2019: Panel 2: Through the Lens of Industry: Integrating New Technology and Implications for Work
So, the, building off that film and building off what we heard this morning in the first panel, the next 30 to 40 minutes will be, really emphasizing. Kind of the industry side of things we taking you through retail, health. Care, and mobility. And, really thinking about how things have changed whether it's tasks, skills, workflow, jobs. Leading this panel, will be a co-chair of the task force david mendel. No better expert, to help help. Moderate, this as he sits across both academia. And industry. As he's the ceo of his own company. Humatics, corporation. And i, really excited to kind of hear from him and hear from these panelists. And. Welcome them to the stage so thank. You. Anywhere here is. Fine. So that was a great introduction, for this uh the panel, um. One of the best things about. Mit, is its uh, general, lack of ivory tower separation, from the world and we. When we talk about these issues as we have all morning we talk about people who are, uh out there working, out there seeing out there out there doing things, as well as the researchers. So uh very happy today to introduce. Itali dayan. Executive, director of strategy. Research, and clinical operations. At, mass general and brigand, and women's hospital. Center for clinical. Data science, very much related to the, regina, bartlett, story we saw, up there and clearly covering. Very interesting, issues in ai and healthcare. David johnson who's, vice president of production. Engineering, and new model quality. For nissan, north america. Which i think translates, that. David owns, most if not all of nissan's, factories, in on this continent. And, dr zainab, tom. Tan. Who is a professor of practice, at the mit sloan school of management. And an expert in retail, and supply, chain work and, author of the the really quite revolutionary. 2014. Book, the goods good job strategy, so, uh. Welcome italia david and zen up, and um i thought we'd just uh open uh, with discussing, uh, technologies. And. In your areas, of. Healthcare. Manufacturing. And mobility. And. Retail and supply, chain, what are the technologies. That are interesting. What are you seeing, out there that's. Changing and affecting, how work is getting done. Type. So. Maybe, selfishly. I would. Allude to artificial, intelligence. Which is one of the big um. Technological. Advancements. Or whatever the application, of ai, to healthcare. I think we mentioned, earlier in a discussion. That in many. Fields and industries. The issue, is about. Uh. Still. Implementing. Computerized. Systems. And being able to actually aggregate, data. I think in the, healthcare, system of, 2019. And going into 2020. The issue, isn't, any more of the existence, of data but rather the explosion, of data. And the ability, to make, decisions. And to. Reach the right conclusions, based on that data and one of the key ways of doing that is by applying, ai. David. And in the automotive, industry it's quite complex. And you've got basically, 30 000, individual, components, coming together to build a vehicle, so all the new technologies, that we see today, are going to have some level of impact either on the front line. And the final manufacturing, plan or out through the supply, chain. But the two that i would say, for me are having the most profound, impact, are, the ai, big data analytics, machine learning. Because that shapes how we see our current performance. And then also, gives us the ability, to predict our future performance, and react. On that, well ahead of time versus, when maybe bad things happen within the production, environment. And the second one is virtual reality. Virtual reality probably hasn't been talked about much. Here today, but. Whenever we look at the the production, environment, most of the time we are making decisions, on new product introductions. Whenever we see, the physical. New product. Virtual reality, allows us to immerse in that new product development. Well ahead of time when it's only lines on screens, we can take the technicians, on the front line, immerse them in an environment, and include them in developing, the processes. To build those new products, and also augment their training well ahead of time. Which allows for a much more smooth rollout of new product. Is it okay great so i would like to take a slightly different perspective. And talk about technology. And people at the same time please, because it's amazing, how, how many leaders think about technology. And people as separate things but technology, affects the work of people the the, jobs of people. So, in my world of, retail. Technology, has always had a profound, impact, on, on retail, the work and and people's, lives yesterday. I had jim senegal. Who is the founder of costco, in my class and some of my students are here in this room. And, his point was technology, has always been a huge factor. Many years ago when you would go to a checkout.
