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Cognitive Business

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Enterprise AI transforms business, impacts performance, and increases efficiencies in multiple ways: (1) Insight generation—using big data and cognitive analytics to extract previously unknown understanding from structured and unstructured data. (2) Customer engagement—using AI, information, analytics, and communications technology to involve someone’s interest, attention, interaction, and participation towards some end. (3) 
Business acceleration—augmenting staff and automating knowledge generation to drive cost savings, competitive advantage, and new business lines through smarter deployment of resources; and (4) 
Enterprise transformation—change associated with the application of digital technologies and artificial intelligence to all aspects of the business, its ecosystem, and human society.

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Cognitive Business

  1. 1. COGNITIVE BUSINESS
  2. 2. This content included for educational purposes. 2 • Lawrence Mills Davis is founder and managing director of Project10X, a research consultancy known for forward-looking industry studies; multi-company innovation and market development programs; and business solution strategy consulting. Mills brings 30 years experience as an industry analyst, business consultant, computer scientist, and entrepreneur. He is the author of more than 50 reports, whitepapers, articles, and industry studies. • Mills researches artificial intelligence technologies and their applications across industries, including cognitive computing, machine learning (ML), deep learning (DL), predictive analytics, symbolic AI reasoning, expert systems (ES), natural language processing (NLP), conversational UI, intelligent assistance (IA), and robotic process automation (RPA), and autonomous multi- agent systems. • For clients seeking to exploit transformative opportunities presented by the rapidly evolving capabilities of artificial intelligence, Mills brings a depth and breadth of expertise to help leaders realize their goals. More than narrow specialization, he brings perspective that combines understanding of business, technology, and creativity. Mills fills roles that include industry research, venture development, and solution envisioning. Lawrence Mills Davis Managing Director Project10X mdavis@project10x.com 202-667-6400
  3. 3. This content included for educational purposes. Introduction 3 Cognitive business — Insight, engagement, acceleration & transformation 
 Enterprise AI creates opportunities to transform business, impact performance significantly, and increase efficiencies: • Insight generation is the production of an accurate and deep intuitive understanding of a person or thing. Big data and cognitive analytics extract previously unknown insight from structured and unstructured data to both identify and act on the opportunities presented. • Customer engagement is the use of AI, information, analytics, and communications technology to attract, involve, and support someone’s interest, attention, interaction, and participation towards some end. We explore application of AI to conversational interfaces, bots, assistants, and virtual agents, and to precision marketing, sales, and services. • Business acceleration is the automation of knowledge generation that drives cost savings, competitive advantage, and new business lines through smarter deployment of resources. Optimization is the action of making the best or most effective use of a situation or resource. We explore cognitive enterprise, cognitive platforms, intelligent automation, and intelligent ecosystems. • Enterprise transformation is the change associated with the application of digital technologies and artificial intelligence to all aspects of the business, its ecosystem, and human society. We explore types of innovation and cycle time, perspectives on cognitive enterprise transformation from selected industry leaders, trends towards exponential value generation, and implications enterprise AI for future skills.
  4. 4. This content included for educational purposes. 4 1. Insight generation 2. Customer engagement 3. Business acceleration 4. Enterprise transformation SECTIONS This content included for educational purposes.
  5. 5. This content included for educational purposes. Enterprise connected intelligence use cases — business and IT Source: WIPRO • Digital Virtual Agents—Enhanced user experience with capabilibes like speech recognibon, natural language understanding; 
 E.g. Collaborabve Agents, Customer Support/Experience, DIY Support. • PredicGve Systems—Extracbng meaning from different forms of data, using tools and techniques – to discover paherns, predict future outcomes and trends; 
 E.g. Recommender Systems, Anbcipatory Systems, Automated Scenario Modeling. • CogniGve Process AutomaGon—Cognibve Process Automabon is defined and executed based on a loose set of instrucbons or logic. These instrucbons are largely machine-learnt, evolve conbnuously and can be user- defined as well; 
 E.g. Automated Problem Resolubon, Sojware Release Automabon, Modal Interacbons and Experience Management. • Visual CompuGng ApplicaGons—Visual compubng applicabons that can acquire, analyze and help synthesize realisbc interacbve interfaces and idenbfy paherns;
 E.g. Dynamic Pahern Clustering, Computer Vision. • Knowledge VirtualizaGon—System that can curate knowledge by using AI techniques. They rely on usage of expert knowledge databases to arrive at decisions; 
 E.g. Diagnosbc Experts, Advisory Systems, Natural Language Generabon. • RoboGcs and Drones—Robobc automabon is powered by a repebbve set of instrucbons. These instrucbons are mostly defined by the user and somebmes machine-learnt. They can be fed into the system by analyzing repebbve paherns; 
 E.g. Smart Drones, Brain-Controlled Robobcs. 5
  6. 6. This content included for educational purposes. AI TECHNOLOGY EXAMPLEENTERPRISESOLUTION Computer vision Acquiring, processing, analyzing and understanding images Video analytics integrated with surveillance cameras provides situational awareness of business operations, delivering insights about risk, safety and security. In retail, video analytics can be used to gain insights into shopper behaviors effectively and systematically. Audio processing Identifying, recognizing and analyzing sounds and speech Speech recognition technologies integrated into call centers automate the identification of callers. Sensor processing Processing and analyzing information from sensors other than cameras and microphones In an agricultural setting, sensors in the field can be integrated with software to deliver “precision agriculture”— sensing and communicating status about temperature, humidity, etc., enabling more precise care for crops. Natural language processing Understanding and generating language in spoken and/or written form Personal assistants on consumer smart phones provide guidance and services using natural language. Increasingly, search capabilities include the ability to understand the meaning of what a person is saying, not just recognizing key words or doing statistical retrieval. Knowledge representation Depicting and communicating knowledge to facilitate inference and decision making Knowledge-based tools provide the capability to link a particular search or piece of content to other relevant content on the web. This is done by tagging all content and then mapping it to a larger representation of knowledge. For example, a search for “Da Vinci” will link one to particular paintings and creations, as well as to Italy, to the Renaissance, and so forth. Inference engines Deriving answers from a static knowledge base such as business rules Solutions can apply rules to make automated loan approval or credit decisions, or granting of visas. Such capabilities can deliver accurate decisions in a fraction of the time of manual decision making. Expert systems Reasoning with rules, algorithms and information available in its knowledge base Medical diagnostics as well as legal research can be significantly aided by the ability of expert systems to sift through millions of data sources, synthesize information and present it to a user. Machine learning Altering the decision process based on experience Software tools and personal agents can learn from users to improve productivity—for example, by sorting email, then extracting calendar entries and action items. Source: Accenture Examples of AI technologies integrated into enterprise solutions 6This content included for educational purposes.
  7. 7. INSIGHT GENERATION
  8. 8. This content included for educational purposes. 8 • What is insight generation? • Analytics continuum • Turning structured and unstructured data into actionable insights • Types of (big) data used in predictive marketing • Types of analytics used in predictive marketing • Machine learning to optimize targeting — with example • How machine learning predictive analytics works • 40+ machine learning and predictive analytics use cases across industries • Sapient cognitive analytics — Cosmos, Idiom & Luminoso Overview of
 insight generation
  9. 9. This content included for educational purposes. 9 in·sight gen·er·a·tion /ˈinˌsīt/ /ˌjenəˈrāSH(ə)n/ The production of an accurate and deep intuitive understanding of a person or thing. Big data and cognitive analytics uncover previously hidden patterns and relationships from structured and unstructured data to both identify and act on the opportunities presented for innovation, growth, diversification, and efficiencies. What is insight generation? !
  10. 10. This content included for educational purposes. Analytics continuum and stages Information 
 Foundation Descriptive Diagnostic Predictive Prescriptive Cognitive RDBMS Information
 Integration ECM Big Data
 Platforms Standard
 Reports Ad Hoc
 Reports Drilldown
 Query Alerts Statistical
 Analyses Forecasting &
 Extrapolation Predictive
 Modeling Rules Simulation/
 Optimization Natural Language Learning Reasoning/
 Explanation Recommendation Prediction Data DecisionInsight Context specific use What's the best that can happen? What will happen next? Why is this happening? What if trends continue? Where exactly is the problem? What actions are needed? What happened? Who? What? When? How often? How much? How do we know? 10 This content included for educational purposes.
  11. 11. This content included for educational purposes. Journey from analytics to cognitive computing — capture increasing value 
 through outcome-driven, actionable insights Source: HfS 11
  12. 12. This content included for educational purposes. | INSIGHT GENERATION MACHINE LEARNING PURCHASETV SMART PHONE DIGITAL An Operating System Turning Data into InsightsSTRUCTURED DATA TV MOBILE DEVICES SOCIAL UNSTRUCTURED 
 DATA | | ACTIONABLE INSIGHTS 80% + of all data is unstructured 12 Source: Publicis•Sapient This content included for educational purposes.
