Access ≠ results.
I talk to teams every week who can’t figure out why AI hasn’t moved the needle.
Then I watch them work.
They’re defaulting to free for decisions that cost thousands to get wrong. Starting fresh chats instead of building systems. Asking generic questions when their actual business data is sitting right there.
Here’s what I’d actually enforce if you want this to work:
1. Stop using the fast model for work that matters
Free defaults to GPT-5.2 Instant. It’s built for speed, not depth.
If you’re making decisions or writing customer-facing content, rework costs more than runtime.
2. Use thinking models when stakes are high
Costs you 30 seconds up front.
Saves you hours on the back end.
Enterprise users report 40–60 minutes saved per day. Heavy users report 10+ hours/week.
3. Match the tool to the job
Fast model: drafts, rewrites, summaries
Thinking model: strategy, tradeoffs, anything with consequences
4. Use Projects, not fresh chats
If your team keeps starting over, you’re paying an “amnesia tax.”
Projects = one place for instructions, context, and files you’d otherwise keep re-uploading.
5. Connect your actual data
Stop asking the internet’s version of your question.
Ask your question against your docs, drive, and systems.
That’s what “apps” (formerly connectors) are for.
6. Build workflows, not random prompts
Teams that structure this are the ones scaling fast.
OpenAI reported 8x growth in message volume and 320x increase in reasoning token use per org, year-over-year.
That’s what real adoption looks like.
7. Audit the thinking, not just the output
When stakes are high, check the reasoning summary.
Spot the gaps. Tighten your inputs.
That’s how you go from “AI said so” to something you’d actually bet on.
If your team had to pick ONE habit to change this week, what would it be?
(a) Thinking-model default
(b) Projects
(c) Data connections
Drop a comment. I’m curious what’s blocking you most.