Costco, Would only accept. Cash, or. Check. Checks because the credit cards. Were a, too expensive. Um the transaction, fees were so hot too high. And b too slow. Uh but over time you know we've seen technology, develop and now they're much cheaper, and and also much faster so that's what costco uses, so there are lots of incremental, changes, like this that's happening in the retail world. Um. But the the, one technology. I think that's transforming, it and let me ask you in this room how many of you have ordered something online during the last week. Yesterday. Exactly so the biggest technology, that's changing retail, is e-commerce, and everything, that has to do with on the channel. The the. You know you can you can order something online, you can order something on your phone and expect delivery in your house within two hours so that has been a transformational. Change. But most of the transactions. In retail still happens, inside the stores. Almost 90, of transactions. And when you look at both the customer, experience. And the employee experience, inside retail stores, that hasn't improved all that much. Customers, still complain, about the same problems. Stockouts, merchandise, not being on the shelves. Long check out. Dirty stores, unhelpful. Employees. And when you look at the, employee, side. We have a huge, problem, in which retail, is the largest, employer. In the united, states. And, it offers bad jobs, with low wages. Unpredictable. Schedules, and people really being used like widgets, one of the retail workers told me. I we are throwaways, who are a dime a dozen. Just human robots. So i think there's tremendous, opportunity. To redesign, the work, and improve, increased contribution, of employees, so that they can have higher wages, and more contribution. But right now, we don't seem to be deploying technologies. Like that, so um maybe to start. Follow up with using up on that, how do, we. You see or how should we be using, technology. To redesign, work. Transform. Tasks, or jobs. We learned earlier this morning they're not exactly the same thing. And, what are what are either examples, or ways that that can become successful. So i can give you i'll, give you positive, examples, and from from companies, that do this really well, um. The the one thing that they do, is there's a clear, purpose. Of what the technology, enables, them to use so one of the best users of technology, i know in retail, is a spanish, supermarket, chain, called marcadona. And when they think about technology, they say, how can we use the technology. To improve, either the employee, experience. Employee productivity. Contribution, or the customer experience. So many years ago they decided to automate, their warehouses. 100. Automation. And the premise, behind that was, people were hurting their backs, the job was very standardized, anyway. And they said we don't want to ask a person to do what the machine can do, so when there's a clear, benefit, to either the customer, or to productivity. Or, to the, employee, experience. That's, that's the that's one good reason to use our technology. But on the other side uh when you look at some other companies. They're using what we the mighty now called so-so technologies. Right there's no clear benefits, to either the customers, or the employees, or the productivity. But they're just they just seem cheaper. And the drawback, of that is. Now you're doing something that doesn't improve your operation, all that much but management's. Time, is being used for that technology. So the one thing is like why are you using it what's the purpose. The other thing that i see among companies, that are really doing a fantastic, job in deploying technologies. Is. Involving. People. In the design of these technologies. So technologies, work great when they are complements, people, and they enable them to, to create better value for their customers do their jobs better. Why not involve, employees, in in designing, these processes. Why not. Involve, employees. In. In in deploying, these practices. And rolling them out so i think, the enrollment, of people.
