he taught me so much, particularly how to survive and function in corporate America. And there are so many things I learned from Martin, but in our previous organization, we were three types of people who are business people. A lot of them who are technology people. A lot of them. And there is Martin, the marketing is a bridge between the business people and technology people. And here's my role model because that's exactly what I try to do. I try to be a bridge between the American people and the Chinese people, and I found translation is the most difficult part because translation involved everything. The cultural translation is not a Google translation, and Martin is a master of translate translation, it translate technology term into business people could understand, and then translate business jargons into the words and the phrases that technology people could understand. So we are very fortunate to have Martin today to share with us his thoughts on automation. I can hardly wait. I really believe in what you're doing and the whole concept that Chang described Martin. So can you talk to me about how you got involved in this something must have triggered your interest what was it. Thanks for your comments earlier comments Chung to kind. So to answer Jay's question. I think I had a very early brush with automation. When I was 1920 is my first nine to five job after high school. And I worked as a data analyst for a syndicate a reinsurance syndicate of the Lloyds of London they had just implemented a mainframe IBM system. And my job was to read the reinsurance contracts and extract seminal pieces of information from those contracts. And I went into a structured database so other reinsurance agents and brokers could find it on the mainframe computer. And I used to think as a 19 year old is kind of interesting what they're asking me to do, which is essentially look for pieces of information and then write it out on this green green sort of grids in with a biro a black biro for data entry person to then type it into the IBM mainframe, and I just saw what a waste of time. And then when I joined a duplication of effort. So as a 19 year old. I always had this appreciation for efficiency around data. And then when I joined Volta's clue are publishing company a couple of years later, then read publishing companies, so much duplication of effort. And so I grew increasingly frustrated with all of the human driven decisions around data, which were getting in each other's way. And to fast forward 30 years. I found, I got access to a new wave of automation tools, which allow those subject matter experts who understand the business process, rather than relying on data analysts data scientists to take control of some of those inefficient tools and really sort them out, really define the process bring these automation tools in and get rid of all these middle man in the middle functions, which were causing delay inaccuracies, additional costs. So that essentially that's my my arc into automation. In practicing law, we had one partner in the firm, who was wild about summarizing deposition transcripts, and he would recruit a young associate in the firm to read the transcript, and then dictate what was in the transcript in a memo. Okay, and it took it took this associate all day long to read the transcript, and it came out in a memo which in turn had to be read. So you had, you know, several people involved in a transaction that is accomplished now. Instantly, by searching for keywords, right. And it was it was extraordinary this is not that many years ago. Yeah, and I guess my reaction is, you can say that Martin but, but the world is still way behind. There are still lawyers out there that are asking associates to summarize, you know, 1000 page transcripts. Yeah. Now, don't forget the importance of context. So, as Chung correctly defined earlier, you need to view information through a particular lens. There is translation, not just, you know, English language translation, but business context translation, as you're reading these documents and turning them into the next form of document which needs to be consumed by the next subject matter expert in the in the design of command or the process. So business context is, it's very early in the day of automation for understanding quickly, or quickly enough, the business context which needs to be pinned down as you start to abstract or extract that data and then pass and it's very early in the days of automation for the machines to really understand business context so humans are going to be needed for this translation work for quite some time to come. I believe. Oh my goodness. There I was thinking that humans would be unnecessary. So why don't you do your slideshow Martin I know you have some graphics and maybe you could explain them to us. Yeah. So I think, you know, you, you're obviously the host Jay but I do have a particular point of view of where automation is. And I want to point to some of the early, early players through through, you know, I'll tell a few stories. So if you could pull up the first slide. I'll just give a summary of my last six or seven years. So I am a consultant. I work with Dow, the very largest Dow companies, top 30 and also federal agencies. When they, when they are when they parachute me in so to speak, they're having a lot of trouble making progress with automation. And my role is normally an architect I'm a solution design architect, but I'm also a program manager so I interact directly with the business or the stakeholder for the project. So maybe multimillion dollar projects broken into various, you know, platforms and data components, but the automation has to sit on top of all of it. And so the first question I asked is what's your goal, you know what is the you know if I could bring you automation in the first 12 months what is it that you're looking to achieve. As a