  13. 13. This content included for educational purposes. From text to actionable insights Source: SearchBlox 13
  14. 14. This content included for educational purposes. What types of (big)data are used in predictive marketing? First-party data ▪ Internal data includes anything sitting in a data warehouse, CRM system, or other sources that have not been integrated into your marketing database. ▪ Examples of internal data include customer service records, transactional data, credit card purchases, or contact information provider by the customer. Second-party data ▪ Internal data purchased from a business or traded for that includes anything sitting in a data warehouse, CRM system, or other sources that have not been integrated into your marketing database. Third-party data ▪ External data is available for purchase by data providers who source and aggregate the data into applicable sets that can be applied to first party databases. ▪ 3rd party data enhances targeted marketing campaigns, because it provides hundreds of detail elements that no consumer would fill out in a single form. ▪ With only a few first-party data elements, third-party data sets can be appended to correct and fill in missing elements such as email addresses, phone numbers, lifestyles, demographics, purchase indicators and more to strengthen customer insights. In-market signals ▪ Today’s always-on and connected consumer leaves a digital footprint indicating in-market purchase signals. Advancements in technology have made it possible to match a consumer’s mobile ID to a piece of PII (Personal Identifiable Information), which can be matched to social IDs and IP addresses to determine search data. ▪ Matching offline and online data establishes rich consumer profiles and access realtime digital behavioral data indicating life events and purchase intent. ▪ For instance, social signals are created when people post to social networks about “Moving to Denver”, “Taking a family vacation to Orlando”, or “Looking for recommendations on a new car.” Also, search data created when consumers research new cars or furniture or browse on e-commerce sites can be used as indicators of life events and intent. 14
  15. 15. This content included for educational purposes. What types of analytics are used in predictive marketing? Descriptive ▪ Descriptive Analytics give hindsight or insight into the past. They use data aggregation and data mining techniques to provide insight into the past and answer: “What has happened?” ▪ Descriptive statistics help understand raw data at an aggregate level, to learn what is going on, and to summarize and describe different aspects of the business. Usually, the underlying data is a count, or aggregate of a filtered column of data to which basic math is applied like sums, averages, and percent changes. ▪ Descriptive analytics allow learning from past behaviors, and how they might influence future outcomes. The past, which can be any point of time that an event has occurred, whether it is one minute ago, or one year ago. Descriptive statistics show things like, total stock in inventory as of a point in time, average dollars spent per customer, and year over year change in sales. ▪ Typical outputs include reports that provide historical insights regarding the company’s production, financials, operations, sales, finance, inventory and customers. Predictive ▪ Predictive Analytics give foresight. They use statistical models and forecast techniques to understand the future and answer: “What could happen?” ▪ Predictive analysis is used when there is a need to estimate something about the future, or to fill in the information gaps. ▪ Statistical algorithms combine historical data found in ERP, CRM, HR and POS systems, and may enhance these with information from public records and 3rd- party data sources to identify patterns in the data, and apply statistical models and algorithms to capture relationships between various data sets. ▪ They estimate the likelihood of a future outcome based on probabilities, hence with some uncertainty. ▪ Examples include forecasting customer behavior and purchasing patterns such as what items customers will purchase together, to identifying trends in sales activities, to forecasting demand for inputs from the supply chain, operations and inventory based upon a myriad of variables. Prescriptive ▪ Prescriptive Analytics: advise on possible outcomes and next best actions. They use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?” ▪ Prescriptive analytics seek to quantify the effect of future decision alternatives in order to advise on possible outcomes before the decisions are actually made. ▪ The goal is to predict not only what will happen, but also why it will happen, providing recommendations regarding next best actions that will take advantage of the predictions. ▪ Prescriptive analytics combine multiple techniques and tools such as business rules, algorithms, machine learning, and computational modeling procedures, and apply these against input from many different data sets including historical and transactional data, real-time data feeds, and big data. ▪ Examples of prescriptive analytics applications include simulation and optimization of production, scheduling, and inventory in a supply chain to ensure delivery of the right products at the right time while optimizing customer experience. 15
  16. 16. This content included for educational purposes. DATA LOGIC EXPERIENCES MEASUREMENT Marketing Database Optimizations Targeting Rules 3rd Party Social Product Data Interactions Transactions Customer Media Performance Sales Data 360° Customer Profile Data Warehouse UNKNOWN DMP KNOWN Campaign Management Personalization Content / Campaign Testing Rules Content & Asset Management Product Info Management Transaction Engine Order Management CRM Tag Mgmt Custom 
 Services EngagementAnalytics Modeling Multi-channel Attribution Customer 
 Value Business KPIs & Scorecards Experience Level Optimization Monetization Call Center Retail/POS Sales Email Social Mobile Web Online Storefront IoT Media Direct Mail DataServiceLayer ExperienceServiceLayer | INSIGHT GENERATION: Machine Learning to Optimize Targeting Source: Publicis•Sapient 16
  17. 17. This content included for educational purposes. Basic Analy+cs • Counts and Averages • Data Preprocessing • SQL Queries • Human Scale • Hand Crajed Advanced Analy+cs • Discovering Paherns • Making Predicbons • Mulbvariate Queries • Machine Scale • Data Driven Image source: http://personalexcellence.co/blog/ideal--beauty/ Sample predictive marketing data 17
  18. 18. This content included for educational purposes. Size of Network Image source: http://personalexcellence.co/blog/ideal--beauty/ Lifestyle ZIPcode Costal vs Inland Marital status Generation Family Size GenderIncomeLevel Competitors Age Revenue Size Life Stages Education Location Sector Industry Legal status City Loyalty and card activity Basic personal data Sample predictive marketing data 18
  19. 19. This content included for educational purposes. Size of Network Image source: http://personalexcellence.co/blog/ideal--beauty/ Lifestyle ZIPcode Costal vs Inland Marital status Generation Family Size Gender IncomeLevel Competitors Age Revenue SizeLife Stages Education Location Sector Industry Legal status City Loyalty and card activity Size of Network Subscriptions Date on Site Wish List Deposits/Withdrawals Device Usage Following Followers Likes Sequence of visits Time/Day log in Time spent on siteVideos Viewed Photos liked Check-ins Number of Apps on Device App usage duration Number of Hashtags used Frequency of Search History of Hashtags Search Strings entered Purchase History Time spent on page In market signals
 and social media 
 acbvity data Sample predictive marketing data 19
  20. 20. This content included for educational purposes. Image source: http://personalexcellence.co/blog/ideal--beauty/ Sentiment Lifestyle ZIPcode Costal vs Inland Marital status Generation Family Size Gender IncomeLevel Competitors Age Revenue SizeLife Stages Education Location Sector Industry Legal status City Loyalty and card activity Size of Network Subscriptions Date on Site Wish List Deposits/Withdrawals Device Usage Following Followers Likes Sequence of visits Time/Day log in Time spent on siteVideos Viewed Photos liked Check-ins Number of Apps on Device App usage duration Number of Hashtags used Frequency of Search History of Hashtags Search Strings entered Purchase History Time spent on page Tone Euphemisms Hedonism Extroversion Face Recognition Openess Colloquialism Reasoning Strategies Language Modeling Dialog Latent Semantic Analysis Linguistics Image Tags Question Analysis Self-transcendent Affective Status Phonemes Intent Insights derived from
 cognibve analybcs Sample predictive marketing data 20
  21. 21. This content included for educational purposes. Gaining customer insight through predictive analytics Prediction data output includes: • Buyer identification • Enhanced consumer profiles, behaviors, and market signals • Specific life event knowledge, facts, and statistical probabilities on which the prediction is based • Type of product or service transaction • Estimated transaction size • Timing of the close • Confidence index (probability) Steps in the predictive process: • Author knowledge about selected life events • Develop predictive models using statistical machine learning based on 1st, 2nd, and 3rd party data • Make predictions of demand and personalization needs • Handle queries from and deliver predictions to activation systems 21 This content included for educational purposes.
  22. 22. This content included for educational purposes. How does a machine learning predictive analytics application work? The mortgage life event predictor POC will deliver significant business value for the bank partner: ▪ Life event predictive marketing can drive quantum improvements in marketing performance compared to demographic approaches. ▪ At any time about 1/3 of bank customers are expecting a life event. Bank customers buy or shed products within 30 days of a major life event. Consumers are 40% more likely to buy a financial product around a life event. ▪ A bank customer typically will have on average 4-to-5 life events that need managing and supporting every 24-month cycle. This means a running opportunity exists to sell at least 4 products per banking household on a rolling 2 year basis. ▪ Life event prediction delivers high value prospects, who are more likely to engage with relevant offers. ▪ Predictive marketing provides early notification and first mover advantage in relationship banking. ▪ Enhanced consumer profiles enable customer-centric segmentation and deep personalization of communications and interactions. ▪ Life event behavior based customer insight has been shown to improve response rates by 2-to-10 times, improve customer lifetime value by 25%, increase loyalty by 30%, and increase retention by 30%. ▪ Life event targeting has been shown to lift conversion rates by 40%, reduce default rates by 30%, and lower customer acquisition costs by up to 50%. 22 Train / Retrain (SML) Model / Algorithm Predict (Statistical + Causal) Notification (API) Knowledge authoring & curation Historical 1st, 2nd & 3rd party data Life Event Knowledge Graph New 1st, 2nd & 3rd party data Recommendation 
 & Explanation Evaluation 1 CONFIGURE AI PLATFORM CAPABILITIES 2 TRAIN PLATFORM 3 MAKE PREDICTIONS 4 ACTIVATE NEXT BEST ACTIONS 5 IMPROVE PERFORMANCE WITH USE AND SCALE Next Best Actions This content included for educational purposes.
  23. 23. This content included for educational purposes. 23 • Go-to-market excellence requires bringing “data sophistication” to all six facets of competitiveness: - Market intelligence and strategic priorities - Product development and portfolio management - Marketing and communication - Sales platform management - Performance monitoring - Organizational enablers such as recruiting, compensation, and training. • The right kind of platform is critical to tap the power of advanced analytics. Organizations need a flexible platform that centralizes their data, and allows it to be analyzed from any perspective. • Also essential are the ability to incorporate external and unstructured data streams, a robust user interface adaptable to varying management needs, and open architecture that leaves room for future innovations. Bringing data sophistication to 
 six facets of competitiveness
  24. 24. This content included for educational purposes. Example marketing & sales predictor applications (1 of 2) • PredicGng LifeGme Value (LTV)— If you can predict the characterisbcs of high LTV customers, this supports customer segmentabon, idenbfies upsell opportunibes and supports other markebng inibabves. Usage can be both an online algorithm and a stabc report showing the characterisbcs of high LTV customers • Wallet share esGmaGon — Working out the proporbon of a customer's spend in a category accrues to a company allows that company to idenbfy up-sell and cross-sell opportunibes. Usage can be both an online algorithm and a stabc report showing the characterisbcs of low wallet share customers • Churn — Working out the characterisbcs of churners allows a company to product adjustments and an online algorithm allows them to reach out to churners. Usage can be both an online algorithm and a stabsbcal report showing the characterisbcs of likely churners • Customer segmentaGon — If you can understand qualitabvely different customer groups, then we can give them different treatments (perhaps even by different groups in the company). Focus is to answer quesbons like: what makes people buy, stop buying etc. Usage: can be guidance • Product mix — What mix of products offers the lowest churn? eg. Would giving a combined policy discount for home + auto result in low churn? Usage: online algorithm and stabc reporting 24
  25. 25. This content included for educational purposes. Example marketing & sales predictor applications (2 of 2) • Cross-selling & RecommendaGon algorithms — Given a customer's past browsing history, purchase history and other characterisbcs, what are they likely to want to purchase in the future. Usage can be an online algorithm • Up-selling — Given a customer's characterisbcs, what is the likelihood that they'll upgrade in the future? Usage can be online algorithm and stabc report • Channel opGmizaGon — What is the opbmal way to reach a customer with certain characterisbcs? Usage can be an online algorithm and stabc report • Discount targeGng — What is the probability of inducing the desired behavior with a discount? Usage: online algorithm and stabc report • CalculaGng the right price for different keywords/ad slots — What is the reacbvabon likelihood for a given customer? Usage can be an online algorithm and stabc report • Adword opGmizaGon and ad buying — What is the right price for different keywords/ad slots? 25
  26. 26. This content included for educational purposes. 9 machine learning use cases Supervised Learning Predict credit worthiness of credit card holders: Build a machine learning model to look for delinquency attributes by providing it with data on delinquent and non-delinquent customers Predict patient readmission rates: Build a regression model by providing data on the patients' treatment regime and readmissions to show variables that best correlate with readmissions Analyze products customers buy together: Build a supervised learning model to identify frequent item sets and association rules from transactional data Unsupervised Learning Segment customers by Survey prospects and customers to develop multiple segments using clustering behavioral characteristics Categorize MRI data by normal or abnormal images: Use deep learning techniques to build a model that learns different features of images to recognize different patterns Recommend products to customers based on past purchases: Build a collaborative filtering model based on past purchases by "customers like them" Reinforcement Learning Create a 'next best offer' model for the call center group: Build a predictive model that learns over time as users accept or reject offers made by the sales staff Allocate scarce medical resources to handle different types of ER cases: Build a Markov Decision Process that learns treatment strategies for each type of ER case Reduce excess stock with dynamic pricing: Build a dynamic pricing model that adjusts the price based on customer response to offers Banking Healthcare Retail 26
  27. 27. This content included for educational purposes. 27 Machine learning applications across industries Source: Forbes
  28. 28. PUBLICIS.SAPIENT 
 COGNITIVE ANALYTICS Source: Publicis•SapientThis content included for educational purposes.