In Both the design, and, scaling, rollout, is what i see as among the good companies. David you mentioned ai, and and virtual reality, as some examples, or or ml. Um. How, have you found. Uh, what makes it successful, at nissan to roll these technologies, out either for the, managerial. Level, in the case of data analysis, or, for the front line workers, in the case of task, design, and analysis. I'll give you an example, on, machine learning and how it's impacting. The business, from a productivity, viewpoint. As well as the other frontline workers. With within the. Smyrna facility. Which is a, fairly large, vehicle plant about 650. 000, vehicle a year capacity. We've deployed. Predictive, maintenance, analytics, on 1500, robots within the body shop. Now. These robots, now stream. All sorts of different, performance, metrics. Torque temperature, vibration, those type of things to the cloud which are then analyzed, to predict, when that machine may fail. Well this gives us a huge benefit, because we can go in and work on the robot, on our own time. Versus, on production, time. And. Of course increase our overall productivity. And we used to have some level of predictive, analytics, that we did but it was all done manually, we had three maintenance, technicians. That traveled around, inside. The shop, they would take grease samples, they would take measurements. And then we would do some rudimentary. Analytics, and try to predict when we need to change out certain components, on the automation. Well now. Those three people don't have to do that job. And that was, a relatively. Low value. Added, task, when you look at the grand scheme of the operations. Now those three people have been redeployed. Looking at new technologies. That can be, deployed in the manufacturing. Environment. And help drive, us, forward. You know and i think. What was said on so-so, technology technologies. That's something that we've got to be very cognizant, of whenever we look at the the manufacturing. Environment, and not deploy. Technology. For technology's. Sake. And involving, all workers, throughout, the management, chain from the front line employees, to the, executive, staff to ensure that we're pinpointing, the right problem. When we look at technology. First. And then try to find a problem to solve with the technology, that's the wrong way to go about it, we need to find what the problem, is, select the right technology, the right people that know the problem well and know the technology. Well. Pair them together. And use that synergy. To drive forward with the proper solution. Now ty in healthcare, you have some special constraints, both around the, human dimension, of the work itself and also, regulations. And privacy, and, many other dimensions. How have you seen within the partners health care system. Uh a tech, ai type technologies. Adapted, or adopted, or not. So, um, you know, ai, and healthcare, has, many headwinds. To it first of all, if you compare, it with the automotive, industry, or retail. All the legal, frameworks. That govern, healthcare, are built to. Slow down innovation. And always prepare for a worst-case, scenario. You'd rather. Have from a legal standpoint, you have you'd ever have people receive, less skill. And people receive more care but some of that can be erroneous. Um. The issue, of that comes, even more into play when you go into. A hospital, setting which is typically, a, high stress, environment. In, which. Healthcare providers. Doctors. Surgeons. Nursing staff etc. Need to make decisions which are always, under, a shroud, of mist. We don't actually know what's going to happen in many cases.
And In fact much of the biologic, mechanisms. That govern, our health are not elucidated. Today. These things mean, that, um. The appetite, for experimentation. And the appetite, for. Scaling, out processes. And actually moving them away from the human. It is fairly diminished, across the system. Uh the way that we deploy, ai, in our, environment. Starts from first of all and i think you you said it very well. And both of you to be honest, uh. Talk with the people and identify, the problems. The best solutions, to date haven't been the most sophisticated. Ai, algorithms. Or the products which are. Seem as transformative. Throughout, the entire. U.s, and beyond. But have been things that have been developed, by researchers. Based in the institutes, for very practical, problems. And, um, deployed, in a clinical setting in which in one service, and then maybe two services. Maybe, three services. Now, uh, from a, point of innovation. And especially in the terms of roi. And actually investing, in innovation. That's a very challenging. Story to tell investors, right. Uh, because you need to do a lot of homegrown, solutions, and some will stick and then some will scale, and some will evolve. And this is like very, upstream, r d like much of, medical, research, in general. Uh which on one hand. And on both ends really makes the hospital, into the ideal, environment, to develop these solutions, from the people who would actually. Use that. Uh, we saw, dr, constance. Lehmann, and dr regina, bauzelai, before that on the film here. Dr, lehmann, is