 So for those who don't know many life Many life is 130 plus years old insurance company. It's based out of Canada and It's present in more than 12 markets in Asia and like any other company these days It is going through transformation Which means we do agile We also work in scrums and we work in sprints. So What what does that mean? This means we are going through a change But ironically This is the most graded word in the organization whenever we use this word and Deal with developers pms and other people and other stakeholders It seems like We are trying to do something which is like this Like we have dropped a nuclear bomb on them and they're like no why we are changing Old design is good enough. Why do we need to change? How do we know this change is better? Why can't we use old design We don't have enough resources why we have to change at all New design is complicated Does anyone feel like that has anyone has faced this similar challenges in your organization? I'm sure most of us face these challenges on daily basis Right, but why this happens why they view our Changes with skepticism. Why don't they don't see it as an opportunity? Is it because? We are wrong like we as designers are wrong trying to do something just for our own sake or is it because they are wrong and They don't understand if they are not wrong and we are not wrong then what's going on? Do they have genuine concerns? Maybe they have are they resistant to change? Maybe they are Or is it that they don't understand the value of design? They think design is Just not good enough It's just like any other design We can just make This looks images more bigger and that's it. We don't need to change design and thanks to previous speaker Saptar. She I Will pick some points from there because this is where it connects and it's nicely sequenced from there So this is a famous quote from Henry Ford and it it goes like this is that if you asked people if they would preferred a car and He would have unscored the answer that they would prefer faster horses because people are blocked or constrained by their own frame of references so So based on our previous experiences we formed the expectation of the future We anticipate the future as we experience the past and our past experiences guide our future thinking and This is what happens when we are trying to deal with change as well When you're trying to deal with change people are stuck in their past bubble and they're not able to break that frame of constraint They get blinded by the expectation they have based on their their experiences and their anticipations So if you ask People if they have better if you give people better camera, they will ask for even better camera And it will never stop until someone comes and disrupts it and change it So what's the reason behind it? Why we have this? this cycle of challenge and then Why people are stuck in this cycle forever? so this is basically Psychological theory where it says that When people have a particular solution in their hand They are Kind of confined with that solution. So once you give some someone something they're happy with that. They don't want to change They don't want to think beyond what they already have and once they have something They think this is what they need So they are unable to think from a fresh perspective They are unable to think beyond what they already see and They create this creates a big creative block or big thinking block Where we can't see beyond what we see and what we do is we just solve basic problems But we don't actually know it and we end up with this circle of disappointment Where we go with excitement and present our designs and then we are shut down and turned down and we are told This design doesn't work Because we can't change we should not change or we already have existing design which works for us. Is this unique problem Is this a new problem which we have because of technology or is this something which has been there for ages and We have been dealing with it Anyone use this before? Okay, so they are people who are old enough We'll use this so that's good Is this available in the market anymore? so Did you guys know Kodak was the first company to invent digital camera? So Kodak invented the digital camera But they never released in the market the CEO Told the inventor that we cannot go with this in the market because We are in the film business and we sell films and if we create a digital camera Nobody will buy our films which means we will be out of business So he saw a problem having a digital camera and didn't see an opportunity which could be there and Where is Kodak now? What about Nokia? Nokia was the market leader Everyone had Nokia phone at least in Asia for sure or at least in India where I came from and Nokia was Innovating its own way It used to release hundreds of new models and Each model tried to push a boundaries of what existed, but it didn't survive It didn't survive when Android came and when iPhone came So what made Nokia lose the market share and eventually the whole business? When it was the market leader It invested in Symbian OS Which was the legacy OS for Nokia and they could not Break out of their legacy their legacy became problem for them And they could not compete and people moved on and people moved on to Android and iPhone from there And more recently Thomas Cook everyone knows the story. I don't need to repeat Right, so we can move on So the reason is that instead of focusing on future those companies focus on past they are more Scared by the future, but they are more comfortable with the familiarity They find so less in the familiarity and they want to