Corporate AI Facilitator

What matters most

• Build role-specific AI learning

  The posting calls for programs spanning foundational literacy, workflow-specific use cases and advanced tool training, with assets for banking, operations, HR and finance. Prepare to explain how you would choose learning objectives and formats across that range.

• Facilitate practical learning for global audiences

  The role delivers in-person and virtual sessions and hands-on workshops using business scenarios. Prepare to describe how you would make LLMs, automation and prompting understandable to non-technical learners while giving them useful practice.

• Turn use-case discovery into adoption

  The facilitator partners with business leaders, advises departments on when and how to apply AI, and helps teams integrate it into workflows. Prepare an approach for moving from a proposed use case to a relevant learning exercise and follow-up support.

• Keep training aligned with responsible use

  The posting requires collaboration with IT, Risk, Legal and Data so materials reflect governance, responsible AI guidelines and security policies. Prepare to explain how you would incorporate those constraints into examples and exercises.

• Measure and improve the program

  The role defines measures of usage, proficiency and productivity impact, analyzes adoption data, gathers feedback and updates materials as tools evolve. Prepare to distinguish participation from demonstrated learning and workflow impact.

Likely challenges to think through

• Scale without losing relevance

  Likely challenge, not a confirmed problem: global delivery favors reusable materials, while banking, operations, HR and finance need different scenarios. Prepare a modular design approach with shared AI fundamentals and role-specific practice.

• Balance experimentation with safeguards

  Likely challenge, not a confirmed problem: hands-on AI workshops need enough freedom to be useful without teaching practices that conflict with governance or security policies. Prepare to discuss how you would review scenarios with the relevant functions and set clear exercise boundaries.

• Show impact while content changes

  Likely challenge, not a confirmed problem: usage may be easier to track than proficiency or productivity impact, and changing tools can date training quickly. Prepare a measurement approach that combines adoption data, demonstrated tasks and learner feedback, with a plan to revise materials.

Topics to prepare

• AI concepts and appropriate use

  Required: understand LLMs, machine learning basics, automation and prompt engineering. For revision, explain each in plain language, then compare when a generative AI tool, an automation platform or neither would suit a hypothetical employee task.

• Blended learning design

  Required: experience with instructor-led training (ILT), virtual instructor-led training (VILT), e-learning and microlearning. Sketch how one AI-literacy objective would be taught and practiced across those formats; the role develops foundational, workflow-specific and advanced training.

• Responsible adoption and governance

  The role coordinates with IT, Risk, Legal and Data so training reflects governance, responsible AI guidelines and security policies; familiarity with responsible AI and data governance is preferred. As a revision exercise, draft questions for those teams before teaching a hypothetical workflow, without assuming any particular firm policy.

• Change enablement and role-specific adoption

  The role helps departments apply AI to workflows; change management and user-adoption experience are preferred. Compare potential learning needs for banking and HR in a hypothetical use-case discussion, and outline how you would gather feedback rather than assume the same training fits both.

• Learning and adoption measurement

  The posting calls for measures of usage, proficiency and productivity impact, plus analysis of adoption data. Draft one possible measure for each, identify what it would and would not demonstrate, and propose a training adjustment based on hypothetical results.

Skills to revise

• Build a compact learning asset

  The role creates e-learning modules, videos, job aids, sandbox exercises, simulations and facilitator guides. Practice producing a one-page job aid and a short sandbox exercise for a hypothetical AI-assisted task, with a clear objective and a way to check learning.

• Facilitate a non-technical workshop

  Teaching technical tools to non-technical audiences and delivering in-person and virtual sessions are explicit expectations. Rehearse a ten-minute explanation of prompting, followed by a hands-on business scenario; prepare a question that checks understanding without technical jargon.

• Lead use-case discovery

  The role partners with business leaders to discover use cases and integrate AI into workflows. Role-play a discovery conversation: ask about the current task, desired outcome, constraints and how employees would judge whether the change helps.

• Refine prompts and explain results

  Prompt engineering is a required knowledge area, and workshops demonstrate AI tools through business scenarios. Using non-sensitive, hypothetical content, practice revising a vague prompt into a clearer one, checking the output and explaining the changes to a non-technical learner.

• Iterate training from feedback

  The role gathers learner and leader feedback and updates materials as tools and practices evolve. Review a hypothetical set of workshop comments, identify one recurring difficulty, and revise a facilitator-guide step to address it.

• Select a training project to walk through

  Prepare one example showing the audience, learning goals, assets, facilitation approach, feedback and revisions. The posting calls for blended learning and continuous improvement; use an actual project rather than inventing results.

• Draft a role-specific learning plan

  As an interview exercise, outline a short AI-literacy path for one audience named in the posting, such as banking or HR. Include a live session, an e-learning or microlearning component, and a job aid or sandbox exercise.

• Rehearse a hands-on AI lesson

  Prepare a brief, non-sensitive business scenario that teaches a non-technical audience an LLM or prompt-engineering concept. Practice explaining the workflow, its limitations and where human review belongs.

• Outline a use-case discovery conversation

  Draft questions for a business-unit leader about workflow pain points, appropriate AI uses and barriers to adoption. Show how the answers would shape a workshop rather than assuming a particular tool or use case is already approved.

• Prepare a governance-aware training example

  Explain how you would seek input from IT, Risk, Legal and Data before teaching a workflow, then incorporate responsible-AI and security guidance into the learning materials. Do not assume specific Houlihan Lokey policies that the posting does not state.

• Sketch an evaluation and update plan

  Propose measures for usage, proficiency and productivity impact, plus a way to collect learner and leader feedback. Show how those findings and changes in AI tools would prompt revisions to the training.

• Write focused questions for the employer

  Ask which employee audiences are priorities, how approved AI tools and governance guidance are communicated, and what adoption data the team can use. These clarify responsibilities described in the posting without presuming internal processes.