  29. 29. AUDIENCE INTELLIGENCE Build deeper relationship with your audiences with One-to-One Cognitive Media Engagements. • Consumer 360 Data Strategy • DMP Activation Strategy • Media Campaign Automation • Media Optimization Maturity • One-to-One Targeting Strategy Engage your customer with rich, dynamic and relevant brand experiences. • CRM Data onboarding • Customer Data Normalization • Persona Activation • Customer Journey Mapping • Customer Lifetime Value Activate cross-channel brand experiences that drive 
 business impacts. • Audience Segmentation • Programmatic Targeting • CRM & 3rd Party Data Alignment • Probabilistic Matching • Cross-Channel Activation COGNITIVE AUTOMATION Automate smarter experiences with artificial intelligence to disrupt your industry. • Fraud & Anomaly Detection • Media Buying Automation • Programmatic Creative • Relevance Down the Path to Purchase MARKETING ANALYTICS Gather, analyze and act upon your customer data in real time and across all marketing • Audience Graph Analysis • Social Sentiments Intelligence • Predictive & Prescriptive Analytics • Regency and frequency Reporting • Cross-channel attribution COSMOS™ Cognitive Marketing Intelligence Platform COSMOS™ Cognitive Services APIs COSMOS™ Audience Intelligence Audience Network COSMOS™ Attribution Analytics COSMOS™ DOMO Integration APIs COSMOS™ Universal Graph ID COSMOS™ Consumer 360 COSMOS™ Social Sentiments Graph BUSINESS IMPACTS DEEP PERSONALIZATION ONE-TO-ONE MARKETING CONSUMER JOURNEY AUDIENCE DATA ALIGNMENT SINGLE VIEW OF THE CUSTOMER AUDIENCE ACTIVATION BID OPTIMIZATION ATTRIBUTION ANALYTICS PROGRAMMATIC TAGS REAL TIME DECISIONNING COGNITIVE MEDIA STRATEGY CONSUMER 360 GRAPH COGNITIVE AUTOMATION MARKETING ANALYTICS | INSIGHT GENERATION: COSMOS™ Powered Media Solutions Source: Publicis•Sapient This content included for educational purposes.
  30. 30. This content included for educational purposes. July 1 July 15 July 30 $10.00 $5.00 $0.00 $30.00 $20.00 $100.00 $90.00 $80.00 $70.00 $60.00 $50.00 $40.00 120 60 80 20 40 100 0 Cost per Action Number of Attributes +2.11 Visited a product page +0.95 Is in the Atlanta DMA +0.89 Saw an ad 7-14 days ago -0.55 Is planning a trip +0.60 Is reading the news +0.51 Searched for luxury products -0.65 On a mobile device +0.46 Made a luxury retail purchase +0.43 Played an online game -0.45 Purchased sporting goods -0.54 Made a non-luxury retail purchase +0.41 Is in the Tampa DMA -0.50 Watched a TV show online +0.38 Clicked on an ad before +0.31 Booked a flight in the last week +0.29 Saw an ad 1-7 days ago +0.25 Is in the Orlando DMA -0.54 Is searching for an apartment -0.21 Is in the Los Angeles DMA -0.30 Saw an ad within the last hour +0.37 Has clicked on an ad before -0.31 Has seen 3+ ads already +0.48 Is in the Houston DMA -0.50 Watched a TV show online -0.54 Made a non-luxury retail purchase COSTPERCUMULATIVEACTION CAMPAIGN TIMELINE SIGNIFICANTMODELATTRIBUTES RELEVANTATTRIBUTES 
 “MICRO-MOMENTS” | INSIGHT GENERATION: COSMOS Learns and Optimizes from Real-time Micro-Moments 30 Source: Publicis•Sapient
  31. 31. PROGRAMMATIC MEDIA • Micro-Moment Targeting • Audience Segmentation • Bid Impression Value (BIV) • eCPM Optimization • RTB Optimizer • Attributions • Ad Creative Personalization • Ad Serving Fraud Detection • Micro-Moment Segmentation • Lifetime Value (LTV) • Propensity • Recency, Frequency and Monetary Value (RFM) • Churn Prediction & Prevention • Universal ID Sequencing • Sentiments Signals • Message Resonance • Concept Expansion • Face Detection • Natural Language Classifier • Speech to Text • Text to Speech • Language Translation • Language Detection • Sentiment Analysis • Dialog • Retrieve and Rank • Image Link Extraction • Tradeoff Analytics • Entity Extraction • Tone Analyzer • Personality Insights • Taxonomy COGNITIVE APIs • Audience Segmentation • Intelligent Search • Frequently Bought Together (FBT) • Cross-Selling (item correlations) • Sentiment and trend analysis • Shipping cost and time estimation • Logistics optimization • Fraud detection and prevention • Supply and demand analysis and forecast • Wallet management and funding source optimization • Various scheduling and optimal resource allocation • Micro-Moment Targeting • Attributions • Content Personalization • Customer Lifetime Value C(LTV) • Customer Propensity • Recency, Frequency and Monetary Value (RFM) • Churn Prediction & Prevention COGNITIVE COMMERCE • Programmatic Creative | DCO • Micro-Moment Targeting • Audience Segmentation • Content Personalization • Micro-Moment Segmentation • Universal ID Sequencing • Text Mining • Sentiments Signals • Cross-Screen Equalizer • Auto-suggest Indexer UNIFIED EXPERIENCE Intelligence AMPLIFY CONSUMER 360 COSMOS Artificial Neuro Network • Author Extraction • Concept Tagging • Relationship Extraction • Concept Insights • Question & Answer • Feed Detection • Keyword Extraction • Visual Recognition • Image Tagging • Text Extraction | INSIGHT GENERATION: COSMOS™ Cognitive Library Source: Publicis•Sapient This content included for educational purposes. 31
  32. 32. This content included for educational purposes. Sentiment Trends Positive sentiment is also highly emotive and is closely associated with artist fandom. Fans proclaim love and excitement around iHeartRadio songs and events, and interact with artist-centric iHeartRadio social content. Association scores range on scale from -1.00 to 1.00.  These are extremes which represent the weakest and the strongest possible relationships: An  association score of 100 represents the relationship between a concept and itself, while -100 is the relationship between a concept and the most unrelated other concept within the same data set.  A score of association score of around 0 represents how much we would expect two concepts to be discussed at the same time as a result of random chance. Above: Top concepts 
 (tool-defined based solely on phrase occurrence) associated with positive sentiment. Right: Examples of stereotypically emotive fans currently listening to & enjoying a song (left), anticipating an event (right). | INSIGHT GENERATION Source: Publicis•Sapient This content included for educational purposes.
  33. 33. This content included for educational purposes. To Create a Single Point of Truth for Each Consumer To Eliminate Siloed Digital Experiences To Empower Cross Channel and Device Personalize Experiences To Connect Advertising, Customer Service, Marketing, and CRM Tools To move beyond rule based optimization Creating 1 to 1 Optimization To Enable Consumer Centric Marketing WHY WE BUILT COSMOS? Source: Publicis•Sapient 33This content included for educational purposes.
  34. 34. This content included for educational purposes. 34 Source: Publicis•Sapient
  35. 35. This content included for educational purposes. | IDIOM CREATES THE SINGLE VIEW OF THE CONSUMER… SARAH Age 26
 Manhattan
 Account Exec $75K smgOS allows for greater insight into a brand’s “true” customer and/or opportunity by unveiling behavioral truths 35Source: Publicis•Sapient
  36. 36. This content included for educational purposes. NOBODY HAS EVER BROUGHT THIS RANGE OF DATA TOGETHER BEFORE • Multi-Client Handling • Export BDE & SAFE HAVEN • Full panel data for search and web DIGITAL • Co-developing exclusive clustering methodology to provide TV data at 30 HH cluster level TV • Extensive data rights review unveiling new insights for potential partners MOBILE • “Reimagining our data in a way its never been sold” PURCHASE 36 Source: Publicis•Sapient This content included for educational purposes.
  37. 37. Source: Publicis•Sapient This content included for educational purposes.
  38. 38. This content included for educational purposes. 38 COSMOS PROVIDES POWERFUL TOOLS FOR MARKETERS TO UNDERSTAND BEHAVIOR Source: Publicis•Sapient
  39. 39. This content included for educational purposes. Concept Cloud 39 At first glance, positive clusters that jump out are the artist names and birthdays. Negative clusters appear associated with news of a celebrity death, as well as songs / concerts. Mentions of iHeartMedia were highly neutral and tended to originate from industry & media sources as opposed to consumers, so we instead analyzed the more consumer-driven, emotive body of conversation around iHeartRadio, keywording specifically to source positive and negative themes (see slide notes). Concept cloud data, as well as data used throughout this report, is drawn from Twitter and Facebook mentions of iHeartRadio from 1/1/2016-6/27/2016. Source: Publicis•Sapient This content included for educational purposes.