stick to that Family environment and family market, so they don't want to disturb that so some of the reasons for that is They fear known This legacy that resist change and there is a filter bubble filter bubble means that They only see things they want to see so they ignore things which doesn't align to their value system They ignore ideas. They ignore opinions which doesn't align with their Goals and objectives and then there's a lack of shared vision and then there is a scale and Lack of agility, so they are big, but they are not fast enough. They are not flexible enough to change So what does this means? If I think it from my own perspective Working in insurance industry, I need to deal with this on daily basis because we deal with compliance and regulatory Departments and if anyone knows financial industry compliance and regulatory departments are too hard to change They don't want to change because it's not in their favor They want to bring in more challenges to you because they want to Make things more complicated. They like to make things complicated. So What we face on daily basis is that when we go to compliance and then we go to regulatory We are turned down and we are told that we can't change because This is not something we have been doing This is not something we did in the past. This is not something we have tried This is not something we want to do and they come up with stupid ideas Like why can't we make the form which needs to be acknowledged as a disclaimer? Show up for at least one minute. Make sure they scroll down. Make sure they tick make sure They double-confirm they have read the acknowledgement and then we proceed So they are more interested in introducing more efforts just to make sure that compliance is met But they are not interested in solving the problems which makes things easier and better for the user And and these are some of the reasons why it happens because they are not Comfortable with Unfamiliar they're not comfortable with unknown So moving on from there. We have IBM IBM used to make computers IBM made mainframe computer as well Which was before PCs came in These days, what does IBM do IBM no longer makes hardware's IBM makes Software and services IBM transformed from a hardware company to a software company Nintendo Doesn't even know how Nintendo started any guesses Yes playing cards. That's right So they started making playing cards and they were quite good at that They made playing cards and from playing cards the transition to making hardware's and games and now they are the One of the world's best gaming company Doing both hardware and games and the Netflix Netflix started as a DVD rental company They used to rent DVDs Then their websites Then they moved into subscription Now they are doing content So they sponsor contents They make their original content and they are quickly moving away from Model they had before so they are transformed the model at least four times in the past So how these companies Can do it successfully compared to other companies which could not do it So we saw that those companies which failed Were more focused on the past Whereas these companies are more focused on the future because they can anticipate what's going to happen and they can change to respond to that change So they can transform themselves responding to the external and External internal changes So what they are doing is they are focusing on what's next What people are looking for what people want what people expect how technology is changing how market is changing And they respond to that and then change So we can see that how These things are totally opposite for these companies Whereas one is focused on the past the other one is focused on the future One is scared of uncertainty and scared of unknown the other one is Willing to explore willing to experiment willing to find what's next what's new While one is stuck in the filter bubble The second one is trying to explore have anticipationary anticipatory mindset and see What they can do to break out of that filter bubble? How can they make them and keep them competitive keep them current keep them Forward-thinking so as we have seen the technology is changing very fast In last few decades things have changed drastically It's only few decades since we have the first computer and Now the mobile we carry has more power Than the computer we had in the recent years So if we are living in such a fast-paced and changing rapidly changing world What happens then if you live in such a fast-paced world and you don't change The gap increases So technology change at exponential rate, but organization can't keep up with that change And if they can't keep up with that change the gap increases and the bigger the gap is The higher the chances to fail if the gap keeps going bigger It's kind of certain if you will fail. So that's that's Common for the organizations to have and that's what I'm trying to do is My role at money life is to make sure that We bridge this gap. We don't let this gap to have widen. We don't let this gap to happen And what we are trying to do is we're trying to build Methods processes and techniques which allows us to think beyond current which allows us to think beyond now Which allows us to think beyond what we deliver day-in-day Paces, so how some of these organizations save or transform or change to respond to these Technological changes and other socio-economic changes One is obviously agile, which is the buzzword everyone knows everyone keeps talking about it and I have to include in my presentation as well so agile Everyone has to be more focused on agile approaches these days. So that's how you