Many engineering leaders struggle to justify AI expenditures to their CFOs, who will demand proof of impact on outcomes rather than just increased activity.
Annually, roadmaps are finalized, budgets approved, and board presentations prepared to project an image of precision. However, many CTOs and VPs often lack complete insight into their operations. They understand their teams but lack a clear picture of workflow, how AI is really changing delivery, or the actual allocation of resources.
Previously, this lack of visibility was manageable. Experience, intuition, and readily available capital helped bridge the gaps. Organizations could address bottlenecks by hiring more staff, over-resourcing key teams, or subtly avoiding problematic areas. The emergence of AI then offered a convenient diversion, with pilots, proofs-of-concept, Copilot licenses, and “AI initiatives” generating visible progress and extending timelines.
This period of leniency is expected to conclude in 2026. Boards and CFOs are transitioning from expecting experimentation to demanding demonstrable impact within the current year. This shift is not due to a loss of faith in AI, but rather a market trend that no longer tolerates ambiguous commitments. Each investment in AI will require a clear link to improved productivity, quality, or customer benefit.
The moment of exposure
Engineering leaders may find this scenario familiar: presenting AI achievements, noting increased adoption, developer satisfaction, and anecdotal evidence of quicker coding and review processes. The CFO then poses a straightforward question: “Exactly how is this budget changing output and outcomes?”
Typical answers lean on:
- AI adoption numbers and licenses
- Time saved on coding tasks
- Roadmaps of what will be possible “once we fully roll this out.”
What is almost always missing is a clear breakdown of:
- Where AI is actually used across the SDLC
- How much capacity does it have that is freed in practice
- How is that time being redirected to customer-facing work, quality, or strategic initiatives
- Whether AI is improving system behaviour, not just individual speed
Consequently, discussions often revert to topics like learning curves, cumulative advantages, and talent acquisition. While these points hold truth, they are insufficient for rigorous budget evaluations.
Why 55% faster does not mean 55% more
AI providers frequently highlight task-specific metrics, such as a coding task completed 55% faster, which appears compelling in presentations. However, a broader view encompassing entire teams and systems reveals a different reality.
Large datasets across thousands of developers show a consistent pattern:
- Approximately half of team members report AI improving team productivity by 10 percent or less, with a significant portion observing no measurable improvement.
- Only a small percentage reports gains of 25 to 50 percent, unlike those highlighted in case studies. Field experiments find that developers do complete more tasks with AI. Still, the gains are much smaller than the “55 percent faster” headlines suggest once you account for real-world complexity, debugging AI output, and integration work. And when you zoom out again to delivery metrics across teams, some organisations see throughput flatten or even dip as AI usage grows, because changesets get larger, integration risk increases, and coordination overhead rises.
- The fundamental issue is that task-level efficiency does not automatically translate to system-wide productivity. Brief periods of time saved get consumed by meetings, support tasks, and context switching. Developers need long, uninterrupted blocks for deep work, but most of their day is fragmented. Even if AI shaves 20-30 minutes off a task, that time easily dissolves into communication platforms, reviews, and incident responses instead of turning into meaningful new output.
The problem is not the tools. It is the lack of a system for where the “extra” capacity goes.
The real productivity question for 2026
Many organizations continue to define AI productivity by speed, such as increased story points, tickets, or deployment frequency. This perspective overlooks a more critical inquiry:
How much of our engineering capacity goes to net new value versus maintenance, incidents, and rework, and is AI improving that mix?
General benchmarks, while broad, offer valuable insights. Developers typically allocate around 45 percent of their time to maintenance, minor improvements, and bug fixes, rather than creating new, customer-centric features. If AI merely accelerates code production within an existing system, potential risks include:
- Shipping features faster with the same defect rate
- Adding new surface area while technical debt quietly compounds
- Making teams busier without creating the product or the business meaningfully better
That is how you end up with impressive local metrics and a leadership team that still feels like engineering is slowing down.
Two moves that turn AI from hype into compounding gains
To present objective evidence in a 2026 budget discussion, it is essential to be intentional about how AI-generated time savings are utilized. Two key strategies are crucial.
- Reinvest micro savings into quality and future capacity, not just speed
AI excels at generating boilerplate code, tests, documentation, and performing basic refactoring. The pitfall lies in viewing this saved time as unallocated “extra” capacity that gets lost in daily operations. Instead, consider:
- Dedicate regular time specifically to quality initiatives: refactoring, enhancing test coverage, improving documentation, and bolstering security.
- Maintain a transparent, prioritized list of significant technical debt and refactoring objectives.
- Leverage AI to expedite these tasks, allowing even brief 20-30 minute periods to contribute to clearing the backlog.
When teams systematically reduce technical debt and improve tests around critical flows, they cut future incidents and rework. Over a year, that frees more capacity for new work than shaving a few minutes off each ticket ever will.
