N-iX
N-iX Engineering Index 2026
A framework for CTOs and Engineering Leaders
Based on N-iX customer engagements between January 2025 and May 2026.
Author: Yaroslav Kisylychka, Head of GenAI Value Lab, N-iX
By mid-2026, most engineering teams have AI tools’ licenses, and some have workflows. Very few can explain why the individual productivity gains aren’t translating to delivery outcomes. This guide is built from actual N-iX engineering engagements between 2025 and 2026 for leaders ready to move past experimentation.
Teams ship code faster, but release cycles don’t shorten. AI accelerates the build stage (code generation, testing, documentation), but the surrounding systems don’t change, forming a series of bottlenecks where the productivity gains are lost.
AI does not accelerate the delivery cycle as a whole. It does not accelerate requirements gathering when stakeholders are hard to reach, nor the acceptance, and sign-off, when review processes were never redesigned to match a faster build cycle. The data on improvements in development speed should be used to explicitly open that conversation at an organizational level.
The average team velocity is no longer a meaningful metric, as AI adoption varies sharply among developers. AI-assisted development has to be enforced as an expectation: in standups, in sprint reviews, in retrospectives, and in the performance bar set for the whole team.
The delivery expectations can only be raised based on data collected within the organization. But there is no incentive for developers to report. Without an adoption framework, all individual productivity gains are converted to free time with no way to track it. The fix is a four-metric stack connected directly to Git, with no developer self-reporting required.
With code volume running 1.5 to 3 times higher, 60% of AppSec teams report feeling strained. The model doesn’t know it has to write code that is secure. The fix is embedding security policy into the coding agent at the point of generation, and using automated risk scoring to reserve human review time for decisions only humans can make.
Through 2025 and into 2026, N-iX set out to answer a question that most engineering organizations were quietly wrestling with: how do you roll out AI coding tools across a team at scale, with real projects and real deadlines? Over half of software developers now rely on AI daily to improve their productivity, and 25% of US enterprises report AI technology having a deep effect on their organization¹. N-iX began investing in AI assistants in response to this shift, but we had two primary questions:
With licenses provisioned across hundreds of engineering projects, we faced three specific problems. Gartner shows that consistently, 30-50% of SaaS license costs go underutilized. Without systematic measurement, you cannot quantify productivity gains or build a defensible business case for continued investment.
As AI-augmented coding becomes the norm, its value is meaningful only if it results in a shorter estimate, an earlier release, or a measurable reduction in hours billed. Without a deliberate effort to convert internal gains into external outcomes, the benefit stays inside the team and never reaches the client.
Our analysis ran across actual customer projects. The output was a measurable framework for scaling AI adoption in software development, with evidence gates at every phase transition.
2,000
Engineers using AI workflows
7
Client programs delivered
150+
Internal projects tested
85–96%
Time savings on piloted tasks
¹ State of AI in the Enterprise: The untapped edge, Deloitte 2026 · ² Stack Overflow Developer Survey 2025
“We tried the “deploy tools and run workshops” approach first. It didn’t work. Zero measurable change. So we changed how we do things. Now we embed with engineers, change real workflows hands-on, and measure everything before and after. If we can’t prove it with numbers, we don’t claim it.”
The N-iX Engineering Index presents the nine lessons we learned arriving at our own maturity as an AI-augmented engineering organization.
Phase 01
Only 25% of enterprises have moved at least 40% of AI experiments into production. This statistic reveals the gap between AI’s promise and its reality. Before any tool is deployed or any metric is set, leaders need an honest read on where AI coding actually stands and what it can credibly promise in 2026.
The pressure to adopt AI coding tools is real, and the marketing is loud. Underneath the noise, most engineering teams are stuck in the same place: nobody yet knows what works, for what tasks, in which conditions.
Tools and approaches are changing every month. Claude Code, GPT Codex, Google Antigravity, Context Engineering, and Planning Mode all emerged only in 2025. Even the teams that built a workflow around such tools will rebuild it soon.
The productivity figures are also unreliable. Someone will cite a study showing a 16%3, 30%4, 45%5, or even 55%6 productivity boost, and then someone else will cite a study showing developers actually got slower7. Both are reading real research. Longitudinal controlled studies do not yet exist because the tools themselves are too new.
In late 2025, an independent survey of 1,149 professional developers found that 96% do not fully trust that AI-generated code is functionally correct, yet only 48% always check it before committing4.
This is the grey zone. The honest starting point is admitting that you are in it. Do not wait for the industry to produce a consensus playbook. It won’t arrive soon. Run your own structured experiments and build the institutional knowledge your competitors are also scrambling to acquire.
