Productivity AI for IT Pros: Tools That Made a Difference

If you've spent any time in IT, you know the feeling: your ticket queue is stacking up, you're knee-deep in documentation you haven't written yet, and someone just Slacked you asking why the VPN is slow again. AI tools won't fix your VPN (well, sometimes they will), but they have genuinely changed how I handle the daily grind. Here's an honest look at the tools that actually moved the needle for me.

Why Productivity AI Matters for IT Professionals

The promise of AI tools often sounds like marketing fluff, but when you start applying them to real IT tasks (writing runbooks, troubleshooting configs, parsing log files) the impact becomes concrete. The goal isn't to replace your expertise. It's to eliminate the low-value time that surrounds it. A solid workflow efficiency gain of even 20-30% on repetitive tasks frees you up for the work that actually requires your brain.

Here are the tools I've leaned on most, and where they've earned their place in my toolkit.

ChatGPT (OpenAI) — The Swiss Army Knife

Available at chat.openai.com, ChatGPT is the one I reach for first when I need to think through a problem out loud. The ability to paste in a block of config or log output and say "tell me what's wrong here" is legitimately useful.

Where it shines for IT pros:

• Drafting and editing technical documentation
• Explaining error messages from syslog, OSPF adjacency failures, BGP state changes
• Writing Python or Bash scripts from plain-English descriptions
• Generating boilerplate Ansible playbooks or Jinja2 templates

For example, paste in an ip ospf neighbor output that's stuck in EXSTART, and ask why. You'll get a coherent explanation about MTU mismatches faster than you can dig through Cisco docs. In practice, what used to take 15–20 minutes of cross-referencing vendor documentation often resolves in under two minutes, freeing that time for the fix itself rather than the diagnosis.

That said, GPT-4o isn't perfect with version-specific CLI syntax. It will occasionally suggest a command that looks plausible but doesn't exist on your target platform. Always verify against vendor documentation before pushing anything to production.

Claude (Anthropic), The Documentation Specialist

Available at claude.ai, Claude handles long-form content better than most. If I need to write a thorough change management document, a network design proposal, or a post-incident review, Claude produces cleaner prose with less editing needed on my end. The context window is large, which means you can dump in a full config file and have a real conversation about it.

A practical example: I've pasted in a 400-line firewall policy and asked Claude to summarize the permit rules by zone pair and flag any anomalies. The output was structured enough to drop directly into a review document with only minor formatting tweaks.

One limitation worth noting: Claude's training data has a knowledge cutoff, so it may not reflect the latest software releases or recently published CVEs. For anything time-sensitive, cross-check with a live source.

Claude also tends to be more cautious and precise with technical claims, which matters when the output is going into production documentation.

Perplexity AI, When You Need Answers, Not a Conversation

Available at perplexity.ai, this one gets overlooked. Think of it as a research assistant that cites its sources. When I need to quickly verify whether a specific Cisco IOS-XE feature is supported on a platform, or find the current best practice for something like ip dhcp snooping configuration, Perplexity pulls from current web sources and shows you where it got the information. That citation trail matters in IT where outdated advice can cause real problems.

The tradeoff is depth: Perplexity is better for quick factual lookups than for reasoning through a complex multi-step problem. For that, you're better served by ChatGPT or Claude.

GitHub Copilot, For Automation Work

Available at github.com/features/copilot, if you're doing any scripting (Python for NetDevOps, Bash for Linux admin tasks, YAML for Ansible) Copilot is the productivity AI tool that pays for itself fastest. It autocompletes functions, suggests entire code blocks, and handles the boilerplate that slows you down. Combined with VS Code, it makes writing a Netmiko script to pull show version from 50 devices feel trivial.

One real-world caveat: Copilot suggestions are based on patterns from public code repositories, which means it can occasionally suggest insecure patterns — hardcoded credentials in scripts being a common one. Treat its output the same way you'd treat code from a junior developer: review before you run it, especially in anything touching production systems or handling credentials.

A Note on Trusting the Output

None of these tools are infallible. They hallucinate. They get version-specific details wrong. They'll occasionally give you a Cisco command that looks right but doesn't exist. The rule I follow: AI drafts, I verify. Use the tool to get to 80% faster, then apply your actual expertise to the last 20%. That combination is where the real workflow efficiency lives.

What's Next

Now that you've got a lay of the land on general productivity AI tools, the next post dives deeper into using AI specifically for network troubleshooting — including how to structure your prompts to get more accurate and actionable answers from these tools. Prompt engineering for IT professionals is a skill worth building, and it's more approachable than it sounds.