Respond to the change faster Another thing is to have a shared reason If your vision is stronger and everyone is everyone shares that vision believes in that vision It makes it much easier But how do you get to that vision? How do you find that vision? That's a challenge in itself And how do you know it's the right vision and then once you have the vision you have to execute it Can you execute it? Do you have the right skills and capabilities to execute it? Can you do it in timely manner? So all these challenges surround these kind of thinking and Solutions which we kind of project in the future because if the future is not known The solution we are building is also not known how challenging it can be So someone needs to make sure that everyone believes in the vision everyone understand the vision and everyone is willing to work on that vision and We find the right people time and capacities to execute that vision And that's what separates the organizations who are reactive Then who are anticipatory? So reactive organization just react To what's happening They react based on what happened in the past they react based on the information which came from historical data They don't react on information based on projections of the future Organizations which are there on the top are the ones Who imagine the future and who make it possible? so hindsight provides what happened And insight is the historical data It tells us tells us what what has happened so organizations looking into past data and just Analyzing what has happened and just based on that They try to make changes then they are just reacting to something They're reacting to something which has happened in the past So if you're using Google Analytics and you're just looking at the data and saying, okay How many people have clicked how many people have which are these pages what they have done? This is hindsight It has already happened You're looking at the data which has happened in the past But what you derive from it is insight it tells you why it had happened why it has happened and What could be the possible reasons? It happened so insights tells us why it is happening What could be the reasons what could be the motivators what could be the Detractors which makes these things happen, but then we have four sides and Four sides tells us what could happen and how to make it happen So insight tells us about the experience the past experience It gives us the information which helps us build the understanding of the experience we had Whereas strategy Derived is derived from the insights which we gather Together the insights we build a strategy and that strategy helps us to optimize our processes optimize our designs optimize our Solutions and optimize the whole organization basically and Then we have four side and Four side is where we get the vision Four side tells us how to get to something and what that something could be So it tells us how to innovate and There we get the vision of what we should do or what we could do and how do we generate four sides The first thing to generate any foresight is to anticipate basically anticipation is How you speculate what could happen and And all the nice illustrations you saw in the previous presentation that's Kind of part of anticipation Where you see how people imagine the future and imagine the uncertain and trying to bring something Tangible to it and say okay. This is which could happen but If it's purely imagination then how it can be useful to us because we are not living in imaginary world We are living in a world, which is very much real and has real consequences and real implications and That's where we have to also think of using other ways to generate four sides Which is not just imagination and our gut feeling but also database and other kinds of Methods which we can use so once we anticipate We can imagine future and we can imagine different kinds of future There are alternate futures and these alternate futures can be of any any kind of possibilities or probabilities These futures belong to different categories because they have different Potential to become real So here we have is preferred probable plausible and possible and then there's another one called impossible So impossible falls in the category where you imagine But it's just imagination and it's very different to make it real So teleporter most likely is an impossible Thing for us at this point because there is no advanced theory yet supporting this notion of teleporter But what's probable right now is high-speed train because It's happening right now But what's plausible is hyperloop because hyperloop is in the construction, but it's not tested and operationalized yet So that's how different versions of the future can vary and Organizations can pick their own preferred version based on where they want to be and how they want to be So that's where the preferred version comes in their preferred future is the future where the organizations feel most comfortable and most Excited about going to so we can say that the future is not a Linear future for organizations which are experimental their future could be a multi-dimensional future where we don't see things and we don't see Progress happening in a linear way it happens in a multi-dimensional way and how can we go there? From where we are how can we go to the future we desire the future we prefer? So we have to move away from the mindset we have right now in that case If you or your organization thinks that you're stuck somewhere and you're not able to break away from Everyday thinking and you're not able to break away from doing the mundane stuff You need to think how much