When your team is operating in the grey zone, you cannot reliably manage the variables that delivery depends on:
AI adoption across a team is rarely even. One developer has rebuilt their entire workflow around AI and is moving twice as fast. Another opened Copilot once, found it annoying, and went back to doing everything by hand. The average of those two numbers tells you almost nothing useful, but it is the number most teams are planning around.
A 2x productivity gain sounds dramatic on paper. It disappears the moment a developer uses the extra capacity somewhere you cannot see: a side project, a second job, or a slower pace because reviewing AI output costs more cognitive load than expected. You cannot plan delivery against productivity you cannot see.
Some clients are skeptical and want no AI involved in their project, full stop. Some have done their research and hold realistic expectations of a 5-20% efficiency gain. Some spent a weekend experimenting with a chat interface, got impressive results on a toy project, and now expect those same gains on a complex enterprise codebase.
Productivity: 1x
They tried AI a year or two ago, decided it was bad, and stopped. They are not wrong about what they experienced. They are wrong about today. According to Copilot data, 19% of developers do not install the license their organization has paid for. This is what the archetype looks like in your usage data.
Productivity: 1.5x
Uses AI consistently and returns the speed gain to the project. This is the archetype you are building toward.
Productivity: 2x
Has achieved 2x speed but is running a side project or second job with the extra capacity. The r/Overemployed subreddit, a subculture of holding up to 5 full-time remote tech jobs, has grown to 330K daily visitors; a threefold increase since the initial launch of ChatGPT.
Productivity: 2x
Has boosted their productivity with AI, but now works fewer hours and has no incentive to publicize their success.
The old concept of ‘average individual velocity’ is gone, for now. You cannot plan deliveries the way you used to until you establish a new baseline that accounts for AI-assisted variability. That baseline requires measurement, which is why Phase 2 begins with tools rather than promises.
When N-iX began tracking commit data, the volume of AI-assisted code generated was even higher than our best estimates. Before we celebrated, we compared aggregated token usage versus client-repository PR throughput. A significant percentage of generated code was not submitted as a PR. The most plausible explanation is that developers were using N-iX licenses to accelerate work on personal side projects.
The insight was not that developers cannot be trusted. The point is that if the organization does not adapt to the world of 3-8x productivity, the rational thing is that the workers will adapt for themselves.
Without measurement, this kind of drift is completely invisible. What N-iX built was a metrics stack (PRs, tokens used per developer, cycle duration), a set of adoption norms, and a structured onboarding process for AI tools; these became the foundation of everything that follows in this guide.
Popular claims about AI productivity range from -20% to +1,000%. Both extremes are real in specific contexts. Neither is useful as a planning assumption.
This approach disciplines both the internal conversation and the client conversation. You are not promising a number. You are committing to rigor. You can deliver rigor from day one.
The field is splitting into thirds:
34%
of organizations are deeply restructuring their business with AI
30%
are redesigning key processes
37%
are still using AI only at the surface level²
Leaders who exit Phase 1 looked through the marketing and accepted the indeterminacy; they say ‘we are measuring’ rather than ‘we are expecting’. That single framing change prevents the conversations that derail adoption before it starts. You have moved to Phase 2 if:
Phase 02
With honest framing in place, the next challenge is choosing the right tools, mindsets, and measurements before the wrong defaults take hold.
Before choosing your tools, understand your team’s needs. The fastest measurable improvements will both win the team’s buy-in and build the case to the C-suite. Across 150 projects, GVL has identified six use cases where AI saves engineering time at a 2x to 10x multiple:
| Use case | Multiplier | Description |
|---|---|---|
| 1. Initial design & code creation | 4x⁹ | AI eliminates the boilerplate stage, letting developers start from a working scaffold. |
| 2. QA and testing | 3–5x | Unit test generation is repetitive and rule-driven, well suited to AI. |
| 3. Security scanning | 8–10x | Pattern recognition across an entire codebase in minutes. |
| 4. Reverse engineering | 5–7x | AI turns weeks of legacy codebase archaeology into days of structured discovery. |
| 5. Onboarding | 2–3x | New developers get a codebase-aware assistant they can query in real time. |
| 6. Documentation | 5–7x | AI reads existing code and generates structured docs in minutes. |
Capture pain points directly from the dev teams
Run a 2-week sprint with the AI champions on real work
Document 10-12 workflow candidates
Document results, metrics and lessons learned
Score effort vs. impact, select top 2-3 opportunities
Roll out validated patterns across teams
⁹ Multipliers are ranges drawn from several task-pair comparisons per use case, contrasting a baseline workflow against an AI-augmented workflow on equivalent task scope. Method: stopwatch-and-output against pre-agreed acceptance criteria. Limitations: not blinded; tasks matched on scope but not on individual developer ability.
