Hi, and happy New Year. Welcome back. I'm Lance Dacy, otherwise known as Big Agile. I'm so glad to kick off our 2026 series with the theme I want to talk about this week: AI Native.
I feel like 2025 was atrocious in many ways. I find it very disappointing how leaders and teams, for the most part, approached AI. It kind of became a bad word, I feel like. And I think that's how learning happens. That's great. Experience it and all that good stuff, but what we do with it is more important. So I want to focus on the continuous improvement of what happened in 2025 to all of these companies and all of this fear that was out there in the industry.
Let's just reset that. I'm still going to use the term AI. I just think that we are dusting ourselves off. I think we learned a great deal, or I hope you did, from last year. So let's dive in.
Here's my catchphrase for this: if your 2026 AI plan is "we bought a tool, told people to use it," well, you're not becoming AI native. You're just speeding up your existing dysfunction—painfully efficiently, I might add.
I'd like to focus the next few minutes and lay out what I think AI native product teams will look like in 2026. I kind of like that term AI native. We're going to talk about what changes in workflow, roles, and operating model. Even more important sometimes is what is going to stay the same if you care about quality and trust. Then I'd like to review three to-dos for leaders, for teams, some failure modes to be mindful of. And I'd like to talk about maybe an experiment you can run that might not wreck morale with the term AI.
A Real-World Cautionary Tale
I want to start with a quick story because this is real life. A few months ago, I actually worked with a team that was rolling out AI. The way they described this is they were really turning on some coding assistance. I'll leave some of the names out of it, but y'all probably know if you're in technology what I'm talking about. Leadership was encouraging everyone to move faster, use these AI tools to do that.
I'll tell you what, for about two weeks, it really looked promising. It looked great. I was kind of baffled myself. I've dabbled in it a little bit, but not seen it really at scale with teams. It took a little bit before we saw what was happening.
You could see this manifested by more tickets being closed. Really, what you look at is the heat map of pull requests. There was more pull request activity and more activity into the code base. But then I saw the system start fighting back. I think this is the end story for a lot of teams: the review queues were growing. The test debt, as I like to call it, started to pile up. Then security team members got really nervous because we couldn't really tell where some of these changes were happening.
The product got flooded—very similar to starting your car and flooding it with gas—but it was flooded with half-baked output. The team really started whispering. The thing that leaders might hate hearing is, "Are we still valued here? Are we going to be replaced?" We're trying to manage that message because it happened so rampantly in 2025.
That's really the moment I want you to avoid because I don't feel like AI is about replacing teams. It should be about replacing dysfunction.
The 2026 Baseline: AI as Infrastructure
The 2026 baseline to me is that AI is really just going to be part of the delivery system now, whether you like it or not, or whether you're ready for it or not. That's what's happening.
There's a lot of good data out there, a lot of good sources that all of y'all can read. Gartner, even though they're a big behemoth, has a top strategy technology trend for 2026 that I thought was really interesting. I see it basically as a signpost that AI is moving from tool to infrastructure. We use that word a lot in technology.
There are three trends that matter a lot for product teams when that happens. First, AI native development platforms—we're going to start seeing AI really baked into the way software gets built. I don't mean just with the coding agents. I'm talking about multi-agent systems, agents coordinating work across steps and teams, not just some one prompt at a time where we learned how to do that. That was so yesteryear.
Second, digital provenance—the ability to prove what created an artifact and whether that artifact is trustworthy. We have to get better at that.
I think Gartner's right that all three of those are showing up as trends in their report. I also like to reflect on DORA's report. 2024 is still kind of valid, but that research gives the grounding that delivery performance is about building a system that can shift changes quickly and safely with stability. AI can help with that, but it can also harm it if it increases rework and instability.
Here's the simple definition of what AI native means: AI native means that AI is basically integrated into how work flows through the system, how trust is maintained, and it's not some bolted-on idea on the side of broken workflows.
What Needs to Change in 2026
Workflow Changes: Manage Flow Like It's Your Job
We want to manage flow like it's your job—because it is. If you're a team member, flow is your job. AI can increase output, and if you've got bottlenecks, you'll just hit those bottlenecks faster.
AI native teams have to get serious about smaller slices of work, tighter feedback loops, fewer handoffs, clear work in progress limits, more automation around the testing, releasing, and responding to incidents. We've been saying that for years. Now it's just making it more important. Maybe the use of AI can help us focus on that a little bit more.
I tell teams all the time, if your work lives in queues, the queue kind of becomes your product. We lose sight of what we're building and what problem we're trying to solve. So we want to be mindful of that.
Role Changes: New Responsibilities, Not New Titles
I want to talk about how new responsibilities will show up. I don't necessarily mean new titles and new org charts, but responsibilities that basically become non-negotiable for product development teams.
