I've Used AI for 3 Years. Nothing Prepared Me for Claude.
- simonechantechetty
- 16 hours ago
- 4 min read
Updated: 5 hours ago
"AI won't take your job. But the operations executive who knows how to use it properly might."
That's not a threat — it's an observation. And it comes from someone who has been in the trenches with AI tools since before most people knew what a large language model was.
Most senior operations executives I know have tried AI. Most of them are underwhelmed. They used it to draft an email. Maybe summarise a document. Got something generic back, cleaned it up themselves, and quietly concluded that the hype was overblown.
I almost did the same — except I didn't, because I'd already seen what it could actually do.
My Starting Point Was Different
I didn't discover AI last year. My first real exposure to generative AI was at Takealot — one of Africa's largest e-commerce platforms — where I used it to redesign our returns process from the ground up.
85%
Reduction in resolution time
R3M
Saved annually
3 Years
of real practice
What Changed With Claude ?
That experience set a baseline. I knew what AI could do when it was properly directed.
From there, like most people, I moved to ChatGPT. It became part of my workflow — useful for drafting, summarising, thinking through problems. A genuine productivity tool. But over time I noticed a ceiling. For the kind of work I do — complex, layered, high-stakes operational problems — it often felt like the output was built for a more general audience than the one I actually needed to serve.
Two months ago I started using Claude AI more intentionally. That's when things shifted.
The difference wasn't dramatic on the surface. But in practice, for senior operational work, it was significant:
It holds context better across long, complex conversations— I don't lose the thread halfway through a working session
The outputs are more precise and less generic— less polishing required to make something actually usable
It pushes back— when my thinking has a gap or my brief is unclear, it says so rather than producing something plausible-sounding that misses the point
It handles layered problems with more depth— the kind that have commercial, operational, and relational dimensions all at once
For straightforward tasks, the difference is marginal. For the kind of thinking senior operations executives actually need support with — it's been game changing.
The Problem I Keep Seeing
But here's what I want to be honest about: the tool only matters if the method is right. And most operations executives — regardless of which AI they're using — are getting this wrong.
The typical approach looks like this: single question, closed prompt, generic answer, disappointment, move on. And I understand why. That's the mental model twenty years of internet use built in all of us.
"The output you get from AI is almost entirely a function of the input you give it. Most people underload it and blame the tool."
What Changes When You Shift the Approach?
The shift that changed everything for me was simple: stop asking it questions, start thinking out loud with it.
Senior operations executives are actually better positioned than most to do this well:
Deep domain knowledge ->means you can direct it precisely
Pattern recognition -> means you can spot when the output is wrong
Stakeholder awareness -> means you can shape the brief with real nuance
The method just has to match the depth you already bring to everything else you do.
What It Looks Like In Practice ?
Pricing Intelligence
Built a structured pricing analysis framework normalising data across multiple regions — in a fraction of the time. The thinking was better because articulating the logic to an AI that pushed back forced me to stress-test assumptions I'd have otherwise glossed over.
Exec Planning
Used Claude as a first-pass thinking partner for senior leadership reviews — sharing raw priorities and direction, using the output as a structural scaffold I then sharpened. More time on the argument, less on the mechanics.
Team Infrastructure
Built frameworks, scorecards, development plans, and escalation protocols at speed. Documentation that lived in my head became structured systems my team could actually operate from.
Product Transitions
Managed a major product transition across a global supplier network — multiple teams, partners, and timelines moving at once. Claude kept the thread consistent across what could easily have become a fragmented mess.
What Actually Changes ?
The real shiftThe biggest change isn't speed — though that's real. It's ceiling.
CEILING |
It doesn't replace the hard thinking. It makes the hard thinking go further.
What It Will Never Do ?
✕ Compensate for weak domain knowledge
If you don't know your field deeply, you won't catch when it's confidently wrong — and it will be.
✕ Replicate relational leadership
The negotiation that required reading a room. The team conversation that shifted something. The trust built through consistent presence. Those remain entirely human.
✕ Replace strategic clarity
If you haven't decided what to prioritise, Claude can't decide for you. Unclear in, polished-but-misaligned out.
The human judgment layer isn't optional. It's the whole point.
How much of yourself did you actually bring to it?
If AI hasn't delivered for you yet, before writing it off — ask yourself honestly: not a task, not a quick prompt — your real context, your actual constraints, your genuine problem. The one you'd normally think through alone on a long walk.
That's the version of you that AI can actually work with.
The operations executives who fall behind won't be the ones who refused to use AI. They'll be the ones who used it badly and concluded it didn't work. |
This tool rewards domain depth, clear thinking, and intellectual honesty. Which means it rewards exactly the kind of people who should be using it most.
What's your experience with AI in your operational work? Comment below · Connect on LinkedIn |
Comments