The average development team juggles four AI tools at once, and 35% of developers access them through personal accounts4. These are the main reasons organizations must provide one AI coding tool:
At N-iX, we standardized on Claude Code after evaluating the field in 2025. The standardization matters more than the choice. If a stronger tool is available when you read this, the lesson holds: pick one, get the whole team on it, and stop comparing. Most leading AI coding platforms now offer comparable feature sets. What matters is how your organization builds these capabilities into delivery. The minimal requirements are:
Leadership should review the service agreement on data retention, training reuse, and privacy protections. The variance between tools10 matters for legal exposure, especially under the EU AI Act. However, enterprise plans typically address these concerns and most issues centre around personal accounts usage.
If you manage engineers and you have not used the tool yourself, you are managing a change you do not understand. This can be fixed in an afternoon.
“Claude Code was released in February 2025. I did not try it until April or May. I was also skeptical, ’just another tool.’ Then I had time between projects and decided to try it properly. I was blown away. The quality compared to a year ago is a totally different world.”
Download your project’s source code. Install the chosen AI coding tool. Run it from the repo folder. Then ask:
The code repository tells you what is actually there. The LLM gives you an answer with no politics, no defensive framing, and no agenda, which often come up in developer surveys.
Metrics are not just an operational tool. If your adoption data shows a consistent 15% reduction in PR cycle time across your portfolio, that saving belongs in your RFQ estimates. That 15% saving on a 1,000-hour estimate is a 150-hour cost advantage. Data-backed claims shape business conversations:
‘Our AI adoption data shows X% faster delivery across N projects, which is reflected in our estimates.’
Transportation leader takes AI adoption to 91% and lifts engineering velocity by 27%
Deloitte’s 2026 report finds that most enterprises have deployed AI tools but are still working on the gap between availability and scaled adoption inside the business. Closing that gap requires measurement, and without a baseline, no difference is visible.
You are ready to move to Phase 3 when:
Phase 03
With baseline data in hand, the focus shifts from individual developers learning to use AI to team-level rituals, enforcement, and raising the performance floor across the whole team.
Once your team has licenses, a baseline, and working metrics in place, there is no longer a legitimate reason for a developer to avoid using the tools. At that point, adoption becomes an expectation.
Sharing statistics directly with developers often produces self-correction without intervention. When a developer can see that their PR contribution is measurably lower than teammates, most will respond on their own, no managerial pressure required.
The pair programming technique: pair your top AI adopter with the developer who is lagging the most. Give them a real feature to build together. This breaks psychological resistance faster than any workshop. One developer shows another how AI is helping in real code, on a real problem, and the resistance is gone within a session.
Audit your metrics every week without exception. A developer showing zero AI adoption for two consecutive weeks needs a direct conversation.
Most ‘bad code’ objections come from developers whose last experience was GPT-3.5 or early Copilot. Since then, a new generation of tooling has been released, and the ‘bad code’ objection no longer holds.
Claude Code shipped over 176 documented updates between its beta launch in February 2025 and the end of the year, evolving from a basic terminal tool into a full agentic development platform. Context windows expanded from tens of thousands of tokens to one million, meaning a model can now reason across an entire codebase at once rather than a single file. Planning Mode arrived, allowing structured architectural thinking before a single line of code is written. Copilot’s code retrieval accuracy improved by over 110% for C# and Java in a single quarterly update.
The objection still has a kernel of truth: AI output needs verification. What has changed is the cost of verification. Repository-level context, planning mode, and better retrieval all reduce that cost materially compared to a year ago.
Sprint cycle
One question per standup
Did you use Claude Code on this task? What worked?
Share the four metrics dashboard
Discuss delta from last sprint. Name highest and lowest curiosity.
One AI practice agenda item
What did we try? What worked? What should move into the team prompt library?
Flag AI-first stories
Identify stories suited to AI-first development. Assign AI-heavy tasks to top adopters.
Show the AI interaction tool
When AI built the feature, show the conversation alongside it. Normalize the method.
13% → 91%
adoption rate
Hold collaborative workshops to channel your team’s creativity. Workshops are the most effective way to drive AI adoption. Working with a team of 150 engineers, a two-day workshop took usage from 45% to 92%. Shared, structured experimentation reduces the team’s reservations about the tool and creates visible early wins that change the conversation around adoption.
