Applying AI & Search in Europe - featuring 451 Research
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In the current climate, it’s now more important than ever to digitally enable your workforce and customers. Hear from Simon Taylor, VP Global Partners & Alliances, Lucidworks and Matt Aslett, Research Vice President, 451 Research to get the inside scoop on how industry leaders in Europe are developing and executing their digital transformation strategies. In this webinar, we’ll discuss: The top challenges and aspirations European business and technology leaders are solving using AI and search technology Which search and AI use cases are making the biggest impact in industries such as finance, healthcare, retail and energy in Europe What technology buyers should look for when evaluating AI and search solutions
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Applying AI & Search in Europe - featuring 451 Research
- 1. 1 Applying AI & Search in Europe – Featuring 451 Analyst
- 2. 2 Todays Speakers Simon Taylor VP Global Partners & Alliances Lucidworks Matt Aslett Research Vice President 451 Research
- 3. 3 Search & AI Application in the Current Pandemic Where to Focus to ease the Burden of doing Business
- 4. 4 We’re now living in a different world… G E N E R A L T R E N D S * o 48% of the market is still formulating its digital transformation planning o 89% of consumers are concerned with protecting their personal data online: Covid-19 bad actors o 76% of consumers are likely to switch to an alternate online vendor: poor experience / Covid-19 o 47% of digitally aware consumers would prefer virtual assistants to save time vs. call center N E E D S * o Measure digital performance relative to the customer experience o Improve personalised digital experiences for content and commerce along with customer data and intelligence platforms o Use of virtual assistants to ease capacity issues relative to accessing relevant information and deflection of expensive call service interactions o Improve employee information access in the new “Working from home” situation *451 Research, LLC – Copyright, March 2020
- 5. TRANSITIONING TO A VIRTUAL DIGITAL WORKPLACE IS CHALLENGING
- 6. 78% of businesses believe Covid-19 has already had a negative operational impact. 75% already have or will be implementing expanded work-from-home policies in response to the crisis 38% that think work-from-home policies will become be long-term or made permanent. 62% have already experienced a fall in employee productivity, or expect to in the next three months. 41% report having already felt an internal strain on their IT resources. 22% have delayed/halted rolling out of new products or services. Coronavirus quick fixes aren’t scalable; business leaders must rethink work itself | 451 Group Research March 20th , 2020
- 7. Top Priorities ACHIEVE A UNIFIED SINGLE SOURCE OF INFORMATION [INC. CUSTOMER DATA] GAIN RICHER SOURCES OF DATA FOR LINE OF BUSINESS DECISION MAKERS Voice of The Enterprise: Workforce Productivity & Collaboration, Employee Lifecycle | 451 Group Research, Q2 2019
- 8. We need to connect people to data insight? Predictable, organisationally defined data access BEFORE Empowered data consumers seek insight and drive productivity on their own NOW
- 9. Data Scientist Driven Activity Business Alignment Meet the “Last Mile Problem” BigDataManagement &Analytics ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ Problems with Activating Data for Digital Transformation
- 10. Data Scientist Driven The Last Mile Problem Reduced Time to Value Quantifiable & Faster ROI Search,Discovery& OperationalAI BigDataManagement &Analytics ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❶ ❷ ❸ ❹ Direct Alignment with Business Needs Problems with Activating Data for Digital Transformation
- 11. The Digital Transformation Question? To AI or not to AI?
