Aaron Levie

Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out: * Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted? * Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts). * Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important. * Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down. * Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out. * Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills. * The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI. Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.

This is a great post if youre thinking about applied AI in the enterprise. The headline of this post is about what companies have huge upside from AI, but the deepest nuggets are about what AI transformation looks like in an organization. It’s fundamentally about changing the underlying workflow or business process. As we move from chat tools to agents, those agents actually have to be deployed against workflows, which usually span multiple functions in an org. This is a different way of deploying AI than solely rolling it out to end users. It takes much more work upfront, but the results are the things that actually drive significant ROI. “Software asks the employee to adopt a tool, but infrastructure changes the operating layer underneath the employee. The employee should still know what happened, and the process owner should still be able to pause the workflow, change a rule, approve an exception, or pull a person back in when needed. But the value should not depend on someone remembering to use the AI every day.” The winners of this will be the platforms that can be deployed for specific workflows and business processes with a deep domain expertise. The playbook will often heavily require FDE support, change management, getting data well organized, be able to have comprehensive evals for the workflows, and much more to get right.