Your AI is Useless Without This.
It Can Make or Break Your AI Strategy.
When I started in HR, the first task I was given was “documentation”.
Fresh into the world of people management, my initial assignments weren’t strategy sessions or leadership meetings - they were pages and pages of process documents.
It was déjà vu moment when I transitioned to being an SAP HCM consultant. My senior colleagues handed me a mountain of documentation tasks - BRDs (Business Requirement Documents), SOPs (Standard Operating Procedures), Solution Mapping Documents, User Manuals… you name it.
In my mind, documentation was something for freshers. The junior team members. The ones who didn’t know enough yet.
But as the years passed, I discovered something ironic: documentation wasn’t just for beginners.
It was for everyone.
It was the backbone of every successful project, every well-run system.
What comes to your mind when you think of companies like McKinsey and Deloitte - fancy PowerPoint slides?
There you go..
But behind those slides? Extensive documentation - because even the smartest consultants know that clarity is power.
And yet, I once heard someone say: "If a system or a process needs a user guide, it is a bad design."
That stuck with me. Because while it sounds profound, in the world of complex systems and AI, it’s not always true.
Even Don Norman, the usability expert who popularized this idea, acknowledges that in complex systems, some level of instruction is necessary. The goal is not to eliminate documentation but to make it intuitive, accessible, and meaningful.
Because even the most intuitive systems need context.
And here’s the twist: Now, with AI, documentation doesn’t have to be a slow, painful process. AI tools can draft, organize, and even maintain your documentation - faster and with less effort.
Why Documentation Matters More Than You Think
Imagine you’ve built an HR chatbot. You want it to help employees with questions about leave policies, benefits, and performance evaluations. You connect it to your HR SharePoint library, packed with hundreds of files, PDFs, and PowerPoint decks.
But here’s the problem: The chatbot cannot make sense of it all.
Pointing an AI agent to a vast, unorganized document repository is like asking someone to find a needle in a haystack. The agent can’t help if it can’t quickly and accurately access the information it needs.
This may lead to biased outcomes, non-compliance, and losing the trust of employees and stakeholders.
The Art of Organization: More Than Just Dumping Files
Here’s the first rule of AI documentation: If your agent can’t find it, it might as well not exist.
So, here are some tips:
Convert to FAQs: One of the fastest ways to make information digestible for an AI is to format it as a Frequently Asked Questions (FAQ) list. It’s simple - give your favorite GenAI tool the document and ask it to convert it to a FAQ.
Logical Grouping: Categorize documents by themes or topics - for example, “Leave Policies,” “Benefits,” and “Performance Management.”
Use Readable Formats: Ensure documentation is in readable PDFs, not images, as some AI may lack OCR (Optical Character Recognition) capabilities.
Maintain Up-to-Date Records: Regularly update documentation to reflect changes to policies and processes. E.g. If Benefits enrollment details change year over year, ensure you have the most updated document in the agents knowledge base.
Use an Orchestrator Agent: For complex setups, where multiple agents handle different tasks, an orchestrator agent can ensure that each query is directed to the right specialist with the right knowledge and documentation.
The Benefits: Faster, Smarter AI Agents
When your documentation is structured this way, your AI doesn’t just answer questions faster - it answers them better. It knows exactly where to look, and it doesn’t get lost in a sea of information.
So far, we covered the documentation that’s needed to ensure your conversational agents/bots handle user queries effectively.
Now let’s look at documentation from a different lens - documenting the overall AI solution.
Blueprints for AI Project Success
Enhancing Transparency and Accountability: Proper documentation helps stakeholders understand how AI solution works, how is it answering questions. This fosters trust because you are making the inner workings of your AI solution understandable. Transparent AI is trustworthy, safe, compliant, and promotes innovation.
Facilitating Collaboration and Governance: It serves as a bridge between diverse teams, ensuring alignment and compliance.
Supporting Ethical and Responsible AI Use: Clear documentation helps identify and mitigate biases, promoting responsible use of AI.
Streamlining Maintenance and Continuous Improvement: Regularly updated documentation simplifies troubleshooting and system upgrades.
Practical Steps for Effective AI System Documentation
Adopt Standardized Frameworks: Use frameworks like CLEAR (Comparable, Legible, Actionable, Robust) to maintain consistency.
Document the AI Lifecycle: Capture everything from data sourcing, building, testing (evaluations) deployment strategy, change management and monitoring.
Version Control: Maintain version histories for data, design, and models to track changes.
Access Control: Define who can view, edit, or update documentation and the AI solutions.
Prioritize Plain Language in Public Reporting: Ensure that public-facing documents (like AI policy pages or data privacy consent forms) are written in clear, plain language. This allows everyday users to understand them, and visual aids can enhance comprehension. This is especially critical for compliance with laws like GDPR or the EU AI Act.
Why This Matters
Documentation is more than a formality. For agents, it’s about ensuring they deliver fast, accurate answers. For your overall AI solution, it’s about building something that’s trustworthy, transparent, and adaptable.
So whether you're guiding a chatbot or maintaining a full-scale AI system, remember - clear, accessible documentation is the quiet hero behind the scenes.
All the best!