@levie: The deployment of AI in the enterprise beyond just interacting with a chatbot will unequivocally take real work to alig…
Summary
Aaron Levie discusses the significant challenges of deploying AI agents in enterprise workflows, including fragmented data, legacy systems, and the need for change management, highlighting the growing role of deployment companies.
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The deployment of AI in the enterprise beyond just interacting with a chatbot will unequivocally take real work to align AI systems to the underlying business processes they’re involved in and drive the desired outcomes.
Most workflows weren’t designed for AI agents to just drop into. Workflows today in the enterprise deal with fragmented data, legacy software systems that agents can’t connect with, institutional instead of documented knowledge, and more.
To deploy agents reliably at scale you need to get data cleaned up, modernize IT systems, figure out evals, drive change management for the new end state process, and so on. This also involves designing where humans remain in the loop (which will mean entirely new ways people interact with the workflows), and figuring out what a company’s new IP looks like.
This is why so many applied AI companies are expanding FDE efforts and launching deploycos, and why the FDE role will be one of the most critical jobs in tech going forward. There’s a tremendous amount of work to be done on this front.
Sheel Mohnot (@pitdesi): MSFT putting $2.5B and 6,000 engineers in “Frontier Co”
Now Microsoft, Amazon, OpenAI and Anthropic are all in the Palantir-like deployco business.
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