you want to change and you need to think how can you change and Some of the things which you could do and you could think about is see if What procedures you are following and is this helping you to solve problems? Because you don't want to get stuck in the procedures you want to solve problems You don't want to just deliver wireframes You want to solve problems which actually makes impact Right, so if it's only a deliverable. It's just a checkbox on your deliverable or in your process It doesn't add any value to anyone So if you're just following procedures, then you should move away from procedures and start Doing problem solving solve actual problems If you are just reacting to what is being told to you if you're just reacting the requirements which are given to you by the by business You're not asking them questions. You're not asking them why you're not asking them why we are doing this Just taking that requirements and converting them into design You're reacting. You're not thinking you have to stop reacting and you have to start thinking You have to start anticipating as well. What this could do to my users. What this could do to my business Yeah, so similarly you can see here We have you have to move away from how to to what if you have to move away from the state of perseverance where you Are comfortable with what's there to the state where? You can disrupt you can do actual disruption. So What should we do? So I would say that first thing we should be is we should do is we should be humble We should not be all confident of what we are doing or what we can do Because ego is the biggest Decremental for innovation or collaboration leave ego aside be humble Don't be more confident and work with others to solve the challenges. You have be more open-minded Be more flexible Understand the confirmation biases you have Understand other biases you might have and try to deal with those biases Find a way to address those biases and don't get into that trap where these biases Becomes a problem or becomes a creative block for you Don't focus on the just on the past. Don't just look into the past data also look into how can you generate? Four sites. How can you generate insights which can lead to four sites? And find a way to innovate find a way. How can you? Get the data not only from the internal stakeholders internal organization data, but also from outside From other sources you could have So try to be open and and try to get as much data as you can from different sources. So you just don't get Stuck into one kind of data or one source of data Obviously have to explore and don't settle for one kind of future But explore multiple kinds of future See what possible futures are and see which one you prefer and that's only Be will be happening when you're not looking into one future. You're looking into multiple futures If you settle for one future You're most likely going for the obvious the least effort way Which kind of defeats the purpose and then see how you can adapt to that future And then definitely we have to share and collaborate because that's what makes innovation possible. That's what makes The thinking which can translate into execution possible. So how do we do it? It all sounds good in theory, right? How can we do it? How should we do it? So these are some of the steps which we can follow and we can see if we can make use of these and try to try to come up with some sort of Ideas imaginations possible futures and then use it in our design and our organization So data is a big part of it. How do you get the data? How do you organize the data? How do you analyze the data and then how do you imagine it? How do you imagine the future? How do you adapt? How do you build the organizational culture and? Practices and processes around that so I'm not going to go too deep into this because This is a framework just for an anticipatory mindset and basically just says that this is what we can do to become more anticipatory So this is where I will stop for this one, but I will just move into something else so we talked about anticipatory mindset and I just showed a framework about anticipatory mindset where we can use the information and we can anticipate what's happening in the future and then we can adapt and react and and Work around that possible emerging future, right? But how do you translate this mindset into an experience how do you translate this mindset into a anticipatory experience and design products and solutions? Which can offer Which can offer an experience which is more anticipatory in nature So it's a little bit on a different level The next part of the talk is a little bit on different level So first talk first part of talk we talked about more on organizational level and individual level Here we are talking more about how do you bring it into your practice and how do you design something? So I will just go through quickly some of the trends we have been saying so far So one of the things we have seen is the how data has increased in the last few years So the data has increased exponentially and then now we are drowned in data And there's so much data that we don't know what to do with the data, but this also brings an opportunity To use that data for something better or something greater At the same time, this is a Gartner hype cycle for AI and this Gartner hype cycle for AI shows that There are lots of emerging AI technologies which are ready for mainstream and there are few more Which will be ready for mainstream in few years and then so on and some of those you can see is ready in two to five years Which is very near Which means we can start using it and we can start using