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AI can generate code in seconds. But software delivery involves much more than writing code.
Before development begins, teams must translate business goals into clear requirements, resolve open questions and create a specification engineers can confidently implement. This early phase is often where complex digital products lose valuable time.
For a recent neo-broker project, Appsfactory applied its AI-Driven Software Development Lifecycle (AIDLC) across both product specification and engineering. The results offer a practical view of what AI-assisted delivery can achieve when it is combined with experienced people and strong quality controls.
A specification completed in two weeks
Based on our conventional delivery benchmarks, a specification of this scale would typically require three Product Owners working for approximately eight weeks.
Using AIDLC, one Product Owner completed the specification in two weeks - equivalent to 12 times the output per person.
Speed, however, was only one part of the outcome. The completed specification passed reviews by Appsfactory’s technical team and the client’s engineering leadership, giving the development team an approved and implementation-ready foundation.
Product Technical Lead Tobias Stienen and Product Owner Marco Schmitz guided the process, combining their product and technical expertise with AI-assisted workflows.
Greater momentum during implementation
The impact continued once active development began.
AIDLC helped engineers spend less time on repetitive work and move more quickly from requirements to implementation. During the infrastructure setup phase, we observed an overall improvement in developer throughput of approximately 15–18% - with even higher efficiency gains expected as we implement the application code.
The objective was never to maximize output at the expense of maintainability or quality. AI-assisted work remained subject to the same technical reviews, engineering standards and client approval processes as any other Appsfactory project.
AI as a multiplier for expertise
The most important lesson from this project is not that AI can replace Product Owners or software engineers. It is that AI can give experienced teams significantly more leverage.
AIDLC supports the people building a product by accelerating research, structuring requirements, improving consistency and reducing repetitive effort throughout the development lifecycle. Human expertise remains essential for understanding the business context, making decisions and validating the result.
For organizations developing complex digital products, that combination can mean a shorter path from idea to implementation - without lowering the quality bar.
Key takeaways
- One Product Owner completed a specification that would traditionally require three Product Owners working for eight weeks - in just two weeks.
- The final specification was approved by both Appsfactory’s technical team and the client’s engineering leadership.
- Developer throughput improved by approximately 15–18% during active delivery.
- AI created the greatest value when paired with experienced product and engineering professionals.
- Existing reviews and quality standards remained firmly in place throughout the project.
Appsfactory is continuing to apply and refine AIDLC across real-world projects. If you would like to explore what this approach could mean for your digital product roadmap, get in touch with our team.
