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Cognitive Automation: What does success look like?

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We hear about cognitive automation. But what does success look like? Meet Cognitive Assist, our virtual agent. These virtual agents, powered by Watson, have ingested a vast corpus of knowledge about the applications IBM support, so they can provide the guidance an experienced coach could give – consistently and in real time. Read more about how Cognitive Assist can help you.

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Cognitive Automation: What does success look like?

  1. 1. C O G N I T I V E A U T O M A T I O N WHAT DOES SUCCESS LOOK LIKE? B A R R Y M I T C H E L L – G L O B A L L E A D , I B M A U T O M A T I O N W I T H W A T S O N
  2. 2. WHAT DOES SUCCESS LOOK LIKE? S U C C E S S C R I T E R I A M E E T C O G N I T I V E A S S I S T L I K E A B I L I T Y B U S I N E S S V A L U E S C A L A B I L I T Y S A M P L E S C O R E C A R D
  3. 3. M E E T C O G N I T I V E A S S I S T ‘Sounds great. But how do we know they’re working?' These virtual agents, powered by Watson, have ingested a vast corpus of knowledge about the applications IBM support, so they can provide the guidance an experienced coach could give – consistently and in real time. Over 25,000 IBMers have access to Cognitive Assist solutions to guide them in their work. Agent Assist For support agents. Provides context- specific technical assistance to resolve issues without the need to escalate to senior support agents. For junior developers Delivers a consistent, full-coverage learning on coding practices, while freeing up senior developers time Coding Assist Employee Assist For employees. Provides context- specific procedural guidance to resolve issues without the need to call the Helpdesk.
  4. 4.  Do users like Cognitive Assist?  Does Cognitive Assist deliver business value?  Is Cognitive Assist a scalable solution? To ensure a good return on investment, we ask three questions when we roll Cognitive Assist out to a new team. S U C C E S S C R I T E R I A
  5. 5.  Do users like Cognitive Assist? Measuring likeability Every time Employee, Agent or Coding Assist answers a question, it requests feedback. Users state whether the answer given was 'Helpful' or 'Not helpful’. Users can also provide further comments if they wish. In some pilots – for example the launch of a new corpus - we run communications campaigns to drive up the proportion of users providing feedback. In some pilots we have also surveyed users, asking them about their preferred method for receiving application support. If they don’t like it, they won’t use it And if they don’t use it, we won’t see business benefits. Plus, because Watson learns from each interaction, we need high usage to continue to improve the tool. WHY IT MATTERS L I K E A B I L I T Y
  6. 6.  Does Cognitive Assist deliver business value? We expect an investment in Cognitive Assist to pay off in months, and ongoing maintenance costs to be offset by the significant savings Cognitive Assist delivers. It is important to begin measuring the savings early to ensure they are on track from the start. WHY IT MATTERS Three drivers of business value 1. Productivity gains. Agents, employees and coders save time by accessing expertise in real-time - especially when a subject matter expert might otherwise take a day to respond. 2. Labor cost reductions. Junior agents and coders are upskilled faster and able to resolve more complex issues sooner. An increase of 20% in the number of tickets resolved by L1 agents is typical - and reduces spend on L2 labor. 3. Reduced ticket volume. ⇢Roughly 40% of L1 tickets are knowledge queries which can be resolved by Watson, when employees use Employee Assist. ⇢Because code being put into production is better quality, fewer issues occur. B U S I N E S S V A L U E
  7. 7.  Is Cognitive Assist a scalable solution? The more user groups an application can support, the more value it will deliver. No matter how well liked a Cognitive Assist solution is, or the savings it generates, if deploying it is harder than launching a space mission it’s not a viable solution. WHY IT MATTERS Assessing scalability Potential usage We aim to expand our successful pilot to a larger user population. Some common opportunity areas: Type of Activity Solution Benefits User Queries ( ‘How to’ Questions) Employee Assist Eliminate 60 –70 % “how-to” queries over time Service Requests ( eg Password Reset, VM Reboot) Cognitive Assist + integration layer + Dynamic Automation Eliminate 40 –60% service requests over time Application Development Employee Assist / Agent Assist Eliminate 60-80% initial training effort Application Management Agent Assist 12% + Productivity gains in a traditional AMS model Time How long will it take create a corpus? For our most mature applications, such as certain SAP or Oracle modules, a vast body of knowledge now exists. Creating a custom corpus can take 6 to 12 weeks depending on the volume and quality of existing documentation, and subject matter expert availability. S C A L A B I L I T Y
  8. 8. Success Criteria Proof points Criteria met? Users like Cognitive Assist 4/5 users prefer using Cognitive to calling Helpdesk  97% of answers were rated helpful by users  Cognitive Assist delivers business value 25-45% ticket reduction because users are resolving queries via Employee Assist  20% more tickets resolved by L1 agents, instead of being escalated to L2  Users claim 4 minutes in time saved per ticket. Average turnaround times for user queries with Employee Assist are minutes, compared with 2.3 days for Helpdesk.  Cognitive Assist is a scalable solution Cognitive successfully ingested documents from 19 sources, and 4 FTE took 6 weeks to create over 450 Q&A pairs.  A Cognitive corpus already exists for 12 of the top 20 apps (by ticket volume)  S A M P L E S C O R E C A R D This scorecard was compiled during a pilot for a major US bank. Following the successful pilot, Cognitive Assist was rolled out to ten additional applications.
  9. 9. WHAT DOES SUCCESS LOOK LIKE FOR YOU? V I S I T W W W . I B M . C O M / A U T O M A T I O N F O R M O R E O N T H E B U S I N E S S I M P A C T O F A U T O M A T I O N . T O T A L K T O A N E X P E R T , C O N T A C T G B S A U T O @ U S . I B M . C O M

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