How should teams approach building real-world applications using OpenAI models in 2026?

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Maha Sarhan

Updated 2 days ago in

I’m exploring how organizations can practically adopt OpenAI models for production use cases such as analytics, automation, customer support, and decision-making.

With rapid changes in model capabilities, costs, governance, and integration patterns, what are the recommended best practices for:

Choosing the right OpenAI model for different use cases
Ensuring data privacy and responsible AI usage
Integrating OpenAI with existing data and BI systems
Scaling from experimentation to production

Looking for perspectives from teams that have already implemented OpenAI in real-world workflows, along with lessons learned and pitfalls to avoid.

I’m exploring how organizations can practically adopt OpenAI models for production use cases such as analytics, automation, customer support, and decision-making.
With rapid changes in model capabilities, costs, governance, and integration patterns, what are the recommended best practices for:
<ul data-start="594" data-end="808">
<li data-start="594" data-end="653">
Choosing the right OpenAI model for different use cases
</li>
<li data-start="654" data-end="704">
Ensuring data privacy and responsible AI usage
</li>
<li data-start="705" data-end="761">
Integrating OpenAI with existing data and BI systems
</li>
<li data-start="762" data-end="808">
Scaling from experimentation to production
</li>
</ul>
Looking for perspectives from teams that have already implemented OpenAI in real-world workflows, along with lessons learned and pitfalls to avoid.

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