 half of us like for us and you know what sticks out the status it checks the reservations and a lot of apps works for us actually and many streaming applications that's you know such as songs movies it's the so-called location and the time where we are so everyday activity everything that we do in our daily activity includes AI directly or indirectly so we are interacting with AI artificial intelligence so integrating artificial intelligence into Drupal the experience is going to make or leak in the user experience so that's a topic about so it's about the synergy between AI and digital experiences has become so interwinded it feels like a magic so why I picked the word magic is like my four-year-old son at home whenever he uses the virtual assistants he asks for writing it seems he asked for a question it answers he asked for any any any topic it replies it asks for an animal sound he replies so he feels it's a magic he asked me mommy it's a magic she talks everything she does everything that's true right like even I feel it's a magic everything with AI is like a magic I know there is other part we come to that later but for now let's take the magic that AI provides to us so this session is all about how we can incorporate this magic into our websites and give a captivating experience to the end users so moving on to the topic so we have to do we cast in this show one is like Drupal so we know Drupal as a content management system from its existence like from 2000 but majority of its existence is considered as like a Drupal content sorry content management system well like the website owner create content you know published down to the end users but with the growing needs of the end users and advancement in technology our community has to complete moving the Drupal from a traditional content management system into a what I say comprehensive digital experience platform which allows integrating with multiple third party tools allows personalization capabilities and allow multi-channel delivery so that is a huge leap and provides a captivating experience for the end user that is what we are actually targeting our business right so moving on to the next past year that is like AI so AI is definitely a buzzing word like wherever you go whatever you talk whatever you do you will hear AI at least once in a minute so we hear AI wherever we go what topic we discuss AI comes in there is a buzzing word definitely it is what is AI so it is about like it is a human intelligence okay it is about you know assimilation of human intelligence missions this program to perform certain tasks that typically needs human intelligence like thinking reasoning problem solving and you know other activities which typically needs human intelligence that's what it asks so AI not only revolution is the way the business progresses not not only revolution is the way the economy progress does it change the way we have our life every day all our everyday activities getting upgraded or impacted by an air so it plays a crucial role in our interaction with the digital world so this above AI so why Drupal VXP and AI must meet so this is a consistently or a constantly seeking innovative ways to elevate their audience presence so everybody wants to meet their customer expectations everybody want to fulfill their user needs and they want to have their consistent online presence and it's all because like they want to retain their customers they want to have more customer re-visits and they want to have more business so this is achievable by integrating AI with an exceptional you know platform Drupal so that is all about Drupal VXP and AI meeting and we want to what will happen in Drupal VXP and AI so first is like AI brings an intelligence to the process so the entire process of digital experience that is that we are providing to the user brings an AI brings an intelligence to the process so it silently observes what the user does on our website it observes what the user process it observes what user patterns and narrative is what user prefers it knows what user likes what user doesn't like and he provides in detail that how we can engage our end users how we can detail our end users so Drupal VXP meets AI gives a captivating user experience that is definitely achievable with this integration and so let's see some of the AI driven enhancements in DXP I have fit you know top and most important ad-driven announcements one is like personalization and recommendation and access conversation interfaces and then automated content management enhanced search data analytics and insights moving on to the first topic which is about personalization and recommendation so what is personalization so personalization is an approach where we deliver tailored experience to the end users right so it is all about thinking about what customer will like so it is an approach or it is a strategy where we keep customer you know in center and friend we analyze what customers like what the customer don't like what engages them the most the website so the moment we understand what our end user like the best in the website the way we in fact or the way we deliver the website to the end users will totally take a new thought it should change totally and it will enhance the user experience so but why personalization is important why vital so let's take this like we are we are in an era where customers does not you know doesn't want to stick with like one-size-fits-all digital experience they want like relevance they want engagement they want to connect between the digital application they are interacting with do we like everybody definitely needs to connect with the digital application we are interacting with right so that is achievable with personalization so let's take a scenario like let's assume our user lands on the like our website and we have two options of presenting the home page one is like a generic home page like with a generic content the other one is like home page with a personalized content that is matching the user preference and you know article suggestions matching the user preference colors matching the user preference and product recommendations matching the user preference definitely user will prefer to stick to the option B that is which is more personalized most more relevant to his interest and you know his likes that's what personalization targets so personalization is