them to design our products and design our solutions and then there's another trend which talks about Personalization and personalization is possible by data in AI So if you look at these trends, it can tell tell us something it tells us that How we are moving from lots of choices and those choices which are generic to the choices which are personalized and contextualized and relevant so that's to give an example if you look at the The internet Five years back or ten years back you go to any portal and The portal looks same for everyone Doesn't matter who you are you logged into the portal and everything is same now at present. It doesn't happen Whether you log into Netflix whether you log into e-commerce website like Amazon or Lazada or whether you log into any other portal There are high chances that portal has been Design in a way that it changes depending on who you are and what you are looking for It understands who you are it understands what you did in the past It understands what you are going to do next and it shows you Suggestions and recommendations which it thinks Might be suitable for you might be relevant for you. So system is anticipating your needs System is trying to guess What you will like? So now we have moved that anticipation process from the organization and our thinking and Embed them into these systems So these systems are anticipatory systems which we are talking about and these systems Take the same concept and same idea of anticipation and apply them in a way that it shows us Relevant information and relevant solutions suggestions and choices So it optimizes the choice for us. It only shows us things Which it thinks which are relevant which means we don't need to deal with making unnecessary choices Which means companies can have better conversion Which means there are higher chances of customer staying through and not dropping off Right, so that's how Choice optimization will help businesses Now the second one is from passive context blind dumb interactions. We are moving to Proactive context of intelligent interactions so Think which we saw in choice and now you can Website now you can see it in any other app you are using these days Even it's a whether it's a grab or any other right-sharing apps. They also have us Some understanding about who you are. Where did you go last time? Where will you go next? Where you are right now? So So they show you something based on where you are. So that's a context. So they are context aware They know where you went last time and they know where you might go next time. So they're intelligent So they provide some of the intelligent interactions where they can prompt you or they can tell you what's happening I'm not sure about grab yet But uber shows you the time when you're going to arrive to your destination. So it predicts or Anticipates when you might reach to your destination. So they are intelligent and They tell you before you actually act on it. So they are proactive You didn't ask uber when I will reach It showed you automatically that this is when you are going to reach So it's it has done that thinking for you on your behalf Before even you have asked for it. So how does it? Affects the design. How does it change the design? So design has been changing a lot in recent years and we know design has Transformed a lot based on data and based on interactions and based on AI so design has been moving from Different levels from objects from physical objects and tangible objects to more intangible objects like digital products We are making so it has moved from architecture to communication to industrial to Interaction design which we talked about now, but We are already talking about system design and what makes this system design possible is that data and AI which will make it possible because you need a strong in integration between different systems You need to have data which can pass through these systems and generate more value than any of these systems could do stand alone Which means We are ready for that because we have enough Tech feasibility at this point which can make it happen. So I Think this is What could make anticipatory experience possible is that you need to have organization culture as well as data driven mindset and Resilience in the process which allows you to quickly change and update your products. So if you talked about resilience I creative solutions agile and scalable and feedback loop in that process has to be there to make it possible For change we have a culture and technology. So we need to have a shared vision. What's changing? Why it needs to change how it affects people? how it affects our behaviors and then for intelligence sensors automation AI and So on so it's an ever-ending list So where are we leading to when we talked about all these where are we leading to we are saying that there will be Personalization we are saying that whether the data there will be AI and and where does it all leading to this is all about reducing the effort and also making sure that The devices and the apps or the products which we are using Becomes more and more personal to us becomes more like a friend and guide to us rather than just a cold Another IT Enterprise kind of website where it doesn't know or it looks very hard. So Matthew Dixon said that if you have to make sure customers are loyal you have to reduce the effort of what they do and This is kind of basic principle because People are lazy People find the least effort way of doing things You give them faster car they are expecting more faster car because they are lazy. They want things better Are we more do we have more free time than before? Just