  40. 40. This content included for educational purposes. Sentiment trends • Negative sentiment is highly emotive, and often in response to sad/provocative/ etc. iHeartRadio music and content. People aren’t upset with iHeart- they’re upset in tandem with iHeart about the emotional content iHeart publishes, or about songs/ albums/artists. Association scores range on scale from -1.00 to 1.00. These are extremes which represent the weakest and the strongest possible relationships: An association score of 100 represents the relationship between a concept and itself, while -100 is the relationship between a concept and the most unrelated other concept within the same data set. A score of association score of around 0 represents how much we would expect two concepts to be discussed at the same time as a result of random chance. Above: Top concepts (tool- defined based solely on phrase occurrence) associated with negative sentiment. Right: Top concepts associated with the more nuanced ‘sad,’ which is largely associated with a celebrity death covered by iHeartMedia in social.Source: Publicis•Sapient 40 This content included for educational purposes.
  41. 41. CUSTOMER ENGAGEMENT
  42. 42. This content included for educational purposes. 42 • Customer engagement • Conversational interface • Bots • Sapient AI platform for chatbots, assistants, and precision marketing • Cognitive marketing, sales, and servicesOverview of
 Customer Engagement
  43. 43. cus·tom·er en·gage·ment /ˈkəstəmər//inˈɡājmənt,enˈɡājmənt/ The use of AI, information, analytics and communications technologies to attract, involve, and support someone's interest, attention, interaction, and participation towards some end. For example, using intelligent agents and avatars to deliver hyper-personalization at scale through all channels, including smarter, more relevant insights and contextual recommendations to amplify end-user experience. 43 This content included for educational purposes.
  44. 44. This content included for educational purposes. Our expectations have evolved. The era of consumer and enterprise conversational computing is dawning. Speech Enabled Devices Virtual Assistants Smart, Speech-Enabled Sites Messaging & Social Media 44 This content included for educational purposes.
  45. 45. This content included for educational purposes. We Amazon the diapersWe Netflix the showWe Uber the car We Spotify the Song We Yelp the restaurant We Google the symptoms 45 This content included for educational purposes.
  46. 46. CONVERSATIONAL INTERFACE
  47. 47. This content included for educational purposes. “‘Conversational AI-first’ will supersede 
 ‘cloud-first, mobile-first’ as the most important, high-level imperative for the next 10 years.” 47This content included for educational purposes.
  48. 48. This content included for educational purposes. CONVERSATIONAL INTERFACE 48 CONVERSATIONAL INTERFACE • Communication enabled by natural language involving: - Multiple contributions - Coherent interaction - More than one participant • Multiple interaction modalities: - Input: Speech, typing, writing, pictures, gesture - Output: Speech, text, graphical display/ presentation, animated face/body This content included for educational purposes.
  49. 49. This content included for educational purposes. What is involved in conversational UI? • Understanding: -What does a person say? ‣ Identify words & other entities from input signals ‣ “Please close the window” -What does the speech, image or gesture mean? ‣ Identify semantic content ‣ Request ( subject: close ( object: window)) -What are the speaker’s intentions? ‣ Speaker requests an action in a physical world 49This content included for educational purposes.
  50. 50. This content included for educational purposes. What is involved in conversational UI? • Managing interaction: - Internally representing the domain - Identifying new information - Deciding which action to perform given new information: ‣ “close window”, or “set thermostat” = physical action ‣ “what is weather outside?” = call the weather API - Determining a response: ‣ “OK”, or “I can’t do it” ‣ Provide an answer ‣ Ask a clarification question What is involved in conversational UI? • Managing interaction: - Internally representing the domain - Identifying new information - Deciding which action to perform given new information: ‣ “close window”, or “set thermostat” = physical action ‣ “what is weather outside?” = call the weather API - Determining a response: ‣ “OK”, or “I can’t do it” ‣ Provide an answer ‣ Ask a clarification question 50
  51. 51. This content included for educational purposes. What is involved in conversational UI? • Access to knowledge and information • E.g., to handle a request, “Please close the window”, the chatbot/assistant needs to know: - There is a window - Window currently is open - Whether the window can or cannot be closed What is involved in conversational UI? • Access to knowledge and information • E.g., to handle a request, “Please close the window”, the chatbot/assistant needs to know: - There is a window - Window currently is open - Whether the window can or cannot be closed 51
  52. 52. This content included for educational purposes. What is involved in conversational UI? • Producing language - Deciding when to speak or otherwise respond - Deciding what to say or display ‣ Choosing the appropriate meaning - Deciding how to present information ‣ So partner understands it ‣ So expression seems natural What is involved in conversational UI? • Producing language - Deciding when to speak or otherwise respond - Deciding what to say or display ‣ Choosing the appropriate meaning - Deciding how to present information ‣ So partner understands it ‣ So expression seems natural 52
  53. 53. This content included for educational purposes. When is a conversational interface useful? 53 • When hands-free interaction is needed: - In-car interface - In-field assistant system - Command-and-control interface - Language tutoring - Immersive training • When speaking is easier than typing and other mode of interaction: - Voice as common interface across multiple platforms, devices and things - Virtual assistant (Siri, Google Now, Cortana, etc.) • When replacing or augmenting human agents: - Voice interface for customer assistance and service provisioning - Process and task automation - Virtual assistance to improve capabilities, productivity, and efficiency of knowledge workers When is a conversational interface useful?
  54. 54. BOTS This content included for educational purposes.
  55. 55. This content included for educational purposes. 55 What is a bot? • A bot is an autonomous program on a network that can interact with computer systems or users to perform tasks. • Chatbots and virtual assistants help customers and colleagues perform tasks in increasingly simpler and more effective ways. Voice and text are the most common modalities for interacting with bots. This content included for educational purposes.
  56. 56. This content included for educational purposes. Bots are the new apps. Conversations are the new UI. AI is the protocol. Messaging apps are the new browser. 56
  57. 57. | CUSTOMER ENGAGEMENT — Visions from science fiction cinema STAR TREK (1966) Natural language 
 command and control HAL “2001: A SPACE ODYSSEY” (1968) Naturally conversing computer HER (2013) A virtual partner with natural dialogue capabilities 57This content included for educational purposes.
  58. 58. This content included for educational purposes. JARVIS is not cool because of what Tony Stark can do with it, but because it is JARVIS 58This content included for educational purposes.
  59. 59. This content included for educational purposes. | CUSTOMER ENGAGEMENT — Evolution of intelligent agents TOOL APPLIANCE CHATBOT ASSISTANT EXPERT SAVANT TODAY 2018 2019-2025 2030+ 59This content included for educational purposes.
  60. 60. This content included for educational purposes. TOOL APPLIANCE CHATBOT ASSISTANT EXPERT SAVANT Tool requires detailed procedural interaction by user to perform a sequence of steps to accomplish function. Chatbot is a conversational agent that interacts with users using natural language and AI. May have its own persona (avatar) visualization. May act as virtual assistant. Expert applies domain knowledge, deep learning, task expertise, and legally defensible reasoning to research, advise, and take actions to solve complex problems requiring human-level expertise. Savant AI demonstrates far better than normal human capacities and abilities. Assistant understands questions, commands and intent; learns and adapts to context, preferences, and priorities; and marshals services and information to accomplish tasks. Appliance minimizes user steps to specify and automate desired function or service. User selects to approve result or redirect. Choosing the level of assistance 60
  61. 61. This content included for educational purposes. CHATBOT ASSISTANTAI CAPABILITIES • Structured and unstructured data ingest, cleansing and curation • Speech processing • Knowledge acquisition • Image processing • Face and gesture recognition • Emotion & sentiment • Avatars • Story and conversation management • Natural language understanding • Task and service orchestration • Natural language generation • Speech generation • Visualization • Presentation • Speech and conversation analytics • Natural language processing • Descriptive analytics • Machine learning & deep learning •Predictive and prescriptive analytics •Knowledge management •Semanticsearch •Symbolic reasoning • Question answering • Advice & recommendation • Next bestactions • Expert assistance • Taskplanning • Command execution • Data and service provisioning Capabilities today 61
  62. 62. This content included for educational purposes. Example: AI in hospitality apps, digital agents, and internet of things CHAT CHATBOT ASSISTANT CONCIERGE BUTLER SMART FACILITY Marriott ‘Mobile App’ Mobile app enables booking, check-in/check-out, digital room key, and requests to staff before, during and after the stay (via chat). Radisson Blu ‘Edward’ Text-based virtual host understands natural language, handles digital checkin, reports on hotel amenities, gives directions and tips, and receives guest feedback and complaints in a matter of seconds via SMS. Go Moment
 ‘Ivy’ Smart texting platform for hotels, powered by IBM Watson AI, welcomes guests, answers questions, advises, integrates with digital room key technology, measures guest satisfaction. Hilton
 ‘Connie’ Virtual concierge embodied as NAO humanoid robot that is approximately 23 inches tall. Connie answers guest questions about hotel amenities, local attractions and dining options. It’s AI uses IBM's Watson machine-learning APIs, like speech to text, text to speech and its natural language classifier. Connie learns as it goes. Starwood Aloft ‘Botlr’ Digital bellhop, or robotic butler delivers amenities to rooms. It knows the hotel layout, is connected to elevators, has avoidance technology to not bump into anything, and has a touch screen for guests to interact with it. Marriott M Beta Hotel innovation incubator in “live beta” From keyless entry upon arrival, sensors beacons enabling digital experiences in the lobby, fitness studio, meeting rooms, cafe, and every corner of the hotel. Infrastructure for rapid prototyping, inviting guests to test and give feedback in real- time, ultimately shaping their future hotel experience. 62
  63. 63. This content included for educational purposes. How intelligent chatbots work Source: Inbenta 1. Captures data in real time The intelligent chatbot captures the customer’s identity, attributes, and engagement data, and any feedback the customer provides—all in real time. For example, the chatbot determines: • Date, time, physical location, and device information • Whether the customer is on the web or a mobile app • Whether the customer requested to engage with a chatbot or received a proactive invitation • Where the customer was on the website or mobile app when he or she began the interaction with the chatbot 2. Uses internal data Using data such as customer profile and preferences, value to the company, location, industry, and amount of money spent in the past year gives the chatbot more insights about the customer. This data is gathered from various sources and is typically available in customer relationship management (CRM) systems. 3. Combines data to predict customer intentions The chatbot develops an understanding of what the customer wants/needs by combining all the data signals. This helps make the conversation contextual and more natural when the customer engages the chatbot. 4. Engages customers Customers can invoke chatbots themselves when they need assistance, or chatbots can proactively engage customers. 5. Understands what is said The chatbot takes each message written or each utterance spoken and runs it through natural language models to understand what the customer said. This interaction is contextual and personalized to the customer. The chatbot achieves this by leveraging information such as the web page the customer was on when they engaged with the chatbot and their customer profile. For example, if a customer is on a bank’s website looking at a page on mortgages and asks the chatbot what the interest rate is, the chatbot will know the customer is asking about the interest rate for mortgages. 6. Formulates a response Once the chatbot understands the customer’s intent, the response-matching algorithm determines the correct response and assembles it from knowledge bases and CRM systems. 7. Determines follow-up actions If the customer provides feedback that he or she is satisfied with the chatbot response, the chatbot closes that intent and waits for a new intent. If the customer requests the chatbot to help “pay my credit card bill,” for example, the chatbot will determine the appropriate follow-up actions such as asking the customer for a password and then completing the transaction. 63
  64. 64. This content included for educational purposes. Source: Inbenta A chatbot should escalate to a live agent when: 1 2 3 4 5 6 The customer’s request is not understandable. The customer appears to be annoyed or frustrated. The customer’s request cannot be handled in self-service (due to rules or policies). The customer’s request is better served by an agent (e.g., conversion or attrition). It is a high-value transaction and the company wants a live agent to close the sales opportunity. The customer explicitly requests a human agent. 64
  65. 65. This content included for educational purposes. 65 Digital advice is computer rendered guidance or recommendations concerning future action. For example, digital advisors for wealth management incorporate AI technologies into their management processes – primarily through the use of algorithms designed to optimize various elements of goal and risk tolerance elicitation, to portfolio construction and asset allocation, to tax management, to product selection and trade execution, to performance monitoring and portfolio rebalancing. Different digital advisors pursue different business models and philosophies, and offer varying degrees of sophistication in services provided. Also, the role of human involvement within digital advisors varies. https://www.blackrock.com/corporate/en-lm/literature/whitepaper/viewpoint-digital-investment-advice-september-2016.pdf What is digital advice?