45% → 92%
adoption rate*
Find senior developers willing to champion AI adoption. Senior developer allies are the second strongest mechanism. When as few as three senior engineers champion AI adoption, their influence can double the adoption rate across the entire team. When peers see respected colleagues using a tool well, the others start trying it.
*Aggregated results from customer engagements between October and December 2025. Adoption rate is defined as the share of contracted developers showing at least daily AI activity on the contracted codebase.
73% of enterprises cite data privacy/security as their top AI risk². Engineering cannot leave the security function to absorb the volume of AI-augmented development on its own. The tooling and processes that make security review sustainable have to scale alongside the code volume.
62%
of security teams admit being challenged by growing code volume
60% of security teams say it’s getting harder to keep up with 1.5-3x incoming code, and 2% admit they cannot meet the demand¹¹. That 2% sounds small until you read it correctly: enterprise organizations openly state their security review is failing.
AI code often appears clean but contains logic-layer vulnerabilities: edge cases, boundary conditions, authorization logic that handles the primary flow but mishandles the exception. 61% of developers agree that AI often produces code that looks correct but isn’t secure⁴. Additionally, developers overrelying on suggestions are found to lose skepticism and push more insecure code¹². The risk is plausible-looking code with logic-level defects passing casual review.
A typical enterprise development organization runs a developer-to-security-engineer ratio somewhere between 100:1 and 200:1. That ratio was already under pressure before AI coding tools entered the picture. Overrelying on code generation weakens alertness to vulnerabilities.
The global cybersecurity talent gap is 4.8M, and demand is growing at 18% YoY, while supply is growing at 9%¹³. Cybersecurity cannot hire its way through this shift. The talent market cannot produce AppSec engineers fast enough to match code production rates driven by AI. The problem compounds because higher-level security reasoning is a machine weakness, and is not as easily automated away. SAST tools are strong at recognizing obvious surface-level patterns, but not logic-level vulnerabilities.
Gartner¹⁴ identifies the most common mistake: adding AI-specific review checklists, separate triage queues, and new tooling stacks on top of already-understaffed AppSec functions. The parallel structure makes the problem worse.
The reason generated code is often less secure than human-written is the model doesn’t know it is supposed to write code that is secure. Integrate security policy into the coding agent itself, so the tool refuses to violate security rules at the time of generation, before the commit occurs. Deploy enterprise-wide instruction files that dictate how coding agents must respond to every prompt. Checkmarx, Snyk, Apiiro, and Cycode all offer IDE-level and agent-level integrations that work this way.
Engineering and product teams that sponsor AI-assisted development should be required to:
The security team moves from absorbing risk to overseeing it. That is a meaningful change in workload that does not require new headcount to implement.
Filter queues for actual reachability rather than theoretical presence. Most SAST-generated findings are real vulnerabilities in the abstract but not exploitable in context, because the vulnerable code path isn’t reachable from user-controlled input in the specific application. Combine that with PR metadata identifying AI authorship combined with automated risk scoring based on what the code touches. Reducing alert volume will free up your AppSec team for the decisions that require human judgement.
Promote at least one internal GRC specialist to oversee AI risk assessments. Without a designated internal anchor for AI security governance, AI-related risks either stall in generalist review queues or move forward unexamined. Given the talent shortage in the market, promoting internally is the best path. GRC professionals are already well-positioned to extend their remit to AI threats: they understand risk frameworks, control assessment, and compliance workflows.
The security function was never designed to absorb infinite volume. It was designed to catch what slipped through. What is slipping through now is not down to negligence. It is a failure of structure: the way code is produced has moved faster than the way it is governed.
Once your metrics show that some team members are measurably faster with AI, do not treat that as an outlier. Treat it as your new baseline.
Some developers are consistently delivering more throughput and shorter cycle times.
Point to specific numbers when raising expectations.
Increase throughput expectations for the whole team.
The developers using AI for side projects or second jobs are rational actors. The bar on the primary project is not high enough to demand their full capacity. Raise the bar, and running two parallel workloads gets harder. You do not police side projects. You make the main project demanding.
A leader in housing management technology, serving thousands of clients worldwide through its own property management system, had over 150 engineers working across five work streams and 300 repositories. The team carried overtime, sustained product pressure, and an incident load, taking resources off feature work. Engineers had no time to adopt AI tools.
A twelve-week engagement restructured the team’s software practice across three layers: workflow integration, quality gates, and custom agentic tools.
These three changes lifted both delivery speed and engineering reliability. Use-case-driven AI adoption produces effects beyond the workflows it directly touches.