The first one is something around AI stewardship: how are models used, how are they evaluated, how are they governed? Provenance and auditability—what touched what and who approved what—is going to become even more important. Platform enablement is going to be a big focus so that teams don't reinvent the same tooling 30 times. We've been struggling with that for many years, but I think AI is going to make that a central theme as well.
Digital provenance matters here because it's how you scale trust, not just speed. If you want to go a lot faster, you also want to prove that the way you're going faster is meaningful. It's not just "we're going faster." It's "we've got checks and balances along the way."
Operating Model Changes: Decision Latency Is the Enemy
Decision latency is really going to become the enemy. If it takes you weeks to decide something, AI won't save you. It'll just generate more artifacts for the meeting that you still can't make a decision in.
We have to be more decisive and we have to have a way to look at that data and analyze it. I'm a data guy. I love analytics and things like that, but I'm also very decisive, and I think teams need to adopt that as well. Leadership needs to embed that, but it's not like you want to adopt that blindly.
AI native operating models are going to reduce that latency with clear decision rights, explicit guardrails on what we're looking at, and faster inspect and adapt cycles.
What Stays the Same: Empiricism, Focus, and Transparency
For our agile folks, because I still play in that space: empiricism, focus, and transparency still matter. They're the antidote to running haphazardly. I have that motto, make haste slowly. We want to go faster, but carefully. We want to have clear boundaries on that.
If you remember one thing, remember this: the complex work that we still do in product development requires empiricism. That doesn't change.
Keep the basics. Transparency on what's in progress and what's risky. Inspection with real user feedback and real delivery data. Adaptation based on evidence, not the politics of the organization.
Three To-Dos for Leaders
Redesign the system before you scale AI. Kind of look at the system and make sure that it's set up and the flow dynamics are set up appropriately. You want to fix workflow clarity, identify where the bottlenecks are in the systems, and address decision latency. Fix all of that first.
Make trust visible. Require provenance. Require psychological safety so that people can give you bad data, bad information earlier. If you're the last person to know something, you're going to have a big cost of fixing those problems. If we can't explain where the code or content came from, we can't scale responsibly. Provenance is a really important part of that.
Address the job fear with respect. Let's practice saying it plainly: "We are changing how work gets done. We are investing in you to grow with it." If you don't say those things, silence can create rumors and resistance and kind of builds this cancer that you can't identify until it's too late. Be clear about that. Yes, we're going to have to change how we work, but we're going to grow with it and we're going to invest in you to do that.
Three To-Dos for Teams
Use AI to tighten up your feedback loops. Don't skip thinking. Just use AI to help summarize data and information. Draft tests, summarize PRs, propose refactors—but humans still own the correctness of the system. Don't lose sight of that. Don't let AI just replace all of your thought. I think that's what a lot of people were getting caught up in during 2025, all this research on how the brain's being affected by not critically thinking anymore. Don't let that happen to you.
Update your definition of done to include AI-assisted work. Include the provenance, the governance around that. Include evaluation checks, security checks, provenance notes if you will.
Instrument outcomes, not activity. Really try to focus on that. Track things like lead time, stability, rework, recovery. DORA's guidance is a solid baseline for that.
Three Failure Modes to Avoid
AI on top of dysfunction. This will really just give you the same handoffs, the same queues, the same politics—really just faster chaos.
Shadow AI. Quiet tool usage where we have unclear data exposure and no audit trail tends to make leadership panic. They're going to ban everything and put all of these ossified and calcified processes in place, these checks and balances. Be mindful of that.
Output inflation. More tickets, more code, more docs—but outcomes don't improve. Burnout could increase because of that, and it just becomes hard to manage.
A Safe First Step: The Two-Week AI Native Flow Slice
One simple experiment that's safe: run a two-week AI native flow slice. Choose one small part of your product. Pick one thin vertical slice and use AI maybe to draft a test plan or generate initial tests and kind of look through them. Use AI to draft release notes, maybe a customer-facing summary. Record provenance: what did AI touch, what did humans approve? Measure before and after. What did the metrics look like? Cycle time, rework, incidents. Pick some of those things and try them out.
The goal is not perfection. The goal is learning within those guardrails to help us become better.
Closing Thoughts
AI native product teams in 2026 are not the teams that have the most AI tools. They're going to be the teams with the clearest flow, the fastest learning, and the strongest trust in the organization. We've known that. We see the data all the time on high performing teams.
Try it out. If you run a two-week sprint or you do Kanban or whatever it might be, try that two-week slice. Measure what changes, keep what works, throw out what doesn't. To me, that's how real teams are going to modernize without losing their soul. Too often, they're just walking around with tombstones in their eyes.
Practice being an AI native team. I'm going to help you along the way. This first quarter, I've got a whole set of topics that I've really tried to sit down during the holidays and think about. I'd love for you to like this video, subscribe, share it with people that you think would like to join us on this journey. And I will see you next week when we talk more about how to incorporate AI in 2026.
See you then.
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