  66. 66. This content included for educational purposes. From voice commands & question answering to intelligent conversation • Siri, Google Assistant, Cortana and Alexa all essentially work the same way — they recognize and parse speech, classify intent and execute commands. • This framework works for building a voice recognition system that can interface with a string of APIs, but it falls short if you expect an intelligent conversation. • Intelligence requires more than a great classifier. You need to balance data, learning, memory, computation and some semblance of goals. 66 Bots learn to converse Source: Semantic Machines
  67. 67. This content included for educational purposes. Source: Inner Circle Guide to Multichannel Customer Contact, NewVoiceMedia, 2016. Why chatbots now? As the number of channels and touchpoints multiply, customer expectations continue to evolve toward tailored, integrated interactions and immediate answers to their questions: • 85% of consumers have used an online channel for support • 40% expect a response within the hour • 60% of consumers change communication channels based on where they are and what they’re doing. 67This content included for educational purposes.
  68. 68. This content included for educational purposes. Source: Inbenta Six ways enterprise chat bots and virtual assistants deliver value 1 2 3 4 5 6 Increase customer self-service engagement. Improve customer satisfaction ratings, lower customer effort scores, and increase your Net Promoter Score. Automate routine customer questions to allow human agents to focus on higher-value interactions. Deflect calls, email, and chats to reduce costs. Minimize menial or repetitive work for agents. Create a seamless hand-off from self- service virtual assistance to a live agent. Maintain context of previous interactions, thus avoiding “starting over.” Reduce average handling time by suggesting responses while the agent is chatting with the customer Generate true “voice of the customer” data through the conversations. Mine agent interactions to learn new customer intents and agent solutions. Six ways enterprise chat bots and virtual assistants deliver value 68
  69. 69. This content included for educational purposes. Business value of enterprise chatbots across industries Telecommunications company Vodafone’s virtual agent “Hani” is an intelligent chatbot that answers 80,000 questions per month and deflects calls away from the contact center for 75 percent of the customers it chats with. Vodafone contact center staff also use the same technology to access accurate, up-to- date information on Vodafone products and services. A leading global airline created an avatar to personify their chatbot. The chatbot serves as an automated concierge, providing customers with instant, accurate answers to their questions about flight status and baggage rules. The chatbot has helped the airline reduce call and chat volume by 40 percent. Canadian Imperial Bank of Commerce, one of Canada’s largest chartered banks, introduced an intelligent chatbot as a virtual agent and saw email volume decrease by 50 percent immediately at launch, and then experienced another 23 percent drop throughout the first year. At the same time, it reduced phone calls by 25 percent. A major health insurance provider improved the experience for its 4 million members with an intelligent chatbot deployed as a virtual agent. With the chatbot answering 150,000 questions per month, the company is saving thousands of dollars in contact center costs by reducing calls to its staff. Canadian utility BC Hydro improved customer service and satisfaction for its 4 million customers and increased operational efficiency by deploying a chatbot on its website. In the first 11 months, the chatbot answered more than 720,000 questions with an accuracy rate of 94 percent. A major retailer implemented an intelligent chatbot to deliver a phenomenal guest experience, answering 45,000 questions a month about order status, shipping, returns, and other common areas of interest. The chatbot deflects informational calls and email away from staff by answering 97 percent of the questions asked, with 96 percent accuracy. Communications Travel Financial Services Healthcare Utilities Retail 69
  70. 70. This content included for educational purposes. The bot platform ecosystem Nearly every large software company has announced some sort of bot strategy in the last year. Here's a look at a handful of leading platforms that developers might use to send messages, interpret natural language. and deploy bots, with the emerging bot-ecosystem giants highlighted. 70This content included for educational purposes.
  71. 71. BOTS CAN HAVE MASSIVE REACH 2.1+ BILLION ACTIVE USERS AND GROWING 900 M 25 170 M 26 2.7 M 27 275 M 28 48 M 29 100 M 30 697 M 31 BOT LAYER API LAYER SERVICES LAYER APPLICATION LAYER DATA LAYER BUILT | CUSTOMER ENGAGEMENT This content included for educational purposes.
  72. 72. BOTS CAN WORK WELL AROSS MESSAGING PLATFORMS • Within chat apps, a bot is essentially a layer that retrieves information for a user or group of users • It can be as simple as extracting information from a database • Or there could be some logic or complex calculations involved - • This is where we would see the application of a technology that rolls up to the AI classification that we just outlined such as Machine Learning or Natural Language Processing | CUSTOMER ENGAGEMENT This content included for educational purposes.
  73. 73. This content included for educational purposes. Conversational AI platform CONVERSATIONAL AI PLATFORM Source: MindMeld 73This content included for educational purposes.
  74. 74. This content included for educational purposes. 74 PEOPLE USER 
 EXPERIENCE CHATBOT/ASSISTANT USE CASESINTERACTION CHANNELS Text Voice Graphics Image Video Virtual world GUI Touch Gesture Dialogue • Product and Service
 Information • Product and Service Selection and Transaction • Trip Planning • Arrival and Departure • Concierge Services • Events and Activities • Customer Feedback Web page IM, Chat, SMS E-mail Activity stream Smart agent Mobile app VR and AR Homes Automobiles Wearables IOT Human / Machine UX Listening (NLP) Cloud Services Open APIs Databases Apps Devices Chatting (NLG) Business Logic Knowledge ML | CUSTOMER ENGAGEMENT: The anatomy of chat bots and virtual assistants
  75. 75. Structured & unstructured data ingest Context, intent and sentiment analysis Story & conversation management Speech processing Natural language understanding Semantic search Natural language generation Descriptive, predictive & prescriptive analytics Speech generation Symbolic reasoning & Real-time decisioning Question answering Recommendation Advice Expert assistance Task planning Command execution Semantic APIs for externally provided capabilities (UI, data, AI engines, external systems and services) UI, task, & service orchestration Visualization & presentation Data/service
 provisioning Knowledge acquisition Image processing Knowledge management Image, face & gesture understanding Machine learning algorithms | CUSTOMER ENGAGEMENT: The functional building blocks for chat bots and virtual assistants 75This content included for educational purposes.
  76. 76. CHANNELS BOT FRAMEWORKS CONVERSATIONAL USER INTERFACE IINTELLIGENT ASSISTANCE (Service APIs) KNOWLEDGE ENGINES DATA & SERVICES Native Apps PlatformsWeb Browsers Mobile GuestsLive Agents MicrosoftGoogle IBM KORE VIV Natural Language Processing Speech Recognition NLU: Words, Syntax, Context, Semantics, Sentiment, Personality, Intent Conversation & Dialog NLG Personality & TTS Vision Processing Image Recognition Knowledge Base Semantic Engines Analytics Engines Process/Workflow Engine Pre-Trip Exploring Immersive Play Discovery Planning Booking Learning Way Finding Personalized Services Arrival Guidance Real-time Suggestions Relevant Notifications Character Concierge Contextual Commerce Virtual Purchases Shopping Sharing Reminisce Moments Cross-Device Messaging User, Task and Service OrchestrationLanguage and Dialog Models, Domain Ontology, Predictive Models, and Task Expertise Knowledge Representation, Common Sense & Causal Reasoning Machine Learning and Deep Learning: Diagnostic, Predictive, and Prescriptive Analytics Data Services 1st, 2nd & 3rd Party Data, Social Networks, Reference Data, RDBMS, Graph DB, CMS Marketing, CRM, ERP, MDM Systems Administration, Monitoring Dashboards Enterprise Services and Operations Apple Nuance SEM PRE YUBIIHOUNDFacebookAPI.ai AI: A CROWDED MARKETPLACE 76This content included for educational purposes.