a breast. Radiologist. A women's health. Advocate. She's not an ai developer, and yet she. Collaborated. With a phd. In computer, science from mit, and develop a product which has a potential, to change. Uh, women's, health screening in the u.s and beyond, that. Um i think it's a great story, i think we're a bit. I think we're in a very early stage. Of that disruption. And i'm not sure if it's going to be, a disruption. As much as it could be in other fields giving the large. Level of heterogeneity. And differences. Between, cultural, tendencies. Medical. Practices. Human and tropometrics. And human disease, across different geographies, and systems. But even incremental, changes, can have a big impact, if we look at the world through the work that people do and through the problems, and say how do we solve these problems rather than saying, how do i apply this technology. In this environment, we could. We could go a long ways in all of these industries, it seems like, i absolutely, agree with you, and, i i think the unique, factor, here is that, um. In order to maintain. Ai. Which will continue, being performant. And maintain, some robustness. To change, or adaptation. To changes. It's an ongoing and very involved, process. So indifferent, from maybe in some places where you can say i've really built a system that knows how to heal itself and knows how to grow and i can deploy, that. And hopefully, i won't create the fascist. Chat about as we often hear about. And if it is when you can. You know stop the bot at least at this point in history. Um, in healthcare you'll need to continue, being involved with upkeep, understanding. The results. And. When we actually deploy. Solutions in the hospital. One of the key characteristics. Is that the, way to monitor, them, and continue, reviewing, the results is often developed, by the staff. Who actually does that because every, every person would have this different dashboard, and one size will never fit on. One of the things that someone told us when we started, this uh task force on the work of the future. About. Uh. A year and a half ago. Was. You know everybody's, going to focus on robots, and ai. But it's really about the computers. And, we were talking a little bit, before we came on stage here about. Many applications. Where you have a simple ipad, or a simple phone. Based. That all it does is present a document, or a form. In a place or in a way that's more convenient, than, a piece of paper at a desk. And. You can say that simple you can also say. That's. 10 years, ago technology, maybe the tablet is introduced. And it takes that long for these, these, devices, to really proliferate. At scale. And i wonder if each of you may have some stories, about. Just um. The slowness, of, proliferation. And at the same time. Once it really happens, how widespread. And, profound, it can become. Yeah i can give an example, of a technology.
I Don't know why it took so long to do this um, actually in this case i think it was the ceo, who had a bad customer experience, and that's why the company ended up fixing, it, um, but since i gave a costco example i'll give a sam's club example, not to be. In, to be neutral in the in the industry, so sam's club recently. Um, implemented, this thing called sam's garage. And, when you take your car, to the warehouse. Club. Um. You have your tire that needs to be changed. And previously. The frontline, associate, will. Get lots of information, about you, about your car. And then figure out what tires, would go with your car but they would put in the information, in one system. They had manuals. They had lots of documents, to look to see. What tires, were actually, fit with your car, and then another system to look at the prices. This whole process. Went anywhere, from 30 minutes to 45. Minutes. Just to figure out, what tire. Fits your car, right, um. And they realized, we can totally redesign, this use very simple technologies. Computers. And and put all those manuals together. And now that process, takes 2.2, minutes. They had over 50. Increase in the throughput, time, they had an increase in their member satisfaction. And and also increase in how associates, feel about their jobs because now. Their job is so much easier. But now they can be advocates, for the customer, they can help them compare, instead of wasting their time trying to figure out the different tires. They can see all these different options, and talk about which one would work but best and and how they could pay for it etc. So this is a great example, of one that, is solving a real problem. Improving the experience, of the associate, as well as the customer. Uh but it's a really simple technology, and again we. We have an opportunity, to look at the world through the lens of work and see what how can we redesign, it in a way that's a lot more efficient, and and, better for. Everyone, involved, so that's one example. David do you have examples, from from nissan, yeah i'll give a uh an example, of our, frontline, supervisors. Every automotive, manufacturer, has some level of production, system we have, the, the alliance production way within nissan. Which requires, frontline supervisors.
