The first time I tried using an AI assistant at work, I thought it would save us a lot of time. But instead, it just sat there, waiting for instructions. It reminded me of when a new teammate joins the team, smart, eager, but totally lost because no one explained how things work.
That’s what AI looks like in many companies today.
People say, "Use AI to move faster!". But AI is like a new junior teammate. It doesn’t know what’s important, what’s old, or what parts of your code are tricky. It doesn’t know your goals, naming rules, or who to ask about that one unstable service. Unless you give it good information, it won’t be able to help much.
If you want AI to help your team, you have to set it up for success first.
The real blocker isn’t the model
Most companies aren’t being held back by weak models. They’re being held back by weak context.
It’s tempting to believe that just plugging AI such as Copilot or ChatGPT into your workflows will instantly make your team faster, more productive, more competitive. But the models already work pretty well.
The real question is: are you giving them the environment they need to thrive?
Let me put it plainly. AI tools don’t replace your systems. They rely on them.
- If your knowledge is scattered across Slack, Jira, Notion, and people’s heads, AI won’t find it.
- If your documentation is outdated or vague, AI will guess and often guess wrong.
- If your workflows are clunky or manual, AI can’t magically fix them without guidance.
You wouldn’t hire an engineer and then leave them blind. So why do we do that to AI?
AI is like a teammate who learns fast, never sleeps, and never complains but only if you onboard them well. That means shifting your mindset from “what can AI do?” to “what do we need to give AI so it can help us?”
This is where a lot of teams get stuck.
They try AI. It gives weird answers. They blame the tool. But in reality, it’s often a data and context problem.
Here’s the truth most people miss: AI needs structure, clarity, and shared context to work well. Just like humans do.
To make that happen, your organization has to start treating its information as a product not just stuff thrown into Confluence or dumped into code comments. That means:
- Centralizing decisions, docs, and code into one place.
- Using semantic structure such as clear labels, tags, linked data to make information meaningful.
- Asking: “can a new engineer (or AI) pick this up and understand it without a call?”
How to set AI up for success
You want real leverage from AI? You’ve got to build the foundation first. Here’s how.
Centralize Your Context
Your knowledge should live where your work lives. That might be a well-structured monorepo, a code-aware documentation system, or a shared workspace tied to version control.
Write for the Reader (and the Robot)
AI can’t read your mind. Neither can your teammates. Whether you’re writing implementation plan, README files, or system design docs, be clear, be specific, and write like someone will read this later to make a change.
Add Semantic Layers
Some companies are moving away from using Saas and third-party tools so they can better control and organize their own data. They want to prepare it for AI use and maybe even fine-tune their own models. This shows how important good data structure has become in AI work.
Some already have strong systems for handling data. But most still use setups that only create reports for humans to read. These reports often remove small details and useful connections that AI needs to work well. We need to improve these systems so they also include meaning, context, and clear links between different pieces of data.
Structure matters. Metadata, naming conventions, tagging, and ontologies, these are the breadcrumbs AI uses to navigate your world. If you want better responses, give it better signals.
Measure What Matters
Don’t just ask if AI is being used ask if it’s moving the needle. Are we shipping faster? Are bugs caught earlier? Are devs spending more time on high-leverage work? Use real metrics: lead time, test coverage, review quality.
If you want AI to help, you need to:
- Give it good context. Clear inputs make for useful outputs.
- Design your organization to be readable. AI learns from your systems, make them clean.
- Make structure the default. Semantics > scattered scraps.
Set your AI up like you do for a new engineer: patiently, intentionally, and with clear expectations.
That’s the work. And when you do it right, AI won’t just help, you’ll wonder how you ever worked without it.