Software going headless is inevitable in a world where agents use the tools 100X more than people do. And the reality is for a lot of software this is actually a huge boon to potential use-cases for these platforms. Software business models have largely been predicated on selling to the number of seats that are in the company in a given function, and the usage of your software is constrained by how much people can do in a given day. This means that your technology is often vastly underutilized relative to what it actually can power for the customer. Enter: agents. Agents can work 24/7, run in parallel, and string together work across systems. This is a big deal because now the agent can do far more than people ever could with these tools. Instead of reviewing contracts one by one, the agent will review all of them. Instead of manually moving data between marketing systems and across campaigns, the agent will let you run 10X more of them. Instead of being rate limited in a client onboarding process by human steps, agents accelerate these. Agents end up using these underlying platforms far more than people ever did, which opens up use-cases that the platform couldn’t go after before. Now, not every software market has the same amount of positive sum use-cases between people and agents, but I’d argue that a significant portion of systems of record, for instance, can be used far more than they are today. Your Salesforce data can be leveraged 100X more to do vastly more customer targeting and sales automation. Your documents can be turned into structured data and analyzed for insights and knowledge to automate other workflows. And so on. Now, of course you have to find a way to make this all commercially attractive, but it’s not hard to picture the revenue from API and agent consumption on these platforms becoming a rich component of revenue streams over time. Seats for the people, consumption for the agents. Lots of upside here for the companies that embrace this trend.

When I talk to enterprises outside of Silicon Valley, most of the use-cases they have in mind with AI are to augment and accelerate how they work, simply because of how much more they can do right now. Most companies are not satisfied with how much they’re doing, and they’re always constrained by some bottleneck. So they’re looking at processes that are slow and inefficient and wondering if AI can make it so they can ship more product, speed up customer onboarding, better resolve customer issues, more comprehensively understand their customers, and more. They’re also bringing intelligence to work that would have never been possible to do before. Tech jobs got concentrated in valley and the tech industry, and enterprises or SMBs have not been able to build the products or bring automation to most areas of work. AI lets them do so now. This will be true of many other fields. And in the areas where there may be some cost cutting, usually that’s in service of funding another area of growth, or it’s temporary. AI cost cutting quickly gets eroded when your competition uses AI to better serve the customer and compete more effectively.

Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today. The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do. First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents. Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do. Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes. Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design. All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it. This is a huge opportunity right now whether you’re doing this internally or as an external business provider.