Navigaite

Scale your engineering team with developers who actually use AI We don’t just build AI – we future-proof it.

At Navigaite, we specialise in helping businesses navigate the complex AI landscape with confidence. From defining your AI strategy to delivering scalable, secure, and compliant solutions, we partner with you at every stage of your AI and data journey. Whether you need a robust off-the-shelf tool or a bespoke system engineered for your unique environment, we combine deep expertise in , , and to deliver end-to-end support that drives measurable impact. Our approach starts with understanding your business priorities, technical challenges, and where you stand in your AI maturity. We deliver actionable, fit-for-purpose solutions backed by full implementation plans – helping technology leaders achieve lasting success. With governance and data integrity at our core, every solution is designed for compliance, scalability, and long-term value. Plus, our tailored and enablement ensures your teams adopt AI with confidence, embedding it deeply into your operations for sustained results. Let’s shape your AI future – with strategic alignment, robust governance frameworks, and measurable results.

06/10/2026

New Atlassian research puts a name to something most teams have experienced: "work slop". Low-effort AI output that someone shares before it's actually finished, leaving a colleague to clean it up.

82% of teams see it happening. Two-thirds of leadership teams aren't addressing it, and nearly 40% aren't aware it exists.

Meanwhile, roughly half the time AI saves is going straight back into supervising AI: checking agents stay on task, retracing their steps, fixing output so it doesn't read as machine-written.

We wrote up what this means for how teams hire: [link]

Personal profile share (Amiel)

Atlassian's new report says 99% of product teams have AI fully integrated. Great.

Then it asks what they do with the time saved. 49% babysitting agents. 48% retracing agent steps. 48% making AI output not look like AI output.

The bit I can't get past: 82% of teams see "work slop", and 39% of leadership teams don't know it's happening.

Work slop isn't a tool problem. It's someone deciding a plausible first draft is finished. That's a hiring question, not a software one.

https://www.linkedin.com/feed/update/urn:li:activity:7513255671912660992

30/09/2026

One of the software industry's biggest AI sceptics just changed sides. At Rails World last week, DHH said his company 37signals no longer writes code by hand as standard. AI agents do it.

The interesting part isn't the announcement. It's the story he told about an earlier attempt, where AI-built features left the architecture full of holes and the team had to step back in.

Our take: letting agents write the code is fine. Making sure someone experienced is steering what that code adds up to is the part that decides whether it works. We wrote up what this means for engineering teams: https://lnkd.in/p/eRpVc7ZM

23/09/2026

Uber burned through its entire 2026 AI coding tools budget in about four months, and capped spend at $1,500 per employee per tool once leadership noticed. Their own COO admitted on a podcast that they can't yet draw a clear line from the spend to shipped value for customers.

You don't need Uber's scale to run into this. A much smaller team spending a few thousand pounds a month on AI tooling faces the same underlying question, it's just easier to miss: is that spend producing reviewed, shippable work, or quiet rework somebody else has to catch later.

We break down why a spend cap doesn't actually answer that, and why we think the real lever is who's using the budget, not how much of it they're using: https://lnkd.in/p/dsYMBi6z

16/09/2026

Here's a genuinely unresolved question in software right now: do AI coding tools actually make developers faster? One rigorous randomized trial found developers 19% slower with AI, despite expecting a 24% speedup. A different trial found the opposite, about 21% faster.

We dug into why these disagree and what a separate line of research on AI-generated technical debt might explain about the gap. The honest answer is we don't think this is settled yet, but we think it points at a more useful question than "is AI fast": what happens to the code after it ships: https://tinyurl.com/2557k54t

24/08/2026

Interesting gap in the latest data on AI coding tools: usage nearly doubled this year, but how much developers actually trust these tools to work unsupervised barely moved. Most still keep a single agent on a short leash rather than letting several run in parallel.

Directing multiple AI agents at once turns out to be a genuinely different skill from being fluent with one, closer to coordinating a small team than operating a tool. We break down what the current data shows and what it means if this is relevant to a role you're hiring for: https://navigaite.co/contact

11/08/2026

There is a lot of research right now showing junior developer hiring has genuinely contracted, somewhere between 9 and 20 percent depending on the study. What gets missed is what that data actually points toward.

A junior hire is a long term investment. The ramp up time makes sense when a company is building a permanent team and the return compounds over years. That same ramp up time rarely makes sense inside a short, fixed contract, where a company needs someone productive immediately.

That is precisely where an experienced, AI Native contract developer fits. Available quickly, productive from day one, without the ramp up cost a junior hire would carry into a short engagement.

We have written a piece on when each actually makes sense, and why the current market conditions make the distinction sharper than before.

navigaite.co/what-we-do

07/08/2026

Once a developer has genuinely good AI tooling, the instinct is to reach for it constantly. It is faster and usually correct, so turning it off can feel like leaving productivity on the table.

But some categories of work are not really about the output. They are about the understanding that only gets built by doing the work directly: learning a new codebase for the first time, debugging something genuinely unfamiliar, making a foundational architecture decision, or getting your first real exposure to a new framework.

Let AI handle all of that too early, and you ship working results without building the understanding you will need the next time something goes wrong in a way no specification covers.

We have written a piece on why knowing precisely when not to reach for AI is one of the clearest signs of genuine AI Native judgement, not a contradiction of it.

navigaite.co/real-stories

04/08/2026

Here is a risk that is specific to AI generated code and did not really exist before it.

AI coding tools sometimes recommend packages that do not exist. Researchers at three US universities found this happens in close to one in five AI generated code samples. Attackers have started registering those exact hallucinated names on public package registries ahead of time, loaded with malicious code, betting that a developer, or an autonomous agent, will install it on the AI's recommendation.

Most security review processes were not built with a step that checks this. We have written a guide on what teams working with AI coding tools need to add to their review process, and what they can safely leave unchanged.

navigaite.co/what-we-do

29/07/2026

There is a lot of noise right now about whether AI is destroying developer jobs or whether demand is stronger than ever. The honest answer is both things are true at once, just not evenly.

Overall demand for developers is genuinely growing. But AI and ML roles are surging far faster than the rest of the market, generalist roles are softening as AI extends what one developer can cover, and junior hiring has measurably contracted, though the exact size of that contraction varies a lot depending on which study you look at.

We have written a piece pulling together what the more credible research, including a Harvard study and Stanford's AI Index, actually shows, and what it means for how companies should be thinking about hiring developers over the next few years.

navigaite.co/what-we-do

16/07/2026

AI can generate a full test suite in five minutes. Whether that suite is actually worth having is a different question.

The most common failure pattern in AI generated tests: they test what the code does, rather than what the code should do. Coverage looks high, CI stays green, and the team stops looking carefully because the tests appear to be doing the job.

We have written a practical guide on how AI Native developers approach testing across the three most common layers: unit tests, integration tests, and Playwright frontend testing. Where AI genuinely helps at each layer, where it fails silently, and what the discipline of good AI assisted test writing actually looks like in practice.

navigaite.co/real-stories

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