The digital Workforce of the future How Mayo Clinic leverages RPA & Bots

Transcription

1 The digital Workforce of the future How Mayo Clinic leverages RPA & Bots Klaus Unger - Mayo Clinic 03/28/ MFMER slide-1

2 Agenda Definitions The digital Workforce of the future Automation & Decision Employee Experience / User Interaction 2017 MFMER slide-2

3 Mayo Clinic 150+ year celebrated history Large, non-profit organization of 65,000 employees Destination medicine of research, education, and practice 1.3 million patients, from 50 states, 136 countries 2017 MFMER slide-3

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6 What is Artificial Intelligence (AI)? AI is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning, reasoning, and selfcorrection. Particular applications of AI include expert systems, speech recognition and machine vision. What is Machine Learning? Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range. (Supervised or Unsupervised) 2017 MFMER slide-6

7 What is Robotic Process Automation (RPA)? Robotic process automation (RPA) is the application of technology that allows employees in a company to configure computer software or a robot to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. or Robotic process automation (RPA) is the use of software with artificial intelligence (AI) and machine learning capabilities to handle high-volume, repeatable tasks that previously required a human to perform. What distinguishes RPA from traditional IT automation is RPA software's ability to be aware and adapt to changing circumstances, exceptions and new situations MFMER slide-7

8 2011 Watson wins Jeopardy 2017-Google s AlphaGo defeats #1 player in Go 1997-IBM s Deep Blue beats chess master Garry Kasparov 2017 MFMER slide-8

9 Fun Facts Oxford University predicts that 45% of jobs will be automated by 2030 Researchers predict that by 2020, 85% of customer interactions will be managed without a human**** Increasing automation is the 2nd most important strategic priority for enterprises* The global market of process automation will grow to $4.98Bn by 2020** AI will likely replace tasks rather than jobs in the near term, and will also create new kind of jobs *** *Source: 2017 Deloitte Global Human Capital Trends: Rewriting the rules for the digital age, Deloitte Consulting LLP and DeloitteUniversity Press, **Source ***Stanford University One Hundred Year Study on Artificial Intelligence (AI100) **** Gartner Research 2017 MFMER slide-9

10 Fun Facts 38% of companies believe they will be fully automated within five years. 41% of companies have fully implemented or have made significant progress in adopting cognitive and AI technologies within their workforce. In 77% of companies automation results in better jobs and retraining of workers (only 20% see job reductions). Yet, in 65% of companies HR is not involved at all. Source: 2017 Deloitte Global Human Capital Trends: Rewriting the rules for the digital age, Deloitte Consulting LLP and DeloitteUniversity Press, MFMER slide-10

11 Fun Facts Robotics and speech recognition are two of the most popular investment areas. High tech, telecom, and financial services are the leading early adopters Healthcare, financial services, and professional services are seeing the greatest increase in their profit margins 65% of students entering primary schools today will work in jobs that don t currently exist. Source: 2017 Deloitte Global Human Capital Trends: Rewriting the rules for the digital age, Deloitte Consulting LLP and DeloitteUniversity Press, MFMER slide-11

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13 Automation Decision: HR 2017 MFMER slide-13

14 Automation Decision: RPA RPA is a good candidate for HR processes that involve high volume, repetitive tasks including data management, talent acquisition, payroll & reporting 2017 MFMER slide-14

15 Automation Decision Making: RPA Recruitment Time Data Management / Entry Onboarding Payroll Compensation & Rewards Engagement Reporting & Analytics Off-boarding Health & Safety Career Development Training Planning General Operations Mobility Labor & Employee Relations Communications & Branding Diversity Benefits & Leave HR Policies & Programs Low RPA Potential Medium RPA Potential High RPA Potential 2017 MFMER slide-15

16 Automation Decision: Use Case HR 2017 MFMER slide-16

17 License Verification Pre/Post Automation Situation: Applicant licenses are checked twice during the interview process to verify a candidate s ability to practice in the assigned state which involves going to a verification website, taking a snapshot of the candidate s license, and storing in their applicant file. RPA Initiative: This process automated the manual license verification and document loading activities via an HR Bot. Results: The Bot automated process reduced errors, rapidly identified missing information, freed up capacity, allowing the recruiters to focus on higher valueadditive activities MFMER slide-17

