How to Allocate Your 2026 Engineering Headcount in the Era of AI Coding Tools

By Ryan Kuchova

Each fall, engineering leaders are tasked with headcount planning for the coming year. But as engineering orgs look ahead to 2026, AI has fundamentally changed the equation. Every head of engineering is under pressure to show the receipts for their AI investments – and integrate AI coding tools more deeply in their 2026 plans. 

Until recently, the conversation around AI in engineering largely revolved around adoption and implementation. We’re now moving into a new stage, where it’s time for engineering leaders to prove ROI and explain how these tools might impact the size and structure of their teams. The engineering leaders who thrive in the AI era will be those who show they have both the vision and the operational command needed to achieve AI-driven efficiencies.

AI is already changing your engineers’ workflows. As you begin headcount planning for the year ahead – and your CEO and CFO call for hard choices – let’s explore how AI can change your team’s structure from top to bottom. 

Mounting pressure to realize AI gains

AI coding tools are helping engineering teams to both write code and perform reviews more quickly. There’s no denying the data. But new bottlenecks are emerging. Engineers are still learning how best to adopt these tools, and change management with AI – as with any transformation – takes time. The issues are compounded by the rapid pace of AI innovation and the lack of clarity on pragmatic vs. promised gains. 

The gains attributed to AI so far have largely occurred only within the code generation phase, but the known impact is quickly extending across the software development lifecycle (SDLC). The new use cases and data will lead to both new improvements and new bottlenecks. 

While someone might be able to squint and make an aspirational assertion on productivity gains for code generation activities, that assertion won’t hold as engineering shifts from the legacy SDLC to the new “AIDLC.”

In today’s paradigm, product managers are using chat-based AI coding tools like Lovable to deliver working prototypes to engineers, many of whom spend more time editing prompts than writing code. Pull requests are automatically reviewed, and AI agents are executing tasks across R&D. 

However, the impact – or at least the timing of that impact – is impossible to predict. LLMs are outpacing Moore’s Law with step-wise leaps in their capabilities, and AI coding tools are seeing unprecedented booms (and busts) as the next hot tool arrives. R&D teams are still learning how best to leverage these tools effectively, safely, and – crucially – in compliance. 

How engineering leaders should adapt

Given these circumstances, what should an engineering leader do? How does a CTO communicate to her CEO or board of directors a 2026 plan she believes in? And how does she protect her job today and in the future, regardless of “real world” AI gains?

The key here is to convey that you are leaning in to AI, and that you have both the ideas and knowledge to drive real, measurable efficiency gains. (To that end, Jellyfish developed an AI Impact Framework, grounded in objective data from nearly 100,000 engineers, that helps engineering leaders drive AI adoption, analyze productivity, and measure ROI.) You also need to prove that you are planning for the future with your realized efficiency gains in mind. 

Let’s work through each of those elements.

1. Leaning into AI-driven efficiencies

Show that you share the same goals as your C-suite peers. Spend time upfront aligning on the state of AI in the SDLC and where you see the trends going. 

Use this shared knowledge to develop bear, base, and bull case objectives. Make sure to note that there is a lag in the realization of AI gains. Jellyfish is seeing that it takes several months for engineers to fully realize the gains of AI code assist and generation tools. How does that impact your 2026 productivity goals given your take on where the market is going?

2. Demonstrate operational command necessary to drive AI efficiencies

Once you align on goals, it’s time to demonstrate that you are in control and charting a course to achieve AI efficiencies. 

Demonstrate that AI is being deployed today in your SDLC. Are you experimenting with multiple code generation tools? Have individual engineers or small teams started working with code review tools? What processes are in place to continue to push for experimentation?

Show your receipts. Prove that you are driving toward the promised AI gains by bringing stats on your team’s level of AI adoption and code written by AI. Highlight your spend to show the level of investment in this area. 

Ground the team in reality. What gains are actually being realized today? Don’t just focus on engineering productivity. Communicate the gains in business outcomes like realized savings or an increase in roadmap and revenue-driving outputs. Compare your data to industry benchmarks so your audience has context for where the team stands. 

3. Develop a forward-looking plan

Now that you are grounded in reality, aligned on goals, and the CEO and CFO both trust that you have operational command, develop a forward-looking plan with realized AI efficiency gains in mind. 

Call back to the bear, base, and bull case scenarios that you and the team brainstormed. While the bull should represent the ideal scenario – one likely aligned to promised AI gains – the engineering leader’s goal should be to build alignment on the bear case and the operational approach moving forward. The primary challenge is to establish a productivity floor as the bear case. This can be hard, especially if you’re early in your AI adoption journey. In that case, leverage industry benchmarks and allow for six months’ lead time to any realized gains, which will allow for the time needed to drive adoption. By putting productivity- or adoption-related gates into the hiring plan, you can build trust that you will push to realize these gains.

If you’re further along in your AI adoption journey, use caution when signing up for additional gains. Your current state may represent the base case, because further tool adoption may present unknown bottlenecks in your SDLC that need to be accounted for, while still leaving room for further upside. To justify this, anchor around the work completed to date and compare that to industry benchmarks and your previous vision for the AI-enabled SDLC; that will help you explain why you may not see more gains in the near-term.

To build alignment with your CEO and CFO on the resulting hiring plan, stagger hiring timelines to leave room to recognize yet-to-be-realized productivity gains and align on regular reporting / governance cadences to determine if / when to lean into more bull-case scenarios.

Once aligned on headcount plans, ensure you also align on AI spend required by quarter to unlock your AI productivity bets. Though headcount plans are a proxy for spend, the nascency of AI tooling and the incremental spend may not yet be fully considered in spend modeling.

Final Thoughts

While many organizations have seen portions of their SDLC accelerate with AI, most have not yet realized the promised productivity gains – but the potential is undeniable. Given the current pace of innovation, projecting productivity gains to develop a high confidence headcount plan is a fool’s errand. 

Instead, an engineering leader should show that she is working to achieve AI-driven efficiencies, has the operational command necessary to drive those efficiencies, and is forward planning with realized efficiency gains in mind.

You can’t make mission-critical headcount decisions without the right data. Quantify your team’s AI coding tool adoption, usage, and impact on efficiency with Jellyfish AI Impact.