actually an overarching technology where you know it interacts with the users in multiple ways like it might be a content personalization it can be a experience personalization interface personalization or it can be a what to say like a recommendation system so recommendation again it is a part of personalization it is a subset of personalization where it specifically targets delivering you know the elements identifying the elements that are specific or like relevant to the user's preferences so the goal of personalization and recommendation is to enhance user engagement and provide satisfaction and delivering content and experience it aligned to the user interest and needs so next moving on to the benefits of personalization recommendation so the default benefit is enhanced user experience user engagement and we can also achieve dynamic content delivery so it is not just we are storing the past user preferences browsing history and the details and then we are analyzing the data and getting the you know content delivery or personalization done here it is also possible the tools to track the current current you know user behaviors patterns and their interest and based on which the content delivery can be achieved dynamically and next is adaptability to user so based on various or different groups of users preferences and their likes the the personalization can be achieved to every user's needs so that is one they tool has a capability to understand what every user needs what every user likes what every user can you know prefer to have in the website so that is achievable with AI tools and next is data driven decision making so definitely with all the data that is available which we capture and we analyze we can make a lot of informed decisions with the data which we capture so we can decide which content will reach the user which content will not make the user happy which interface will make the user more engaging in the website so that is one and also increased conversion rates so with all these possibilities definitely the retention rate of the user on the website is very high it will be very high and the conversion rate that is like the user's coming back to the website will also be high so that is all about the benefits and moving on to the types of personalization recommendation we have a lot of types like one is like content recommendation another one is like user journey personalization we can do product and service personalization and email personalization search personalization social recommendations like which social activities can be recommended to the user event recommendation so if a person is in a particular location the events around the location can be recommended to the user and knowledge based recommendation based on their interest respect respect articles podcast and you know blocks can be recommended to the user so these are the types of recommend personalization and recommendations and moving on to the techniques so we have a lot of techniques involved in personalization recommendation so starting from mission learning nlp and you know filtering techniques so let's start with the mission learning techniques one is like clustering algorithms we know like these algorithms helps to group the content based on the preferences based on the attributes so that helps to you know club the content and deliver to the respective user groups and we have nlp techniques like named entity recognition like any r which helps to identify the entity in the content and tag the entities based on which the content can be delivered to the users with respect to preferences and it uses the intent recognition where it identifies what is the intent of the content what is the intent that can be shared with the end users and sentiment analysis if we can understand what is the sentiment behind the content and we have other techniques like collaborative filtering and content based filtering that is basically a recommendation techniques and when it comes to collaborative filtering it is about like identifying the user preferences based on the other relevant user preferences and the item preferences so we have two types in collaborative filtering it's like user behavior based collaborative filtering where like let's say a user likes ai and user y with similar preferences might also like ai so that's the reference or the preference we identify based on the user behavior collaborative filtering technique and the other one is like item based let's say like user x likes ai user x might also like ml so there the relevant between the items can be established and the products can be delivered to the users and next is content based filtering it is like we can map the contents which are relevant to the user preferences so we collect the contents that are like with similar attributes together and we collect users who are like having similar preferences together so where we can establish a mapping between the user preference or like user groups who like to have an attribute text and the product with attribute text can be mapped to them that can be shown to them that can be recommended to them so that is achieved that is achievable using content based filtering so we can also have we also have predictive analysis where we predict what the user might like so using these techniques we can predict what the user might like what the user might prefer to look into our websites and we have time series analysis where like the collection of the information based on the times are analyzed and plotted to identify how the you know the responses or preferences vary based on the time for example the market rate might be like high at this point market rate might lower might be low at this point and you know the travel for particular destination might be very high at this season and for this and particular no destination might vary at a particular season so all these time series analysis you know all these can be handled with time series analysis and next is context of our recommendation so this technique helps to understand the context of the user expectation so it is not just what user prefers it understand what user expects from you to understand the context and based on which the recommendations are done to the user so these are some of the techniques I've picked actually and moving on to the tools I have picked a couple of tools so one is like aquia personalization