 So, in order to create a very first impression, a positive first impression, it's essential to create experiences for the products which kind of build a sense of trust and confidence on day one. And to build on that on day two, it's really essential for us to make sure that we build experiences for not just day one, go beyond day one, day two, day three, and then make sure that entire service experience is well taken care in order to build on that trust that we've built on day one. And Apple, as we all know, has been championing this ever since, and then they have been building customer confidence and trust, and now they are well known as the most trusted companies in the world. But why trust? I mean, why trust in this age of emerging technologies, and then how can we make sure that we continue to design for trust, right? So, previously, most of us used to purchase things from Windows directly, so what it actually meant is we used to know them really well, very well familiar with the people, and that's the very first step to build trust. But lately, with the retail coming in, whether I'm not endorsing this brand, I'm not really sure if this brand is trustworthy or not, but with the retail coming into the picture, I think that gap is widening, right? And moving further, once the goods got online, the gap has increased further, and reliance on systems in between which enable that communication and transactions have increased a lot. Amazon has been championing this cost of this, right? So, Amazon's experience, as we all know, I think they make sure that the experience is top notch and to end by making sure that each and every touch point is taken care, in that sense, from day one, day two, day three, and beyond. And how does this translate to something that we are all facing these days with emerging technologies? So trust in the consumer world with emerging technologies, as in, please. So this is something that we all face these days, right, so with internet connectivity going wrong, on and off, which we've just seen, right, so what will you guys do when this happens? Either you call customer service, or before calling customer service, you would want to do a self-service all by yourself. In order to do that, you need to be well equipped with all the tools, all the things that you need to know to fix that problem to start with. But more and more, technologies and companies who believe in building trust, they make sure that they are embedding those things that you need to know into the product itself. So in this case, in case if your Netflix experience breaks because of connectivity, they have embedded those steps that you need to take care as to fix in the problem. So that goes a long way in building trust into the product. And as this gets sophisticated, these products like Nest and things, so they also do something very similar wherein they give you visual guidance about the problems that you could face, so which means they're anticipating the problems way ahead in advance. And then it's these four problems which happens to be the most common problems with Nest, and then they give you visually guided troubleshooting experience. I think this we are all familiar with right now, right? So I think this example has been taken multiple times. Anybody any guesses about this? If you're there in the conference on day one, day two as well, I believe to give you another hint, Chris Nossel has referred to the same as thing, agent to technologies, and then we are talking about this guy, right? So what Roomba does is, obviously it's a mini robot. They are built in a language within Roomba which is based on sounds. So if anything goes wrong, it beeps once, it beeps twice, means something else has gone wrong. Beeps twice means something else, something else. And also it gives you audio clues to how to fix the problem. So what it all means, right? So this troubleshooting technological issues in the consumer world, which is on one extreme and on the other extreme, it is this complicated technology which is getting further and further complex. What it actually means is these companies are kind of relying more on intelligent machine analysis to enable fixing all these problems, right? So that humans don't have to do analysis. They embed this intelligence into the machines and then that way they are building trust. And we have taken those lessons, right? So not just building trust like that, but in case if it needs you and me, consumers to take a phone call and call support, support folks go a step further, right? So they don't even, don't just fix the problem, but they also make sure that they do follow up and make sure that the problem doesn't come up. So it is these kind of essential experience elements we need to embed into a design going forward so that consumer experience in the new world becomes all the more seamless, and that way we can build trust. So these learnings we have taken back to the product company that I work with. Basically I work from California. I work for infrastructure IT company which is trying to build advanced cloud technologies. So we've taken these learnings into our thing and the target user for us is the Enterprise IT Administrator. I don't know how many of you folks work in the domain. I think it's a pretty common thing, right? So Enterprise IT Administrator means on one side he's a human like us and then he's also getting used to all this new cool technologies and US and gadgets, but at the same time his domain is very complicated. He needs to deal with servers, cables, and all those things. So that seemed like a very promising opportunity to bring all the learnings that we had in the consumer world to the enterprise, right? So what it actually means is so in terms of his journey, right? So his journey starts from day one where then he needs to install, set up all those things, and day two he needs to manage and all those things. And then at day three is when in case if there is any problem or he needs to do planning, troubleshooting, and all the stuff. So that he ensures seamless experience for his end users that are using this infrastructure. So what is actually happening is it's given that I think most of the systems in terms of advancements in technology. I think we are doing a lot of this based on machine learning wherein we help them with, help them on day one with pre-created templates and packages. Which will help him take those templates and install it. And then on day two, with machine learning, we do analyze the environments and then we do identify, detect anomalies and stuff so that the machine is understanding under the hood. And then it's fixing the problems by itself as much as possible. And on day three, it is not just fixing the problem, but it's also recommending the requirements that you need to have so that you can support your changing requirements in terms of the resources you are end users need. So resources in this case are nothing but how many computers you are end users needs, CPU storage memory, all the stuff to keep it simple. Like that I don't want to get too deep into that. But all in all what I meant to say is I think with machine learning, I think we are helping this space to simplify the case, get the problems fixed, and then scale up automatically. So which actually gives these admins enough time to focus on their life and their goals. All in all, I think this diagram tries to summarize how risk and trust go hand in hand. Because risk is an important factor we need to deal with, especially in the case of this domain that I was talking about. If machine takes actions by itself, it might take some critical actions which might be detrimental to the entire setup. It might result in cost, failures, and whatnot. So there I think the approach right now that we are taking is to automate certain actions which are low on risk, but high on yield. So in that way, risk and trust to start with their university proportional. But with machine learning and design thinking, as I mentioned, I think we are trying to drive it towards a point where they both converge. Not only converge, but also cross each other, right? So that trust and risk to meet and then machines should be able to do more advanced things. So to start with, they're doing basic rudimentary things. But going forward, I think as we understand things more, I think they will be able to do more and more advanced things. But in order to do all this, in order to do all this, I think we as designers will continue to use the same approach that we have been using for everything else, right? Everything else as in any products that we design, any services that we design. Design thinking is still the key. That first step in there, right? So first step in there, we still need to understand what is all about. Even in the machine learning world, autonomous world. We need to understand that we need to engage more with the engineers and product managers to understand the data behind the scenes, the data technology behind the scenes. And then participate with them to define the experiences so that we solve the problem. So the role of designer is more crucial now in this emerging world. So that we need to dive it inside out. So is designing for trust versus risk new? I guess not. We have been designing things around risk ever since. So this is the 12th century kitchen wherein humans have been designing things, controlling things like fire and everything from those days to our convenience. And we'll trust in those technologies to start with. So this is where I think we have tried to contain and control energy and then use it for our needs accordingly. And by good design, I think we are not just controlling those things, but also building trust in technologies. And this is the very first email that got generated, right? So most of you probably know that, right? So but in here, the risk was for security, main risk was security. With design, I think it is evolved over a time, over a period of time. And still are successful in successful in retaining the trust factor in this. And not just that, in not so distant future. So this is a poster from 1950s advertisement, which is interestingly it is being published by an electric company. Electric company that has envisioned autonomous cars back then. So even in this case of extreme automation, I think it's all about understanding the requirements and then designing around those risks, right? So that will help us inculcate more trust into technology by design. All in all, I think to start with as I mentioned, right? So just the way we teach our kids how to take risks, by doing the actions by themselves to start with, low risk actions. It's in the same way I think we should design for systems of which can take low risk actions to start with and then build on them gradually. And someday they will fly and take us to the autonomous world.