former product developer, I can go along with somebody who would say to me, we're trying to reimagine our entire business model, I would love that to be the response. But very rarely do they say that what they say is we're looking to save costs. We have 30 people doing this one task we would like to reduce it by a number of five. So automation can do that. So that slide I've just shown you is really at the far left. It's very tactical, and it's very piecemeal. They have found a process which takes 30 people to do. And they want to reduce that 30 people down to 25, but they have no interest in going either upstream or downstream to connect with other people who may be doing parallel type of work. These are very large companies. It's not that they don't have the imagination, but the way that they are funding these projects, it's very, very tactical. Now where I would like to go or where I would like the industry to go is to say yes those types of tactical projects need to be done. And strategically speaking, how does this organization want to change this business process to improve its services to improve customer experience, and I'll come back to this point later. So that so that slide is really just a summary of my work over the last five or six years. And to your point, Jay, I think you made earlier that you feel it's very early on in the automation industry, and I completely agree with you that there's plenty of opportunity to use automation in much more dynamic and more exciting ways. In our real estate practice, we always say real estate is not about land. Real estate is about people. And I would say that automation is not so much about automation as it is about people. Do you agree? Absolutely. I've got my last slide is on this. So that that's going to be a central theme through all these five slides. That's the combination of process because automation does need, you know, these are computers, they need to be told what to do. And the, and the way you tell them what to do is to first define your process, and then codify your process you've got to write it down and put it into the ones and zeros, which the computer or the platform you're working with can make sense of. So that's the first, that's the first part of the three legged stall the second one is data. It has to be readable. It has to be consumable by the applications by the platforms by applications which the subject matter experts used to do their job. And the third part, you're quite right to J is the people element. So what are those people doing performing a creative cognitive role. And if they are, how does automation, or the processing of data support that cognitive creative role, if that person is not doing a creative cognitive role. If you're doing something which is rules based repetitive, something which, which a computer can do better than the human, then that's the kind of job that will be eliminated or certainly avoided through the use of automation. So when you have that conversation around the people element, you've got to be very clear. What is the subject matter expert trying to do. What's the support that the automation can provide. And finally, is there some change management here where the split of menial repeatable tasks and creative tasks need to be rebalanced. You know, it's, there's another side to the people thing. And that's the resistance side. You know I've been doing it this way for the past 20 years. Why should I do it differently it works the way I've been doing it. Don't tell me about new thing. A good example is medical records, you know, automated medical records doctors don't like that they don't like to do it. And that that that bleeds off to the staff who don't like to do it. And the result is that automated medical records had not made as much progress as we had hoped. And that is probably so in every capacity I remember, it almost, it almost got it was political. When the courts in Hawaii, starting with the lower court there, you know, we're faced with automation of their, you know, records. They opposed it the clerks opposed it the judges opposed it. Nobody wanted it, and it took much longer than it should have taken to get them to accept it. And I'm sure that's still the case isn't it that's the other side of the people question. And I think there's a, there's a multi layered way of attacking automation if you're an attorney in the medical medical field or medical devices field, and you're you're familiar with some of the compliance privacy HIPAA. There's a lot of arguments for saying that an unattended bot that's going on a rampage in your PII database is something you you can't even you know you can't go there. So and I do come across this what I what I tend to do just to you know is a kind of a rule of thumb is when I it's very easy to see somebody's role being reduced because they're doing menial tasks. What I do is take them to one side and I you know I've been doing this recently actually taking them one to one side and giving them a career development opportunity to become what they call a bot line manager a BLM. And it's actually a term of reference to somebody who is the domain expert in the process, even if they're doing repetitive repetitive tasks, they still are the SME the subject matter expert for the process. There's a variety of job development opportunities for them to take ownership of not just a single bot, but a, and I'm using bot to refer to a piece of software, which understands the process and will perform those tasks pulling in data manipulating the data and then creating an output. There's a lot of bots a lot in this talk, but somebody has to manage the bot, and it can't be somebody too high up in the organization. You actually have to know the process you have to know the exceptions the problems, technical issues. And it's a great opportunity for somebody whose job is changing, because they no longer are