start time so Im gonna get started thank you all so much for joining this webinar my name is Mimi on and I run hub stop research where todays webinar is presented by us alot Academy and the IBM Learning Lab these are both great resources for continuing education they should definitely check out after this webinar so as a reminder for folks online we are recording this webinar and you will all be sent this link later day.
so to start we have two amazing panelist alyssa simpson scotland and on a pass a to alyssa to introduce herself briefly hi ELISA Simpson director of product management at IBM Watson my portfolio covers what we call these sensory services so teaching Watson to see and hear speak and and the emotional portfolio of feeling and understanding emotional intelligence Thank You mr Scott this is Scott lemon Im the co-founder of equals three we are a very.
close business partner to IBM and in my past Ive had the great opportunity to build marketing services and add tech companies that have had great deal of success working with some of the worlds biggest marketers on how digital technology is transforming their business excellent thanks so much for joining were so happy to have you too so lets start with an easy question and if I saying easy its not easy um how would you describe AI and lets keep it brief cuz were gonna dig obviously.
deeper into this in the next hour but you know when you’re talking to someone you say I work for Watson I work for equals 3 and we dowel in an AI how do you explain the concept to someone who may not be familiar with it so from my end what I look at is different than traditional computing there are two attributes we really focus on one is the.
ability to look at immense amounts of content and understand it in a fairly human-like way I mean truly the computers dont understand it as a human does but different than traditional computing cognitive allows that understanding and the others the learning nature these platforms these cognitive learn by usage the more you use them the more you train them the smarter they become and I look at that as these kind of two big differences versus a traditional computer yeah that’s really well aligned with how we look at it Watson we take it a little bit of a step further sort of that basis.
of understanding large amounts of data reasoning right and and understanding what that is in the context of where that information is coming from being able to learn by interacting with humans as there’s feedback from that and that interactive piece right so being able to interact and partner with humans who are providing that training data or you know making decisions from that unstructured data I love that because because Im not from that space I tend to say its though enabling you to be a better marketer if you make better decisions help you be faster and smarter.
leveraging data that you probably couldnt analyze yourself because there’s just tons and tons of data being generated but now I see that using that term enablement can be really really confusing because it it mixes up with all of these existing technology technologies that are available for marketers today so what is some of the confusion that youve seen with marketers around the topic of AI we think we think robots we think cars that drive themselves and a lot of people cant really grasp how marketing can be affected by AI so when you’re speaking.
to you you know prospects and customers what is the message that you give I think you know the first sort of myth that we like to dispel at Watson is that this is not magic there’s no magic ball anywhere and that you know AI like anything else is a technology and you can break it down into you know small pieces to be used for a particular.
application you know such as marketing and so I think the first real hurdle to get sort of folks who are unfamiliar with the world over is that there’s not a magic button there’s you know its really exciting companies like equals three who have built what appears to be magic you know and and magic buttons on top of you know a lot different technologies but at its core you know these are you know sort of transactional technologies that can be.
applied in specific applications such as you know personalized marketing and maybe equal so you can expand a little bit on what you’re doing there but that’s the myth we like to dispel here yeah that’s actually has a great set up because we do run into that problem where people see a demo they see it all work and its like oh it just magically works and so one of the.
things weve had to educate people on is we have found there’s a significant difference between a cognitive project and a traditional one in a traditional or at least for us you know IT kind of does all the development all the backend work and the business user waits for the result but here because it isnt magic we actually can implement loosely very quickly but then its incumbent on the.
business user to train their new associate their new companion and they actually have to spend a fair bit of time in training and mentorship so their companion can be effective and so rather than a finished system and then waiting for IT to do more its actually on the business user to do the training and really do a lot of the work interesting so out of the box you’re not going to get this magical assistant that can automatically sync with all the data sources that you have and give you the.
recommendations you need you need a little bit of time to work with the system itself because while its smart its not that smart yet I want to assume Scott well you know that’s actually a that will end up being a great segment segue into the demo because I can actually show how this all works excellent well going back to Alyssa tell us a little bit about IBM Watson in the product portfolio that you manage youve.
talked about things like emotional elements that you teach Watson can you give us a little bit more color on that because I think that’s something that we as marketers at least today without touching the the product know like very little about yeah absolutely and so Watson is a fabulous part of the broader IBM company were like a very well-funded startup within and so were you know sort of out bleeding-edge here creating technologies that are at sort of the platform level.
are a series of apis that each do discrete functions and each one of those functions serve something a little different but they are basically like Lego blocks and companies like equals three are stacking them together to solve a particular challenge in a particular industry right so in this case marketing what a lot of people dont necessarily realize about watson since there’s you know a lot of attention and hiker out AI is that how much its actually already being used in.