To Do certain audits. And their particular, zone on the manufacturing. Line. These were, traditionally, done as paper audits and then they were. Transferred, into a computer, database. Once the, the paper audit was complete. And it tied up a tremendous amount of the supervisor's. Time. And a group of folks got together within, our, in-house, apw, group and says you know we can take, ipads. Tablets. And. We can do all of this work on the tablet have it seamlessly. Updated, into the database. And it's going to free up the supervisor's. Time. So we got the the group of the apw. Team together, along with the manufacturing. Systems, team which is, an in-house, i t team if you will. And they work together to develop, the supervisor, tablet system. This tablet system, although it's very simple, it's basically, the same audit that was being done before. But, the time that it saves. Is now given back to the supervisor. To have. More dialogue, with the workers online, develop better relationships. Look for, solutions, to either ergonomics, or quality, issues that may be happening. And you know it's a simple technology. That we use in our personal lives every day but it had never been deployed. Into that environment, because, of the most dangerous, words within manufacturing. We had always done it the other way. We'd always use the paper copy so that's what we're going to continue to do. Well. Innovative, people got together and said no, there's a better way, let's change it, and you know the pace of adoption. Was slow we had, pilots, that would, use and feed back, and now you know it's completely, deployed, within the manufacturing. Environment, and, paying great dividends. Ty do you have a favorite example within health care. I i think a revolution. Which is, happening, right now but is. Yet to receive, a lot of publicity. Is the pale provider, interaction. Today. The way that payers. Try to control, their risk and also to control utilization. Is a very labor intensive, process. It typically. Required, at least. In many cases until recently. Paper, forms, and filling out stuff and sending, things in fax machines, etc. Most of that has been digitized. And, algorithmized. To an. Small, extent. But the next leap which is happening right now. Also with companies, that spun out of large providers, such as ourselves. Is about applying, ai and more machine learning, methods, into these interactions. And that could actually really. Move a needle in terms of. The pales, cost base. With some transfer, these savings, to the provider, side. So how in each of these cases or in the industries, that you work in. How are jobs changing, whether the high level, skill of the doctor, or the the retail, worker. Um. And how are organizations. If at all. Responding, to the need for. Different skills, maybe lower skills, different tasks. Do you have a good example, or maybe a bad example, of how it should work. Maybe starting with incentive. I don't have either good or bad examples, i will say that i mean in terms of number of jobs we haven't, seen much change in retail. Uh 10 years ago there were 4.4. Million, retail sales people now there are four 4.5. Million, retail sales people, same thing with cashiers. There could be some wholesale. Change perhaps, in the cashier. World, if, if we have. Scan and go technologies. In terms of skills. You know. Someone, who, used, a computer, now it's going to use an ipad app there's not much change, in the skills that i see at least from outside.
But It could be very different in different industries, does the rise and the presence and the pressure of e-commerce. Change, the way that the in-store retail. That has changed and that has i think that is one of the reasons why we are still seeing some slight, improvement, in the number of jobs. So while some of the technologies. Have made things more efficient. The e-commerce. Means that now there's more work to be done inside the stores. So previously, we would go to the stores, just to buy our merchandise. Now the store employees. They, assemble. Our, um. Our order for us and we either pick it up at the store, or we pick it up at the curbside. So technology. Has added more work. To the retail store employees. And it has also increased, the. Importance. Of competence. In the operations, and people side. So if you're relying, on technology, to pick up your orders, uh curbside, delivery, or, look check, inventory, at the stores. You better make sure that those data, are correct data, from which you're you're you're promising, customers, of delivery. Uh and many retailers. Tend to have. Tremendously, inaccurate, inventory, data. Point of sales data. So so so so these technologies, are i think increasing, the, importance, of paying attention to your store processes, store operations. And also. The competence, of your employees, so if you operate with 100. Turnover. It becomes, very difficult. To get all these things done because, you're you're teaching somebody, to do something, and then, two months later some other