The productivity revolution is already happening. The GENESIS-AI project is a way for Europe not to fall behind
GENESIS-AI is a research project in the inLABs programme. The capabilities described are research goals, not an available product. Project status: inLABs
Since late 2022, US labour productivity has grown by 2.16% a year — well above the previous trend. According to the St. Louis Fed, generative AI may account for about 1.3 percentage points of this growth.
In this context, the question for European companies is: how to join this growth curve instead of watching it from the sidelines? The answer we research in the GENESIS-AI project is simple: start where value is created in the organization – in the software factory.
The curve shortened to months. AI accelerates results like no other technology before it
The history of every great technological revolution has been similar: first there was hype, then long years of investment, and only after a decade or two did productivity gains appear. This was the case with electrification, the combustion engine, computers, and the Internet, where real effects in statistics took 15–40 years to materialize.
This time it is different:
- the first effects on productivity appeared within a dozen or so months,
- and the growth path resembles the post-war golden age and the Internet revolution – only compressed in time.
This means that organizations that ignore generative AI today will not “miss one cycle,” but an entire window of historic change.
Generative AI: the fastest-adopted technology in history
Technology diffusion analyses show that generative AI is beating the pace of adoption of personal computers and the internet. Mass adoption (approximately 40% of users) takes:
- more than 10 years for PCs,
- 5–7 years for the internet,
- 2–3 years for generative AI.
Why is this important from a CIO/CTO perspective?
- Because we are not talking about an “experiment,” but about a technology that has become the standard for knowledge-intensive work.
- Because organizations that do not embed AI into their SDLC will create an internal “productivity gap” between how people work and how systems work.
The GENESIS-AI project aims to fill this gap: to integrate generative AI not at the level of individual tools, but across the entire software development cycle.
15 percent productivity increase and 6 hours of time per week – per individual employee
Research shows that employees using generative models report an average 15 percent increase in productivity and savings of about 6 hours of work per week. This translates into very specific micro-improvements:
- analysis of a 40-page report in 30 minutes instead of 2 hours,
- responding to a complaint in 1 minute instead of 10 minutes,
- a significantly higher number of ideas generated in the same amount of time.
If these figures apply to the average knowledge worker, the potential in development and product teams is even greater. In the case of software, every reduction in cycle time:
- delivers value to the market faster,
- reduces maintenance costs,
- frees up resources from maintenance to innovation.
GENESIS-AI aims to multiply these micro-improvements across the whole SDLC across the entire SDLC and the entire IT organization.
GENESIS-AI: the concept of a software factory in the AI era
Instead of asking “what can AI do for a single developer?”, it is better to ask “what should the entire software factory look like when every element of the SDLC has AI built in?”. The GENESIS-AI project’s answer:
- Planning and analysis: Transforming business requirements into technical specifications, user stories, and backlogs with the help of AI agents that understand the domain, systems, and constraints of the organization.
- Implementation and code generation: Generating code that complies with internal architecture, security, and compliance standards, with full auditability and reviewability by senior developers.
- Testing and quality: Automatic generation of test scenarios, unit and integration tests, and code quality reports.
- CI/CD and maintenance: Support in preparing pipelines, IaC, rollouts, and rollbacks, followed by log and incident analysis.
At each of these stages, GENESIS-AI is meant to share common features:
- operating on on the data and context of a specific organization,
- working in a sovereign model in a sovereign model (on-prem/own DC/air-gap),
- being auditable and prepared for regulatory requirements (AI Act, NIS2, sectoral regulations).
Micro-gains × thousands of commits = macro-effect for the CFO
If we are talking about 6 hours per week for the average employee, then in the case of an IT team, the calculation is straightforward:
- 50 developers × 6 hours = 300 hours per week,
- 300 hours × 50 working weeks = 15,000 hours per year.
Even assuming conservatively that an AI software factory delivers only part of these savings of these savings, we are talking about:
- thousands of person-hours shifted from manual SDLC to architecture and innovation work,
- a reduction in the time-to-market for new features and products,
- a real impact on EBIT – not only in the form of lower costs, but also faster revenues.
This is exactly the same effect that we already see on a macro scale in productivity data – only transferred to the specific organization.
Europe versus the US: there is no longer a “safe distance”
Why is this particularly important for European companies?
- The pace of AI diffusion is highest in the US, and investment in this technology is many times higher there than in other developed economies.
- Cognitive work accounts for a larger share of the labor market there, so AI has more “material” to work with.
- The effects on productivity are already visible in statistics, not just in case studies.
If Europe wants to remain competitive, it must:
- adopt AI not as a curiosity, but as a standard for software development,
- do so in a sovereign manner, in compliance with regulations and the requirements of regulated industries.
We run the GENESIS-AI project with exactly this scenario in mind: European, regulated, sensitive to data sovereignty, but at the same time aggressive in terms of exploiting the potential of generative AI in the software factory.
If productivity in one large economy can accelerate by 7% in three years, then similar changes will be expected from companies – there will be KPIs, benchmarks, and questions about why some can do it and others cannot. GENESIS-AI is the answer to a very specific challenge: how to build a software factory within an organization that uses AI in a systematic, compliant, and financially measurable way.