  77. 77. Native AI platforms for chatbots Leading internet technology companies providing native 
 AI-based personal assistants and bot frameworks for building 
 and deploying conversational interfaces: 77 ▪ Amazon—Alexa provides voice interaction with devices and services. Alexa Skills Kit provides a collection of self-service APIs, tools, documentation and code samples for adding skills to Alexa. ▪ Apple—Siri artificial intelligence and natural language processing enable conversational interface, personal context awareness and service delegation. SiriKit lets developers integrate services with Siri. ▪ Facebook—Facebook Bot Engine is based on WIT.ai, which trains bots using sample conversations. API calls extract meaning and intent from sentences. ▪ Microsoft—Cortana voice or text activated intelligent personal assistant platform supports multiple devices, languages, and operating environments. Microsoft Bot Framework provides APIs and functionality needed to build, connect, manage, and publish intelligent bots that interact conversationally. ▪ Google—Google Assistant (AI) extends Allo, Now, Hangouts, Home, and other products into conversational 2-way dialog that understands the users world and helps get things done. Google provides many best of breed APIs needed for conversational UI. ▪ Samsung Viv—Virtual assistant framework with dynamic programming to handle complex queries. This content included for educational purposes.
  78. 78. ▪ Facebook M—is an instant messaging service that provides text/voice communication and web chat. ▪ Google Hangouts— is a communications platform that includes instant messaging, video chat, SMS, and VOIP features. ▪ Kik— is an instant messenger application (app) for mobile devices. ▪ Line—is a Japanese messaging app. ▪ Skype— is a communications platform for text, voice, and video. ▪ Slack—is a multi-environment, cloud-based platform for team and community collaboration ▪ Telegram—is a cloud-based encrypted service for sending messages and exchange photos, videos, stickers and files of any type. ▪ Twilio— provides infrastructure and software as a service for business communications, enabling phones, VoIP, and messaging to be embedded into web, desktop, and mobile software. ▪ Twitter— is a free social networking microblogging service. ▪ WeChat— is a mobile text and voice messaging communication service that provides text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video conferencing, video games, sharing of photographs and videos, and location sharing. ▪ WhatsApp— is a cross-platform mobile messaging app that allows exchanging messages without SMS charges. Messaging chatbot channels Messaging platforms providing chat bot frameworks, APIs 
 and SDKs that support building, publishing, and managing of
 chat bots and personal assistants: 78 This content included for educational purposes.
  79. 79. ▪ Arria— NLG Platform generates natural language by extracting information from complex data. ▪ Narrative Science—Quill is a AI platform for NLG that converts data to relevant information to professional prose ▪ X.ai—Amy is an AI that arranges meetings ▪ Clara Labs—Clara is an AI who schedules meetings. ▪ Conversica—is an AI platform that acts as a sales assistant to qualify and communicate with leads. ▪ Creative Virtual— is a virtual agent platform for self-service and hybrid AI customer support solutions. It trains by reading manuals and other documentation. ▪ Equals3Media— is a cognitive platform for audience research, segmentation, and media planning. ▪ Kasisto— KAI is a conversational AI platform powering virtual assistants and smart bots across mobile, messaging, and wearables. KAI Banking is pre-loaded with thousands of banking intents and millions of banking sentences. ▪ Kensho— Warren is a Siri-, Watson-style intelligent investor with significant financial services domain expertise. ▪ Ross Intelligence—ROSS is an artificially intelligent attorney that helps power through legal research. Enterprise chatbots and virtual assistants 3rd-parties providing and deploying enterprise chat bots and virtual assistants that combine domain expertise, causal reasoning, and machine learning to handle complex tasks: 79This content included for educational purposes.
  80. 80. This content included for educational purposes. 3 Leading internet technology companies providing enabling technology for AI platforms and frameworks to build and deploy virtual assistants that augment employee productivity, automate complex tasks, and improve customer experience: ▪ API.ai—is an AI platform for bots, that provides speech-to-text, NLU, intent recognition, context and conversation management, and fulfillment of user requests. ▪ Artificial Solutions—Teneo is an AI platform for building enterprise- class assistants that let people talk to apps in free-format, natural language using speech, text, touch, or gesture. ▪ CyCorp / Lucid—is a causal reasoning platform that combines a large common-sense ontology and knowledgeable with natural language interfaces. ▪ IBM—Watson is a cognitive platform that enables software, services, and apps that think, improve by learning, and discover answers and insights to complex questions from massive amounts of data. ▪ Inbenta—is an AI platform for delivering intelligent chatbots for customer service. ▪ IPsoft—Amelia is a cognitive agent (or digital employee) who can take on a wide variety of service desk roles and communicate with customers using natural language. She speaks 20 languages and trains by reading manuals and other human readable materials. ▪ Kore—provides an enterprise-grade platform-as-a-service to build and deploy AI-based enterprise bots on a large scale for varied business use cases. ▪ Luminoso—is an AI platform for NLU that uses machine learning and semantic knowledge graphs to put text into context, map concepts, analyze sentiments, and derive insights from varied sources. ▪ Nuance— Nina is an intelligent cross-channel virtual agent platform that converses via voice or text, and delivers instant, accurate, successful outcomes in a natural, human-like way. ▪ Robin Labs— is an open, expandable AI platform for building conversational virtual assistants that communicate through natural language including speech, text, gesture, and visual imagery, learn from examples, can accomplish tasks for a user. 80 Enabling technologies for chatbots & assistants Leading technology companies providing enabling NLP, AI technologies and bot frameworks to build and deploy consumer and enterprise virtual assistants that improve customer experience, augment employee productivity, and automate complex tasks: This content included for educational purposes.
  81. 81. PUBLICIS.SAPIENT 
 AI PLATFORM FOR CUSTOMER ENGAGEMENT (YUBII + KAAS) Source: Publicis•SapientThis content included for educational purposes.
  82. 82. YUBII + KAAS Ubiquitous cognitive experience framework + Knowledge as a Service approach allows you to leverage best-of- breed AI capabilities from the widest marketplace YUBII Framework • • The Sapient Cognitive Experience Framework which enables multi-channel user experiences with seamless cross channel integration, and is built to support dynamic user experiences through chat, image, video, AR/VR, and a flexible approach to support new UI paradigms as they are born. Supports Interactive Learning with the ability to monitor and learn interactions as part of a user experience automatically in order to minimize training and pre-configuration. • Enables the seamless integration of live human agents and supporting AI capabilities to deliver the greatest experience possible. KaaSApproach • • • • The Sapient Knowledge as a Service approach provides an architectural plan which enables flexible delivery of knowledge services across a diverse set of knowledge engines, enterprise integrations, and data sources. Provides a single, central resource for user experiences to establish their ability to access, modify, and interact with the world outside of the user experience. Provides future-proofing through it’s ability to allow migration of integrations and knowledge engines without modification to the user experience. Provides a common deployment for choosing best-of-breed solutions from the market. Source: Publicis•Sapient 82This content included for educational purposes.
  83. 83. | CUSTOMER ENGAGEMENT: “YUBII” accelerator for ubiquitous brand engagement Source: Publicis•Sapient 83This content included for educational purposes.
  84. 84. This content included for educational purposes. • Yubii is a Cognitive Experience Framework that orchestrates the technologies and information needed to have an intelligent conversation (NLP, Knowledge Engine, Experience Framework, State Management, Live Agent, Data) • Yubii allows us to build user experiences and deploy the underlying knowledge models to multiple endpoints (mobile app, website, messenger, connected device, virtual / mixed reality).
 • Yubii also allows us to manage a conversation across endpoints, without aggravating the user by asking them to restate their intentions.
 • Yubii is technology agnostic. It can work with multiple ML and cloud vendors, nlp components, knowledge engines, content management systems and analytics solutions. YUBII: OVERVIEW 84 Source: Publicis•Sapient This content included for educational purposes.
  85. 85. YUBII: VIRTUAL ASSISTANT TRAINING LIFECYCLE Input Collection Data Prep & Analysis Conversation Design & Training Testing Go Live Ongoing Improvement • Geo location data • POI details data • Guest data • Park operations data • Audio: Speech to text processing • Data cleaning & normalization • Pattern analysis • Conversation structure design • Training data creation • Testing plan and data creation • Dialog • Intents • Entities • Contexts • Fulfillments • Training synonyms • Fulfillment micro- services • Review • Curate • Improve • Release new Virtual Assistant • Monitor • Curate • Improve FEEDBACK FEEDBACK Source: Publicis•Sapient 85This content included for educational purposes.
  86. 86. This content included for educational purposes. KAAS: HARD AI / SOFT AI Intelligence: The ability to acquire and apply knowledge and skills Perceive Understand Intelligence Act Decide Observe Direct The World “SOFT AI” – Cognitive Computing Conversation Communication Representation Perception Generation Production “HARD AI” – Machine / Deep Learning Calculation Computation Classification Regression Reason Memorization Through it’s bringing together of data, enterprise integrations, knowledge engines, and user experience frameworks, KAAS becomes an AI platform. As a system, it provides the ability to acquire and apply knowledge and skills through the natural pairing of cognitive computing and machine / deep learning. ACQUISITION APPLICATION 86 Source: Publicis•Sapient This content included for educational purposes.
  87. 87. COGNITIVE MARKETING, SALES & SERVICE
  88. 88. This content included for educational purposes. ASK 88 Cognitive marketing, sales, and service: • Customer journey focus rather than product • Customer & prospect big data and predictive analytics for better decisions sooner & at scale. • Adaptive, context-aware websites and microsites • AI-first mobile apps and seamless multichannel user experience (smartphone, tablet, wearables) • 1st/3rd-party message apps • Dynamic pricing, delivery, service, and support • Concierge QA, advice, and personalized service • Customer digital engagement, feedback, reviews, ratings, and loyalty programs This content included for educational purposes.
  89. 89. This content included for educational purposes. Rich landscape for customer-centric digital experience today 89This content included for educational purposes.
  90. 90. This content included for educational purposes. Use of cognitive and analytics to drive customer engagement Source: IBM 90 This content included for educational purposes.