- 12. What goes wrong with AI in Digital Transformation Projects? BAD DATA IN = BAD RECOMMENDATION OUT. FALSE POSITIVES REDUCING CONFIDENCE IN MACHINE LEARNING LONGER TERM COMPLACENT DEPENDENCIES ON “INFALLIBLE” MACHINE LEARNING IN ABILITY FOR AI TO TAKE ACCOUNT OF THE WIDER CONTEXT OF BUSINESS NEEDS / OUTCOMES
- 13. Challenges Adopting AI & ML IT Teams May misunderstand both the business objectives and the machine learning model. Business Leaders Define the business goals that want to pursue with AI, but they don’t usually understand the challenges and limitations of building ML models Data Scientists Understand machine learning, but they might not truly understand the business objectives and they may build “hungry” models that consume too many IT resources 1 2 3
- 14. Artificial Narrow Intelligence (ANI) is the AI that exists in our world today, programmed to perform a single task — whether it’s checking the weather, being able to play chess, or analyzing raw data to to write journalistic reports. Artificial General intelligence (AGI) or refers to machines that exhibit human intelligence. In other words, AGI can successfully perform any intellectual task that a human being can being conscious, sentient, and driven by emotion and self-awareness. ANI systems can process data and complete tasks at a significantly quicker pace than any human being can, which has enabled us to improve our overall productivity, efficiency, and quality of life e.g. assist doctors to make data-driven decisions, making healthcare better AGI is expected to be able to reason, solve problems, make judgements under uncertainty, plan, learn, integrate prior knowledge in decision-making, and be innovative, imaginative and creative. It’s all about Operationalising AI
- 15. “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.” SAS INSTITUTE INC. 2019
- 16. How to Implement Machine Learning A FRAMEWORK FOR APPLYING AI IN THE ENTERPRISE GARTNER INC. 2017
- 17. ML-Powered Search Learning Maturity Self-Learning Taxonomies/Entity Extraction Automated Relevancy Tuning Query Intent Prediction Keyword Search
- 18. Many AI tools’ designs start with just data, not the human question. Thus, a great AI platform focuses on an answer to the question by using search, rather than just being tool based. AI AUTHORITY CHAO HAN, HEAD OF DATA SCIENCE, LUCIDWORKS SEPT 2018
- 19. FILTER VISUALIZATION ACTIVITY CONTENT INDEX NATURAL LANGUAGE BOOSTED RESULTS MACHINE LEARNING QUERY RULE MATCHING USER SIGNALS FACET, TOPIC & CLUSTER D ATA Human Generated System Generated Application Generated S O L U T I O N Digital Workplace NATURAL LANGUAGE MACHINE LEARNING QUERY RULE MATCHING USER SIGNALS FACET, TOPIC & CLUSTER
- 20. 20 HYPER PERSONALISATION Go beyond using static rules and profiles. Instead dynamically customise the experience for each data consumer. Use AI and machine learning to predict user intent and give employees the insights they need, when they need them. Maximise the opportunities to tailor content that fits each and every employee’s wants and needs. Explore Curate Integrate MACHINE LEARNING
- 21. Use Case Examples Digital Commerce Predictive Merchandising Catalog Search Sentiment Analysis Personal Assistant/Chatbots Digital Workplace Scientific Research Call Center Prioritisation Support Deflection Natural Language Search Fraud Detection
- 22. Advanced connectors and AI enrichment, delivered by intuitive applications created with App Studio, deployed on-prem or as a multi-tenant cloud managed service. D ATA Any format, any platform S O L U T I O N Personalised insights for each individual STORAGE & SEARCH INTENT PREDICTIO N APP CREATION DATA INGEST & PREP F U S I O N P L AT F O R M Human Generated System Generated Application Generated Digital Workplace
- 23. STORAGE & SEARCH INTENT PREDICTION APP CREATIONDATA INGEST & PREP NLP: NER, phrases, POS Document classification Anomaly detection Clustering Topic detection Search engine & data processing Connectors ETL pipelines Scheduling & alerting SQL engine Rules engine Query pipelines Query intent detector Automatic relevancy Signals & query analytics Recommenders A/B testing Modular components Stateless architecture User-focused experience Geospatial mapping Results preview Rapid prototyping S C A L A B L E O P E R AT I O N S SECURITYCDCRCLOUDSCALABLEEXTENSIBLE
- 24. ML for Anomaly Detection on Data Ingest
- 25. Signals ML for Query Rule Rewrite
- 26. D ATA Any format, any platform Human Generated System Generated Application Generated Index Search Intent BuildApp S O L U T I O N A S S E M B LY Digital Workplace RULE ENTITY ML NLP BOOST SIGNAL R A P I D A S S E M B LY P L AT F O R M FUSION Q&A Chatbot FUSION Risk Analysis FUSION Workplace Search S O L U T I O N T E M P L AT E S
- 27. R E A L - L I F E E X A M P L E S Lucidworks Fusion powers connected experiences Customer Care Investigation Employee Q&A Supply Efficiency Compliance & Audit
- 28. C A S E S T U D Y Single, global source of truth in their knowledge management application An accurate picture of client interactions and expertise Content disambiguation 10MD O C U M E N T S M A N A G E D 40KEMPLOYEES
- 29. C A S E S T U D Y Better customer support Putting the right information in front of customers in fewer clicks Improved support calls Shorter wait times, and a more engaged support Reinvest savings 200%I N C R E A S E I N C T R 50KF E W E R S U P P O R T T I C K E T S 34%C A L L D E F L E C T I O N 91%R E D U C T I O N I N T C O
- 30. C A S E S T U D Y More searches converted into sold tickets Full-site search on iOS & Android mobile apps 33%I N C R E A S E I N C O N V E R S I O N 63%I M P R O V E M E N T I N V I S I T O R S 15%R E V E N U E AT T R I B U T E D T O S E A R C H
- 31. 31 Q&A
- 32. 32 THANK YOU Get In Touch! Email: teameurope@lucidworks.com
Editor's Notes
- SCRIPT: You’re not the only one who feels the challenge transitioning to a more agile digital workplace.