reality and you might not know it because its sort of like this sort of I had a friend at one point call sort of a a sort of this digital toilet paper right its something that you dont necessarily know exists but its there right so major banks major insurance companies that youve already interacted with lots of companies large and small.
are using this today and they may or may not you know be super clear that theyre using it behind the scenes to help automate some of the customer interactions up there Im serving with you know were really big into transparency at Watson and so we like to be really clear and open around you know what data is being used to train you know why and so in the emotional intelligence basis you were saying you know that’s a really fun and exciting area for us we have sort of three.
different technologies in that space today we have tone analyzer which takes text and analyzes the tone of that conversation so today actually really released a major update in that space focusing on the customer care market so for example if you’re interacting with a customer service agent and you are perhaps calling a company because you’re frustrated likely but we can understand that you’re frustrated and help escalate that conversation faster or direct you.
to the right place and really be attuned to what those emotions are in the customer care space right that might be a little dip from emotions that you’re expressing more generally on social media or other venues yeah the emotion stuff is really interesting and because its not language that we as humans often talk about and theyre reasonable humans can disagree around what emotion is contained in a particular phrase emotion is a very sort of multi-faceted way of expressing yourself right you have a facial expression and you have your tone of.
voice you have the actual words that you’re saying themselves you have the context in which you’re coming into the situation right are you on the phone are you on social media you know what is that background that’s gone on to that interaction that you’re having you know what do I know about you but you know you may look happy today ELISA is pleasant or shes you know not angry but.
maybe I am angry and Im not telling you so its a really tricky space its really exciting were really proud of what were doing there and one I know that equals three is using quite a bit as personality insights which is you know taking personalities and you know understanding you know sort of intrinsic natures about people and you know when we say someone has a really strong EQ right we mean theyre good at reading people and understanding and so how can we build Suites of technologies that help understand people better and.
interact better and apply those towards delightful client experiences yeah that’s fascinating the minute you talked about scanning for tone I I was thinking of a certain airline that issued an apology that nobody appreciated under what Watson would have had to say if we had run the text through the system I was actually a pretty classic use case around you know understanding you know what what people are saying or even you know if youve.
written an email right and you might not be aware that it comes across as really assertive right and say you can have there’s companies that have built little widgets that integrate with Watson and say hey like yeah this is an aggressive email do you really want to be aggressive you know why you know heres some suggestions or how you can alter things so there’s a lot of exciting work going on yeah and I know some email marketers would probably love to get their hands on that type of technology and make sure that their emails are effective and.
conveying the tone that theyre you know right that matches with your brand right and that matches with what you’re trying to communicate because not everyone has a good sort of third-party independent you know editor to review what theyre saying and how it comes across right in the same way that me as a human Im always interested in how people are responding to what Im saying and how Im communicating it and I might think.
that Im being you know Pleasant and open and conscientious but I may come across as abrasive and assertive and you know different from how Im interested in communicating my emotion so fascinating Scott so Alisa kind of set you up to tell us a little bit more about Lucy or your product what what is it what what is looking to do a little bit about what you know you’re working on today so yeah the so Lucys the cognitive companion to the marketing professional shes built for the Fortune.
1000 and the agencies that served them the problem that we set out to solve working with IBM and Watson was really the idea that marketers have so much content in so many different systems they have the content that they own on their own databases marketing analytics website analytics media data they have third-party data like forest or eMarketer cantar and others and they have all the own documents all of the powerpoints PDFs and the like and if.
you’re ok should I bring up Lucy totally so Ill just turn on screen sharing yeah I love seeing real-life applications of what AI can do especially again in the marketing space because even some me Ive done a lot of research into it its kind of just this cloudy topic where I dont really know what it is so to see an actual example is always super valuable in case for the audience that we have here absolutely so this is Lucy.
shes a software-as-a-service she lives in ibms bluemix environment and the N she has three major components research audience persona modeling building really type persona models along the lines what a list was just talking about and then helping with media planning and so what ima show here is research and you can see Ive asked a question of Lucy what is the latest information on self-driving cars in this instance Ive got a demo that’s around automotive marketing and Tesla specifically Lucy gets trained around the data of the company that hires her so we have to be.
pretty specific in that regard so heres what shes done when I asked the question what is the latest information on self-driving cars she comes up with a list of responses these can come from databases they can come from powerpoints and PDFs and documents on our file systems or income from third-party relationships like the e marketers and foresters and the like so shes showing the list of responses and you can see on.