which speaks majorly about user segmentation we can do user profiling user grouping user segmentation and we use content targeting like we can deliver content you know to the specific targeted users and we have recommendation engines which suggest which which product can be delivered to you know to the respective target groups and it also handles behavioral tracking of the user and moving on to the next tool it is optimistically this is specifically for experimenting and optimization so it does a lot of ab testing so it does it tests a lot of ab testing identifying which content or which data reaches the use which group of users so based on repeated ab testing the and experimentation experimentation results the optimization of the personalization is achieved using this technique using this tool and next is lift igniter so this provides real-time personalization recommendation that is it captures the data in real-time and the responses and recommendations are done near real-time Lee and it has a scalability ability so it grows with the user you know growing user base and it provides a real-time analytics data and it provides predictive modeling support as well so next is vue.ai this is specifically for retail industry and I like actually like this tool because like I found a lot of interesting elements here like one is like visual search rather than typing and you know giving information I can do a visual search I can post an image and I can ask for the relevant image for the from the website and next is like it provides size recommendation it provides style guiding it you know it automates fashion designing and it it specifically automates the entire merchandising like the inventory management is automated here and next is amazon personalized it is a it does again supports the real-time recommendations and it has it has an automated ML you know support where the machine learning algorithms are available where the developer can use them personalize them to their based on the requirements and next is IBM Watson personalization again this provides real-time personal personalization technique and it does behavioral analytics and reporting so reporting in the form of dashboard which helps to track the you know customization personalization if from a dashboard and next is dynamic yield so this is again supports email and mobile app personalization and unified platform like where multiple channels of personalization can be grouped together in a single dashboard a single instance that is achievable in dynamic yield so this is all about personalization recommendation moving on to the next it is conversational interface so yeah so conversational interface is like it is a technology where we enable humanist communication so where you know we allow the users to interact with the system in a more natural language and in turn these interfaces understand the you know input user input interpret them and identify and respond to the user in more natural language making anti-communication between a human and the mission in a natural way in a natural communication way so this you know the goal of conversational interfaces to make human computer interactions more you know in a natural way so we use a lot of techniques in you know for this conversation interfaces and it is it is achieved by chatbots, virtual assistants, voice assistants and it's all possible with that moving on to the benefits so conversational interfaces you know they are they are they provide real-time engagement they are available or they can respond instantly and they provide 24% unavailability so whenever the user reaches the interfaces are available to respond to the request and they have an efficient problem resolution technique so they can handle or they can identify what exactly the user needs and they respond instantly for that and it also helps content discovery next is it provides multilingual support with the integration of multilingual translation models it supports multilingual you know conversations and it provides multi-channel interaction like where the user can interact it can handle interactions across multiple channels like website, mobile, email and all different channels can be handled together and maintained and next is consistent user experience so it is not like there will be a dip or drop in the performance so the user experience which you know which this conversation interfaces provide is like very consistent across the throughout the website and it scales based on the growing user base the more information they get the more information or motor interaction it gets from the user the more it grows and you know it it learns from the user inputs it's trains like we can train the model repeatedly based on the user and new user inputs we get and we can you know scale the tool based on the requirements and next is data-driven it's again like here we can use the data analytics and we can make informed decisions that what kind of responses can be given to the end users based on the user preferences and next is automating routine tasks this is important like it's not just about talking or chatting it's also about automating a lot of you know routine tasks like some of the chatbots helps to book tables for us make reservations for us check the order status for us so all these automated routine tasks can be automated with the help of you know a conversational interfaces and time and cost savings so tree place is a huge you know team who needs to sit consistently to handle and support the end users so this saves a lot of time and cost when it comes to you know a conversational interfaces and moving on to the types we have text based chatbots so these chatbots like where we can enter the text and we will get the response accordingly and otherwise like voice assistance where we use speech to you know get the communication happen and access virtual assistance which is like it is beyond the chatbots it's not just you know answering a question it's beyond it's beyond the chatbot where it helps to provide certain information beyond like answering a particular question and like multimodal chatbots so this is actually like it handles various types of inputs like it can be an image it can be a voice it can be a text so it has the capability to handle different inputs and provide the response in such a way and next is multi-lingual chatbots again like it can support