If you’re trying to figure out how to actually deploy AI at scale, not just experiment, this guide from OpenAI is the most results-driven resource I’ve seen so far.

It’s based on live enterprise deployments and focuses on what’s working, what’s not, and why.

Here’s a quick breakdown of the 7 key enterprise AI adoption lessons from the report:

1. Start with Evals → Begin with structured evaluations of model performance. Example: Morgan Stanley used evals to speed up advisor workflows while improving accuracy and safety.

2. Embed AI in Your Products → Make your product smarter and more human. Example: Indeed uses GPT-4o mini to generate “why you’re a fit” messages, increasing job applications by 20%.

3. Start Now, Invest Early → Early movers compound AI value over time. Example: Klarna’s AI assistant now handles 2/3 of support chats. 90% of staff use AI daily.

4. Customize and Fine-Tune Models → Tailor models to your data to boost performance. Example: Lowe’s fine-tuned OpenAI models and saw 60% better error detection in product tagging.

5. Get AI in the Hands of Experts → Let your people innovate with AI. Example: BBVA employees built 2,900+ custom GPTs across legal, credit, and operations in just 5 months.

6. Unblock Developers → Build faster by empowering engineers. Example: Mercado Libre’s 17,000 devs use “Verdi” to build AI apps with GPT-4o and GPT-4o mini.

7. Set Bold Automation Goals → Don’t just automate, reimagine workflows. Example: OpenAI’s internal automation platform handles hundreds of thousands of tasks/month.

Let me know which of these 7 points you think companies ignore the most.