the Left her confidence score her confidence is based on her natural understanding language which comes from Watson as well as the training that is given to her by the companies hired her so here I can see her responses to this question on self-driving cars with a 94 percent confidence now keep in mind this is a train to Lucy she has founded some information from emarketer on level of interest attitudes and opinions about self-driving cars so.
great stuff in the bottom right where it says was this answer relevant I can say yes give Lucy four stars that’s the training that edifies and gives her confidence and as I go through this I can see other examples I see more information from eMarketer I can see yet additional reports from emarketer and as I go through this shes giving us the components of emarketer reports that she.
thinks best answer the question just below her confidence in those emarketer reports she has some great data from statistic reading these responses saying how she did a great job that impacts her confidence if I see something I like I can save it to a project so by clicking on the the star here I can pick a project and save this to it and so.
that’s how we end up interacting with Lucy we ask a natural language question she goes through all the data this available tour shows her confidence and gives you her best responses now another example is I want to find a SWOT analysis for Tesla so Im going to ask do you have a SWOT analysis for Tesla now in a world without Lucy what would happen is I would think Im.
in a marketing department we have dozens or hundreds of people here and I might say I know somewhere we created this but where and I might post to an internal social network like a facebook for business or to a chatter I might email around I might knock on some cue balls but the chances of my finding such a specific component as this within the.
thousands of documents that are in an enterprise is very very difficult Im more likely to recreate it than anything else but here I asked do you have a SWOT analysis for tests I just as easily could have said what are the strengths for Tesla what are the weaknesses what are the threats and Lucy found it so where did she find this and on the bottom left I see the source in the emarketer insta teesta reports that.
source would have taken me to my subscription in this case the source is going to take me to the specific file and so when I click on source you can see Lucys downloading a file its a PDF its a 46 page PDF that is like so many documents that are within an enterprise where a singular document could answer dozens of different questions so as I scroll through this this is a document that Lucy read but she answered with the.
precision of the specific answer that’s within this document so as I eventually get to page 19 I see that SWOT analysis and so its not just you know Lucy saying heres a series of documents or heres a document its heres the component in that document and then I can save that component for later reuse now a lot of what Watson has been known for you know we saw Watson on Jeopardy was the amazing ability looked through huge amounts of content text and we.
think of that as unstructured data so these ignition examples either my own documents or the licensed documents from emarketer and other sources are examples of unstructured content the thing is marketers need to work with data that’s structured and unstructured they need to be able to ask questions of their marketing automation platforms like HubSpot they need to be able to ask questions of databases like Google Analytics or Omniture or other website.
analytics they need to ask questions of media data from sources like comScore or cantar or Nielsen so hiraman asked question which is how much did the end of you spend by month last year this is an example of a natural language query is going to go against a database without Lucy I would have to go into a platform like cantar or Neil Center comScore to ask this question Id have to be trained on there how to write scripts or how to do reporting but here we bring a natural language interface to this source of.
data so here were working with cant our data you can see the data that we connected by a API to cantar and extract it to answer the question and you can see the visualizations of this data that were able to provide so if I ask something like who are the competitors for BMW that’s another question that can be answered from data that existed cantar and here we see the competitors if I want to ask a question like how much did the MW spend versus a Jaguar.
vs Audi and versus LED like for them Kia and some others by month last year you can see that she will go out to cantar and formulate this question come up with the response and its really pretty amazing how we can work with various structured sources of data all through this natural language interface and Lucy works with this very quickly to give us what can be some fairly complex reporting and brilliant so there’s one.
other thing I wanted to show you and you brought up United Airlines and you brought up what could we learn from chucking things like cone from messaging and so one of the things were doing with Lucy is we are combining multiple sources new sources social all together in one component so an initial example this is brand insights and what Lucy does is she is reading through roughly a million pages of content a day and here.
weve looked at United Airlines over the last 10 days were looking about 10 million pages of news content coming from common news sources like Washington Post New York Times CNN Reuters and a thousand others and what Lucy is doing is shes saying the sentiment in articles about United Airlines is really really negative 74% to the negative only 12% to the positive there is a ton of.
content here that Lucy has gone through you can see under the sources and articles you can see the volumes of mentions so New York Times has written about United Airlines thirty three times in the last ten days and if I click on this I can see which articles were considered negative or positive were looking at that tonality per article and so we can easily go through the list if I want to see when did it get bad for.