multiple language inputs and respond to the user in different languages based on the requirements and accessibility chatbots which is like it focuses on inclusivity of the users like it addresses divers user needs and you know for example like navigation supporter screen reader compatibility tools like this will help the you know this will help a wide audience or a wide diverse group of audiences with different needs so this is about the types of conversational interfaces and next is techniques so whenever we talk about any content related you know enhancement nlp is default natural language processing is default like all natural language processing techniques is involved in content management and content related enhancements we have intent recognition technique which is used to understand the intent of the you know the content or the conversation or the query the user is posting to us and entity recognition again we can identify what is the we can identify and tag the entities in the query for example let's take a travel chatbot where the user posts that i'm traveling to paris so in this like the entity recognition will identify the terms like paris and travel so this will help the chatbots to identify what exactly the query is about and respond accordingly so the entities are captured tagged and the responses are provided accordingly and next is core reference resolution this is like this provides a relation between the content making the communication very naturally for example let's take the same example so a user post in the chatbot that i'm traveling to paris this week and i need to book flights for there so which means like there is rated to paris so this relationship is established with the help of you know core reference recognition technique and we have dialogue management this particular technique helps to maintain the conversational pattern in the you know the conversation interfaces it holds the dialogue management and we have automatic speech recognition which is about whenever a user gives the input via like you know voice speech so that is the recognition converted into text which will be further passed and identified and the response will be given to the user accordingly and next is text to speech so yeah once the uh once your datas are analyzed and the responses are ready and it will be converted into a speech again from the uh structured data so that is done with the help of text to speech technique and multi-ton conversations so multi-ton conversations it's like where the user can interact in multiple for example let's take some of the chatbots where i say like i wanted to do this particular action and the chatbot will respond to me and followed by that i don't want to mention this name i repeatedly again rather to create the context and maintain the conversational like multi-ton conversational aspect of that like every time i don't need to mention a specific term to remind the chatbot this is what we are talking about so it'll understand what exactly it is it will stay with the context it'll retain the dialogue and it'll you know may maintain the multi-ton conversation with every user so next is contextual understanding yeah so every AI conversational interface understands the context behind it it understands what context users trying to communicate it breaks down the query into simpler formats and understand the context so that it can respond to in a more relevant way and next is emotion recognition sentiment analysis this is very important because it understands the emotion of the user i recently used one of the chatbots where i was like i was literally upset i don't have anybody to you know talk to i message the chatbot telling that i'm feeling low chatbot immediately respond as if it's like a friend don't feel low i'm here to support you this kind of emotion understanding is is possible with the with integration of AI so these are the techniques that is like covered in conversational interfaces and moving on to the tools we have a dialogue flow that is by google which uses natural language understanding this is specifically to build chatbots and it has an integration model which supports Drupal to integrate this dialogue flow and we have IBM Watson assistant so this again provides a facility for the developers to build chatbots it uses a natural language processing intent recognition named entity recognition and dialogue management integration and with different channels so it has an ability or it allows users to interact this with multiple channels like different channels like a website or a mobile application so and also it provides multi-lingual support and next is microsoft bot framework so it provides an SDK which comes with tools and libraries which you which the dev and also it supports multiple languages like python java steps so my developers can use it to build the respective you know chatbot based on the requirement and it uses a composer and emulator to test build and deploy and it also provides multiple channel and connectors facility and it specifically uses a language understanding integration service by microsoft to understand the inputs and maintain the natural conversation technique and moving on to the next tool it is rasa so it is an open source tool and it also uses natural language understanding technique and it also uses dialogue management technique which is mainly or primarily you know targeted for like more natural way of conversation and it has a visual interface like where the or where the models can be built or like maintained by even a low code knowledge person and it provides multi-channel support and it builds it has a built-in analytics where we can get the data analytics and reporting in a dashboard which will be very integrated and moving on to the next tool which is amazon lex so this is by amazon aws and it uses natural language understanding again and it supports multi-turn conversations then it supports voice and text interactions as well so these are some of the tools that you know supports conversational interfaces and moving on to the next enhancement is automated content management so so the content pandemic like in the current era like content needs to be dynamic personalized and it needs to be delivered efficiently to the users so integrating AI with this you know management system helps to handle the digital content efficiently right so in the traditional