AI Deployment Surfaces: Why Enterprises Fail
The most powerful AI agent in an obscure platform sits unused. These agents have become hyper-intelligent, to the point where capabilities are no longer the issue. Enterprises are still struggling to disseminate these new tools through their organization, and it’s because of where the models live. Adoption lives where the team already works. Expecting teammates to adopt new platforms is not a durable way to adopt new technology.
When companies deploy AI outside of existing systems, new bots, apps, and accounts, adoption collapses. Teams live in Slack, Linear, GitHub, Outlook. Oftentimes spending their entire workday jumping between those tools. Adding another tab and another software vendor adds unnecessary friction.
Powerful models hit the same wall. ZAI released GLM-5.3, which had frontier capabilities at a smaller-model speed. They gave unlimited tokens for a while, yet it launched through the CLI. Great for programmers, non-programmers can’t touch it. It’s unreasonable to expect someone who’s never opened a terminal to try it out. Some early adopters dabble in MCPs and side tools, but that’s not how you reach a business. The surface is the bottleneck, not the capability.
And the opposite mistake is worse. Connecting your agent to everything at once also doesn’t work for the enterprise. Grok Bot has tried this. A beautiful interface, broad integrations, paired with easy onboarding. The concept is elegant, but the problem of permissioning remains. Expecting users to connect their agent to everything and then delegate permissions later is a failure case and a security concern. Giving unfettered access to all of your files and organizational logins isn’t something anybody should be readily excited about.
I asked Grok Bot to send me a daily calendar summary and prep me in the morning. First, it fired off inconsistently and broke. Annoying, but expected for a new product. What wasn’t forgivable: it dug into Slack and a random Desktop folder with personal files unrelated to calendaring. After perusing files and articles it didn’t need, it eventually completed the task. It started to work. But that made me wary of asking for anything sensitive or broad, when Google Calendar and Gmail alone would have been enough.
Tools that survive and see adoption look like Claude Tag in Slack, Atlassian’s products in Jira, Instinct in iMessages. The friction to onboard is lower. Oftentimes it feels a step above zero. Teams don’t have to learn a new tool if their LLM is a taggable object in slack. Adding AI to what you already know beats out adding two new tools at once (the model and its surface).
The model I have adopted is to treat AI as a remote employee who can’t hop on Zoom. Train and onboard them like any other employee, give them the tools needed, scope to a specific problem set, allow them to learn, and most importantly, put them where I already live. Nobody would add a new tool to talk to a single employee. Instead, we would invite them to Slack, Jira, and more. Treating AI the same, as a new teammate who can contribute as a normal teammate, is the abstraction I expect to stay.
The next phase of enterprise AI is not about better models. It’s improving the current surfaces, meeting teams where they work. The bleeding edge is interesting, but we have to move on from being beta testers. For AI to be adopted in the workforce, we have to be comfortable deploying it in our own environments. Treating it as a teammate who won’t sleep or eat. The companies that move beyond beta-testing and into embedding just added a new teammate for 10% of the cost.