United I can click on United Airlines the brand under the topics and I can see there are 500 negative mentions on the 10th a thousand bad ones on the 11th 900 more on the 12th you know this is just a crisis its just been you know you can see that bubble and when the news which is so bad we can see hashtag analysis we can see image associations and you know you see the what was the United customer being dragged out of the plane so all this is being combined by bringing.
social and new sources together into one place and by the way we also so compare how sentiment runs in news which is seventy-four percent negative and social which isnt quite as harsh which is a little surprising in any case what you’re seeing are the research components of Lucy the ability as natural English questions against unstructured content that’s licensed like emarketer ask questions against your own data like the PDFs ask.
questions against databases like the cantar database as well as how were able to use Watsons ability for measuring tone and sentiment to look at huge amounts of content to do things like a brand insights so Lucy has all kinds of other features would love to show but this gives you a good idea of how were working with those core watts and components into a package solution for marketers and Lucy being able to scan assets I know that that’s a pain that we.
even feel at house ah you know were out perfect we have lots and lots of PDFs and files and we have it in our internal kind of wiki system and it gets lost to totally being able to search our own archives like that would be just I want it sign me up even so I wanted to go back to the first example where you were training Lucy by simply giving it a star rating I think that’s just a wonderful visual testament to how simple it can be because I think a lot of people when.
they think about AI they think about having to dump in a lot of data to Train it right and then complicated algorithms get spit out and you have to have a PhD to really navigate your way through the system to make it do the thing that you want to do but these are overlays of the technology that you’re building what iBM is enabling it can be as simple as the.
Netflix you know thumbs up thumbs down you’re going in the right direction it could be as simple as that you kind of teach or train or AIC to do what you needed to do which is a wonderful example I have no question for that I just wanted to point that out that’s fabulous alright so thanks got for the demo super super interesting so from the hustlers perspective weve been trying to dabble in AI I dont think were as far along as as YouTube obviously but we.
wanted to definitely kind of share with our own customer base on what marketing automation can become with the help of AI weve got our own kind of natural language processing bot where we allow people to dig into their CRM prospect look for new leads and also create new blog posts just by bypassing kind of menu navigation system completely right we just have the plot safe create a new.
blog post for me and Ill pop out and give me the link I think for a lot of folks though the concept of AI is a little bit frightening and so you know do you think that with all these new technologies that are being built I think it will definitely change our jobs for sure but do you think that jobs will be gone that were going to be obsolete that the machines are gonna take over.
obviously you can tell from my tone that I have a bias and how they the answer I think it is something that is part of every single conversation nowadays that’s around AI so love to get your thoughts on that so you know for us the whole idea of the name equals three is about the idea that one plus one equals three that better than the individual or better than the Machine are the two together so I think that we will see scenarios look at how they can make.
changes to staff based on automation or certainly seen that in many industries I think the business that compliments the talented individual with the AI companion will outperform those who dont adopt or embrace AI at all or those that rely too heavily on the AI to do the job itself and so were pretty bullish an idea that that this is all about supplementing and enhancing the individual now I think whats going to.
happen is that were going to see more expected or demanded by the marketing department more expected and demanded of the agency and that the way they keep up with that is AI its going to enhance their service delivery in their performance but we look at it that more will be expected and more will be achievable people have able to drive better results in better outcomes.
because of their embracing of the AI versus the displacement of people yeah I think that that’s a hundred percent with how IBM really comes to market and talks about this you know we see this as man plus machine right and and Jennys gone on about that many times its about the partnership here between humans and you know cognitive technologies we actually.
at IBM when we say AI we meant we meet we sort of talked about augmented intelligence right which is all about augmenting you know what a human is already doing and extending that to be able to do things that they could not have done before so one example in the marketing space from another client working actually with Ikea a company called I trend they I can.
use you know interested in social media listening right similar to what Scott just demouth and they actually wanted to understand if youve ever put together an IKEA product it can be a challenge sometimes and sometimes people get frustrated or they do really creative things with a bookshelf like turn it into a bed that IKEA didnt necessarily anticipate or think of and so they did a project that again extended the reach of.
the marketing team by looking at Google Video YouTube videos right and understanding visually speaking where were the IKEA products that they were particularly interested in appearing in those videos right and then what was going on in the context of those videos was it positive was that negative what did it associate with in the products keys that IKEA actually sold and so that was an example where there were you know hundreds of thousands millions of videos they couldnt possibly have done that with their marketing team right its.
something that from a human perspective its way too hard way too overwhelming but if you can do that using AI by training a visual classifier to understand you know visually where are those products and when which ones look like ones I sell you can start to have an intelligence that you that was not possible before but that was only.
possible because the humans trained the visual classifier to say hey heres what it looks like heres what I want to see can you go find that tell me where this you know exists so that’s just a good example where the nature of the work may be shifting a little bit or someone may have different responsibilities and they did before but you know to Scotts point the winning.