employee, comes in and and these technologies, are much harder to adapt with high turnover. David, how does nissan, see. Skilling, across the workforce. It's a very interesting, question, because. To understand. The skills and the need for new skills within automotive, you've got to understand. Automotive, industry, on the manufacturing. Side, is largely defined, by a lot of legacy, systems i mean we have very large plans huge capital, assets. That, evolve, over time and that evolution, usually takes place whenever, new products are introduced, and with legacy, systems, and legacy, processes. Also come, legacy. People. Legacy. Ways of thinking. So it's how to bring. The new skill sets, and the new technology. In without, losing your best know-how, which is housed, in your legacy, employees, they're the ones that actually know how the business works and how to, bring the manufacturing. Systems, together, to produce. A final product. I'll give you an example, of how we're doing it at nissan. And since we're at mit. I'm going to use an mit, student, in the example. In the body shop we have a vision, for, predictive, quality. And in body, this is where, all the metal components, come together, to build the foundation. Of the car very complex, and it requires a lot of technologies, coming together to be able to predict, the outcome, of the quality. Well one of the elements that we wanted to look at is, fit and finish and surface, quality. So we paired, a 20-year, veteran. In the body shop. With, an mit, student. And we said guys. We want to know, how we can do this. Autonomously. And feed the data. Into. Big data do some analytics, and predict what the outcome is going to be. And i have a lot of meetings, in in my world, and the meeting that i look forward to the most was the meeting between these two individuals. Because it was absolutely, amazing. You had this 20-year. Nissan, veteran. That didn't know anything about advanced, vision, and data analytics, that now talks like he. Invented the stuff. He is absolutely. Immersed, in the technology. He understands, it and that understanding. Now gives him the ability, through his. 20 years of previous, experience, to look at other areas within the body shop and how to deploy. And then on the flip side i've got this mit, student who had never been inside of a body shop before. That is talking in detail, how the body comes together. How the processes. Work, how the new technology. Fits into the process. So, it's not just developing. You know the skill. For, a new employee, coming in on the legacy. On how to do the the legacy, work of the company. But you know it's also, those new employees. Teaching, the the veteran, employees. About new technologies. And only through that synergy. When you purposely, pair these people with a common goal. Can you really drive the skills forward with it where they need to be for mass, new technology.
Adoption, And deployment. That's a great example because we we hear a lot about companies who try, to. Mix the foundational, skills of of older workers, with the kind of data and, and technical, skills of younger workers. And we actually have a cognitive, scientist, on our, uh task force who's taught us that, you know the the a lot of those skills just decline, linearly, over. One's lifetime. Right and uh but other skills. Relational, skills. Uh social relationships. Uh more foundational. Skills, actually increase. Pretty linearly. Um. Unfortunately, they tend to cross right about. My age, um so. It's no wonder that they don't let me write code anymore. Um. And um. I wonder itae if you see any of that kind of interaction, in healthcare, or you. How, how. The partner system, deals with. Uh changes in skills, uh especially, around. Information. Technologies. Across. Uh, different. Types of providers, and age groups. Yeah so, um. First of all. In our case i actually, see, a lot of interest, from. Fairly, senior and experienced. Physicians. In, ai, and in new technologies. So i definitely said with out of appetite, for adoption. I think there's also a certain, level, of. Perhaps, a, narcissistic. Trait here. Because a lot of the idea behind building ai products, is to scale up cognitive, processes. And if you believe it you can. Your cognitive, processes, are worth scaling, up you would be interested, in contributing, to that product. Um. Know that mit feels that way, i'm sure our students don't know. There's also another, piece, i'd say that we were now, after the. Wave of in, medical informatics. In which we had a, lot of. Kind of post-residency. Programs, in which uh, doctors, actually learned how to operate computers. Understand. More basic stuff like icd, codes. Basic data analytics, tool. Um, and now. There's a huge, interest, now in the next wave of more advanced, analytics, as well as ai. We run a, fellowship, program, at the center for clinical, data science of which i'm the executive, director, of. And we get. Holds of candidates. Of. Post, residency. Board certified. Md phds. Want to. Stop