From Demos to Deals: Insights for Building in Enterprise AI

Summary

Summarized wtih ChatGPT

AI is now a top enterprise priority and AI startups grow much faster than legacy SaaS.
Winning enterprise AI means deep integrations, constrained reliable models, and customer-specific implementation.
Takeaways: prioritize integrations into workflows; invest heavily in safety and testing; sell outcomes that replace labor.

Highlights from Article

Creating a flashy AI demo is relatively simple with modern tools, but the last mile of product work is exceptionally difficult. In the real world, users behave unpredictably, customer data is messy, and success depends on handling the long tail of paths a user might take.

In other words, buyers are actively seeking and pulling AI software into their organizations, unlike previous generations of software that often required a sales push.

This is because AI software often sells the work output itself, instead of selling software to help the people do the work.

there’s still a heavy human-to-AI interaction loop, as humans are still often overseeing and auditing the work being executed by AI today

Getting users comfortable with their product’s UI/UX has created powerful workflow moats, making customers unlikely to switch to new tools.

All material owns to the authors, of course. If I’m highlighting or writing notes on this, I mostly likely recommend reading the original article, of course.

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