companies are going to be the ones that embrace this idea of both excellent so before I ask the next question Im just going to pause and let folks on the webinar know that you can ask questions of our panelists I tweeting at HubSpot Academy use the hashtag hub sype webinar and though someone will come in with the questions and well be sure to answer them if you have any time left over so if you have.
any burning questions for the panelists please do tweet at us and we will try to get them answered at the remainder of the webinar so I just have a kind of a not a personal question but more about why you two decided to you know you started your own company Scott on around AI like what was the potential that you.
saw what kind of motivated you to get started with this what what caused you to think this is this is it and Im gonna dabble in it Im gonna build it because I see X amount of potential in return and what Im gonna build yeah so you know Ive always been fascinated with the AI space and when IBM you know showed up on Jeopardy it was like wow you know like and that’s something I just you know explored it was super interesting and then it was about two years ago it became clear to.
us that the Watson platform was being made available to developers and so I sat down with my business partners and said what could we do if we had this what is the problem domain where if we could apply everything IBM is invested you know the billions and years they put into developing this platform if we had at our disposal what could we do with it and we thought about all the marketing technology platforms we have stood up for customers over the years and thought.
about just how much data is in so many different platforms if we could find a way to bring the power of Watson to all that data whether it was structured unstructured under license what we do with that and that became the impetus and then we started to we worked with IBM they were great to work with we loved the tech and we started to build out the MVP around Lucy and bring it to.
some of our trusted contacts they were blown away and that gave us the energy and excitement that lets lets go for it make it happen I think for me I was really attracted to Watson and this space generally because I see the potential to change the world for the better its a cheesy answer but its really its what gets me out of bed in the morning is exactly a sort of what Scott is and many of our other customers are doing with the technology and how theyre.
applying it to a whole host of different industries and business problems and it delights me you know to hear our customer stories and to work with those customers around how theyre actually using this to make something easier or better or delightful for that end customer that really is exciting and something they they could not do before so that’s that’s what gets me out of bed in the morning and I think you know its.
its certainly a privilege to be in my position do you think that so the reason why we had hot spots you know held this panel even though were not really in the AI game is that we undertook a lot of research into AI because we saw a lot of potential obviously but a lot of confusion at least among our marketing audience people could tell that it was something that was important it may disrupt their jobs but they just didnt know what it.
even was and so we kind of wanted to unpack that a little bit and weve been you know just had the pleasure of working with you to even develop this webinar to kind of educate our audience I kind of asked you this question before but why are so many professionals just not aware of what the potential impacts.
of AI is is it because its nascent and so the tools are still developing there’s not a lot of messaging is it because weve been told in popular culture that this is what we should expect AI to be and so we have this preset notion there’s so much potential there’s so much interest and yet so little clarity why then I think one of the challenges that this basis has is its been a promise for the last 50 60 years right Hollywood has promised this.
this magic you know future world and so there’s a lot of pre-existing ideas around what it will be or what it should be and I think you know in some ways this technology and this space in general is very nascent right were just starting to see in the last you know five years I was a real you know businesses use this in reality at scale in production to really and truly solve hard problems but its not.
new technology but there’s sort of a confluence of data of hardware of you know accessibility of this technology that is new right and were able to do things that we were not able to do 10 years ago or 15 years ago and so I think that’s one of the the challenges that we face is sort of re-educating people around you know whats magic and then whats now and then I think the other side of it is that one of the biggest pieces.
that I think we really work with our customers on which is what Scott touched on a minute ago which is that he sat down you know wasnt starting equals three and thought about what problem do we want to apply this to and that really is the hardest problem with any technology right I can sell you a knife or a MacBook or anything but its its just technology the magic happens when you apply it right as a chef and you.
create a masterful you know dinner or if you put a MacBook in the hands of a you know iOS developer and they create wonderful mobile apps right like anything this technology is a tool its a new tool right you know new ish but its really up to our customers and the users of this to make that magic happen and apply it to particular industries particular business problems and so I think there’s a lot of people who one you have to understand how the tool works and learn it right and so that can be a challenge to understand hey heres.
what it does heres what it doesnt do right you cannot make breakfast with your MacBook but you might be able to make dinner with a knife so you need to understand sort of the limitations of the tools and what they do and then you have to think about hey like you know am I gonna make Vietnamese food tonight or am I gonna make Italian right you have.
to get specific around what you’re going to do with that and and how you’re going to create a delightful experience so I think that that hurdle with AI is around you know do I want to do customer listening in social media do I want to optimize my call center do I want to you know apply this to medicine do I want to applies to health care do I want to cure cancer right how do you want to take this tool and apply.
it to the problem that you care about and breaking down that problem that you care about into its parts and pieces can be a big challenge right Id want to do social media listening I want to understand everything anyone has ever said about my company ever and magically have an insightful dashboard that’s a lot of work right and Scott and seumas have have proven that and and really.