employed to do those menial tasks to become a bot line manager, and it's, and it's multiple bots. The other show is very interesting is that you, you, you had before, you knew the technology but not the business. Now, to be efficient, you have to know the business. And so you bring somebody in who knows the business and then you'll get a much better result. Well somebody who's in the business one of you know your bot managers. You definitely should know the business. So what's missing with the bot manager is to know the bots. Yeah. And, and so if you can train them to do both, then he or she would have a career outside that particular office outside that particular process because now it's a merger of those factors within that person. It's very persuasive argument when you take them aside. But I think I want to ask Chang, Chang, is this something you've done in your office. This is something you've seen done. Is this something that you support, or do you oppose it. And how about your staff, and why not have a small legal firm to all of this and have a benefit by it. Well, it depends on what type of law you practice. And I teach constitutional law and I practice art law and the legal law. So I work with artists and the immigrants. And those people are hard to manage. And because, you know, I expect, you know, when the young people ask me why don't I want to go to law school. My answer is, it depends. And in most cases, don't, because most legal parties areas will be automated and in the coming years. But the three areas I'm most interested in constitutional law, art law and an immigration law, they require a tremendous human involvement. In this case, I think that correct me from the wrong marketing. I think it's very difficult to, to be automated. And because you cannot it not like a mortgage application, and for the immigration case, and for the artist. And so all of them involve human tremendous human judgment. FME subject matter expertise requires of decades of training and human interaction with both Marty and Jay, both you said about people really interested me because I do want to throw this question back to you. And for the automation, what would be because we hear these comments all the time, the data is a new field. And, and because the both automation and AI, all of them require, you know, tremendous astronomical amount of data, and who has most amount of data. China, and 20% of human beings are in China. And because of the, the, the pandemic, and they collected just a unbelievable amount of data and people are so easy to be managed. If you ask, you know, somebody in London, and in Hawaii, okay, gave me your footprints in the past 14 days, so so I can check whether or not you into an infected area become a high risky person. So, you know, a person of British or willing to do that, but people said, okay, yeah, I know that you've already made really five or six really important points Chung so can I can I kind of step back and break and break this apart so the first the first point you made which is really important is you brought in the summarizing here, the process of law, and the process of law, you know, is it's a moving, it's a moving target in its own right. And the emergence of structured data and when I mean structured data that is controlled either from the courts, or from providers like Westlaw and Lexis on that data, as it grows will will deliver insights. And new ways of looking at the law, which I think will impact the process of law. So whether that is looking at the structured nature of case law, or it's looking at opinions, or it's looking at the results of litigation. So that data is being amassed already by the major players, but also from the court system itself. You know these courts are starting to get on the bandwagon of structuring their data. And when they look at their data, it will bring insights and recommendations, which may impact the actual process of law so I think it's not a, I don't think the law itself is a fixed a fixed object but it's going to be impacted by data. Now the going on to your next that goes, I'm sorry for the digression but that goes to everything. Yes, this process you are describing throughout this discussion is iterative. Yes, you can't get in there and say okay we're going to automate it this is the new world that's it, and then wait and wait 20 years to change it. It's going to have to be iterative all the time. Right, right, but where I don't agree with some of the commentators in legal technology is that somehow AI is going to tell us what needs to change in the law or some of the practice of this. I don't believe that at all for a second. What I think will happen is some of the leading law firms or even the courts themselves will start codifying their own processes. And it will be a combination of codified process and the data analytics or the data insights together, which will have the impact. In these big sort of IBM Watson initiatives where they're just going to suck all the data they can from the US legal system and somehow eradicate the need for lawyers that that is just not a future I see it as feasible. There is work to be done, both by the court system and by some of the larger law firms to clean up some of the practices and codified it codified now whether they see it as a strategic advantage. So one court system may invest in their own state based processes, or it may happen to the federal level or you may get a major player, you know, a large New York based firm who decides that it's going to be a strategic advantage for them to create automated legal services at the lower levels, going back to what Chung was saying that the law is, you know, can be complex needs interpretation, but some some some areas of the law don't. So who's going to, who's going to start automating those processes, and how will they describe the value around that automation. I know