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Getting Started with AI in L&D


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Marketing. Logistics. Safety. HR. Artificial intelligence is changing the way work is done. But what about L&D? While we will not be the first team to introduce AI in our organizations, we cannot risk falling behind when it comes to the impact AI is having on our workplaces. So how should L&D teams prepare for adoption of AI-enabled technologies? How can L&D professionals apply AI to improve the impact of learning on employee performance? And, most importantly, what should the L&D function look like in an organization where AI and automation have fundamentally changed the way people approach their work? Let's discuss!

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Getting Started with AI in L&D

  1. 1. AIGetting Started with in L&D
  2. 2. JD Dillon Chief Learning Architect Axonify
  3. 3. Today we’re going to talk about 4 things … Mindset Application Data Preparation
  4. 4. Why are you interested in AI?
  5. 5. AI is NOT about robots.
  6. 6. This is what AI can do today. § Pattern Recognition § Natural Language Processing § Conversational Response § Discovery § Visual Recognition § Sentiment Analysis § Text <> Speech
  7. 7. Broad AI General AI Emerging Pervasive Revolutionary We are here. Narrow AI
  8. 8. Pixabay L&D will not introduce AI in your workplace. AI-enabled technology is likely already in use by your Sales, Marketing, Logistics or Recruitment teams.
  9. 9. Before we can talk about applying AI in workplace learning …
  10. 10. … we have to talk about boxes. Pixabay
  11. 11. internet social mobile AI represents the next technology inflection point for L&D. L&D tends to put new concepts into isolated boxes, thereby missing the larger potential impact of the innovation.
  12. 12. 75% of commercial applications will use AI by next year. L&D within 3 years. IDC – Worldwide IT Industry 2018 Predictions
  13. 13. How can you apply AI within workplace learning?
  14. 14. Pixabay AI can be applied to solve problems in different ways. AI-enabled traffic signals can alleviate congestion and help vehicles move through a designated area more effectively. Self-driving cars are also AI-enabled and focus on transportation challenges but from a different perspective.
  15. 15. Before you decide how you want to apply AI, you must determine what problem are you trying to solve.
  16. 16. Authoring Translation Administration Search Chatbot Personalization Gap Analysis Recommendation Coaching Impact Analysis 10 Real-world AI Applications in L&D
  17. 17. Administration A host of L&D administrative tasks, such as class scheduling, report generation and course assignments, can be automated through emerging AI-enabled tools.
  18. 18. Authoring AI can be applied to develop digital learning content from source material but still requires human review and revision to ensure accuracy and context.
  19. 19. Chatbot Many chatbots are complicated branching programs and not true applications of AI. Some do use AI capabilities, such as natural language processing.
  20. 20. Search AI can be applied to help users find specific information rather than just providing links to related content based on keywords.
  21. 21. Recommendation AI is sometimes used to recommend content to a user based on how their data profile matches other user activity.
  22. 22. Coaching Fittonic AI can be applied to provide actionable coaching recommendations, just like a personalized exercise application.
  23. 23. Janet Let’s continue with your guided learning today. Michael You have a certification due today. Adam Here’s your chance to practice your knowledge! Personalization AI-enabled personalization goes beyond just content recommendation by adapting the learning experience to meet the immediate, proven needs of an individual.
  24. 24. Impact Analysis Machine learning can be applied to large data collections in order to establish causation between learning activities and performance outcomes.
  25. 25. Gap Analysis ü Capability #1 ü Capability #2 ü Capability #3 ü Capability #4 ü Capability #5 ü Capability #1 ü Capability #2 q Capability #3 ü Capability #4 ü Capability #5 ü Capability #1 ü Capability #2 ü Capability #3 q Capability #4 q Capability #5 AI can be applied to proactively identify employee capability gaps and match people to roles with both speed and scale.
  26. 26. VALUE ADOPTION Personalization Search Translation Authoring Administration Chatbots Recommendation Gap Analysis Impact Analysis Coaching Many AI applications within L&D are just now emerging and represent a different potential value based on each org need.
  27. 27. Is L&D ready for AI? Kinda …
  28. 28. AI Machine Learning Analytics Information Architecture AI requires data. An AI-enabled application must have the right information architecture at its foundation to power the solution.
  29. 29. Software-Enabled AI Limited IA Required Authoring Translation Administration Search Chatbot Applications may be able to function without a robust information architect.
  30. 30. Software-Executed AI IA Foundation Required Personalization Gap Analysis Recommendation Coaching Impact AnalysisApplications require a strong information architecture in order to function.
  31. 31. AI Machine Learning Analytics Information Architecture
  32. 32. Information architecture is to data as libraries are to books. Many L&D teams just don’t have a lot on their shelves with which to start.
  33. 33. Even organizations with data may not have applied solid analytics practices to clean and organize this data so it can be used effectively by an application.
  34. 34. A data-enabled AI application requires clean, consistent, bias-free data to help you solve the right problem.
  35. 35. L&D does not have sufficient data to power AI. The Kirkpatrick Model
  36. 36. Fixing the measurement problem begins with mindset – the way we think about learning in the modern workplace.
  37. 37. data-rich solution design Agree on a clear, measurable business result Define the observable behavior required to achieve the result Define the knowledge required to execute the expected behavior Determine the right-fit solution DESIGN + MEASUREMENT START HERE
  38. 38. Reinforcement Analytics Motivation Shared Experience On-Demand Resources Events Online Content Experience Messaging Knowledge Growth Business Results Coaching data-rich learning tactics
  39. 39. multi-dimensional data Who is this person? Demographic What has this person reviewed? Consumption What does this person know right now? Knowledge What is this person doing on the job? Behavior What impact is this person having on business outcomes? Results What else is happening around this person? Context What does this person say they want/need? Feedback How does this person engage with the org? Connections
  40. 40. How are people engaging with learning opportunities? Engagement How is people’s knowledge changing over time? Learning How is learning impacting business results and delivering ROI? Outcomes How are people’s behaviors changing on the job? Behaviors How are we projected to perform in the future with our key business goals? Prediction How can we continuously adapt our support tactics to ensure optimum results? Adaptation Learning is continuous. Measurement must also be continuous.
  41. 41. What steps can you take to prepare for the application of AI in L&D?
  42. 42. 1 | explore AI within your organization 2 | do your AI homework 3 | find the problems 4 | establish a vision for AI-enabled L&D 5 | fix your measurement practices 6 | partner with experts + providers 7 | solve specific problems 8 | evolve your role
  43. 43. As we explore AI, L&D must continue to focus on solving problems to help people do their best work every day.
  44. 44. To summarize … 1 | L&D is late to the AI party, but that’s OK. 2 | AI is very good at very specific tasks. 3 | AI is not about tech. It’s about solving problems. 4 | Meaningful AI is out of reach until we fix L&D measurement. 5 | To fix measurement, we must change how we approach learning. 6 | Don’t try to solve this one on your own. Get expert help.
  45. 45. @JD_Dillon
  46. 46. AIGetting Started with in L&D