How AI agents are changing the way you buy software

Nate B. Jones (0:00)

What if DoorDash was the clue to how software was sold and bought in 2026 and 2027? I think it kind of might be, because on September 30th, DoorDash announced a way to let you text your agent at DoorDash and have your agent at DoorDash get you what you want, your favorite pizza, your favorite sandwich, whatever it is. This is the future of software. And there are twists and wrinkles in business software that DoorDash doesn't have to face when getting a sandwich. And now, I want you to take that in to work with me. Just walk with me for a second. A new customer is going to sign up for whatever you're selling at work, and your agent is going to read the contract, your agent is going to look at sales notes, your agent is going to read the email thread. Agent, agent, agent, right? We get tired of it. And then your agent is going to write the kickoff plan for you. You stop opening the tool that you used to use all the time.

That's what agent disruption looks like. The company holding your customer records is fine. They still get paid.

But over a few months, you teach the agent everything that nobody has really written down at work, like this customer needs security approval before kickoff. But that's kind of a different process and we use it this way. And so in this world that we're all barreling toward, three things are happening at once. First, the extra tool that used to put that plan together, the software that got less sticky, that got easier to leave. That's the risk for SaaS right now.

The agent you train can be harder to replace if all of those lessons learned live in your custom partners, maybe in your individual machine or in the memory associated with your individual agent at work. And if your CTO, your finance team or other teams you work with want you to switch AI vendors, that's about to get really, really tough because you may all be counting very different costs. They may not see the way you're using your agent. This is the battle that is coming to teams in 2027 But first, what does this actually look like? What are the kinds of stories I'm seeing? I'm going to give you an example. It's rooted in a real story of anonymized and lightly. Let's say every week you open up a recruiting tool. You look for candidates, you copy your notes into a spreadsheet and you write up a short list for the hiring manager. That's part of your role. Then your AI agent starts doing it and you think, I'm more productive. This is great. I'm doing my job. It pulls candidate profiles from a data provider. It drafts a short list. Now, you review it, of course, but when you give the okay, it then goes to the hiring manager. You stop opening the recruiting tool over time and the company is selling the candidate data. What happens to that?

They still get paid because they're still selling data. You're still using the data.

You're now paying for different parts of the work on top of that data. And so you start liking how the agent is working. You stop thinking about the recruiting tool a lot. Your agent gets really good. It gets full of context. And you've taught it a lot about how you personally screen for candidates that are successful for your roles, how you screen for candidates that don't really fit the company, and what a great candidate looks like. It's also starting to learn what particular hiring managers want. It even knows who you actually hire because you feed that information back in. And that seems to be helpful in helping it to pick better candidates and reduce the time you need to spend manually reviewing over time. And you're responsible, and you're ethical, and so you still manually review. But it's helping you a ton.

Now, that story can be hard to reconstruct if you haven't been in the trenches. But I have seen versions of that, obviously not just in recruiting, across product, across engineering, across sales. Everywhere I look, you have old tools that were single purpose, like the recruiting tool I described, that are getting easier to leave. And the agent you trained on is getting much harder to leave. And the moment leadership wants you on a different AI vendor, that's often because of a cost conversation, they want more optionality. That suddenly turns into a really tough conversation across the company that leadership isn't ready for, because everyone at the table has really different priorities. So I'm going to show you how to get through that conversation as an employee, how to lead it as a leader. And really importantly, you're going to walk away with a sense of where software is going from here. How do we think about software in the age of agents? What parts of the software stack are actually more valuable? Spoiler alert, I have a lot of confidence in software going forward, it's just going to look different. So we're going to get into what that looks like, and how we think about the combined and messy world of software and agents. And if you're like, oh, this is going to be hard, it won't be hard. It'll be fun. We'll have real stories. We're going to have fun with it. And you're going to walk away with real tools. Okay, let's get started with what you are still buying when the agent is doing the kind of work that I just described. When the agent is doing a lot of what I would call the information processing part of a role, and you are managing that agent, which is happening in every role in 2026