  91. 91. This content included for educational purposes. CLIENT EXPERIENCE Research Options Life Event Select & Engage Associate F2F Digital Enablement Client Portal & Reporting Peer Networking / Community Develop Network Customer 
 Goals, Needs,
 Constraints Custom 
 Product/Service Construction Guidance & Resources Credit, Financing &
 Payment Plan
 Enrollment & Onboarding Product/ Service Training &
 Operations Regulatory, Risk & Compliance Visualization & Reporting Reassess Goals, Life Events Modeling & Adjustment to Strategy & Plan Extended Networks & Resources Networking Support Community Building Broadening the Relationship Optimization & Tuning Execution and Management Acquire & Understand Client Initial Engagement, Strategy & Planning TEAM MEMBER EXPERIENCE Cognitively enable the customer experience of both clients and employees 91
  92. 92. This content included for educational purposes. 92 • Big data and advanced analytics give firms a host of ways to target and connect with the kinds of investors they want. • Marketing is shifting from supporting sales and distribution to actively targeting clients and engaging investors in the right place at the right time to build and nurture relationships. • Big data and analytics are key to better identifying which marketing strategy is best suited for a given prospect at a given time and to providing a custom experience tailored to every individual client. • Semantic data mining and machine learning automate gathering prospect information such as contact information, demographics, income, interests, and in-market behavioral signals (e.g., site navigation, emails, voice conversations, content downloads, etc.) that prioritize leads and better target messaging, offers and market interactions. • Sales agents and managers can mine this trove of information to create SEO-optimized content, customize it for specific user contexts, and tailor it for delivery across different media such as email, social media message, tweets, and other information channels and devices. Precision marketing technology gives firms a host of ways to target and connect with the kinds of customers they want.
  93. 93. This content included for educational purposes. Source:AccorHotels Source: Third Door Media Search engine optimization success factors 93
  94. 94. This content included for educational purposes. 94 • Before the rise of social platforms and interactive digital media, corporate communication was generally a one-way street; even websites and email are mainly broadcast media that offer limited interactivity. • Social media exploded those limits, empowering even big, anonymous corporations to have meaningful conversations with their customers, employees, partners, colleagues, and the world at large. • Even regulators have shown enthusiasm for social media, officially recognizing their value in helping educate investors and prevent fraud. • More advanced companies use social media to “listen at scale” to learn customer characteristics, and to understand emotional motivators of investor behavior and feelings, including some factors of which customers may not be aware. • Some firms sift tweets, emails, and voice communications from traders, investors, and analysts for market insights and investment signals to better inform decision-making. Meaningful social media conversation creates value
  95. 95. This content included for educational purposes. 95 • AI technology helps enterprises engage the right clients with the right offerings at the right time and through the right channels. • AI CRM tools do more that present internal information in an organized way. The best portfolio managers are also the best relationship managers. • AI-based CRM tools can continuously monitor customers’ social media posts, tweets, credit factors, and other data points and can alert investment managers accordingly. • AI applications can initiate event-driven personalized communications with customers, and engage them in near-human ways that traditional software cannot. • AI-based CRM platforms can interface with customer portals to provide customized user interfaces. The customer is presented with the information he or she is most likely to need, based not only on previous interactions, but also on big-data predictive analysis. AI CRM service personalization
  96. 96. This content included for educational purposes. 96 • Enterprises need infrastructure that provides instant access to account data and documents, a 360-degree view of their assets, rapid information processing, and effective tools for easy access to self-service research and advice. • AI-enhanced content is provided through a knowledge base or resource center that clients can access at any time to get the insights and latest research they need to either inform their own decisions or drive discussions with their human and digital agents. • The data that results from clients and prospective clients accessing and downloading specific content assets can provide deeper insights into the specific needs of each individual, enabling managers to reach out with precise messaging that answers their most pertinent questions – without clients ever having to ask. It’s this level of personalized service that enables enterprises to lead the competition in the digital age. • As speech processing and natural language processing technologies mature, AI applications handle many customer service queries without human involvement. AI applications handle many customer service inquiries using speech processing and natural language processing
  97. 97. This content included for educational purposes. Marketing technology landscape 2016 97 This content included for educational purposes.
  98. 98. This content included for educational purposes. Precision marketing company briefs and case examples* • Company briefs and case examples highlight vendors that provide predicbve markebng solubons for B2B and B2C customers. • Vendors enable access to aggregated data sets of enbbes, individuals, and behavior from internet and other sources. • All provide some level of pre-packaged predicbve models and DIY training. • All develop predicbve models through machine learning that analyzes 1st, 2nd & 3rd party data and historical conversion outcomes to some extent. • Most provide demand generabon and lead scoring based on predicbve models as an alternabve to hand-built models. • Martech players provide interfaces to markebng automabon and CRM packages — e.g., HubSpot, Marketo, Salesforce. • More compebtors and potenbal partners exist if we generalize the POC pla…orm concepts to handle more types of life events and more industry segments. • The slide depicbng vendors providing machine intelligence for marke+ng idenbfies 150 vendors in 24 categories. • In parbcular, we may see compebbon from vendors of customer analybcs and advanced analybcs as covered in Forrester and Gartner reports, and summarized in two charts. • The highlighted vendors provide toolsets, workbenches, pla…orms, integrated environments and whole solubons that can support precision markebng. • Capabilibes provided vary, but can include descripbve analybcs, predicbve modeling, prescripbve analybcs, data mining, text analybcs, forecasbng, opbmizabon, simulabon. 98 * Not part of this research deck
  99. 99. 24/7 6Sense Adobe AgilOne Aginity Alteryx Angoss AYASDI Bluecore BlueShift BlueYonder Datacratic DataMentors Deloitte DynamicYield Emarsys EverString FICO Grey Jean IBM Infer KNIME Lattice Engines LeadSpace Mintigo Oracle Pitney Bowes RapidMiner Radius Reach Analytics SalesForce SalesPredict SAP SAS Teradata Tiny clues Versium WealthEngine wise-io * Not part of this research deck 99 Precision marketing* • Machine learning • Descriptive analytics • Predictive analytics • Prescriptive analytics This content included for educational purposes.
  100. 100. BUSINESS ACCELERATION + OPTIMIZATION
  101. 101. This content included for educational purposes. 101 • Business acceleration • Cognitive enterprise • Cognitive platform • Intelligent automation • Intelligent ecosystemsOverview of
 Business acceleration 
 + optimization
  102. 102. This content included for educational purposes. 102 busi·ness ac·cel·er·a·tion + op·ti·mi·za·tion /ˈbiznəs//akˌseləˈrāSH(ə)n/+/ˌäptəməˈzāSHən,ˌäptəˌmīˈzāSHən/ Business acceleration is the automation of knowledge generation that drives cost savings, competitive advantage, and new business lines through smarter deployment of resources. Optimization is the action of making the best or most effective use of a situation or resource. For example, using machines to replicate human actions and judgment with robotics and cognitive technologies, automating repeatable tasks to improve efficiency, quality, and accuracy of processes, while lowering costs and freeing profits and revenue from the scale constraints of manual labor. What is 
 business acceleration?
  103. 103. COGNITIVE ENTERPRISE
  104. 104. cog·ni·tive en·ter·prise /ˈkäɡnədiv//ˈen(t)ərˌprīz/ Enterprise refers to a project or undertaking, typically that is difficult or requires effort. Cognitive enterprise is the future of public and private sector businesses or organizations that utilize knowledge acquired by artificial intelligence and digital technologies to better understand and respond to customer, colleague, citizen, and/or stakeholder needs; provide products, services and information digitally; and improve operations to reduce cost, drive revenue, and maintain compliance. 104This content included for educational purposes.
  105. 105. This content included for educational purposes. 105 Cognitive enterprise is based on machine learning, natural language processing, and intelligent human interface technologies. • An enterprise’s cognitive systems can learn and build knowledge from various structured and unstructured sources information. They can understand natural language and can easily interact with users, other devices, and other data sources. • To illustrate, instead of surfing web pages, simply talk into a single input box, e.g.: “I lost my card.” A quick chat with a rep (you didn’t even notice was not human) and a new card is on its way. A cognitive enterprise provides consistent and personalized service. • Further, cognitive enterprises leverage machine learning and big data to predict customer needs (e.g., it interprets based on analyzing a lifetime of customer data, web data, and social media) and proactively suggest a personalized product or service. It can do this at a scale not possible with manual only methods, and it can learn and improve as it handles more cases. • Cognitive systems capture the expertise of top performers, accelerate development of expertise in others, and enhance the decision-making of professionals across the enterprise. Cognitive enterprise is based on machine learning, natural language processing, and intelligent human interface technologies $
  106. 106. This content included for educational purposes. 9 Acquired Sqream, which uses machine learning to detect behaviour patterns of wealth customers Goldman Sachs invested $15m to help fund Kensho, the natural language search engine designed to analyse news events and answer detailed questions about financialmarkets ING mobile app allows transactions to be made through voiceactivation Sage has developed a chatbot called Pegg that acts as a business accounting personal assistant Zest Finance – AI underwriting which offers 40% improvement over best in class industryscore Siftscience – can helptheir clients detect 89% of fraud while reviewing only 1% of customercases Digit – an automated savings app that reviews your spending habits and proactively saves money you canafford 106 COGNITIVE ENTERPRISE IS ALREADY HERE This content included for educational purposes.
  107. 107. what’s my Quicksilver card balance?” EXAMPLE: ENGAGING CUSTOMERS Capital One has deployed a new skill to Amazon Alexa that powers voice activated banking. This application is already integrated with transactional systemspermitting payments in addition to balancequeries. Manage your Capital One accounts using nothing but your voice Credit Cards • Checkbalance • Get duedates • Pay Capital One cardbill Checking and Savings • Checkbalances • Review recenttransactions Auto Finance (New) Home Loans (New) • Check principal balance • Check principal balance • Get payoff quote • Get due dates • Make a Capital One payment • Make a Capital One payment “Alexa, ask Capital One 107This content included for educational purposes.
  108. 108. This content included for educational purposes. IS BECOMING THE NORM Dominos Pizza one of many fooddelivery chatbots CognitiveCommerce CognitiveService Donotpay the world’s first robot lawyer that hasoverturned 160k parkingfines Uber cognitive commerce via chat integration with Googlemaps Twyla AI driven support chatbot that learnsfrom human agents in order to improve FAQ content which is only 50% effective H&M personal stylist chatbot, creating a service that was uneconomical with humans Ivy Go Moment’s hospitality systemcapable of handling 90% of guestrequests 12 COGNITIVE BUSINESS 108 This content included for educational purposes.