18 Reporting Pre / Post Automation Situation: This repetitive and rule based process involves manually extracting data from different systems and then formatting it as per the business needs. RPA Initiative : This process was standardized with 80% of manual activities being fully-automated via an HR Bot. Results: Standard Reporting templates: ensures consistent format used for report delivery. Faster delivery : More number of reports can be delivered per day when compared to humans Reduced Risk of Error: programmed to extract the report as per criteria and also format it via filters ensuring 100% accuracy MFMER slide-18

19 Compensation Data Entry Process Pre/Post Automation Situation: The existing business process required hiring contractors for 3-4 months annually to manually enter data into the compensation planning tool. RPA Initiative : This process was standardized with 80% of manual activities being fullyautomated via an HR Bot. Results : The compensation Bot took 20% less time to complete a transaction, delivered 3 times more volume, achieved 100% accuracy and eliminated the need for 90% of contractors who were hired annually to support this process MFMER slide-19

20 Automation Mayo Clinic 2017 MFMER slide-20

21 Automation Mayo Clinic Chat Bot Amelia Content: PTO- Paid time off (accrual, request, balances) Employee Recognition (options for recognizing an employee or co-worker) Transactional: I am moving (address change, phone number update) MFMER slide-21

22 Inside Amelia s Brain Conversational Intelligence Episodic Memory Understands what your customer wants in context, and provide immediate answers. She is also able to leverage her past experiences and that of other agents to build new processes. Semantic Memory Ability to learn about all relevant topics and answer user questions as needed. EQ Ontology Adapts her responses to your client s emotional state Supervised Automated Learning Observes Agents and learns from them by modifying existing processes or generating new processes Smart Workflow Executes a process for your customer in order to address their needs Advanced Analytics Advanced and Big Data analytics help inform Amelia where improvements can be made Experience Management Customers can have a very natural conversation with Amelia 2017 MFMER slide-22

23 Automation Mayo Clinic Catalytic Pushbot Quarterly Goals process for Executives and approval automation for Employee Learning and Development Can we talk? course Lawson Security Office access request for ESS, 100+ hours per year Trip Request? RPA proposals for HR - SBAR Amelia qualitative surveys- will be pushed to users after interacting with Amelia to measure the pilot success MFMER slide-23

24 Automation Decision: Technologies 2017 MFMER slide-24

25 AI based unconscious bias - pilot 2017 MFMER slide-25

26 Automation Decision: Chatbots - Mya 2017 MFMER slide-26

27 Automation Decision: Chatbots - Mya 2017 MFMER slide-27

28 Video Interviewing / Analytics Cell phone camera can identify 40,000 facial location points Video-based assessment can capture up to 1 million data elements in a 15 minute interview 40% of interviews are done digitally AI software can now detect race, emotion, gender, and tendency to exaggerate or lie through video While use of this data is not legally defensible yet, companies are actively using this data to select candidates 2017 MFMER slide-28

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30 IBM - Career Website MFMER slide-30

31 Workforce Experience / User Interaction / Trends 2017 MFMER slide-31

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35 "Coming out of CES, we're going to clearly have established that voice is going to be the go-to user interface," said Steve Koenig, senior director of market research for the Consumer Technology Association. "Wherever we go or whatever we're doing, we're going to have some form of digital assistant at our side ready to help us." Steve Koenig, Senior Director of Market Research for the Consumer Technology Association: 2017 MFMER slide-35

36 Workforce Experience Employees are empowered to seek the answer to their questions AI goes beyond just answering and initiates functional processing Workforce Experience will meet workforce where they are headless apps imagine a world where your notifications come to your phone, whether you re logged in to the app or not. It says, Hey, you ve got to enter your time sheet, or The sentiment in your team has declined, or, It s time to have a conversation, you haven t spoken to Joe in three weeks. Those recommendations, which are based on artificial intelligence, taking me where I need to go in the system as opposed to my logging in MFMER slide-36

37 Artificial intelligence Augmented reality Voice recognition Sensors 2017 MFMER slide-37

38 Future technology will make location even less relevant 2017 MFMER slide-38

39 Risks - Regulatory, financial, reputational hazards Are you in compliance? What about cyber security and data privacy? What s the backup plan? Risk of Artificial Stupidity Are you ready for change? Is it a fit for us? 2017 MFMER slide-39

40 We must find the proper balance of man / woman and machine 2017 MFMER slide-40

41 Questions & Discussion 2017 MFMER slide-41