I've been, I found that website for you Jay. In terms of you know some of the emerging websites for helping individual consumers push back on parking fines for example, I mean it's not the law, but it's the, it's the thin end end of the wedge. There's plenty of opportunity to go up the value curve for law firms to automate some of these processes ahead of, you know ahead of where institutions, like, you know, institutions within states or federal, maybe it becomes a commercial advantage first so I, I think there's a lot of work needed to be done. So I was to put my day job hat on this is not a good use case for your automation. So, this is something which I would push back on to say you've got to be much clearer on what you think the positive outcomes of this word cloud or, you know, or whatever the solution is or not. It's not it's not a crisply defined use case. So my role as an architect it was to say, this is not a good project. The other thing I'll just mention is that I do, I do know a couple of other than Chung, I know a couple of talented artists who helped me on these kind of conversations. Damian real from fast case and Laverne Pritchard who's a local entrepreneur innovator in Minneapolis. And one of the first things I would do is go to those two guys and give them what Jay has just said and take their opinion, but from a technology point of view, this is not this is not a good discussion. I would I would back away from this. I just understood and let me add that understood but we've agreed that this is all about change including the architecture itself, including the technology AI today who knows what tomorrow, and who knows, you know what happens when you have a world of not 10 billion but of 10 or 12 billion hopefully we get there. And, you know, we may need greater efficiencies we may need to plant this kind of automation model everywhere in order to have society work. And so I say to you, can't. Can you envision the change within your own specialty. How is your specialty going to change going forward. So we probably don't have enough time to go through my slides but I've got a couple of slides on examples of some of the players who are using automation to reimagine their business model and that's what needs to happen in the law. They needs to take a step back. They need to define the ecosystem of all the players, the data sources what what expect is expected to happen to the data what a positive outputs what a negative outputs they need to do a full process discovery process analysis. View of the law, very difficult to do, but but not not impossible. I think that's something from that view. The question I think you're asking Jay is how much change. How much change do you want. Do you want to go from zero to hero in 12 months 12 years 120 years. So I think the pace of change. There are lots of supply chains in the legal industry. You don't you know you don't want to throw the baby out with the bathwater to use a user an expression. Be careful and do the analysis absolutely do it from a high level, all the players all the supply chains and then look at the pain points what things aren't working. What could save costs what could create new value. I've got a couple of slides on this if you go to I've got a couple. So the the Uber business model is a good one to start with. I'm an Uber driver to really flesh out my own thoughts about the two personas in the Uber ecosystem. There is the persona of driver, and then there is the persona of passenger, and I was convinced as a member of the public that there must be something tying them together and there must be an automated ecosystem, which has been put in place to improve the efficiency of essentially being a taxi. And that's exactly what I found, both as an Uber driver passenger, but also somebody who had contacts at Uber and I actually started asking some questions about the technology and absolutely. They have, they have started out with a business model, which was defined very early on. And the question was, how are they going to use data to improve the management of safe driving and the service delivery to passengers, but at the same time, making sure they cost cost out the drive in a effective manner, or the journey I should say the cost out the journey. So, you know, this is what that that Uber drawing from Tim O'Reilly is really what needs to happen in the law. What are the main components to a successful successfully run legal system and do a deep dive on all of those components. And of course data is going to be the heart of it but exactly what do you want to happen to the data, you know, is it structured is it unstructured. Do you need to pull information from other sources which you don't have access to now. You know you talk about public opinion, or public opinion is a very sort of fluid type of data but there are tools out there, sentiment analysis and other tools, which can give you more of a codified sense of what public opinion is. But are those tools used today in legal system probably not but should they, I don't know. That's a question for people, the stakeholders of the ecosystem. You're selecting a jury I think they would be very valuable maybe that that's already the case. Right. Yeah, I mean this is this is really interesting and I wonder I wonder where where it all goes to me. You know the world is like a fellow. It doesn't appear at the office. He's at home. He's by remote. He pushes a button. It starts to firm up. He goes place golf. And there's nobody else worked for the firm. Yeah, I don't know. Yeah, I don't want that I don't want that vision. I go nuts. Oh, that's that's that's the ultimate. Okay, but we are we are on a path to get there. So I don't know, I don't know. I'm not sure I, I don't think I think there are others like me who don't want to play golf. So, I mean it's not I'm nothing against golf is just that I get huge amounts of