imagine a world before we had smartphones. Do you have more time than that? You really have more time. I don't think so you have more time Imagine a world before computers when everything was manual and you have to do with paper Did computers help you save time and you got more time you got more time, but you got other things to do So your time is required in a different way Now you spend more time on Instagram So that's why you use your time so So people are lazy they want to save time not necessary. They have something to do. They just want to save time so how does it fit into the matrix of Intelligence and choice optimization So, how can we use intelligence and choice optimization? To have some guiding principles for our design So if you see There is a horizontal axis which says choice optimization and there is a vertical axis which says intelligence and the higher on the axis it means it uses foresight and the lower on the axis means it uses hand side and on the left side it uses the It offers you more choices it doesn't help you reduce choices on the right side. It helps you reduce choices So if you look at Google products, which uses it quite a lot and quite well So here you can see I'm not sure if you can see it clearly, but basically this is a email So Google Google mail or Gmail It nudges you about The email you got few days back and you didn't reply And it will remind you that you have to reply there. Do you want to reply to this email? So it's just kind of optimizing it because if you have to reply to this email You have to go and find this email click on that email and then reply it automatically Put it on top of your email list and then add some message to it and says Do you want to follow up? So that's optimization It's anticipates It anticipates that you might have to reply to this email and Based on that anticipation it just surface it up Have you seen Google duplex demo? So I'm not showing it here But if you have not seen Google duplex demo, I will just recap it a little bit So it's it's a wise AI driven chat bot from Google which can make calls Which can make calls and handle that calls by itself Like booking an appointment at a restaurant Or at a barber or anywhere That chatbot will make a call Have a conversation as humans will have and then make that booking or appointment possible for you So what Google is doing there is lying on automation You just need to feed in what do you want and then that chatbot will handle everything for you prevention Prevention is based on anticipating anticipating something might go wrong So you anticipate something might go wrong and you want to prevent from happening so prevention is basically trying to say okay, this is what has happened and You should not be doing this for example, this is Android Usage behavior on Android and and this is Google Android 9 and Android 9 has these usage behavior report Just like Apple has Apple has a weekly report and Google Android has this one It tells you how much time you spend on mobile and how much Social media you have used and how much other applications you have used So it's kind of anticipating that if you spend more time It's bad for you and because it is bad for you Maybe you should use less and then because you should use less it kind of anticipates What should be the right amount of time you should use so that's prevention but Going a step further it's anticipation it projects into future Just not talk about What happened so this is Google Maps and in Google Maps You go to a restaurant And in that restaurant You click on the restaurant details and in one of those details there is a detail about timings and schedules of the restaurant So there you see this when you see it on Google map and it will show you the most busy Times for that restaurants and less busy time for those restaurants So it will show give you a number it will show you a graph It will tell you okay. This restaurant is busy at this time and this restaurant not busy at this time And even if the time is beyond today It will give you a projection Based on what happened in the past So it is anticipating that it's going to be similar Based on previous pattern, but now one more step further. It's augmentation The difference is that in anticipation you didn't get any choices You just got the information you got the data but when you look at Google map and You try to set a Set of directions find a direction or just try to set a route for the car or It will show you alternate Alternate ways to go there You go you type in your destination you type in your Type in your address type in your destination then you type Then you select how do you want to go and you select car? It will show you the route and then you can see there are three different ways to go And it can tell you how long it will take based on each each of those ways So it's not only telling you How long it will take it's also telling you which one is better It's giving you a choice It's helping you make a decision And Something which has not happened yet, but going to happen in future going to happen in near future by the time you will leave your Leave your location. Yeah, just some examples here For optimization you can Sorry, it's a wrong title for optimization. You can just basically see how you can optimize your Interactions and Which kind of data can you put? by itself Just like you can see here. So it optimizes the interaction by just Automatically filling details or data or fetching the API data or fetching the sensor data Which are most likely correct? So this way you can reduce some of the effort efforts a user has to make Automation for automation you can see this