GPT-6 in the Real World: How I’m Using It to Automate the Boring Stuff (and Avoid the Weird Bugs)
It was a muggy Thursday afternoon in Bengaluru when I first spun up GPT-6 in our sandbox environment. I’d just wrapped a week of patching legacy SharePoint servers (don’t ask), and I needed a break—something new, something smarter. So I thought, “Let’s see if this GPT-6 hype is actually worth the bandwidth.”
Spoiler: it is. But not without a few surprises.
Why I Gave GPT-6 a Shot
I’ve been automating workflows since the PowerShell 2.0 days—back when half the scripts were duct tape and the other half were Stack Overflow. But GPT-6? This thing doesn’t just follow instructions—it remembers, adapts, and (sometimes creepily) anticipates what you’re trying to do.
I was skeptical at first. I mean, I’ve seen enough “AI-powered” tools that were basically glorified regex engines. But GPT-6 came with persistent memory, multi-app coordination, and natural language interfaces. That last one sold me. If I could get my junior techs to trigger workflows by typing “generate weekly report,” I’d finally get my weekends back.
How I Set It Up (and What I Didn’t Expect)
I started small—running GPT-6 alongside our existing RPA stack (Power Automate + a few UiPath bots) on a Hyper-V VM with 32GB RAM and a modest Azure backend. First use case? CRM hygiene. You know, the stuff no one wants to do: deduping contacts, flagging stale leads, summarizing call notes.
I piped in data from Salesforce and let GPT-6 loose with a few prompt templates. Not gonna lie, I was winging it at first. But within a day, it was updating records, tagging sentiment, and even suggesting next steps for the sales team. One rep actually asked if we’d hired a new assistant.
Then I got bold. I wired it into our ticketing system (ServiceNow) and had it triage low-priority requests. It started grouping similar issues, suggesting KB articles, and even closing out resolved tickets. The kicker? It remembered which departments preferred email over chat and adjusted its responses accordingly.
The Weird Stuff No One Warned Me About
Here’s where things got interesting.
- The memory is powerful—but also a little clingy. I had to manually flush context after a few test runs because it kept referencing old workflows I’d already deprecated. Lesson learned: always set memory boundaries.
- Prompt injection is real. One of our interns (bless him) tried to “jailbreak” the bot by feeding it a rogue prompt inside a ticket comment. It didn’t go full Skynet, but it did start replying with markdown headers. We’ve since sandboxed all user inputs.
- Integration quirks. Most guides say “just connect via API,” but I found using Microsoft’s Admin Center to monitor GPT-6’s behavior gave me way more visibility than raw logs. Also, GPT-6 plays nicer with SAP than I expected—but throws a fit with older Oracle setups.
What I’d Do Differently
If I were starting over, I’d:
- Set up a governance layer from day one. Think: prompt templates, audit logs, and a human-in-the-loop for anything customer-facing.
- Train the team on prompt engineering. It’s not just about writing instructions—it’s about thinking like a bot. We now have a cheat sheet taped to the wall: “Be specific. Avoid ambiguity. Use examples.”
- Keep a “weird responses” log. Trust me, you’ll want it. One time, GPT-6 summarized a finance report with “Looks like someone’s cooking the books 👀.” Accurate, but maybe not the tone we wanted.
Final Thoughts
GPT-6 isn’t magic. It’s not going to replace your sysadmins or your analysts. But it will absolutely make their lives easier—if you treat it like a junior teammate who’s brilliant but needs guardrails.
I’ve gone from cautious curiosity to full-on integration in under two months. And while I still don’t trust it with production deployments (yet), I can’t imagine going back to the pre-GPT days of manual triage and endless report formatting.
Your Turn
Anyone else tried GPT-6 in a real enterprise setup? What’s the weirdest or most useful thing it’s done for you? Drop your stories—I’m all ears.
