Artificial intelligence (AI) is a hot topic, and yet most people don’t distinguish between the two primary AI types — generative and automative.

First, let’s quickly define artificial intelligence. Simply put, AI is a computer science practice that aims to mimic (and sometimes better) human intelligence and creativity. This can range from copywriting and image creation, recognizing speech, learning and predicting, planning, and problem-solving.

So, what exactly is generative and automative AI? Let’s break them down.

Generative AI in Business

Generative AI can create new content from learned data. In marketing, for example, this AI could generate engaging social media posts after learning from successful past campaigns. It can also generate sales emails personalized for each customer based on their behavior and preferences.

Examples in practice:

  • Writing first drafts of blog posts, emails, or ad copy
  • Generating images, product photography, or design variations
  • Creating personalized outreach at scale
  • Summarizing long documents or meeting notes

Automative AI in Business

This is when AI automates routine tasks. It might be follow-up emails or scheduling calls for sales reps, saving valuable time. In reporting, AI can automate data gathering and report generation, providing up-to-date insights without the need for manual effort.

Examples in practice:

  • Automatically routing support tickets to the right team
  • Triggering follow-up emails based on user behavior
  • Generating weekly performance reports from raw data
  • Scoring leads based on engagement signals

The Key Difference

So, generative AI creates, and automative AI streamlines — both making business functions more efficient and effective.

This is a simplified explanation. The world of AI is much more complex and nuanced, but understanding the difference between generative and automative AI is a great first step into this fast-moving field.

As AI tools become more accessible, the businesses that win will be the ones that understand which type of AI to apply to which problem — rather than treating all AI as the same thing.