Unpacking AI Use Cases
AI is a tool. It's a strategy. And it's an accelerator. But, like any powerful tool, to leverage it effectively, you need to understand how you can effectively use it. That's where your AI use cases comes in.
What is an AI use case?
An AI use case is a specific example of how AI can be used to achieve a business objective. A great use case details both ‘how’ and ‘why’ you’ll be using AI giving you a solid foundation for implementation.
An effective AI use case touches on four crucial elements:
📑Process: The current process that you want to enhance using AI.
❔ Problem: The issues you're facing with the existing process.
🔁Task or Interaction: The specific tasks that humans and AI will perform in the new process.
🎯Goal or Measures: The desired outcome or improvement you're aiming for with AI.
By defining these four elements, you create a clear picture of how AI will transform a specific business operation.
Let's consider an example.
You're an L&D consultant specializing in instructional design.
The process you're looking to enhance is your development of interactive, scenario-based learning modules. The challenge is the time and resources it takes to create realistic scenarios that directly relate to performance goals. Your goal is to create more engaging, contextually-rich, and relevant learning experiences.
Your AI use case for this could involve utilizing generative AI like ChatGPT to create diverse and realistic learning scenarios.
The task for the AI tool would be to generate various contextually relevant scenarios based on the parameters you set, like the learners' roles, industries, or specific challenges they face. Meanwhile, your role as an instructional designer would be to curate these scenarios, validate, integrate them into your learning design, and ensure they align with learning objectives.
This use case not only streamlines the process to create learning scenarios. It also allows you to focus on the strategic aspect of instructional design — enhancing learning outcomes. With AI in your toolkit, you're set to deliver immersive and personalized learning experiences that resonate with your audience and drive their professional growth.
Learning & Development AI Use Case Examples
Browse use case examples below for inspiration.
Repurpose Training Content
❔ PROBLEM: Resource-intensive to create microlearning from existing training content
📑PROCESS: Creation of microlearning modules from existing training content
🎯GOAL: Efficiently create microlearning modules using existing content
💡 Use ChatGPT to extract key points and script it, then use Synthesia or other tools to create an AI avatar video
Translate Learning Materials
❔ PROBLEM: High cost and time in translations
📑PROCESS: Translation of written materials
🎯GOAL: Decrease translation time and cost
💡 Use ChatGPT to draft initial translations, which are then verified and edited by a translator
Enhance Internal Communications
❔ PROBLEM: Difficulty maintaining consistent and engaging communication across the organization
📑PROCESS: Communicating company updates, policies, and announcements to all employees
🎯GOAL: Facilitate regular and engaging communication across the organization
💡 Use generative AI to draft initial company-wide announcements and updates
AI Avatars for Role-Play Learning
❔ PROBLEM: Limited opportunities for employees to practice skills in a risk-free environment
📑PROCESS: Providing practical training to employees
🎯GOAL: Enhance the learning experience by providing opportunities for practice
💡 Use AI avatars for simulated role-play scenarios
Collaborate w/Subject Matter Experts
❔ PROBLEM: Time-consuming content creation requiring dedicated SME time
📑PROCESS: Drafting initial instructions or content requiring SME's input
🎯GOAL: Minimize the required time of SMEs in content creation
💡 Use ChatGPT to draft initial content, which is then reviewed and finalized by the SMEs
Adaptive Training Scenarios
❔ PROBLEM: One-size-fits-all training doesn't cater to individual learning needs or pace
📑PROCESS: Delivering training to employees
🎯GOAL: Personalize the training experience to better fit individual needs and enhance learning outcomes
💡 Implement generative AI in training modules to adapt scenarios and difficulty based on individual learner progress and responses
AI-Powered Research Assistant
❔ PROBLEM: Manual research is time-consuming and may overlook valuable sources
📑PROCESS: Conducting academic or industry research
🎯GOAL: Improve efficiency and quality of research
💡 Use appropriate AI tools to automate data collection, literature review, and synthesis of findings
❔ PROBLEM: Taking meeting notes distracts from full engagement in the discussion
📑PROCESS: Documenting key points and action items during meetings
🎯GOAL: Ensure comprehensive meeting records without sacrificing participation
💡 Use AI to transcribe and summarize meeting conversations and action items
Analyze & Draft Policy Documents
❔ PROBLEM: Time-consuming to draft comprehensive policy documents
📑PROCESS: Creation of initial drafts for new policies or legislation
🎯GOAL: Streamline the drafting process
💡 Use generative AI to analyze rules and regulations, identify impact, and create initial drafts of policy documents
❔ PROBLEM: Traditional risk assessment can be labor-intensive and may overlook some potential risks
📑PROCESS: Identifying and assessing potential risks in business operations
🎯GOAL: Enhance the comprehensiveness and efficiency of risk assessment
💡 Use generative AI to analyze historical data, identify potential risks, and generate risk assessment reports, which can then be reviewed by risk management experts