2. Point AI at the ugly, high-friction work that commonly blows up roadmaps
The biggest productivity wins are not in everyday code generation. They are in:
- Framework or language migrations
- Large-scale legacy refactors
- Systematic security vulnerability remediation
- Architecture simplification and platform consolidation
Such activities consume significant time and hinder strategic projects. Employing AI to quickly comprehend legacy code, suggest refactoring strategies, create migration frameworks, and identify common failure patterns can substantially shorten the timelines for this work.
In parallel, there is real leverage upstream in the problem space. Teams that reach higher levels of AI adoption report better gains when they:
- Utilize AI to refine requirements and user stories
- Summarize customer feedback and support tickets
- Investigate alternative solution methods sooner
This approach minimizes wasted development efforts and directs focus toward changes that genuinely matter to customers. The most substantial benefits arise not from replacing human ingenuity, but from enhancing it and applying it to more clearly defined challenges.
You can be elite on DORA and still waste 45% of your capacity
DORA metrics are valuable tools, with deployment frequency, lead time, MTTR, and change failure rate serving as strong indicators of delivery performance. However, a potential pitfall is to consider them the complete assessment.
It is entirely possible to:
- Deploy many times per day
- Recover quickly from failures
- Maintain a low change failure rate
and still:
- Burn nearly half of the engineering time on maintenance and bug fixes
- Ship features that do not move product or revenue metrics
- Exhaust teams with constant pressure and hidden after-hours clean up
Leading organisations are already expanding their scorecard to include:
- Customer-facing changes shipped and adopted.
- Time and cost by value stream or product
- Ratio of new work to maintenance, support, and incidents
- Developer experience signals, such as focus time and satisfaction
By 2026, boardroom discussions will likely evolve from assessing DORA metric excellence to evaluating the proportion of capacity dedicated to customer-perceptible efforts and whether AI is enhancing this balance. A comprehensive answer requires more than DORA metrics; it necessitates a method to link AI utilization, workflows, quality, and business results throughout the entire system.
Engineering intelligence as the new operating layer
Engineering intelligence platforms are transitioning from optional tools to essential components. Organizations that succeed in 2026 will achieve this not through additional AI tools or isolated dashboards, but by integrating existing, often underutilized data into a unified, cohesive view:
- Git and code review activity
- Issue trackers and planning tools
- Signals about AI usage across the SDLC
From there, leaders can answer the questions that actually matter:
- How is engineering time really allocated by product, initiative, and work type?
- What does “before and after” look like for teams that adopted AI heavily?
- Where does flow break: planning, development, review, testing, release, or operations?
- Which teams are stuck in reactive work, and which consistently deliver high-impact, customer-visible changes?
Instead of defending AI spend with anecdotes, leaders can present:
- A baseline of throughput, quality, and resource allocation prior to AI implementation.
- A distinct trend line post-AI, highlighting areas where AI proved beneficial and where it introduced new challenges.
- Concrete decisions made based on these insights, such as reallocating licenses, modifying processes, or restructuring teams.
That is the difference between “we believe in AI” and “here is how AI changed our delivery engine in measurable ways.”
A checklist to be ready for 2026
To prepare for the more intricate questions anticipated next year, this planning cycle should focus on four key actions.
- Measure your baseline – Track where time goes today: new features, maintenance, incidents, rework. Capture DORA metrics, as well as customer-facing changes and defect trends.
- Instrument AI adoption properly – Look beyond license counts. Track which teams actually use AI, for what kinds of work, and watch what happens to lead time, failures, and incidents in those areas.
- Decide how you will reinvest AI time – Pick one or two big quality levers, such as refactoring hot spots or increasing tests around critical flows. Block time for them, and support teams in using AI to go faster on those specific tasks.
- Choose one flagship, high-friction initiative – Take a migration, refactor, or remediation effort that usually drags on and make it your test case for using AI plus engineering intelligence to compress time and reduce risk.
Do this, and you will not just have “AI activity” to show in 2026. You will have a credible, data-backed story from AI spend to business outcomes.
Who thrives in engineering leadership in 2026
The engineering leaders who will excel next year are not those presenting the most impressive AI demonstrations or the boldest “AI strategy” slides. Instead, success will belong to those who:
- Know where they sit on the AI adoption curve, beyond anecdotes
- Have honest visibility into how their engineering system behaves, not just how busy it looks
- Use AI to fix fundamentals like ownership, workflows, and quality
- Answer hard questions with numbers instead of narratives
Engineering intelligence platforms are central to this transformation. They provide the intricate data needed to illustrate resource allocation, AI’s actual impact on delivery, and the sustainability of current operational speeds. The move towards data-driven engineering leadership is inevitable.
The gap in 2026 will be between teams still guessing and teams that can prove, in detail, how their engineering organisation works.