- How are these challenging industries to evolve It is difficult to integrate AI and Machine Learning into enterprise operations. This because most companies have adopted a “project-based” deployment model for ML. This is challenging because three groups need to work closely together, despite having different skills and interests: Business leaders define the business goals that want to pursue with ML, but they don’t usually understand the challenges and limitations of building ML models, Data Scientists understand machine learning, but they might not truly understand the business objectives and they may build “hungry” models that consume too many IT resources, IT Teams may misunderstand both the business objectives and the machine learning model.
- See search-maturity keynote in presentations gdrive
- SCRIPT: At Lucidworks, we know that these are very complex challenges, because we began working on them 12 years ago. We began with a deep understanding of search and Apache Solr, and we wrote code to ingest data generated by systems, humans and applications. We’ve developed advanced functionality for indexing, clustering, classification, faceting, filtering, relevancy, ranking, analytics, visualization, natural language processing, ranking and boosting results. We’ve bundled it all into a Digital Workplace solution available with Lucidworks Fusion. Because we’ve done the work and become the experts at delivering the solution in the world’s largest organizations (including one third of the US Fortune 100), you can begin using our platform without diverting time, attention and money away from your core business. Focus on your business, your customers and your employees instead. REFERENCES: Indexing Data: https://doc.lucidworks.com/fusion/2.1/Indexing-Data.html Faceting: https://doc.lucidworks.com/fusion/2.1/Search/Faceting.html Collaborative Filtering: https://doc.lucidworks.com/fusion/2.1/Recommendations_and_Boosting/Collaborative-Filtering.html Relevancy: https://doc.lucidworks.com/fusion-server/4.1/solr-reference-guide/7.4.0/relevance.html Ranking: https://doc.lucidworks.com/fusion-server/4.1/solr-reference-guide/7.4.0/learning-to-rank.html Analytics: https://doc.lucidworks.com/fusion-server/4.1/solr-reference-guide/7.4.0/analytics.html Visualization: https://doc.lucidworks.com/fusion-server/4.1/system-administration/dashboards/display-panels.html
- Use Case Examples Here is a list of use cases likely to need AI (we will add to this list as we see different AI-enabled deals come through): Digital Commerce B2C Predictive Merchandising (e.g. Lululemon) -- this is the classic ecommerce search like we support at Lululemon, Bed Bath & Beyond or Barnes & Noble B2B Catalog Search (e.g. Getty Images) -- suppliers of industrial equipment who support very large and complex product catalogs. The buyer can search the catalogs or the seller’s employees can advise the buyer about which parts and components might be best Digital Workplace Scientific Research (e.g. IQVIA) -- for engineering, drug formulation or public health research in an area where experts have access to thousands of content-rich studies or reports Call Center Dashboard (e.g. Fidelity) -- for help highly skilled consultants respond quickly to in-bound calls from the most valuable customers IT Support Ticket Deflection (e.g. Red Hat, SAS) -- help distributed teams solve their own problems, when they may not be able to explain the issue according to pre-defined terms. Synonym detection, misspelling detection and head/tail analysis can help users solve their own problems. Pharma Clinical Trials Tracking -- search data on many individuals with many, varied drug interaction signals. Investment Advisory Platform (e.g. Morgan Stanley) -- give private bankers the power to make real-time recommendations when they are consulting with their high net worth clients. Customer Sentiment Analysis (e.g. ExxonMobil) -- model keywords used in search and let the ML model changing trends in net positive or net negative sentiment expressed in those queries Investigation of Fraud, Waste and Abuse (e.g. Government agencies) -- let the ML flag subtle but suspicious patterns that individual humans might not notice across multiple dats sets and channels Instant Answer Engines (QA Systems) -- help employees or customers find their own answers with natural language search Personal Assistants for Self-Service (e.g. Bank of America) -- power digital personal assistants like Bank of America’s “Erica”