made that easy but there’s a lot of smaller tools and smaller parts and pieces that go into making that magic happen and so I think when people get overwhelmed sometimes its because theyre trying to break down that problem into smaller and smaller components of which AI can be applied to yeah Ill just add to that a little bit which is you know one of the practical challenges from marketers is they’ve never done this before so I totally agree with everything Alice has said that you apply that to yes identify the.
problem but if youve never bought it I mean for us there are no RFPs for cognitive agents there’s no marketing departments that have pre-existing budgets for Im going to put X dollars into AI and so you know because of that you dont have people with the experience of having run AI projects before they havent bought it before theyre not quite sure what its going to look like so there’s a huge level of market education events like this are immensely helpful because people can walk away and say oh I get it you know I.
get at least that’s one facet of of my job that could be impacted by AI and as much as we all have AI permeating our daily lives you know Google is an amazing tool for AI its no longer a search engine your Facebook newsfeed is completely driven by AI to some degree people use you know services like Syria and things like that so we have AI in.
our day-to-day lives but we havent put the necessarily into our business lives in this way and so its new for people so market education is just a huge huge element to get to that point where you can say what are the problems I could solve excellent so I do have a question that actually kind hi it ties into what were just discussing so Im going to ask it its for Scott and it has a little to do with this made confusion around like what AI enables versus something that exists today so the.
question is how is Lucy different from other listening platforms so the third example you gave in your demo a marketer is asking well that kind of looks like on the shirt kind of looks like something good so whats what are those of you product that makes it more advanced so heres the thing if your whole life is in social media you know you’re gonna.
live in products like you know sprinkler system OHS and you’re gonna go a mile deep if your life is as a data scientist you were going to use a dome or a tableau or Watson analytics type product if you’re in marketing automation youll use one of the marketing clouds but to the VP the product manager the the strategist the planner somebody whos omni-channel somebody who either isnt using all of their data or theyre sitting there with twenty windows open at once Lucey becomes amazingly helpful to them because through one login through one.
natural language interface theyre able to get at that data that would otherwise be an Omniture and perhaps is only in the hands of very few people in organization theyre able to get to that cantar data though otherwise only be in the hands of a few people thered be able to fully utilize emarketer because ii marketers got great content but too often an enterprise its not used as universally as it ought to be or that be true a Forrester and others as well and.
so were saying through one login one natural language interface I can query dozens of different sources and have it all come together now if my life is only in one source then the deeper tools are going to be use use that you know use that tool if Im a cancer operator and I know how to write a script and I know how to do the reporting I should just.
live in cantar but if Im the agency Account Director the VP and I just want to know how much did we spend versus our three top competitors and I dont want to ask the decision science or the media team was overworked I can assess Lucy alright last question and then well dig into some more of the submitted questions um so where do you see AI heading in the not-so-distant.
future and how can we get started in using AI today we dabbled a little bit in that piece but tell us about what you’re excited about new developments that are coming so from our end and Alyssas probably got a broader perspective on this being at IBM but from our standpoint we see that its going to permeate everything I get so excited when we connect to you know a new source and Lucy learns it and she.
becomes smarter smarter and its amazing to see how that data gets stronger you know the longer somebody has an AI companion the better it performs for them and so and then the other part is for us just product roadmap were constantly inventing that whats next its exciting to sit around with the team listen to customers and get their feedback on what they would.
would want to see and then make it real yeah were similar at Watson with a you know potentially a little bit of a broader scope you know were really excited about sort of the future of everything that were bringing out you know Mike teams have releases I think we have three releases this week so were constantly iterating and releasing new stuff and that’s just my team there’s you know many others that are that I work closely with so were developing sort of at a.
lightning fast pace to keep up with market demand for different features and functionality as I mentioned today you know the customer care tone models are just available last week we released a visual recognition tool around making training easier and were coming out with some more exciting stuff in the next couple of weeks I think it you know more broadly though there’s a perception that this is hard and its difficult to use and take advantage of something.
people dont know about me is I dont have a background in computer science I have a liberal arts degree and I dont code on a regular basis but I use AI right and I can use these developer tools I there’s a 13 year old tongue mate whos gotten a lot of press with IBM and hes always the first to adopt you know whatever we put out there even before you know been some pre-release you know beta right and hes 13 years old but this stuff is is there’s free versions of all of it its easy to use and if.
its not easy you know call me Im not doing my job well but the idea is that you know this stuff is easy for developers of any skill level to get started with and you know there’s certainly an expertise and a training as you get more advanced and more sophisticated with the thule what we want to do but at its most basic level these are API so if you to integrate an API or even better some.
