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The AI Dilemma - Bridging the Gap between Experimentation and Customer Expectation

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A deeper look at how you can bridge the gap between experimentation and customer expectation when thinking about using AI based support.

If you'd like to read about our latest offerings in the AI space, have a look at our blogpost - https://blog.freshdesk.com/social-signals-ai/

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The AI Dilemma - Bridging the Gap between Experimentation and Customer Expectation

  1. 1. The AI Dilemma Bridging Experimentation and Expectation Presented by Girish Shenoy
  2. 2. Creating moments of wow
  3. 3. Freshworks in numbers $149M in funding 7 Years Old 150+ Countries 5 Global offices 9 Acquisitions 7 Products 150k+ Happy Customers
  4. 4. Our Products
  5. 5. Office Data Centre General Data Protection Regulation (GDPR), compliant by May 2018Global Presence
  6. 6. 150,000+ customers from 150+ countries
  7. 7. Our experiments with AI
  8. 8. Artificial Intelligence Learns Understands Responds
  9. 9. Three Questions
  10. 10. Three questions led us Where can AI help in a customer support environment? Will AI replace an agent or assist them? Is AI good enough for customer support? 1 2 3
  11. 11. Where can AI help?
  12. 12. Our Support Framework Avoidance Routing ResolutionDeflection
  13. 13. AI for Avoidance
  14. 14. “Where is my cab?” Uber wants to prevent this question from reaching their support Arrival Time Cab Location Pick-up Point
  15. 15. AI for Deflection
  16. 16. AI can suggest solutions
  17. 17. Chatbots can initiate and deflect
  18. 18. AI for Routing
  19. 19. Skill-Based Routing AI can intelligently assign tickets to the best agent available
  20. 20. Noise Cancellation AI can filter the noise from social media
  21. 21. AI for Resolution
  22. 22. Direct Responses AI will be able to respond to customers directly and solve problems
  23. 23. Better Context AI can provide better context and recommend responses to agents
  24. 24. Replace or Assist?
  25. 25. Intelligent. Helpful. Economical. ● B2B Chatbots can respond to about 40% of the questions ● Accuracy of answers are between 8% and 25% ● The more questions AI tackles, the better it becomes ● AI can help businesses save a lot of money as well as time
  26. 26. Is AI good enough?
  27. 27. Microsoft Tay soon after launch
  28. 28. 12 hours after launch
  29. 29. 14 hours after launch
  30. 30. Two Methods of Learning ● AI can learn from any source ● This is how humans learn ● Skynet Google Assistant, Microsoft Cortana Free-Form Learning ● AI needs specific sources to learn from ● This is how most bots are learning ● Your local chatbot Curated Learning
  31. 31. ● Comprehensive self-help articles, or other content will be a necessity ● A dedicated curator might be required to teach the AI the right answers Curated AI For Deflection
  32. 32. Curate what your AI learns
  33. 33. ● Free-form learning AI will be better suited for resolving tickets quickly ● Need to teach right from wrong, to prevent another Microsoft Tay Free-Form AI For Resolution
  34. 34. ● Free-form AI needs constraints and safeguards ● Free-form AI needs to be taught how to differentiate right from wrong ● Free-form AI is not ready for customer-facing role. Yet. What can we learn from Tay?
  35. 35. The places AI can help in a customer support environment AI entities right now are capable of assisting agents AI and business should be ready for each other 1 2 3 So far we’ve seen
  36. 36. Should you use AI?
  37. 37. 1. What problem in your support do you want to solve with AI? 2. How “controllable” is your AI going to be? 3. How open is your support to experimentation? Ask yourself this
  38. 38. Bridging the Gap
  39. 39. Here’s how we bridged the gap ● AI should not stop a customer from contacting an agent ● AI should provide extra value without compromising core support experience ● Experimentation is fine, if the customer is aware that they are talking to an AI
  40. 40. Is your support ready for AI?

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