everything, for a year. And or two years and only learn about ai. Develop, ai. Products. Contribute, to publications. In the field etc. And, i actually, asked some of them, you as a neuroradiologist. Could you know in some cases maybe, making. North to half a million dollars a year. What drives you to actually come back and do something which is similar to a postdoc. And many people would tell you. It's clear to me that the way that. My job isn't at risk. And my the field i work in is not at risk but the way, i conduct, my affairs, is going to change dramatically, over the next few years. So i'd say that's definitely, um. You know an example, of that. Interesting. Such a wealth of experience. On the panel here i'm sure we're going to have a lot of questions so, maybe, i don't have the questions yet on the uh. Uh. Monitor here. Uh, but. We have a few uh. Emailed in here yep um i'll i'll read the first one. Technology, always strives, towards doing more with less. Or enabling. That could not have been possible, before, so how should externalities. Be. Managed. Big question. A little unclear. How we think about. Externalities. Again. One of the things i always teach is that what you define as internal, and what you find as external, is a huge value judgment, around, how you draw the boundaries, of the system. Maybe another question here. Um it's paul varder from, toronto. Management, consultant, and an author. Um. Largely you've had a conversation, about. What we would call. Competition, within, existing, firms, here. But we've got kind of two big elephants, in the room, in competition, these days we've got the global giants, the. The google, amazon, et cetera that are entering, many adjacent, businesses. And we've got startup, so you have uh, in the case of google, they're entering, both. Automotive, and healthcare. And we've got startups. Tesla, and automotive, and a myriad of healthcare, startups. Maybe speak about. How you perceive, competition. From those, you know. Existential. Threats i feel like. I'm happy to take the retail perspective. The big threat in the retail world is amazon, which accounts for almost half of all the e-commerce, sales. Um. And i think for. Many. Companies. One of. The. One of the issues, have. Been, that, they focus. Too much on, amazon. And not enough on their customers. So retail, is still a big space there's still a lot of. Opportunity, to come, compete, uh price, and convenience, are not the only thing that matters to customers. So i think the the the way to, um. To address that threat in in at least my world of retail, is to re-offer.
Your Customers, a compelling, reason. To buy from you and that compelling, reason can't just be, more products, and more convenience, it has to be something else, and for that to be good you have to invest in your store processes, and you have to invest in your store employees. So that customers have a fantastic, experience. Okay hi everyone, my name is antonio trigoso, uh, i am pharmaceutical. Chemist and i work at johnson johnson, in peru. So. My, my question is for, itai. Maybe if we talk about sales marketing. And other. We. We need to communicate. Uh, at human level. But if we, are talking about a treatment, a medical treatment. Maybe, we need. A, treatment, at human level but also. At molecular. Level. So. Wha how are you using. This ai, to. Maybe, know about the dna, of the, patient, and, design, a specific, treatment. For, for that. Patient. Thank you. That's an uh, an excellent, question, first of all, um. I'd say that, one of the main limitations. Uh. In implementing. Ai in healthcare, as i said, earlier. Is lack of understanding, of biologic, systems. And, in that. Aspect, ai, has a huge potential. Um. In order to make these connections, and really, converge, different data sources. Which is something that us as humans, do a very poor job. At usually. I'd say that in terms of. If you if you look at the hospital, tumor board. That's always one of the most. Confusing. Meetings, one could attend. Because each specialty. Brings its own angle, of view towards something. And, based on their experience, and based on the data sources, and you'd have a geneticist. Based on genomics, and you'd have the surgeon, based on morphology. Etc. And in that sense there is a lot of work including in our system. Of how to fuse these data types together. Um, the current generation. Was, um. You could use different, neural nets but eventually, you had to merge them together. The different, data, sources, you'd still go back to classic, methods and in that sense you were limited to some extent. But the frontier, of science, and computational. Science, is now progressing, beyond, that. I would assume. That. The area. Of. Big advancement. In that field is still not in the hospital, environment. But rather in pharmaceutical. R d. And i am looking forward to seeing more bio stencils, and more, ways to efficiently, capture. End points and, patient biomarkers, in order to really implement, these and clinical, kill.