nest has an automation Uses automation AI so basically it automatically adjusts the temperature of the room based on your preferences Similarly this car by Jaguar also use automation and it detects the face or facial expressions and Based on facial expressions it will make a guess that you might need some help So it will try to for example, you are feeling stressed and that AI will detect you are feeling stressed So it will try to change the temperature try to turn on the music try to do certain things which will In its understanding, I mean it's in the understanding of AI it will try to reduce that stress So it's kind of automating the whole mood control process So you don't need to think what I need to do you don't need to turn on the AC you don't need to turn on the music It will do it automatically for you prevention in insurance there is a for for automobiles we use something called telematics and for Healthcare there's something called Health healthcare for our smart bands like Fitbit or Apple smart watch So what these do is these also provide you some prevention Suggestions so basically why it does this it collects all the data how a driver is driving the car and Based on that data it maps that data It analyzed that data and it says okay, this is the score This is how you have been driving the driving. This is your report card, but it also provides some of the ways driver can improve Offer the ways to train offers the ways to learn something better offer the ways to control Emotions so so those things can be offered based on the past data. It is analyzing on the right side This is a patent done by Apple and it's for iPhone. So basically this patent is about a device When the device is going to fall it can detect it's going to fall and it will change its orientation So it's kind of anticipating the fall and then it's going to change its orientation. So it doesn't fall on its glass It falls on the back or side So that's how the patent is and Amazon has started doing something called anticipatory shipping So anticipatory shipping is basically based on what you are buying how you are buying your past behavior Amazon predicts that You're going to buy a particular product in next few days and Amazon will ship that product to the nearest hub So that it can be delivered to your home within a day So they are predicting all these behaviors and patterns Based on what you have been doing and then we have More examples for augmentation. You can see here how it can help you make decisions Help you make your work easier. So it for example on Gmail again It gives you option to reply and it's anticipating again that What could be the possible replies? You might want to give So providing an option providing a choice providing not making decision on your behalf That's augmentation and not automation if it makes decision on your behalf. That's automation Same example similar example here It's for call center and in this app for call center Basically, it records and analyze the voice of the agents and based on that voice It can provide suggestions what you should do so that you can deal with the case better Yeah, so as I was saying that That the choice optimization intelligence there are little bit differences in terms of how you Handle each case differently. You can't optimize everything. You can't automate everything. You can't augment everything There has to be a reason why you want to augment why you want to Automate and why you want to optimize? Not all of the interactions are equal, right and not all of the problems are equal so what we are supposed to do is we are supposed to make a call and and one of the ways to do it is basically see how much confident are you that The decision I'm going to make on behalf of the user is the right decision and how much Confident are you that? The cost of that decision is not going to be high So if you are confident enough then you can make that decision on your on the be on the behalf of the user But not then you can leave it as any design or any other Process and framework we we need to be careful about certain things and this last section is all about Dealing with design in a way which is not considered not considered unethical not considered Something which can make us fail not considered something which can Bring more challenges for us So we have to be cautious about few things and I will just walk through some of these things Which we need to be cautious about because these these while new data and then AI these are good to have and these are Good for our design and good for solutions and augmentation anticipation in general But there are certain things we need to be careful about so Trust is fragile, so It takes some time to build trust right so we we build trust we we get customers we we gain their trust and we don't want to break their trust and It's very easy to break break that trust so we don't want to abuse their trust If you know about this case about Facebook Facebook has been selling the data for political advertisements so I'm not sure if you are guys are aware of these cases It was quite popular in news recently and And in US elections especially presidential elections, they have the case where Facebook sold the data of profiles To a third party and the third party Basically use that data to segment the profiles and target these particular users of the Facebook with a biased advertisements To make sure that these guys vote for a particular candidate and This you might be aware as well Like Amazon admit that Alexa listens to people's Conversations So unless you turn it off It's listening to you So we talked we just saw that Trust is fragile and