A few years ago, before generative AI (gen AI) made headlines, three things seemed true.

1. Models were limited. Machine learning has long been transformative, and natural language understanding was becoming more powerful. But without generative capabilities, AI value was technical-only.
   
   
2. There was no good way to work with AI models. Interacting with AI required prohibitively technical tools—no generative models, no general-purpose value, and no interface for non-technical users.
   
   
3. Most people didn’t care. Because of the model limitations, the lack of access didn’t pose a problem. Most of the workforce didn’t know anything was missing.

Here’s what seems to be true today:

1. Models are no longer the bottleneck to AI value. Large language models (LLMs) are already changing the way we conceptualize work. With every release, model providers redefine what’s possible.
   
   
2. Everyone cares (a lot). The majority of employees today are using AI in their work—with or without company-wide strategies in place. Many see AI as a path to profound progress, professionally and personally.

What's missing? 

Most teams still don’t have a good way to work with AI models. Since generative AI expanded the value proposition and became relevant to non-technical teams, we’ve seen point solutions proliferate, promising to make work more productive, efficient, and automated. Most employees are experimenting with AI through “interfaces” like their productivity apps or chatbot-style conversation. And most companies are stalled somewhere in that experimentation stage, without seeing ROI from these trial efforts.

With real customer stories, this e-book explores how companies are finding the ROI. Download your copy to learn more.

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