What this means here.
An AI workflow is a defined work path where AI supports a bounded task, approved source material is visible, human review is required, and a person still owns the final decision.
Topic
AI Workflows
Practical ways to use AI in planning, drafting, review, QA, knowledge management, and production without turning the work into a tool demo.
Why this work starts to hold together.
AI gets useful when we attach it to a real workflow. It gets messy when we add tools before we agree on standards, review habits, and ownership.
What this helps with
Use this when we need a cleaner next move.
Attaching AI to the planning, drafting, review, and QA work we already do
Reducing blank-page work without weakening review standards
Turning tool access into repeatable team habits
What usually makes this harder than it needs to be.
- People use AI alone, so output quality depends on who happens to be prompting that day.
- We have prompts, but we have not agreed on review standards, source standards, or risk levels.
- AI gets treated like a faster content machine before we fix the workflow around the content.
The early warning signs usually show up here.
- Prompt sharing replaces workflow design.
- AI output moves faster than source review, SME review, accessibility review, or risk review.
- The team cannot tell which work is safe for AI assistance and which work needs tighter boundaries.
Look at the work before choosing the fix.
- Where AI enters the workflow and where human judgment takes over
- Which source materials are allowed, current, and safe to use
- Who reviews facts, tone, accessibility, privacy, risk, and final fit before anything goes live
Small moves that can change the conversation.
- Pick one low-risk workflow and mark where AI assists, where we review, and who owns the final call.
- Add a source check and SME check before any AI-assisted learning asset goes live.
- Create one reusable project brief with audience, task, constraints, tone, source material, and review criteria.
Before we choose the fix
The useful move is usually upstream.
These prompts slow the conversation down before we add another course, tool, report, or AI workflow.
- Responsible AI guidance keeps coming back to accountability, transparency, privacy, safety, and human oversight. For L&D, that means we cannot treat AI output as finished learning content.
- The useful question is where AI changes the workflow. If the handoff, review, and source standard stay messy, the output just gets messy faster.
- Where does AI enter the workflow, and where does human judgment take over?
- What source material is allowed for this task?
- Who owns the final decision before the asset goes live?
- Do not build a prompt library before we know which workflow the prompts support.
- Do not let polished AI output skip source review, SME review, accessibility review, or risk review.
What this looks like
The same problem can show up in more than one way.
These patterns help us name what is happening before we commit to a fix.
Prompt-first adoption
The team collects prompts, but we still have not defined source standards, review gates, or accountable owners.
Private productivity gains
One person moves faster, but the shared workflow, QA process, and review standard stay the same.
AI output without evidence
Drafts look polished, but reviewers cannot tell which sources were used or what still needs human judgment.
Three ways we can respond
Start simple, then add tools only when they help.
Start with the smallest useful move. Then add common workplace tools or AI only when they help the work.
Write the rules for one AI-supported workflow on paper: allowed tasks, not-allowed tasks, required source checks, review owner, and publish criteria.
Create a shared Word, Google Doc, or SharePoint page with approved prompts, source checklists, review criteria, and before-after examples.
Use AI to draft first-pass outlines, SME questions, accessibility checks, and QA checklists, then require human review for facts, tone, risk, and final decisions.
Best starting moves
Start with the resource that matches what you need next.
Use the note for context, the template for the working artifact, and the example when you need to see the shift before trying it.
The 31% Problem
Start here when the team has AI access but needs a better operating pattern for using it in real L&D production work.
AI Workflow Brief
Use this when a team needs to define source material, allowed AI tasks, review gates, and human ownership.
AI Prompt Without Context to Useful L&D Prompt Brief
Use this when AI output feels generic because the work around the prompt is unclear.
Setup guides
Build this with tools the team likely already has.
Use these when the topic needs to become a repeatable setup in a document, spreadsheet, List, Sheet, or shared workspace.
Give Claude Code a Memory
Set up Obsidian as persistent context and living documentation for Claude Code projects, with Mission Control, a MEMORY index, session handoffs, and reusable L&D project notes.
Field notes
Read the notes tied to AI Workflows.
Use these when you want examples, explanations, and next actions for this part of the system.
The 31% Problem
Systems thinking, not tool access alone, changed development cycle time. AI worked when it improved planning, review, handoffs, and production habits.
Why L&D Professionals Need a Personal Knowledge System
L&D teams research constantly but retain too little between projects. A personal knowledge system turns scattered notes into reusable context.
Using Obsidian as Claude Code Memory
A practical setup guide for giving Claude Code persistent project context, session handoffs, and reusable L&D working memory.
The LLM Wiki Pattern For L&D Research
A research knowledge base can help L&D teams maintain reusable context for tools, frameworks, examples, and implementation decisions.
Related PDF guides
Download guides connected to AI Workflows.
Claude AI and Cowork
Use Claude and Cowork for planning, drafting, review preparation, reusable project context, and daily L&D production workflows.
Microsoft 365 Copilot
Use Copilot across Word, Excel, PowerPoint, Teams, SharePoint, Power Automate, Forms, Power BI, Viva, and Copilot Studio.
Google Gemini
Use Gemini, NotebookLM, Canvas, Deep Research, Gems, and Workspace workflows for practical learning team work.
Claude Code + Obsidian
Use Obsidian as persistent context and living documentation for Claude Code projects.
ChatGPT Images 2.0
Use the carousel as a quick reference for better AI-assisted visual production briefs and iteration habits.
Related templates
Use a working artifact for AI Workflows.
AI Workflow Brief
Define one AI-supported L&D workflow, including source material, allowed tasks, review gates, risk checks, and the human decision owner.
Related examples
See what better looks like for AI Workflows.
Use these when you want the before-and-after move before you open the template.
AI Prompt Without Context to Useful L&D Prompt Brief
Give an AI tool the role, source material, task, constraints, review standard, and output format it needs to help safely.
- Use when AI output is generic because the prompt asks for content without giving the workflow context.
- Try this Before asking AI for content, write a three-line brief: source material, output needed, and what the model must not assume.
References
References worth keeping nearby.
Microsoft responsible AI principles
Use this when we need to check the idea against source material or platform documentation.
Microsoft 365 Copilot enterprise data protection
Use this when we need to check the idea against source material or platform documentation.
Google Workspace Gemini data protections
Use this when we need to check the idea against source material or platform documentation.
Docebo Future-Ready Framework for L&D
Use this when we need to check the idea against source material or platform documentation.