After Agile: Why AI-native engineering is the new competitive frontier

Agile is no longer the differentiator it once was. In fact, it’s now table stakes, with nine in 10 organisations practicing it.

Agile originally emerged as a response to the rigid, adversarial Waterfall model, replacing years‑long requirement gathering and disappointing final releases with rapid iteration, continuous learning and tight alignment between business and technology. By the mid‑2010s, agile thinking spread beyond software into the broader enterprise, becoming a baseline practice, rather than a competitive differentiator – most teams now use it, differing only in execution quality.

Because Agile inherently keeps teams moving in the right direction through constant feedback, the real shift today isn’t about methodology, but about what AI‑native engineering introduces: a new step‑change in capability. Just as early adopters of Agile once dramatically outpaced those on Waterfall, organisations who adopt AI‑driven engineering practices can achieve a similar – but far greater – velocity advantage, amplified by machine‑speed iteration rather than human‑speed process.

The next competitive edge isn’t about how you run ceremonies, it’s about how you produce software – and that’s changing fast. AI-native engineering (AINE) is rewriting software creation. It’s not about using AI as a bolt-on, but instead as a new means of production for intelligent organisations.

The impact of this shift is already visible: early enterprise adopters of AINE report a 20% productivity lift across development and service functions, and some engineers adopting coding assistants noticed their productivity increase by 10-20%. That’s before you even account for agents and closed-loop learning.

What is AI-native engineering?

AI-native engineering is all about using AI tools to create AI solutions – resulting in systems that are built from the ground up, and designed to scale, with AI. The agents execute governed tasks, while the AI-native architecture compounds performance over time, ensuring the system keeps improving.

As an example, at Nearform, we recently embedded governed agents inside an AI-powered cross-product search for a global pharma client. This enabled usage to feed the adaptation of prompts, retrieval and policies. Naturally, relevance improved instantly. But the biggest impact was seen in the discovery timeline, which compressed from six weeks to just two, and the AWS infrastructure came up in minutes, instead of weeks.

But why is this relevant now? Because enterprises aren’t struggling with model accuracy anymore, they’re struggling with operationalising AI at scale. Most companies still haven’t been able to move pilots into measurable production value, only 26% have the capabilities to move beyond proof of concept, and 74% are still failing to realise tangible AI value. AINE is the missing operating model.

The new innovator’s dilemma

Unlike previous delivery models, AI-native engineering introduces compounding velocity. This means automated code generation and tests reduce release cycles from weeks to a matter of hours. Governed AI agents run multi-step workflows, keep context inside the system and reduce rework and handoffs. Meanwhile, continuous evaluation pushes improvements back into prompts, retrieval and policies.

Early AI-native engineering systems may look ‘worse’ on legacy control metrics, as they’re packed with unfamiliar governance paths. But AI-native startups, unburdened by process debt, adopt AI-native engineering from day one – driven by smaller, senior teams supported by streams of governed agents. This immediately results in higher velocity and less waste – and as tooling and methods mature, cost advantage and learning loops become an insurmountable advantage.

McKinsey’s State of AI report evidences this, showing how most organisations are still experimenting, while a minority of high performers – who are redesigning workflows – are realising outsized value.

Failing to adopt AI-native engineering risks the AI-edition of the innovator’s dilemma – protecting today’s governance and delivery model, while tomorrow’s competitors compound away from you.

AI-native engineering erodes yesterday’s moats

The old moats were legacy codebases, hard-won internal knowledge and proprietary data. AI-native engineering attacks each of these.

With AI-native engineering, automated refactoring and code generation significantly drop the cost of rebuild vs. maintain – eroding the value of legacy codebases. Similarly, copilots and agents encode decision history, meaning internal expertise becomes portable across teams, instead of being trapped inside handoffs. And the proprietary data advantage is narrowed by foundation models and synthetic data. In fact, foundation models already encode massive general knowledge, meaning smaller players aren’t starting from zero anymore.

We’ve also seen institutional knowledge become more portable. In the pharma use case above, copilots and agents codified decision history and evaluation criteria into the system, meaning context is able to travel with the work.

The barrier to high-performing systems is collapsing. It’s no longer about “who owns the most data” or what you’ve built – it’s about how fast you’re able to learn and improve.

The measurement problem nobody wants to admit

Currently, most CTOs trying to prove AI’s value are measuring the wrong things. Velocity, story points, lines of code, etc, are all proxies for human labour – friction points getting in the way of progress. When you move at machine speed, these measures collapse to zero. What actually matters is whether AI is solving the problem, not how fast a human would have solved it.

Agile teams often ask the business for time, trust and a year of dedicated resources, so they can ship iteratively, learn continuously and ultimately, deliver the most valuable outcome – without knowing upfront exactly what that will be. Finance however, works on annual planning and wants clear commitments on cost, scope and ROI at the outset, creating a long‑standing tension between agile delivery and fiscal predictability. Traditionally, software engineering could bridge this gap because it relied on decades of experience, solid estimation models and deterministic systems.

But AI-native engineering’s value is compounding. It’s not just seen in sprint velocity, it’s seen through the reduction of rework cycles, the acceleration of the second and third release after the first, and the rate of technical reduction debt over time. These are harder to measure in the short-term, and therefore often easier to dismiss – which is precisely why many organisations stay trapped in pilots. Not because the technology failed, but because large delivery models and metrics aren’t designed to capture compounding value, making it difficult to justify the next investment.

The CTOs realising true value aren’t necessarily those with the best agents, they’re the ones who started by instrumenting their workflows before introducing AI. This gives them a genuine baseline to measure against, meaning ROI conversations become more of a demonstration than a negotiation.

A pragmatic path forward

1. Start with a thin-slice in production: pick a workflow that has real P&L impact as a starting point. Then ship a governed agent and closed-loop evaluation and measure against the baseline.
2. Build the AI-native engineering backbone: introduce governance as code into the workflow, embed evaluation into CI/CD, and centralise shared memory to preserve context and knowledge across teams.
3. Reshape the talent mix: create senior-led pods that combine domain leaders with engineers who are fluent in agents, retrieval and testing automation.
4. Scale by compounding: each release becomes a reusable capability, which you can use to grow horizontally over time, into adjacent workflows.

AI-native engineering moves beyond bolting AI-features onto existing workflows, towards changing how you build. While Agile made delivery scalable for almost every organisation, AI-native engineering makes it compounding.

The organisations that internalise that now will set the pace for the next decade. The rest will be catching up… at human speed.

By Jeremy Vianna, Vice President, Strategic Growth, Nearform.