content management system the you know every task needs manual effort and it's time consuming so with the help of you know AI integration in the content management we can automate most of the task starting from content creation content tagging content categorization moderation duration and personalization delivery everything can be automated with minimal or less or like no human effort so this actually you know frees up the space for the team to focus on you know setting up the content strategy and enhance the content quality when it comes to delivery of the content and it also you know and it like the primary goal is to enhance the content management process and provide an exceptional user experience with a good quality content delivery so that is about content management and moving on to the benefits it provides a real-time content management it's not just works on the store data but also whenever the content is generated instantly the content management process also starts like it starts to read the content it starts to understand the you know intent of the content any moderation is required it is done in real time and next is content quality with proper mechanism and tagging and categorization and moderation and other content content management techniques the quality of the content is like well maintained and it gives an exception content quality and it can be delivered well to the end users so next is like efficiency and scalability so with like with an automated content management of the AI tools the efficiency of the content management system is like very high and it can be scaled like based on the growing content growing content or large amount of data the the tool also scales to it seamlessly it can handle the content management effortlessly seamlessly which is like kind of challenging when it comes to human-involved management so that is one and it provides multilingual accessibility so for global accessibility especially we can translate you know content to different languages without affecting the context of the you know content and it's it also handles user safety and compliances like it ensures that the you know high encrypted data should be secured and authentication authorization is a you know process to be kept in place and and also like it provides privacy and concentrated you know measures will be is taken here and it also has the ability to identify the anomalies and reduces spam or irrelevant content it allows users to report about the content so that we can know that these content is affecting a particular group of users and based on that feedback the models can be trained further to eliminate those kind of you know content from the websites so this is one and also it provides improved SEO so these automated content management system knows what exactly reaches the user and how to bring the content in the top ranking in the search engine so that is achieved with this and it provides data driven insights again so all the you know AI driven enhancements has this data analytics and they bring in a lot of data driven insights in every aspect of you know improvement when it comes to UI and the next is user trust and satisfaction as I said we are maintaining a lot of user safety and compliance we ensure that these tools ensures that we are meeting the user policies company policies the compliance is that allows to gain the user trust and satisfaction so and moving on to the types we have automated content generation we have content personalization we have moderation content governance analytics content versioning and workflow automation so all these are the types of automated content management and moving on to the techniques again we use natural language processing again like intent recognition named entity recognition sentiment analysis all these are like helps to understand what the content is about and it helps to categorize the content according to the intent and the key words and the respective context of the content and we can we use energy natural language generative techniques which helps to generate content in more natural way so there can be like a template based generation where at a pre-defined template will be there and the content can be prefilled can be filled into the template or it can be a narrative way like where the content can be generated in a more natural way and RNNs so this recurring neural networks especially helpful in sequential data management where the sequence of the data is understood and the sequence is maintained throughout the content generation and also it is helpful in content summarization like tech summarization when we when we convert a huge amount of content into a small summary then the sequence of the data on the context should be retained which is helped with which it can be achieved with the help of RNNs and next is like convolutional neural networks this helps to analyze an image analyze a video this is primarily to focus on this visual content across the website so this identifies the object in an image in a video to identify the entities in the video on any image it helps to tag them convert them into a text and maintain or like maintain them in a particular order so that it can be delivered to the user and next is generative adversarial networks so this has an ability to create a content especially visual content like it you know generates a lot of images and it can it also has the ability to style transfer where it can transfer a style of one image with you know collaborating with the other image generating a third image which which which is actually maintaining the style of the image one which is like this is primarily used to maintain the theme across the entire website having a similar or you unite you know theme across the website and computer vision this is one more technique where it uses facial recognition to identify the faces in the picture and tag them with the user's optical character recognition where the images where where the scanned images can be converted into a structured text for further processing and visual search where we can use the images to go ahead and search and these are the computer vision techniques and next is language translation so we have a lot of language translation techniques and models that can be incorporated that helps to translate the contents across different languages based on the different requirements business needs