Planning your 2026 AI tooling budget: guidance for engineering leaders

Engineering Enablement Podcast

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Planning your 2026 AI tooling budget: guidance for engineering leaders

Abi Noda and I share how engineering leaders can plan effective 2026 AI budgets that balance innovation, cost, and measurable ROI.

Abi Noda

Oct 17, 2025

Transcript

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In this webinar, Abi Noda and I explore how engineering leaders can plan their 2026 AI budgets amid rapid change and rising costs. Using data from DX’s recent poll and industry benchmarks, we break down how much to spend per developer, how to allocate budgets across tools, and how to balance innovation with cost control.

We also discuss practical strategies for building a multi-vendor approach, choosing the right metrics before and after adoption, and proving ROI through continuous measurement. Along the way, we share insights on communicating AI’s value to executives, avoiding cost-cutting narratives, and investing in enablement and training to make adoption last.

Some takeaways:

Planning Your 2026 AI Budget

Budgets are shifting from experimental to essential. In 2024–2025, most AI tool spending was discretionary and exploratory, but by 2026, it becomes a recurring line item with clear ROI expectations.
Expect rising costs. Tools are expanding their capabilities and pricing accordingly; organizations should plan for year-over-year increases.
Average spending is climbing. The floor for AI tooling is now about $500–$1,000 per developer per year, but multi-vendor setups can easily push that higher.

Allocating your AI budget

Plan for diversity across the SDLC. Modern AI adoption means using multiple tools—chat interfaces, IDE copilots, background agents, and specialized tools—to cover distinct use cases.
Shift from headcount expansion to efficiency. Many companies are reallocating budget from new hires to automation and tooling—not cutting jobs, but slowing growth to focus on productivity.
Track budget maturity. Move from “experimental” AI spending to a structured budget that includes telemetry, measurement, and enablement layers.

Vendor and tooling strategy

A multi-vendor approach is now the norm. Locking into one platform risks missing out on faster-evolving tools and model improvements.
Enterprise licensing vs. stipends. Enterprise plans streamline enablement and reporting but carry overage risks; stipends are predictable but lack team-level visibility and economies of scale.
Cost control is an active process. Use telemetry and regular reviews to ensure spending aligns with actual value delivered across teams.

Measurement and ROI

Use data before and after adoption. Measure both during proof-of-concepts and in production to understand which tools truly move the needle.
The right metrics matter. Track usage tiers (daily, weekly, monthly), time savings, developer satisfaction, and percentage of AI-generated code—while acknowledging how hard these are to measure accurately.
ROI is more than throughput. Developers spend only about 14% of their time coding; improving that slice doesn’t guarantee overall productivity gains.

Communication and leadership

Frame AI as acceleration, not automation. Messaging that focuses on cost-cutting can backfire—leaders should position AI as a way to stay competitive, not reduce headcount.
Language matters. Talk about time recaptured or capacity gained instead of cost savings to avoid triggering budget cuts.
Leadership advocacy drives adoption. Developers are far more likely to use AI tools daily when leaders promote them clearly and consistently.

Enablement and training

Training is non-negotiable. AI adoption stalls without enablement; companies investing in experiential training see stronger, sustained usage.
High-quality programs pay off. Workshops and accelerators that pair real business problems with hands-on learning cost roughly $500–$2,000 per developer—and are worth every dollar.
Measure enablement ROI. Track adoption rates before and after training to validate impact and justify continued investment.

Trends to watch

AI add-ons are expanding. Expect price hikes as existing tools add AI features—often as premium tiers or separate modules.
Custom models are not yet mainstream. Fine-tuning and bespoke models are still limited to large enterprises; most companies can focus on leveraging general models effectively.
Data-driven decisions will define leaders. Teams that collect usage data, track ROI, and adjust budgets proactively will outpace those relying on assumptions.