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The Digital Workplace Powered by Intelligent Search

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This presentation covers the landscape of AI-enabled enterprise search.

The presentation was given at Sinequa's INFORM2019 events in both NYC and Paris.

Learn more about AI-enabled enterprise search on Emerj:

Publicado en: Tecnología
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The Digital Workplace Powered by Intelligent Search

  1. 1. The Digital Workplace Powered by Intelligent Search, Today and Tomorrow Daniel Faggella CEO at Emerj Artificial Intelligence Research
  2. 2. Presentation Outline ● Background in Brief ● Enterprise Search - Then and Now ● Intelligent Search Use-Case Overview ○ 1 - Tagging and Clustering ○ 2 - 360º View of the Customer or User ○ 3 - Concept and Advanced Entity Search ● Future Forecast ● /end @danfaggella
  3. 3. We help large organizations (the World Bank, global pharma giants, etc) make critical strategic decisions about AI strategy and AI impact. ● AI market sizing, growth-rate analysis ● Competitive intelligence and strategy ● Vendor selection and AI adoption Presenting our AI Research at United Nations HQ, NYC
  4. 4. Then and Now
  5. 5. Search structured documents. Emphasis on predictable formats and direct-match keyword searching. Metadata is applied manually and painstakingly. Difficulty: Integration, defining metadata ontologies, solving a defined use-case. Digital text is searchable. Enterprise Search, Then and Now Search unstructured documents. Emphasis on “understanding”, clustering, and metadata. Metadata is applied programmatically and at scale. Difficulty: Integration, defining metadata ontologies, solving a defined use-case. Digital documents, scanned paper documents, images, microfiche -- all is searchable.
  6. 6. ● There is still plenty of value in older enterprise search approaches, by allowing information accessibility, and in organizing previously unorganized data. ● AI and ML approaches take these benefits to the next level, by: ○ Making more information available (OCR, advanced metadata, etc…) ○ Allowing users to ask more questions of the data itself (reporting on broader patterns, finding more direct answers) Enterprise Search, Then and Now @danfaggella
  7. 7. ● Newer AI vendors underestimate the significant integration challenges to bring AI into the enterprise. ● AI-enabled search and discovery applications are not unique in this respect. Enterprise Search, Then and Now @danfaggella
  8. 8. @danfaggella
  9. 9. AI Vendor - Geo Analysis @danfaggella
  10. 10. Use-Case Highlight
  11. 11. ● Adding tags and meta data manually, and training systems ○ (Note: The value here still relies on human ability to determine the use-case and the meta data ontology! That’s beyond AI) ● This data can be added retroactively to an entire corpus, or added upon entry ● Potential metrics of success: ○ Improved speed and efficiency of any business process involving search 1. Enrichment and Classification Examples: Proactively protect confidential information by having AI categorize the confidentiality of documents - based on initial human training (rather than relying on all employees to intuitively know the confidentiality level). Manufacturing: Search through production orders for mentions of specific cluases or terms.
  12. 12. ● Enabling sales and support people with a full view of a user or customer’s situation / history ● Potential metrics of success: ○ Improving customer service satisfaction ○ Reduction of time-to-resolution for CS ○ Improved upsell close rates for salespeople ● 75% of the applications of enterprise search in the financial services sector feature Customer Information Retrieval as a main featured capability, more than any other use-case. 2. 360º View of the Customer or User Examples: A call center rep might see (a) a summary of recent support calls and chats with a customer, (b) what those calls were about, combined with (c) the ability to find contracts and docs related to that customer. In the future, this use-case may also involve suggesting next actions or approaches to the call center rep (“coaching”).
  13. 13. ● Previous search systems could search for terms: ○ “Wells Fargo” ○ “Pharma” ○ etc ● ML enables broader inquiry ability, including: ○ “Contracts for X service over 18 months long” ○ “Invoices that don’t reference the service paid for” ○ etc 3. Concept Search Examples: Banking: Search for all documents that reference LIBOR, or LIBOR-related terminology. Life Sciences: Search toxicology reports that mention specific types of complex or broad symptoms.
  14. 14. Into the Future
  15. 15. Present A huge bulk of the value of enterprise AI search comes not from advanced AI features, but from: ● Tagging and clustering ● Entity recognition ● An established process to integrate and connect data systems, and determine meta tag ontologies and structures and help the client Today, value doesn’t lie in the fanciest AI tricks. Value lies in accessing data and making it reasonably accessible to the people who need it. Proper integration, knowledge of workflows, and basic, working functionality seems to be most important today. 5-Year Future Forecast
  16. 16. Source: Emerj Artificial Intelligence Research “Enterprise Search and Discovery - AI Capability Overview” Level of Advantage Competitive Advantage High Client relationships with data access (storage, analytics, etc) Middle-High Client relationships without data access (trust) Middle Knowledge of the subject-matter (types of data) Middle Knowledge of systems and workflows (processes, IT systems) Middle-Low Data science talent (experience with applied AI, ability to iterate models)
  17. 17. Source: Emerj Artificial Intelligence Research “Enterprise Search and Discovery - AI Capability Overview” Level of Difficulty Feature High Providing “Answers” (Receiving sentence answers, not reports or lists, e.g. “There are 47 contracts with XYZ type of clause included in them since 2012”) Middle-High Insights (e.g. Predictive analytics, notifications of anomalous activitiy – notifying users to activity before) Middle Natural language search (e.g. Receiving a text answer to a natural text question like “How many of our client accounts have spent over $1MM in the last 6 months?”) Middle Enrichment and classification (e.g. Automatically tag documents on intake - and/or suggest relevant metadata for human users to approve) Low Entity search (e.g. Finding places, people, things, companies, or concepts - in text data) Low Reporting (e.g. Finding the number of docs in various categories, instances of entities over time, etc)
  18. 18. 5-Year Future Forecast 5-Year Future More specific search functions will be built as part of workflows (in compliance, in customer service, etc), allowing human users to instantly reach the info the need when they need it. Purpose-built solutions suited to specific use-cases will become more the norm. Insight applications and increasingly broader “concept search” will define future development. So will accessibility. Interfaces will develop to allow non-technical experts to set up custom searches, and derive insights as they see fit. Developing meta tag ontologies and determining how to connect to disparate data silos will still be a massive challenge, but there will be best-practices that make it less painful than it is today.
  19. 19. That’s all, folks @danfaggella