  109. 109. Artificial Intelligence + Experience Design Logic Magic Only by addressing both elements can compelling experiences be ones that surprise and delight customers and colleagues, making the bank feel more human. Source: Publicis•Sapient 109 COGNITIVE ENTERPRISE SITS AT THE INTERSECTION OF LOGIC AND MAGIC This content included for educational purposes.
  110. 110. COGNITIVE PLATFORM
  111. 111. This content included for educational purposes. 111 cog·ni·tive plat·form /ˈkäɡnədiv//ˈplatfôrm/ A cognitive platform is a software platform based on the scientific disciplines of artificial intelligence and signal processing that encompass machine learning, reasoning, natural language processing, speech and vision, human-computer interaction, dialog and narrative generation, and more. A software platform is a major piece of software, such as an operating system, an operating environment, or a database, under which various smaller application programs can be designed to run, together with externally provided devices and services that are provisioned using application program interfaces (APIs). What is a 
 cognitive platform?
  112. 112. REASONING ENGINES INTEGRATION & CORE SERVICES KNOWLEDGE SERVICES SERVICES API (KNOWLDEGE ENGINE ABSTRACTION LAYER) Free-Text Search Contextual Search Graph Search Reasoning / 
 Explanation Logging Caching Security Monitoring KNOWLEDGE BASE MGMT Ontology Creation Ontology Evolution Concepts/Entities Extraction Relations/Fact Extraction CONTENT DELIVERY Query Parser Inference Deductive Relevance-based Results Ranking CONTENT INGESTION Indexing Content Storage Tagging/Metadata Extraction Facets/Filters Concepts Disambiguation Results post- processor User Entitlements Check Query Formulation/ Expansion Structured
 Data Access Text Generation User Entitlements Indexes, Structured Data Triples Knowledge Base, Reasoning Topic Modelling, Query Expansion, NLP/NLU, Ontology USER EXPERIENCE FRAMEWORK TEXT, AUDIO, VIDEO, BIOMETRIC, AND IOT SENSOR INTERFACES ASR / TTS ServiceMobile framework Text Interfaces Voice & Tone REST / SOAP APIs Managed File Transfer Ontology Natural Language Translation NLP / NLU Audio/Video Capture Audio/Visual Output Reporting / Portal Email Heuristic MACHINE LEARNING Deep Learning Engines Neural Networks Constrained Conditional Models Deep Learning Engines Chat Agents NLG This diagram depicts functionality to support cognitive enterprise. A business would incorporate various portions of this architecture in phase, for example 
 a crawl, walk, run approach. SaaS AI Layer PaaS IaaS PUBLIC/HYBRID 
 CLOUD STRUCTURE Source: Publicis•Sapient Example business context diagram for a cognitive enterprise 112 This content included for
 educational purposes.
  113. 113. This content included for educational purposes. Cognitive enterprise business context diagram — partner overlays This overlay to the cognitive enterprise business context diagram depicts where 
 partner platforms might be deployed to address various functions. Indexing Tagging/Metadata Text Generation Facets/Filters Extraction Structured Data Query Parser Relevance-based Access Content Storage ResultsRanking Results UserEntitlements Post-Processor Check CONTENT INGESTION CONTENT DELIVERY Ontology Creation Concepts/Entities Extraction Ontology Evolution Relations/Fact Extraction KNOWLEDGE BASE MGMT Heuristic Inference Deductive REASONING ENGINES Text Interfaces Audio Capture AudioOutput TEXT & AUDIO INTERFACES Chat Agents Voice & Tone Mobile Framework ASR / TTS Service Reporting / Portal Email USER EXPERIENCE FRAMEWORK Concepts Reasoning/ Disambiguation Free-Text Search Contextual Search Graph Search Explanation Query Formulation Query Expansion NLP /NLU SERVICES API (KNOWLEDGE ENGINE ABSTRACTION LAYER) Ontology Indexes, Structured Knowledge Base, Topic Modelling, NaturalLanguage Data Triples Reasoning Query Exapansion, Translation NLP/NLU,Ontology KNOWLEDGE SERVICES Deep Learning Constrained Engines Neural Networks ConditionalModels MACHINE LEARNING REST / SOAPAPIs ManagedFile Transfer Security Logging Caching Monitoring User Entitlements INTEGRATION & CORE SERVICES BESPOKE AND POINT SOLUTIONS SEMANTIC AI ML PLATFORMS SEMANTIC AI ALIGN WITH INTERNAL ARCH ML PLATFORMS NATURAL LANGUAGE & VISION PLATFORMS NATURAL LANGUAGE AND SEMANTIC PLATFORMS 113 Source: Publicis•Sapient
  114. 114. This content included for educational purposes. Enterprise AI platforms company briefs and case examples* • Company briefs and case examples highlight trends toward enterprise AI and cognibve pla…orms in the following sectors: - Internet & compuGng — Amazon, Apple, Baidu, Facebook, Intel, Nvidia, Google Alphabet, Microsoj, HPE. Deep learning, proacbve agents, open source pla…orms - IT services (BPOs and consultancies) — TCS, Infosys, Wipro, Mphasis, Accenture, Booz Allen, Deloihe, EY, KPMG, PwC. AI pla…orms for robobc automabon, cognibve compubng, intelligence augmentabon for knowledge intensive services. - Financial services — Cib Group, Goldman Sachs, USAA, UBS, Amex, MasterCard, BofAML. AI for finch innovabon, business disrupbon, and collaborabve services automabon. - Retail — Amazon, Lowes, Staples, Target, NorthFace, WayBlazer. Customer- centric predicbve markebng, cognibve travel services, virtual assistance. - Manufacturing — Siemens, Fujitsu, Hitachi, Maana, General Electric, Ford, Toyota, General Motors, Tesla. AI & cognibve pla…orms for engineering, manufacturing, and product lifecycle management 114 * Not part of this research deck
  115. 115. This content included for educational purposes. Accenture Amazon Apple Baidu Booz Allen CYC/Lucid.AI Dato Deloitte Enterra EY Facebook Fujitsu GE Google HPE Hitachi IBM Infosys IPsoft KPMG Maana Microsoft Palantir PWC Rage Frameworks Siemens Skytree TCS WayBlazer Wipro WorkFusion Enterprise AI platforms* * Not part of this research deck 115
  116. 116. INTELLIGENT AUTOMATION
  117. 117. a good rule of thumb for automating knowledge work 100:1 NEW BUSINESS • Intelligent search • Broader coverage ENHANCEDINSIGHTS • Automation of manual activities • Conversion of unstructured dataCOST REDUCTION • Reduce time required • Scale human effectiveness COMPETITIVEADVANTAGE | BUSINESS ACCELERATION 
 + OPTIMIZATION Source: Publicis•Sapient !117This content included for educational purposes.
  118. 118. This content included for educational purposes. Big data to intelligent applications: a lifecycle view 118 DATA INGESTION Data preparation • Data integration • Data enrichment • Data imputation • Data versioning • Data provenance • etc. Natural language processing • Entity extraction • Entity resolution • Relationship extraction • Taxonomy generation BIG DATA Web content
 (web sites, blogs, …) Social networks
 (Twitter, Facebook,…) Online activities
 (Search, shop, games…) Enterprise apps
 (ERP, CRM, …) Internet of things
 (Sensor, device data…) Processes
 (logs, data lineage,…) Textual content
 (Documents, reports, …) Knowledge-bases
 (taxonomies, ontologies,…) SEMANTIC GRAPH MACHINE REASONING Sensemaking engine Recommendation engine Process automation engine Context engine Semantic search Inference engine Rule engine Semantic query engine Machine learning 
 (classification, clustering, anomaly detection INTELLIGENT APPLICATION Find
 (people, content, …) Compare
 (products, companies, …) Detect
 (incident, anomaly, opportunity, …) Discover
 (Insight, pattern, …) Analyze
 (Performance, problem, …) Design
 (Product, svc, process…) Predict
 (demand, inventory, …) Prescribe
 (Next best action, …) Network of: people, places, organizations, processes, rules, policies, events, documents, devices, etc. Semantic inferencing Learning from usage patterns Automated 
 update cycle
  119. 119. BOT 1 
 PRODUCT SELECTION BOT 4 
 GIFTING BOT 3 HOW-TO CONTENT BOT 2 
 COMMERCE BOT 5 
 REGISTRY personal assistant (the conductor) 119This content included for educational purposes.
  120. 120. This content included for educational purposes. 120 Intelligent collaboraGon Intelligent processes are 
 goal-oriented & event-driven. Processes adapt and self- opbmize when events happen, excepbons occur, or needs change. Emergent projects learn. Knowledge models evolve. Fixed Transacbon Dynamic
 Case Emergent
 Project VALUE KNOWLEDGE INTENSIVITYLow Hi LowHi • Semanbc data models, process models, and rules connect Info and systems across organizabons, jurisdicbons, and geographies. • Goal-oriented acbvibes to perform • Decisions required to take acbon • Rules & condibons to be met to choose • Data & calculabons determine condibons • Semanbc, machine learning, model- driven methodologies for knowledge- and data-intensive collaborabve knowledge work. • Authoring, collaborabon, analysis, and communicabon tools are semanbc. • Design = Model = Applicabon = Explanabon = Documentabon. 
 (Model executes directly) • System learns, project models evolve • Simulabon tesbng & automated version control are nabve.
  121. 121. The fastest workflow travels the fewest steps, touches the fewest hands, and does as much 
 as possible for you. 121This content included for educational purposes.
  122. 122. This content included for educational purposes. PERSONALIZED AND FRICTIONLESS EXPERIENCES Companies are using real time and customer personal data to automate and predict needs starting from prompting the dream phase, to awareness, engagement, and every step of the customer journey. DIGITAL PLATFORM TRANSFORMATION Companies are replacing old or homegrown systems with digital platforms that enable seamless experiences, support customer services and drive business decisions. BUSINESS MODEL EVOLUTION Companies are expanding their products and new services to better the serve the needs of the new customer expecting the the ultimate experiences at the touch of a finger. Three business acceleration power moves 122
  123. 123. BACK STAGE FRONT STAGE Organization
 &
 Operations Service
 Value Chain LINE OF VISIBILITY OmniChannel
 Communication User
 Experience Customer 
 & Employee Experience 3rd Party Partner Experience Distribution Partner Experience B2B and B2B2C Enterprise services 
 cognitive transformation 
 and business acceleration 
 framework Source: Publicis•Sapient 123This content included for educational purposes.

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