of our services have tooling on top of them to your business user like me you can log-in and build a chat bot Im you know Im using our conversation service so as an example I got sick of people asking me the same question over and over and over again about visual recognition and pricing and weird if I Docs everything else and I was like Watson could handle this and I built a.
little chat bot right and again Im a business user I dont code right and I was in my hotel room and an hour later I was done and I launched it right and so I think that’s dispelling that myth that this is hard is something that I tried to reiterate and dispelling the myth that its expensive because these API costs costs you know fractions of a cent and its.
something that can its easy to get started with and scale up as you grow this is gonna be a natural question that some folks are tweeting at us where can they find if these resources that you’re talking about Alyssa oh just go Google go to IBM Watson comm get started with a bluemix account just like equals three did you know a couple years ago and.
start making API calls but walk IBM Watson platform is hosted within bluemix accounts are free checkout reach all right so lets bring it back to marking a little bit I think you you two are definitely really well aware of whats available on the market so there’s some questions around all right how can he help me deliver the right content to the right person at the right time what exists out there that I can leverage to do that and if at all wants a ticket.
ahead well Ill just say that as of the moment Lucys fairly unique in what she does the ability to do both some of the research things you saw as well as the audience persona modeling and the media planning capabilities within a package solution are currently unique for the most part so my answer the question is well you know talk to us about Lucy we have yet to have any significant client.
interactions theyre evaluating us head-to-head with another cognitive solution its really the evaluations are they ready for cognitive what are their use cases what are their sources of data what are the expectations for how that data looks within a platform so you know our point of view in the marketplace is we certainly know there will be competitors there has to be but at the moment we havent seen a lot of that as of yet I think you know certainly what equal service build is is unique and and unusual in the space I think there are.
many agencies that were working with that are doing components of this right and using different apis for similar types of use cases around ad targeting personalization as an example or something that are a little bit more on the fringes of what equals 3 does so ocol V is one that has had a lot of attention and case study and what they’ve done for the US Open or Wimbledon is well-documented examples of how theyre delivering you know with brands right the right messaging or the right person at the right time right.
with all of their sponsors and many different brands involved yep to that end you know agencies like Sashi did some interesting work that was you know put into the Cannes Film Festival that was you know AI driven hes got agencies like Havas developed cognitive practices but in general the cognitive practices are also creating bespoke solutions versus have providing packaged offerings got it I think we were partnered with Salesforce and they have quite a engine that’s a being built out for their.
marketing in their CRM platforms and even on the hosts outside we were seeing a lot of interest and were certainly working on allowing our own customers to examine that you know previous emails they sent content they’ve written channels that they’ve explored and then eventually our goal is to package at in the way using AI and natural language processing to allow people to understand.
ok this was a successful channel that we where we pursued a prospect this is a great version lane that we can kind of enrich and enable in the future using some of these AI capabilities I think there’s definitely a lot of Murphy Automation companies like HubSpot definitely feverishly working on it its such AI think you you mentioned Salesforce specifically and Id be remiss if I didnt you know sort of bring up our we.
announced recently a really large partnership with Salesforce and many of those use cases are in the marketing automation space you know I think Salesforce is really interested in around how do they take the richness of the data that they have around customers and really use it to deliver personalized messaging and enable sales teams and marketing teams to really delight that end customer so were.
really excited about the work that Salesforce is doing on integrating the Watson technologies into their platform on to that end a lot of the effort in AI in marketing which is not the area of our focus isnt that area of cognitive engagement how can I use cognitive to optimize how Im fitting a media how can i optimize you know performance at the.
e-commerce level or conversion of some kind and there are a fair number of solutions out there that are working in that space so very different than what we have been talking about as far as research and things like that but there’s a fair amount of energy there certainly that’s a big area that Einstein is focusing on and trying to drive optimization of their marketing cloud and you know creating better one-to-one experiences for their.
customers I do some very cool work there yeah the ad buying piece is really really I think compelling for a lot of marketers I saw one of your colleagues actually demoed pieceof of Watson that was kind of just focused on advertising optimizing getting the right type of you know channels hitting the right people at the right time all powered through the I engine I think.
for a lot of folks advertising is like the biggest crapshoot you know for marketers because we just dont know for hitting the audience that were getting the conversions are actually going to generate the revenue and I think tinyeye into that to know that the the purchases that you do make are the smartest possible and hopefully with all the data.
sources coming in and tie it back to an actual sale right that’s super super powerful and I think a lot today just dont have that ability to track it in that type of detail yeah right the Holy Grail is the market is the attribution and the automation right of all that and I think even you know looking ahead around the attribution.