intellectual value from pursuing something which is on the edge, something on the edge of, of, you know, of changing the world as we see it. I love that. And I love these conversations with yourself Jay with Chung and others. Would we have the same conversation at golf or maybe I don't know. But no I think that there's, I think humans have. I've got actually I do have a slide on this. If you can go to my very last slide I'm jumping ahead now but it's a relevant point. So in relationship, you talk about, we just press a button the business runs itself. I don't see that I see a new synergy between the human human creative spirit, and then those tasks, which really are beneath the humans capability. So machines are good at certain things, and it's not just turning a one to zero zero to one. There are patterns which can be created by machines, which a human has to interpret. But the all important lens to look through is context. Right. That's a very difficult thing to codify. And so I think you'll have an emergence of a new type of subject matter expert who expects there to be automation on their first day of the job. And the question is, how do I plug the right type of automation together into an ecosystem to get the positive results that I want either to provide to my customer to provide a service. You know, so there's a lot of design thinking in building automation libraries and and putting those automation components together for a higher for a higher goal. And so that's higher goals we have a question from a viewer. And you know as as the journalists at UH say, the most important news story of our lifetime is climate change. And the question from the viewer is how can automation speed up solving the planet's enormous climate change problems. You're talking about business context, but there are other contexts, larger in a way, you know and more more potential than business, I think, what's your answer to that. My answer, I guess it's twofold. First of all, climate change I think is a very difficult ecosystem to define a bit like the process of law but it needs to be defined. I think there is an, there is a need for AI in climate change data can come in structured form and unstructured form. And that data which is difficult to quantify and I think climate change is is one of those areas where there's going to have to be predictive modeling and predictive modeling is something where you actually don't see something happening today, but through correlation of machine learning and statistical modeling you can see it's going to happen tomorrow. I think it's a combination of of science policy machine learning modeling, which, which a government or, you know, an institution needs to get their arms around and then speaking as a product manager, provide a brand. The data from that model means something in the journalistic world, you know I was listening to NPR today. And we had, you know, there were extracts from the BBC extracts from there was a US consultant, and there were others from representatives from all quoting from their own sources their own academic journals or their own data models, but there was no unifying brand, which, which gave each of those sources any sort of hierarchy, they're all opinions. They're all based on science, but there was, it was, there was a lack of cohesion amongst all of the protagonists that for for change to prevent climatic disaster. So I think there's a huge opportunity for a unifying model, which plugs in data analysis, great literature, academic article, and makes make something out of it as a brand and use that brand proactively to change policy. That's my own. That's my own two. I love it. I love it. And I think that's the future. And it's building credibility. It's using tools to build credibility so anyone looking at it can get a handle on how valuable this source is. This is so important. This would be important in so many ways in so many areas, but we're almost at a time chain. Can you, can you do what you always do. Can you summarize that come up with the, you know, the 50,000 foot take away on this. I will try, but I will, I would just say this, you know, a marketing has been my friend for 15 years and I learned the two most important things for marketing. The fourth is lifelong, be a lifelong learner. And then I met very few people as intellectually curious, curious as marketing, he always interested in new things and different subject area different to discipline. He always asked questions and learn from people from various background, and I really appreciate that, that intellectual curiosity, and I try to, you know, follow that model as well. And secondly, it'd be positive, you know, you probably heard that me so this are passive aggressive. I would call Marty is a pessimistic optimistic. So when there is a crisis when there's a challenge and he immediately realize these things. We are in a precarious situation and we are in dangerous situation, but then quickly he analyzed and he analyzed the situation and he and solution, and he sees the opportunity so that's why I call him a pessimistic opportunity. I like it. So that's it. I learned from Martin, and I really delighted that we, we are on this panel, and to hear as for the opinion from Martin, and we have, we haven't finished everything we want to discuss today, but very much look forward to our next conversation. Thank you, Jay and Martin. Thank you. Thank you, it's very provocative discussion, and lots to come and we could, we could do this again and go much further, such as the issue of using technology for better or worse. We got to that. Yeah. And the private the privacy thing and persona is a definite candidate for another session, a full 30 minutes on that for sure. Thank you, Martin, Martin Hinman and Chang Wong. I hope you do send from the session Chang, and we'll be back. Thank you so much gentlemen. Thank you. Thanks for having me.