Which Today. I find fairly limited. I have a question, from the online slido. Which has a lot of votes. Um what has been the reaction. Of the employees, who see parts of their job being automated. And how do you communicate. Upskilling, opportunities. To your workforce. I'll take that one, because i think i probably got. More in the automation, side than uh, by the two panelists. So most of the time people are excited, to see their job automated, because. We're automating. Jobs that are traditionally. Very difficult jobs to do. Maybe an ergonomically, challenging, job. Or. Have some other constraint, that's around it, now as far as the redeployment. Of that workforce. You know there is, all kinds of opportunities. That are communicated, within nissan and i'll take the engineering. Group, as an example. You know within engineering. We have a group of. Frontline, technicians, that we've brought online. And worked, very closely with our engineers. As, they know the production process, is better than anyone. So, they work with frontline, technicians. Back with the engineers, to go out and solve, problems, so those are opportunities. That we create. As we automate, jobs, and repurpose, people to a higher value use similar, to the example that i use with the maintenance, technicians, and the predictive, analytics, around, uh robotics. One more online and then one more for the audience, yep absolutely so uh, so there's a shortage, of labor. Around technical, skills, in the us. Like welding and nursing. So, why do you think that students are not enrolling, in vocational, schools to fill those. Gaps. Probably, take that one as well. Uh. You know i've made the comment. Uh. Locally, within tennessee. You know. A lot of. I think is a societal, thing. Because, we say, go to school get a four-year degree, get a good job, and we don't talk about the vocational, opportunities. That are out there. That you know, welders, and pipe fitters, and the skilled trades. You know you can make a very. Good living. Doing those, and you know and it's one of the things that i'm proud to say in tennessee, that has a lot of push behind, it because those skilled trades are in short supply. And they are, very good paying jobs. And, we have many initiatives. To, help support, students that want to take that. That direction. But it's that's definitely something that as a country, we have to continue, to promote, and celebrate, whenever people make the decision, to go and get a skilled trades vocational, degree. One final question from the audience hi uh. Hugo here a fellow from mit. Last month, a. Publication, from. Researchers, at uc berkeley. Uc chicago, and partnered healthcare. Found that several artificial, intelligence, algorithms. Handling. The health care of over 200 million people here in the u.s. Were, infected. With the same racism, and racial, bias, that human decision makers have. So basically the algorithms. Were. Denying. Critical medical interventions, to. Black patients that were sicker, than non-black, patients. So the question that i have probably for for ty and david as well so how, what. You as a senior leader is actually specifically, doing. In order to identify. And tackle. Uh the automation, of discriminatory. Practices. Another excellent question. I'd say that. Each, type, of. Use case, has a potential, for different risks and different biases. The main two that i'm the most. Concerned, about these days. Is diagnostics. In terms of generalizability. Across different populations.
And Being able to. Provide, quality, of care. For a diverse, set of, an equitable, care for, diversified. Patient population. We are managing. That, by reaching, out to additional, data. Providers. I'd say. And. Identifying. Different strategies. And technologies. Such as federated, learning. And ways in which you can. Improve, the robustness. Of your product. Without, actually jeopardizing. The. Safety, and privacy, of a patient data. There's another, aspect, which is. More, hospital, operations, management. In which you could make, optimization. Decisions. That would, be unequitable. To different. Socio-economic. Strata, of society. In that aspect. I can say that part of it is about the thinking about how you minimize, your cost function. And which techniques. And which data points you would do or wouldn't do, and where you'd maybe. You know if to take from the computational. Sciences, you'd stay at the local minimum rather than the global minimum. If it does in fact create inequality. And i'd also say that with positive. Optimistic. Note on that. Is that all of our commercial, sponsors, who, we are. Also involved, in collaborating. Within developing, these solutions. Are actually, extremely. Um. Attentive. To that issue. And are actually interested, in um, resolving, that, ahead of time and making sure that. Models, can't be actually used on. To stray purposes. Uh some of it is because, of. Reputational. Risk, and some of it is because i think the real, understanding. That. We are in it in order to improve. Throughput, and hospital, operations. But also maintain, a very high level of respect, towards. Human, dignity. And. Cultural, diversity. Great great answer to a very important question and on that note please join me in thanking our panel for a, wonderful. Conversation.