and the to fulfill the global accessibility needs and we have mission learning classifiers this is basically to group the content across different groups so that it can be maintained organized and can be delivered to the user based on the preferences whenever it is needed so these are some of the techniques of automated content management and moving on to the tools we have open AI GPT so this supports with content generations code generation and language translation content summarization and next is like we have google cloud natural language apia this particularly handles sentiment of our content generation it knows what is the sentiment of the requirement and generates the content accordingly and entity driven content can be handled here dynamic personalization language adaptation automated tagging and categorization and moving on to the next tool it is nuxio it is an open source CSP like content service platform and it uses the dam capability like which is like a digital asset management capability and it supports workflow automation and it supports multi-tenancy that is like maintaining content across different projects different repositories as possible here and it also supports cloud base it is a cloud native platform which helps to handle the content across on the cloud next is accrual links so this is a content optimization platform so it understands the company policies and guidelines and it you know ensures that the terminologies industry terminologies is maintained across the content generation it ensures the style is maintained across the content generation and the tone guide that has to be followed across the content generation matching the company policies and the compliance and the next is curator this is actually a content marketing platform so this helps in content curation so whenever there is an update in the real time that will be brought into the you know brought into the content and it curates the content based on the real time updates and it distributes the content it also automates the workflow and it has a feature for content calendar where the content can be you know posted or published based on the needs and the plan dates so these are some of the tools of automated content management yeah moving on to the next enhancement it is an enhanced search so we know search itself is like primitive you know functionality which is needed in the website so it allows users to find what the information they want from the website rather than traveling you know browsing through pages and pages it helps to get the information in just small in a click and a swipe so AI powered search actually enhances the website search functionality by providing or by improving the accuracy relevance and query handling of search using air techniques in ML algorithms so the goal is to create more user centric intelligent and engaging user search experience that leads to higher customer retention like reduce the bounce rates so the when user gets what they want in a simpler way they will definitely not go to the website so that we can retain the customers in providing a good the search experience to the customer that is achievable with the help of AI powered enhanced search yeah so moving on to the benefits of enhanced search it provides improved relevance so it is like it understands what the user wants it knows what the user prefers okay so the results can be provided in such a way matching the user preferences and it provides multilingual support like it can provide the search results in different languages based on the user requirements and it can optimize the search it can you know match the it can understand or analyze the user preferences and can provide the search results accordingly and real-time updates so these search results you know can be based on the real-time data that is happening and it gets the data from the real-time and it provides it literally a near real-time response to the end users and efficiency in query handling so it will understand the context of the query it understands the intent of the query and provides more accurate relevant response to the user that's how the query handling is very efficient when it comes to enhanced search and scalability it's not just a search mechanism it grows with the more user queries it get the more search data the more user queries the the tool also enhances so next is types of search so we have semantic search where the query is broken into some smaller semantics and based on the analysis the search results will be published and next is personalized search to where the search results can be you know delivered based on the user preferences if the user prefers to see videos the search results will show videos first and if the user prefers to see certain kind of content and that will be shown first so personalization can be achieved in personalized search and next is voice search which allows so here like we are not sticking to a traditional method where we have to type in the search rather we can like speak you know to their system we can you know we can show or we can tell the system what we actually want we are looking for so that has achieved with voice search and next is visual search as I said we can use an image we can you know search using an image click the image post it and the techniques like you know visual recognition and image recognition techniques will help to break down the image and they'll identify what exactly the user expecting for and the results can be shown and next is conversational search where the search will you know mechanism can be like in more natural language and multilingual search having the search in different languages and next predictive search here the search knows what the user might prefer to search so it will auto suggest what the user wants to search for so it provides you know multiple options for the users to select and search accordingly and next is dynamic faceted search where the the research based on the research that is getting populated the the facades are like put in place so that we can apply filters and the facades will be dynamically populating the filter values based on the user preferences and the user focusing on so these are some of the types of enhanced search and moving on to the techniques yeah as I said anything related to content nlp and nlg will be definitely there so next is contextual analysis this is like it