It's no longer enough to be an individual contributor who pulls cards off the board, completes tasks, raises PRs, and merges them. AI has made implementation easier. What once took days now takes hours.
This means implementation is becoming commoditized. To stay valuable, you need to focus on everything else. And you can't wait to be asked.
Here's what I'm doing.
1. Use and Evangelize AI
Be the person who's forward-thinking and actually using these tools. Show your tech team and the broader organization what's possible. Lead others in adopting AI to streamline work and bring more value to the business.
Being the AI guy wasn't in my job description. I just started using the tools, talking about them, and showing people what was possible. Recently I presented at a company all-hands about Agentic AI. The productivity gains and value we're shipping are starting to get noticed at the C-suite level.
You get hands-on experience with what works. And as your colleagues start adopting these tools, they see you as the expert. That makes you more valuable.
2. Become a Generalist
Specialization made sense when implementation was the bottleneck. If building anything took significant effort, you needed deep expertise just to be productive.
That's changed. I'm no longer just working on the core web app. I'm reaching into DevOps, writing Terraform. I'm looking at QA processes, evaluating whether Playwright could replace manual tools like Ghost Inspector.
The breadth of what a single engineer can contribute to has expanded. Take advantage of it.
3. Automate More
We've always automated the core application. But engineer time was expensive, so the cost-benefit analysis rarely justified going beyond that.
The value proposition is different now. It makes sense to apply engineering effort to places we used to ignore.
Start by automating your own workflow. My interface is Claude Code. I manage my Jira cards and GitHub PRs through it. I respond to PR comments, address QA feedback, and incorporate PM input in real time.
Here's what this enables: when a PM finds an issue after delivery, I can address it immediately instead of creating a follow-up card for later. The implementation cycle is short enough now that this makes sense. The result is more complete features shipping faster.
Then see what you can automate for your team. I automated our deploy process. It's now a simple /deploy command in Claude that finds the diff between main and production, posts to Slack showing the commits about to be released, and performs the deployment. The developer doesn't have to do any of it manually.
Another example: I saw QA asking in Slack to change the session timeout on staging. No interface for it, so they needed a developer to push a code change. Back and forth, waiting on availability, then again to revert it when done.
I built a feature that lets QA manage it themselves and posted to Slack asking if there was buy-in. There was. Now QA doesn't wait on anyone.
Finally, work cross-functionally. Start looking at other teams in the company and what you can automate for them. This is where the huge value comes in. Developers adding automation across the business, not just within the tech team.
4. Build More Complete Features
Robust error handling. Full test coverage. Rate limiting. Extensive documentation.
These things weren't always worth the investment. Our team didn't prioritize a ton of tests. We definitely didn't prioritize documentation because there wasn't time to write it properly, let alone maintain it. We were applying the Pareto principle. Get 80% of the way there, accept diminishing returns after that.
That calculus has changed. These are trivial to add now, so add them. Tests and documentation also serve as guardrails for AI during implementation. They help ensure what gets built actually matches the requirements.
5. Expand Your Product Knowledge
When you're not spending all your time on implementation, you have more cognitive bandwidth to focus on other parts of the software development life cycle. Use that bandwidth.
Stop relying on product managers to write detailed specs. Get comfortable operating with ambiguity. Focus on what the business really needs and how to ship the right value.
When you deeply understand the product domain, you make better judgment calls. You anticipate edge cases. You push back when requirements don't make sense. You become a partner in product development, not just an executor.
6. Manage More Work in Progress
Before AI, it was smart to limit yourself to one or two code changes at a time. Context switching was expensive.
Now I have more changes in flight at once than I ever could before. AI tooling holds context for each workstream and helps me switch between tasks without losing momentum.
When you're waiting on code review or blocked on a dependency, you can pivot to something else without losing your place. The throughput increase is real.
The Point
The engineers who thrive in 2026 won't be the ones who resist these tools or use them passively. They'll be the ones who rethink what it means to be an engineer when implementation is no longer the hard part.
You can't just be reactive, pulling cards off the board and completing them. Look at the business, find places to contribute.
These are the strategies I'm using. They're working.