you don't want to break their trust Which means you don't abuse the data if users are giving you their data They are trusting you they're trusting you with the data and if you misuse abuse or use that data for some other undesired Things then it won't be good for sure right, but machines are also biased. We can't trust machines completely as well machines Have bias because we have bias and we are the one who makes the machine So machines are not free from the biases because we are not free from the biases Right. So for example here you can see Yeah, I can predict Who is going to be criminal based on certain characteristics and these characteristics are given by who humans and You can see here that it's not perfect It can racially profile black people as criminals because someone told it to do that and then Microsoft's chatbot within a day People trained it to do something which it was not designed to do So you can't control how your design will be used, right? That's another challenge you have is we never Be sure what's going to happen once the design is out once the product is out and how people will use it so it's it's a It's another thing which we need to be careful about like when we are designing and we are anticipating the future We also need to think of negative sides of it and machines can fail. They're not as biased. They can fail and this is the most this is the Unfortunate disaster which everyone knows how machine has failed and this machine basically the Boeing Boeing plane failed because of a sensor and This sensor gave a wrong reading of data and that wrong reading made it Dive down. So it's it's basically AI sensor used in a place where it should not be it's a mission-critical task and and They didn't provide any kind of failsafe to get out of it quickly and that's where things happened In a way should not happen Also, we are very prone to miss prone to manipulations And everyone knows about dark patterns, right? So the way we anticipate It can be hacked as well For example here a booking dot-com uses all these dark patterns all the time and and I'm sure most people know these So I don't want to repeat But you can use The anticipation in your favor sometimes so it's not always in our against it's not always about dark patterns You can many plate people to do something good So how this works is basically this is a company which makes bicycle and expose these bicycles and But These bicycles were found to be broken when it was open at the other end so it was getting damaged in the shipping process and The company was quite Frustrated about this and they were trying to find out a way to make it safer to ship without getting the bikes damaged What they did is they Package the bike in a TV box now Now the People who handle the cargo They see is a TV box and they anticipate that because it's a TV. I should be handling it carefully So their mental model has not changed their mental model is same as based on their past experiences what they have been doing and Based on their past experience a TV is fragile and TV needs to be handled carefully and because this is a TV box This is a TV So sometimes you can use anticipations in a way which can work in your favor but When you are having lots of data when you are deciding on behalf of the user You need to be careful about How far can you go? What responsibilities you have and what you can do it can easily be misused it can easily be abused in a way where it can control your freedoms control your Decisions you are making and it does that It can lead to a life which is different than what it could be So everyone is aware of China's social care system and how it uses the data and AI to track people and blacklist them or Profiling minorities or profiling criminals so if you're making a product and If you are using AI and data It's your responsibility to make sure that data and AI you are trying to use Doesn't cross a line doesn't cross a boundary doesn't cross a limit where it is acceptable and Of course machines can't replace humans. So Yeah, so machines can replace humans. So we have to make sure that We humanize our interactions and minimize the conflict of machines So I will just recap so basically this is what I talked about today Change resilience anticipation intelligence and this is what we need if we have to Create a process which is anticipatory and which help us create anticipatory experiences and That's it and thank you. Thank you. Thanks. So we have time for one question still How did Anticipatory experience in manual life take it to where it is now are there any success stories that you can share with us? So I joined my new life this year. So I'm not there For a while But what we are trying is we are trying to build Solutions which uses the rich data we have about the user and we are trying to see how we can help them and pre-empt their needs So we can pre-empt what kind of policies they might have what kind of policies they might need because as they grow older They might need certain policies and as as we get more information about the user and we know we can help Find them right products with right coverage. So they are not under insured if they're not sure it's good for them because if they're not understood ensured, so it's like They're safe and they are they can manage the risk when the risk comes So what we are doing is basically taking those data and trying to integrate with the product So as I talked about system design right now that data sits separately and we have to just bring that data into the systems We are building So that's where we are right now Okay, thank you and we'd like to give a token of appreciation to you