understand what the content what is the context of the user query it knows it tries to break down the user query into smaller pieces and understands the context of the user query and next is like query expansion some some cases we might the tool might needs to expand the query so to put in some of feeling you know feeling contents and understand the exact query context and next is ranking algorithms it is used to it is basically used to rank the the results based on the user preferences and entity recognition, intent recognition again it you know understands what is the the intent of the search query and entity recognition helps to identify the entity that is involved in the search query and next is visual recognition so visual recognition is like it uses it is helpful when it comes to image search where we use visual search and then we have speech recognition techniques which is used in voice search techniques and relevance feedback so it is like feedback mechanisms which can be put in place to get the feedback of the search results and you know that has helped to improvise the models based on which the AI based on which the models can be updated every time on the feedback so these are the techniques and moving on to the tools we have elastic search with mission learning so it supports dynamic filtering and relevance tuning it is like it fine tune the results based on the user preferences based on the user likes and anomaly detection it knows what is the pattern of the content and if there is any abnormal data identified it can be deleted or eliminated from the system and next is Apache solar with learning to rank so LTR is an M we all know like Apache solar is a popular search engine and this integration with LTR brings more relevant search results and supports real-time ranking real-time ranking and Algolia it provides fast and real-time search autocomplete and suggestions like it predicts what the user might be searching for and it shows the preferences which the user can select and you know and do the searching and next is it supports facet search as I said like it once the results are published it will provide the facets to filter out and narrow down the results and based on the facet applied the results can be given to the user and Corvio it is again a clown-based platform so it supports relevance tuning and it it reports faceted search and also search driven content recommendations so based on the search queries we have applied in the website this identifies the contents that will be that the user will be interested off and it will be sure it will be recommending those content those products in the different pages of the website and next is Microsoft Azure cognitive search so this is a cloud-based you know service and it has a full-text search like it does not stick to one particular search type it covers different types of content and it also supports relevance tuning multi-lingual support like different languages monitoring and analytics is also achieved with this so this is about enhanced search next is data analytics so data analytics and insights so data is like is it over i'll i'll try to wind up so so data is like everything like whatever clicks actions we do and the website is the data it is a goal for us we use we should use this data to convert them into an important information so data analytics basically does that it captures the data it captures every user interaction and converts them into an important information based on which informed decisions can be made we can decide what the user likes what the user does not like what the content has to be published the user to attract them to keep them engaged to the website so the goal is basically to optimize the digital experience and engage users effectively by you know understanding what the user preferences and that is achieved with the help of data analytics and insights it provides next benefits of data analytics is like personalized content management okay whatever we have covered so far whatever the air tech announcements we have seen like everything involves data analytics and insights so with the data analytics and its reporting and its insights we can achieve a lot of user experience announcements in this system and next type of analytics is like we have predictive analytics we have personalization analytics optimization analytics optimization analytics is used to um you know understand what sections of the website needs to be optimized what is the pain point of the user where exactly the user drops where the conversion rate is low and that points can be identified in another section or can be optimized here and next is like visual analytics where the visual content can be analyzed and updated and we have voice and speech analysis analytics we have multi-channel analytics so these are some of the types of data analytics and insights yeah move on to the techniques so again like every you know air driven announcements includes a lot of algorithms mi algorithms ml algorithms so we have automated data preprocessing so to train a model data has to be processed and like automated data preprocessing can be achieved uh can be can be used for training this data analytics model and next clustering and segmentation and time series analysis all this can be used to uh you know perform this data analytics and we also use deep learning to you know break down some complex patterns and you know do the recognition and also to do these anomaly detection and next is like we have we can use a we use a text and data mining intent recognition predictive analytics and prescriptive analytics all these techniques are a part of data analytics and you know the insights and moving on to the tools we have a lot of tools actually and these are like some tools i've picked so google analytics four which actually uses a drive ml driven insights for advanced analytics and predictive metrics and we have ibm cogniz analytics so it is like uh it has an it provides an interactive data visualization so it provides a visualization dashboard a reporting dashboard where the huge complex content or patterns is presented very simply to the user to understand to break down the complex patterns and next is domo it is a it also supports predictive analytics anomaly detection and automated insights and we have microsoft power bf azure ai so data so it supports data preparation for training the