AI Tools for IT Productivity: My Personal Workflow

Over the past couple of years, AI tools have gone from novelty to necessity in my daily IT workflow. I want to share what actually works, not just what sounds impressive in a product demo. This post is a personal look at the tools and habits that have genuinely made me more productive as an IT professional.

The Mindset Shift: AI as a Thinking Partner

The biggest mistake I made early on was treating AI tools like a search engine. You type a question, you get an answer, done. The real productivity gains come when you treat AI as a thinking partner. You bring context, constraints, and domain knowledge. The AI helps you move faster, think through edge cases, and draft things you would have stared at for an hour.

That shift changed everything about how I integrate AI into my IT workflow.

The Core Tools in My Stack

ChatGPT (OpenAI)

This is my daily driver for anything text-heavy: drafting documentation, explaining complex concepts to non-technical stakeholders, and writing first drafts of runbooks. The key is being specific with your prompts. Compare these two approaches:

Vague prompt:

Explain BGP.

Effective prompt:

Explain BGP route selection to a junior network engineer who understands
basic IP routing but has never worked with a service provider environment.
Focus on the decision process and use a practical example with two upstream providers.

The second prompt gives you something you can actually use or share with a colleague. Specificity is the skill.

GitHub Copilot

If you write any Python, Ansible, or shell scripts in your role, Copilot is a serious multiplier. It is not perfect, and you still need to review its output carefully. But for repetitive tasks like writing boilerplate Netmiko connection blocks or looping through a list of devices, it saves real time. I use it inside VS Code and have it enabled for .py, .yml, and .sh files.

Perplexity AI

This one replaced a lot of my Google searches for technical topics. Perplexity pulls from live web sources and cites them, which matters when you are troubleshooting something version-specific or looking up a vendor advisory. It is available at perplexity.ai and the free tier is genuinely useful.

Notion AI

I keep all my project notes, change records, and lab documentation in Notion. The built-in AI assistant helps me summarize long meeting notes into action items, reformat messy notes into structured documentation, and generate table of contents for longer runbooks. It keeps everything in one place, which matters for consistency.

A Real Workflow Example

Here is how these tools come together for a typical change management task. Say I need to document a firewall rule change for a production environment:

1. I use Perplexity to quickly verify the port requirements for the application being opened up.
2. I paste my raw notes into Notion AI and ask it to format them as a change request template.
3. I use ChatGPT to generate a rollback plan based on the change description.
4. If a verification script is needed, GitHub Copilot helps me write a quick Python snippet to test connectivity after the change.

What used to take 45 minutes now takes closer to 15. That is the compound effect of building AI productivity tools into your workflow systematically rather than using them one-off.

What to Watch Out For

A few honest cautions from personal experience:

• Hallucinations: AI tools will confidently state incorrect information. Always verify anything that will go into production documentation.
• Data sensitivity: Do not paste customer data, internal IP schemas, or credentials into public AI tools. Use private or enterprise versions where your organization requires it.
• Over-reliance: These tools are a complement to your skills, not a replacement. The better your foundational knowledge, the better you can evaluate AI output.

What's Next

In the next post, we will go deeper on prompt engineering specifically for IT professionals. Knowing how to write effective prompts is the skill that unlocks the most value from every tool in this stack, and there are patterns and frameworks that make a real difference. We will walk through practical examples built around networking, automation, and documentation tasks.