Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Ilya Kazimirovskiy, Outsource People_2016_Minsk


Published on

18+ years of overall experience in IT field. Director of the Engineering. Key skills: - Build highly effective development process (agile delivery model) - Coordinate and enable successful project execution by working with offshore & onsite teams - Team productivity - Scope and risk management - Knowledge sharing framework - Quality process improvement

Хочу поделиться success story, в которой инновационный подход к технологиям победил сильных конкурентов и рассказать, как можно обьединить людей вокруг сложной задачи. A также на этом примере рассказать о взаимодействии групп Sales<->Pre-Sales<->R&D<->Engineering и на выходе получить Customer Success

Published in: Business
  • Be the first to comment

Ilya Kazimirovskiy, Outsource People_2016_Minsk

  1. 1. Success story Outsource People 2016 April 2016
  2. 2. Outline 3 Facts about me What problems banks are trying to solve? What prevents them to do that? Smart Process Automation Team Collaboration Q&A Facts about WorkFusion
  3. 3. Facts about me 4 BSU. Faculty of Mechanics and Mathematics. 1995 – 2000 NT lab – 2 years. VHDL developer IBA – 8 years. Developer -> Team Lead Exadel – 4 years. Department Manager Strevus – 3 years. Co-Founder. Director of Engineering WorkFusion – 1 year. Co-Founder. Director of Engineering Institute of Technical Cybernetics, National Academy of Sciences
  4. 4. Facts about WorkFusion 5  WorkFusion is in 10 NYC Startups That Raised the Most Amount of Capital in December  WorkFusion Raises $14M to Drive Smart Process Automation in Enterprise
  5. 5. What problems banks are trying to solve? 6  Reduce cost  Configurability  Automation process  Reduce FTE: 25% or more
  6. 6. What prevents them to do that? 7  Tech team can not deliver  Leaving Legacy systems as is  Huge amount of low quality documents  All optimization was considered done by moving to Offshore  Support Chinese, Korean, Japanese, etc.
  7. 7. Smart Automation Process April 2016
  8. 8. What is “Robotics” and “Cognitive”? Why are they new? 9 Head work Hand work Cognitive Automation Robotics “aka” RPA i.e. entering data from one application into another i.e. extracting information from unstructured documents Why now? End of labor arbitrage + strong adoption of self-service across enterprise Why now? Breakaway progress in AI tech + availability of data and compute in cloud
  9. 9. 50% impact can be expected from full-stack implementation of smart automation, as high as 70% if starting from onshore 10 Initial state Future state Plain old offshoring and outsourcing Sourcing CognitiveRobotics 10-15% robotic automaton on offshore, 40% on onshore resources 10-20% cognitive automation on top of robotics 5-10% human worker analytics / UX improvements 30% +50% Smart automation Automation of onshore FTE remaining due to regulatory reasons
  10. 10. WorkFusion is Smart Automation 11 Human-in- the loop Crowdsourcing Statistical Quality Control 2011 MIT CSAIL lab research leads to R&D on human-in-the-loop computing Microtasking Robotics 2012 WorkFusion launches first SaaS platform for Microtasking in enterprise Machine Learning 2013 WorkFusion launches Machine Learning automation Smart Automation 2014 WorkFusion becomes first full stack robotics + cognitive + human platform Full stack Automation 2015 WorkFusion patents Worker Fitness, Virtual Data Scientist
  11. 11. CASE STUDY Processing of Invoices to extract header information and individual line-items 12 Situation  A Human Resource software company processes up to 150k invoices on a monthly basis  The current processing method is fully manual, which limits the amount of information that can be extracted Approach  Optical Character Recognition (OCR) capabilities are applied to turn each invoice into structured text, which is then passed through a workflow  Machine Learning models are applied to automatically extract values where possible  When human effort is required, a semi-automated information extraction task is used to speed-up the manual work Impact FULLY MANUAL TO 80% AUTOMATION 1 LINE PER DOCUMENT TO ALL DOCUMENT LINES KEYING REPLACED BY HIGHLIGHTING AND AUTO- SELECTION
  12. 12. CASE STUDY Standardizing on processing format to collect key values from tax documents Problem  Customer is storing and analyzing tax documents for its customers. To add each document to the database correctly, the Company Name and Jurisdiction need to be extracted from the document. The current process is fully manual due to the large variety of PDF types and tax documents Impact  Utilized WF’s OCR technology to convert all documents to a format maintaining context. Configured web scraper and information extraction tools to be able to collect data. Incorporate in a workflow that incudes normalization of values and human exception management. 1
  13. 13. How does WorkFusion enabled enterprise automation architecture? 14 Enterprise Infrastructure / Enterprise Cloud Systems of Record (ERP, CRM, …)Data Warehouse / Data Lake Cognitive Automation (VDS) Digitization (OCR, Scraping, …) Workforce Orchestration AutomationAutomationenablers Mobile Business Process Management Messaging UXDataIntegration(ETL,DQ,MDM) Robotic Automation (RPA) AutomationBI/Analytics EmbeddedAutomation
  14. 14. Team collaboration June 2015
  15. 15. Team structure Sales, marketing Pre-Sales R&D Engineering Professional Services Customer Support 16
  16. 16. Process Improvements Top priorities focus Feature Team Approach Continuous Delivery Collaboration Ownership Seniority Expertise 17
  17. 17. Q&A 18
  18. 18. Contact 19 Ilya Kazimirovskiy Director of Engineering Mobile: +375 29 163 3393 Email: Site: