The Rise of AI in Software Development
Software development teams are increasingly turning to AI coding tools like Claude Code and Cursor to accelerate product development. However, as these tools become more widespread, business leaders are encountering new challenges. Many organizations lack clear visibility into how these tools are utilized, the associated costs, and whether they deliver measurable value at scale.
Journi has introduced a new platform called DevOS to address this specific problem. By offering full visibility and control over AI-assisted development sessions, DevOS helps businesses manage resource consumption and track return on investment. We spoke with Jake Rickhuss, MD (Commercial) and Co-Founder of Journi, to understand the platform’s capabilities and how it aids organizations in optimizing their AI investments.
Measuring ROI for AI Coding Tools
Q: Why do so many organizations struggle to measure the true return on investment for their AI coding tools?
Jake Rickhuss: Most organizations 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 optimize how teams use AI.
Local Control and Security
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 organizations, 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.
Efficiency Gains Through DevOS
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, optimizing 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 optimize the platform alongside the latest model releases, so as the AI landscape evolves, DevOS evolves with it to ensure customers continue to maximize efficiency and value.
Identifying Inefficient AI Usage
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 organizations to establish governance, measure the real impact of AI, and continuously improve how their engineering teams work.
Pilot Program and Expected Outcomes
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.
The Future of AI in Software Development
This conversation highlights 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.
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