On Dec 4, 2025, Nomain will sponsor the Nordic Banking Forum in Helsinki.
A Short History of AI in Software Development
AI in software development is nothing new. In fact, already in the 80’s developers utilized AI-tools, like syntax highlighting to write code faster. Since then, the development of AI-aides for programming has been accelerating, giving birth to tools like low-code platforms and todays AI-powered coding assistants like Copilot, Cursor and Devin. The focus being, how to enable humans to write code as fast as possible.
And on an individual level, these tools have absolutely delivered. Developers can prototype faster, fix bugs in seconds, and even generate complete apps from prompts. But when we zoom out and look at how much these advances have improved the efficiency of entire software teams in enterprises, the numbers tell a more sobering story.
For instance, GitHub Copilot has been reported to increase developer efficiency by 20-50%, but the efficiency gain for complete enterprises has been reported to be around 5-20%. That’s remarkable progress for individuals, but less impressive when viewed at an organizational level. If you keep zooming out and look how big of an impact AI has had on complete economies, then the efficiency gain is barely visible.
It seems that the farther you zoom out, the less of an effect AI has. Why is that?
What Makes a Software Team Truly Efficient?
To dig into why there is an efficiency gap between the performance of individual developers and complete software organizations, let’s take a look at what makes a software organization truly efficient. I would split the essentials into three pillars:
- A shared goal – A clear, aligned purpose that drives every decision.
- The right context – Knowledge about users, business, system interdependencies, architectural decisions etc.
- Psychological safety – The freedom to ask, challenge, and collaborate without fear.
Most enterprises don’t suffer from lack of goals or safety. The organization that I’ve seen, have had clear, company-wide goals and mostly excellent psychological safety, enabling trials with new technologies like AI.
The thing that most software teams in enterprises miss however, is the right context. Decades worth of information, humongous code bases and complex processes is a lot to share across hundreds of teams.
The lack of context is not only limited to the software team, since they don’t work in isolation from the rest of the organization. On the contrary, the most efficient organization share all the context with everybody. That’s why startups are usually more efficient than large enterprises: everybody shares basically all the context. In mainframe organizations this means that the business analysts, the mainframe programmers, product owners etc. should all have the same set of facts in their head, when they think of new features, are fixing bugs or doing prioritization.
The Effect of Missing Context in Mainframe Organizations
Context becomes especially complicated in mainframe organizations, where silver-haired mainframe experts have been retiring in masses, taking the context with them and eroding the foundation for efficient software development. Significant outsourcing of central operations to 3rd parties have made the situation worse and the walls of silos have grown thicker. Very little context is being transferred between functions.
A 2024 Forrester study reported that mainframe developers spend just 16% of their time actually programming. The other 84% is spent doing everything else, like searching for functionality in massive legacy code bases, sitting in meetings, re-discovering business logic and so on: finding or sharing context.
The most interesting part is that the single most time-consuming part of mainframe development is code comprehension. The average mainframe developer spends 3-7 days just searching where they need to input their code in the code base. And that’s for each new feature, which results in thousands of hours wasted yearly. Not to mention the security and compliance risk that comes along with not understanding how these critical systems work.
Nomain: on the Quest for the Perfect Context
Nomain is built to bring the right context to everyone working with legacy codebases. By mapping logic, dependencies, and business rules across entire systems, Nomain helps teams understand how their mainframes work, without spending weeks reading code or deciphering documentation. Through our integration layer, we can add virtually any information like ticketing systems and telemetry data into one single place and connect it together with the insights drawn from the mainframe code, enabling full context to developers and business analysts alike.
In other words, Nomain doesn’t just make coding faster, it makes understanding faster.
When everybody has the right context, they can focus on creating value, reduce MIPS consumption, and modernize with the big picture in mind, turning the burden of mainframe code into an asset.
A Final Thought
AI has doubled the speed at which developers write code. But only by accelerating understanding, organizations can unlock the real potential of AI.
The next era of mainframe modernization won’t be defined by who codes fastest, but by who understands their systems best.
And that’s exactly the problem Nomain is built to solve.
AUTHOR
Henri Kasurinen is an AI expert and entrepreneur with over a decade of experience driving innovation in artificial intelligence and machine learning. With a Master’s degree in Artificial Intelligence and a patent in the field, he combines deep academic insight with a strong record of real-world impact. As a startup founder, he focuses on transforming cutting-edge research into practical solutions that deliver measurable value. Known for his clear and inspiring communication style, Henri is an engaging public speaker who brings complex AI concepts to life and motivates audiences to explore the transformative potential of intelligent systems.
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