- SCRIPT: Lucidworks Fusion incorporates AI and Machine Learning throughout the platform to intelligently ingest, explore, and curate the data. When you ingest data into the Fusion platform, it uses AI to cluster, classify and organize content, so that it will be available for user queries. Once the query comes into Fusion Server, it invokes Search AI to understand query intent and personalize results. Fusion App Studio allows teams to quickly create new, personalized, data discovery experiences. All of this can either be deployed on-prem, self-hosted on public cloud service, or as managed by Lucidworks in a multi-tenant cloud PaaS. REFERENCES: No customer left behind: How to drive growth by putting personalization at the center of your marketing | https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/no-customer-left-behind
- SCRIPT: Here’s some deeper detail on the functions performed by each of Fusion’s components. For example, AI on data ingest includes NLP analysis, document classification, anomaly detection, clustering and topic detection. Fusion Server delivers connectors to hundreds of data sources, and very fast search results, even with extremely large numbers of documents and thousands of concurrent users. AI at query time helps with intent detection, automatic relevancy, query and signal analytics, recommenders and A/B testing. And App Studio helps our customers deliver powerful knowledge discovery through a user-focused experience. Rapid prototyping helps our customers get the experiences right in the beginning and then easily tune them as conditions change. Fusion has a distributed architecture, so its operations are extensible, scalable, and cloud-ready, all with cross data center replication (CDCR) and enterprise-grade security. REFERENCES: SolrCloud: https://doc.lucidworks.com/fusion-server/4.1/solr-reference-guide/7.2.1/solrcloud.html CDCR video: https://youtu.be/fAvO8bHTh-Q Webinar “Secure Solr with Fusion”: https://youtu.be/unREzFNIa7Q
- SCRIPT: I’ve described how Lucidworks Fusion powers exploration, integration and curation. I’ve shown some fictional examples in my hypothetical “Brazilian oil & gas case study”. Now I’d like to share some real-life customer examples of major companies using Lucidworks Fusion to power their Digital Workplace initiatives.
- SCRIPT: PWC powers their global knowledge management solution with Fusion. Fusion is the single, global source of truth, painting an accurate picture of client interactions and employee expertise. PwC’s application augments internal data with external sources such as Wolphram Alpha and Wikipedia and presents the information side by side within the app, offering content disambiguation. It is used by 40,000 employees to explore 10 million documents. REFERENCES: PWC slides from Activate 2018: https://www.slideshare.net/lucidworks/strategic-value-from-enterprise-search-and-insights-viren-patel-pwc
- SCRIPT: The open source Linux pioneer, Red Hat, uses Lucidworks for customer support. They’ve doubled their click-through rate, avoided about 50,000 support tickets and reduced their TCO by 91%, compared to their previous self-service portal. REFERENCES: Case Study: “How Red Hat Cut Costs and Improved Relevancy with Lucidworks Fusion” | http://programs.lucidworks.com/rs/579-JML-927/images/Lucidworks_Red_Hat_Case_Study.pdf
- SCRIPT: Digital commerce on Vegas.com is powered by Fusion, and after migrating to Lucidworks: conversion improved by one third, visitor engagement improved by 63% and now 15% of the site’s revenue is attributed to search. REFERENCES: Lucidworks customer video: https://lucidworks.com/video/vegas-com-and-fusion/