+94%
Team velocity
1.8 → 3.5
PRs per developer per week
55% → 89%
Test coverage across 300 repos
47,493 → 31,060
Total annual incidents (-34%)
4 hours → 30 minutes
Incident investigation time (-87%)
More N-iX case studies: Enterprise software leader makes knowledge base search 120x faster with AI · Housing management leader cuts bugs reaching production 60%.
By Phase 3, a developer opens their IDE, drops context into their AI tool, and starts building (no scaffolding and no browsing Stack Overflow). The team has a shared prompt library, weekly-reviewed metrics, and a performance bar that keeps moving. The data shows what is working, the ceremonies reinforce it, and new joiners reach full productivity in a fraction of the time.
You are ready to move to Phase 4 when:
Phase 04
The final phase addresses the remaining objections, the bottlenecks that AI coding cannot fix on its own, and the question of who actually owns AI adoption in your organization.
The only model that scales is one where GVL owns the APEX framework, evidence gates, and standardization layer, while the delivery leads own enforcement, champion selection, and sprint-by-sprint adoption.
A central team like GVL can maintain the framework, curate the best practices, track what is working across the portfolio, and bring an external perspective on how the landscape is shifting. What it cannot do is sit inside each delivery team’s sprint, know which developer is struggling, or notice when a champion is being quietly sidelined. It cannot push back when adoption slips because a deadline is approaching. That requires proximity, relationship, and accountability that only the delivery lead has.
Proximity to the work is necessary to turn a framework into results.
When either side tries to own both layers, something breaks. A central team that tries to own enforcement becomes a bottleneck and loses the trust of delivery teams who feel managed externally. A delivery team that tries to own the framework reinvents it differently on every project, and the organization learns nothing collectively.
GVL currently runs 2-3 projects simultaneously with full coaching depth. Even at the planned growth target of 10 FTE, the team cannot cover 150 active projects. Delivery managers and engineering leads must own adoption on their projects; GVL provides a framework, tools, and coaching.
If every team owns its own adoption, there will be inter-team discrepancies, and the company-wide pre-sales argument gets weaker. True. But waiting for perfect standardization means another 6-12 months of license misuse and lost baseline data. Start now, imperfectly; the framework improves as the data accumulates.
AI coding optimizes one stage of the software development life-cycle: implementation. It does not automatically speed up anything that happens before or after the sprint.
The pattern across all three is the same: AI removes friction from execution, but the non-execution parts of delivery, the conversations, the approvals, the decisions, move at the same speed they always did.
Use the data on speed improvements to initiate a conversation with the client or business about accelerating related processes. A faster engineering team that still goes through the same slow acceptance process will frustrate everyone and prove nothing.
While some teams are still debating whether the tools are good enough, the most advanced organizations have measured in their processes, built toolkits and prompt libraries, and are running experiments on the next layer of capabilities. Stay close to the data. Be willing to expand what you thought AI could do.
Ranked by use-case maturity from research-stage potential to emerging industry standard.
7/10
Broadly practiced, even if not always named or structured. Developers now use an average of 2.3 tools simultaneously; consistently structuring AI use around well-defined tasks, while keeping design decisions for themselves, requires deliberate context engineering.
5/10
Moving faster than expected. Gartner recorded a 1,445% surge in multi-agent system inquiries between Q1 2024 and Q2 2025, and McKinsey data shows 23% of organizations are already scaling agentic AI systems. However, Anthropic’s research shows developers can fully delegate only 0-20% of tasks, and effective multi-agent work requires new skills in task decomposition, agent specialization, and coordination protocols that most teams have not yet built.
4/10
CI/CD pipelines that use AI to predict failures before they happen are identified as an emerging standard architectural pattern in 2026. The infrastructure story has matured, but the operational patterns have not.
3/10
Still early. The Anthropic Agentic Coding Trends Report frames the direction clearly: engineers are orchestrating agents that write code rather than writing it themselves. They are evaluating output, and providing strategic direction, but the tooling for pure spec-driven workflows is not yet mature enough for consistent production use.
Each challenge described in this report has the same shape. The measurement gap, the adoption gap, the security strain, the slow decision-making: none were caused by AI. They were always there, hidden behind the development bottleneck that AI has now removed.
The organizations that extract lasting value from this moment are not those that moved first. They are those that treated AI adoption as a reason to act on what they already knew, then built the systems and habits to back it up.
In 45 minutes, we map your team's position on the adoption curve, identify the use cases with the highest measurable return, and surface the operational blockers between AI productivity and delivery outcomes.
Yaroslav Kisylychka,
Head of N-iX GenAI Value Lab