Software development teams are rapidly adopting AI coding tools like Claude Code and Cursor to build products faster. However, as these tools spread across organizations, business leaders face a new set of problems. Many companies lack clear visibility into how these tools are used, what they cost, and whether they actually deliver measurable value at scale.
Journi designed its new platform, DevOS, to solve this exact issue. By providing full visibility and control over AI-assisted development sessions, DevOS helps businesses manage resource consumption and track return on investment. We sat down with Jake Rickhuss, MD (Commercial) and Co-Founder of Journi, to discuss the platform’s capabilities and how it helps organizations optimize their AI investments.
Q: Why do so many organizations struggle to measure the true return on investment for their AI coding tools?
Jake Rickhuss: Most organisations are measuring AI by adoption rather than outcomes. They know developers are using tools like Claude Code or Cursor, but they can’t see whether they’re actually improving productivity, where costs are going, or which workflows are creating value. Without that visibility, it’s very difficult to justify further investment or optimise how teams use AI.
Q: DevOS allows deployment entirely within a customer’s own technology environment. Why is this local control so critical for businesses today?
Jake Rickhuss: For many organisations, particularly those handling sensitive intellectual property or regulated data, sending source code outside their own environment simply isn’t an option. DevOS can run entirely within a customer’s own infrastructure, so source code, AI context and engineering data never leave their environment. It also works with the AI coding tools and workflows teams already use, inside their own tenant, so there’s no need to replace existing tools or change how developers work. That makes AI adoption both more secure and much easier to scale.
Q: Early testing indicates up to a 55% reduction in AI operating costs for certain tasks. How exactly does the platform drive these efficiency gains?
Jake Rickhuss: A significant amount of AI spend comes from agents repeatedly rediscovering information they already know – re-reading code, rebuilding context and processing large volumes of output. DevOS reduces that unnecessary work by giving agents persistent codebase intelligence, optimising the context they consume and compressing execution output.
In our latest testing using current-generation AI models, we’ve seen AI operating cost reductions of up to 55% on the enterprise development workflows DevOS is designed for. We continuously benchmark and optimise the platform alongside the latest model releases, so as the AI landscape evolves, DevOS evolves with it to ensure customers continue to maximise efficiency and value.
Q: Beyond cost savings, how does DevOS help managers identify inefficient AI usage and improve the way their software teams work?
Jake Rickhuss: Cost is only one part of the picture. Through DevOS Vantage, engineering leaders gain visibility into how AI is being used across their teams, including token consumption, delivery performance and AI quality metrics. They can see where AI is creating value, where it’s introducing inefficiencies and where teams may need additional guidance or better workflows. That allows organisations to establish governance, measure the real impact of AI and continuously improve how their engineering teams work.
Q: Journi is currently offering a free three-month pilot to five companies. What specific outcomes do you expect these teams to see during the trial?
Jake Rickhuss: We want participants to come away with more than just lower AI costs. Our goal is to help them build a repeatable operating model for AI-assisted engineering – one that’s measurable, governed and scalable. Alongside improved efficiency, they’ll gain clear insight into how AI is being used, where value is being created and how to confidently expand adoption across their engineering teams.
This conversation makes it clear that adopting AI in software development requires more than just access to the newest tools. Without proper oversight, businesses risk overspending and losing control over their engineering processes. DevOS offers a practical framework to measure cost savings and ensure AI actively improves team productivity.
As AI coding agents become a standard part of the development lifecycle, companies must treat them as measurable investments. By prioritizing visibility and security, platforms like DevOS give engineering leaders the exact data they need to manage these resources effectively and scale software production with confidence.
To learn more, visit https://devos.journi.uk