problem which you know is often a disparate data problem its also looking at the impact so lets say you did reach that customer right and they they did make a purchase but how did they feel about that purchase you know was it a good was a successful one how are they feeling about that product are they that encouraging others to buy so there’s more than just did it happen or not did I get that buted I get that click or not.
but was that click meaningful was it positive was it you know can you can you get further right then just a more sort of wrote it happened or not it didnt so I think oh one more question this is from some from Southeast Asia so in turn as international the top of mind for this particular person how does AI being adopted around the world and specifically when it comes to localization as people are going across boundaries across geographies across languages is a nice-lookin that can help.
us bridge that gap yeah were really focused on that problem at IBM and we have a huge amount of resources and attention on solving that internationalization problems IBM operates at 170 countries I believe and so we need to have Watson understand not only the languages but the cultures and the context of our global client footprint right because Ill go back to you know tone and emotion right those cultural norms impact how do you understand and apply AI and in different places so one example that I use there is something that like color right for.
example in Japan the notion of green is a concept that is different than green in the United States right so a simple tag like that around hey this you know this tree is green that that concept is different right because green is not a fact right its this abstract creation that we have of color right and so how when we do global expansion how do we not just we translate something into you know a.
different language but how do we make sure that we are being aware of the cultures and the learnings that we can create you know context specific relevant a solutions for that market so its a lot more than just language its got any thoughts I think its a great question its something that were were mostly focused on on really us all of that said Lucy takes in questions from.
dozens of different languages although providing english-language responses and we have plenty of global customers again working against English data its interesting to think through how do you start to compare different cultural norms how do you have content from different geographies and how do they compare equally within that aai environment and then IBM just has a much better better and bigger perspective on.
how to solve that because theyre immersed in it I think some of the other challenges for expanding globally are around security and data sovereignty laws right for example we just opened a a data center in Frankfort that were really excited about to serve our European customers and then were also working with partners so you mentioned Southeast Asia you know we have SK a big partner in in Korea and.
we have you know other clients who are partners of ours serving those end customers you kind of mentioned it tangentially the last question is are there any concerns of AI being hatched for trade secrets for data how how does IBM approach this how does you know how do you Scott kind of when you’re building up this product you know you’re.
compiling quite a lot of inside data is uh whats the approach there yeah we take security really really seriously at IBM its its not trivial at all you know we try to differentiate from our competitors in the space actually on security and on the approach that we take to data so when you’re you know weve reserved the you reserve the right.
as our customers not for IBM to store or learn your data right you could use Watson without us storing or using anything that you’re sending to us so that’s the first sort of big way that we differentiate and then we offer different sort of levels of separation right we offer our public cloud we also offer premium and dedicated options for different security environments and whats appropriate given what type of data that you’re looking to do analysis.
on right so one example would be our healthcare IBM Watson health right is a HIPAA compliant totally separate type of environment then if you’re just looking to understand you know social media is someone posted this image what is this image of right its a very different types of data security requirements yeah on our end the security side has to be supportive of the enterprise and so we have a couple of really important.
tenants for the agency customers Lucy needs to help them stay in compliance with their MSAs to their end customers if they’ve got multiple brands they’ve got internal firewalls the right users can only see the results of the content that they should have access to and then four are the data partners those that are the providers of third-party data we need to ensure that Lucys helping our customers stay in compliance with their third-party data rights so that you know if youve got ten named seats to source.
X those ten people in Lucy will see those results whereas the rest wont and that sense of being a very important part of how weve architected Lucy the other thing is partnering with IBM leveraging the security they have for the data that is within their environments has been really critical because they’ve got you know world-class infrastructure to support us in that excellent all right so now is the real.
last question thank you everyone for joining this session my question is when the first self-driving car rolls out of the factory are you guys buying one Ill say I already have a Tesla and I used its autonomous driving features a ton and I loved it so its not true self-driving but I got a lot of miles during wheel Im not there’s no swearing rule at all how about you Alyssa Im really excited about self-driving car technology of a number of friends of Teslas and I I.
think its really exciting I think were just getting started so I was at an accident a car accident last week Ive got a concussion and I was thinking to myself oh I cant wait till its self-driving and this doesnt happen right because its human error today so I cant wait for it to drop me off and then drive home and then pick me up so I dont have to find parking that’s like Im excited for that so I have an early.
adopter of everything driving cars have a way to go were not there yet Im looking forward to it all right folks well thank you so much for your time and your insights Melissa its amazing that you are so coherent after a concussion oh my goodness but thank you again I hope for the audience that this was an insightful and interesting topic of conversation continue it on Twitter.
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