Takeaways: How engineering leaders are planning AI budgets for next year

With the rapid pace of AI innovation, how should engineering leaders plan their AI tooling budgets for 2026, especially for tools that don’t even exist yet?

In a discussion hosted by DX’s CEO and CTO, Abi Noda and Laura Tacho, in October 2025, explored this topic and shared data on how organizations are planning AI budgets for next year. They offered insights and observations about what organizations are currently spending on AI, how budgets are evolving, and what actions they’re taking to optimize their investments.

This article shares some of the key moments and takeaways from that discussion on how leaders are shaping AI budget planning for 2026. You can rewatch the full session here, or listen to it on your favorite podcast platform.

Q: What’s the new floor for AI investment in engineering?

Laura: 2026 marks a shift from experimentation to baseline investment. Nearly half of the companies we surveyed (47%) are allocating between 1% and 3% of their engineering budget to AI tools, while another 27% are already planning to allocate 4% or more. Leaders should expect spending to increase as the year progresses; budgets in Q1 will likely be smaller than those in Q4, as adoption grows and vendors adjust their pricing.

Today, about 40% of teams spend between $101 and $500 per developer per year on AI tools, and nearly 20% are already spending more than $500. By 2026, that $500 mark should be viewed as the minimum viable investment. Anything below it risks leaving teams behind. And given the pace of feature expansion and usage growth, leaders should anticipate prices continuing to rise, not fall.

Q: Is a single-vendor AI strategy a budget risk in 2026?

Abi: Yes. Multi-vendor strategies are no longer optional. In fact, both poll data and industry research show that no single vendor can cover the full SDLC. As a result, organizations that try to standardize too early risk overspending on the wrong tools—or missing out on better-performing ones altogether. Ultimately, the smarter budget move is to avoid lock-in and instead select more intentional options for each workflow.

Laura: The pace of change is extraordinary. Tools and models are leapfrogging month to month, which means locking into one vendor quickly leaves you exposed as others pull ahead. At the same time, AI now spans multiple use cases such as chat, IDE assistants, background agents, code review, and observability. Since no single provider can cover it all, most enterprises are therefore taking a multi-vendor approach. The key is managing it well: one paved path per use case, minimal overlap, and clear standards for data and security.

Q: Where is money for AI investment coming from?

Laura: In 2024 and 2025, most AI spend was net new, treated as experimental. One of the patterns we’re seeing in the ICONIQ data is that organizations are redirecting money that would have gone to headcount growth into engineering productivity tools. This doesn’t mean taking jobs away. It means hiring more slowly while emphasizing automation. We saw this during the DevOps and cloud transformations, when budgets moved from expansion into tools that gave more leverage to existing teams.

Abi: In other words, budgets that once went toward headcount are now being reallocated toward leverage. AI spend isn’t additive—it’s a shift in how engineering resources are deployed. Instead of hiring more people to increase output, leaders are investing in automation and tooling that scale the impact of the people they already have.

Q: What metrics should leaders use to justify budget?

Laura: As AI budgets move from experimental to core investment, measurement has to evolve with them. While traditional delivery metrics like DORA still have value, they do not show how AI is changing the way software is built. To truly understand the impact, leaders need data that connects AI adoption to developer productivity and experience—fewer blockers, faster feedback cycles, and higher-quality output. The AI Measurement Framework gives organizations a consistent way to evaluate those outcomes and show ROI in terms the business understands.

Abi: The most effective engineering organizations treat AI impact as part of the overall productivity system, not a separate effort. They are layering AI-specific metrics into existing frameworks to track how automation reduces rework, improves flow, and accelerates delivery. Ultimately, in 2026, AI investment will be judged the same way as any other major engineering spend: by how clearly it translates to leverage, efficiency, and value delivered.

Final thoughts: Key considerations for AI budgeting in 2026

Laura: Spending patterns are shifting quickly. In early 2026, budgets will start lower and ramp up through the year as adoption increases and vendors expand pricing. At first, most spend will focus on licensing and pilot programs, but as the year progresses, more investment will move toward integration, enablement, and platform support. As AI becomes embedded in day-to-day engineering, leaders need to plan for that shift now by funding training and automation work, not just developer seats. Additionally, leaders should reserve a percentage of their budget for new tools that challenge incumbents and expand capabilities across the SDLC.

Abi: Pricing and scope will both expand. As vendors move up the stack and organizations roll out multiple AI use cases, per-developer costs will rise. Leaders should expect budgets to increase quarter over quarter as tools move from experimentation to infrastructure. 2026 is about scaling what works while planning for AI as a permanent line item in engineering spend.