model it supports visualization of for presenting the content once the data analytics is done and it supports forecasting so these are some of the tools of data analytics so we have covered some of the enhancements now so let's see like how this integration i'll just quickly give a overview of how the tools can be integrated with the Drupal well basic steps so we know we know like identifying an ai tool is very important when it comes to the you know integration but like integrating it correctly also matters a lot so even a small miss can like create a lot of data violation problems cost overruns and maintenance challenges and a lot of time and resources so we have we should properly have a plan and expertise team to handle this integration and so these are some of the steps a quick steps that can be taken into consideration when it comes to ai tools with Drupal integration choose the optimal ai services so we understand our requirement and pick the right ai solution for that and next is get the api access for that particle from the respective ai tool and install and configure models so we need to use the models modules to install and configure and you know use the api key to provide the authentication for that and data integration so we need to establish proper data integration to ensure that the data flow between the Drupal system and the AI system works well and we need to you know when when there are cases like search and automated chat we need to have a proper user interface integration so the user interface needs to be very user friendly and matching the requirements and it should go well with the ai tools and services and testing and optimization so whatever we do multiple levels of testing and you know multiple levels of optimization is mandatory when it comes because it has to be enhanced every then and there and next we should focus on privacy and security so this is a more most important section of you know the ai tools we pick like we need to ensure that the user data secured user data is like safe in our system so privacy and security measures has to be taken it should match the user needs it match to the company policies and the business needs and next is monitoring and maintenance so once it is implemented the monitoring continuous monitoring maintenance needs to be put in place and next is user training so every user like who is not aware of the new technology that has been implemented should be trained and proper documentation should be in place like starting from the decision of tool and the integration steps and the api integration and what all the changes we have done to the integration that has to be documented properly for future references so these are some of the steps and moving on to the challenges i'll quickly wrap this with wrap up with this so moving on to the challenges limitations uh every every every integration every tool has a lot of challenges and integrations sorry challenges limitation definitely so when it comes to ai like we are in a crawling face there are a lot of challenges like you know it's there in the ai tools and some of them are like integration complexity so when it comes to integration there will be a lot of complex scenarios or like difficult scenarios of integration cannot be achieved we need an expertise team to handle that predict that guide the team to do that and like data quality and content so data quality is much needed when it comes to a training a model so the huge amount of data and the quality of the data is mandatory when when it comes to training a model and we need to focus on data privacy and security cost and performance impact like what impact this you know ai tool will bring in when if it is not properly done these all should be considered and interpretability we we will never know that to how this tool made this decision so that justification should be taken care when it comes to you know integrating an ai tool so these are some of the challenges and moving on to the limitations we have so not everybody understands ai so there will be a limited scope of understanding here and we need to depend on a lot of third party vendors we need a lot of training and expertise involved in and there might be biases in the ai tool which we are used which we need to primarily focus and you know eliminate at the beginning stage itself and limited a capabilities and user adoption user might not be familiar with the product or the tool and they might be find it very difficult so we should provide a proper user guidance to handle this scenario and these are some of the challenges and moving on to the best practices so it's very let me quickly run through this this is like we need to understand the requirement well step one and choose the right tool step two and get technical expertise you know involve the core team to understand what is the tool what is the requirement is what the tool can fulfill and you know you can identify what exactly can be done from this and next is we have to prepare data and you have to ensure the quality of the data is sure so with bad quality of data we can end up in a worst product we end up in building a worst product so we should ensure the data quality and quantity is like proper and we should ensure the data privacy and compliance is met when we are training a model and regular maintenance like repeated maintenance repeated updates you know it is needed for any AI tool and interpretability and explainability as I said we we should ensure that explainability is involved in every AI tool we are integrating with and user training and adoption so like we need to train the user groups we need to train the technical team so that they are aware that what this tool basically does and how what how this tool can support the users so I think that's all so like I was like when I was like interacting with a lot of people outside like they were like repeatedly asking me whether it is good or bad so it's subjective so it's it's it's how we are used it like we use it wisely we can you know bring a you know very good impact on the society but when we use it badly like it's our problem like so we have to decide how we can utilize the technology we can we should embrace the technology we should you know grow with that and we can have to evolve with the technology that's that's all about like having an AI in place so that's my session thank you thank you so much