AI in Business: CHORA’s Practical Experience

After analyzing in the previous article why artificial intelligence in business often fails to deliver expected results, it is useful to shift the focus to what happens when theory turns into practice.

At CHORA, AI was not introduced as a miracle solution, nor as an exercise in innovation for its own sake. Instead, it was gradually integrated into our processes through a structured path made of study, experimentation, and real-world validation.

The goal was never “to do AI”, but to understand if—and where—it could genuinely support technical work, without distorting roles or compromising quality.

AI in Business: from Understanding to Experimentation

The first concrete step was to stop and understand.

About two years ago, CHORA undertook a preliminary training program on artificial intelligence, with the aim of building a shared knowledge base. This was not about learning how to use specific tools, but about understanding what AI can do, how it works, and—most importantly—its limitations.

This phase proved essential to avoid unrealistic expectations. Without a shared understanding, AI risks being perceived as a black box or as an automatic solution to be applied indiscriminately. Only after clarifying these aspects was it possible to begin experimenting with new approaches, adapting them to the real dynamics of a technical engineering firm.

Technical Processes and the Gradual Integration of AI

One of the most important lessons learned concerns processes. In a technical studio, workflows are rarely linear or easily changeable. They are the result of years of experience, incremental adjustments, and operational compromises. For this reason, not all processes are immediately compatible with the use of AI.

At CHORA, we deliberately chose not to force integration of artificial intelligence where it would not generate real benefits. AI was introduced only in specific phases where it could support the work without creating friction or additional complexity. This gradual approach helped us avoid isolated experiments and maintain control over our processes.

The Engineer Remains Central, the Way of Working Evolves

Practical experience has reinforced a key conviction: the role of the engineer and designer is not replaceable. AI, instead, can help redirect time and energy toward activities that are often sacrificed due to tight deadlines or workload pressures.

One example is review of project data and technical documentation. Integrating human–machine cross-checks makes it possible to identify inconsistencies or critical issues that might escape human eye, often due to cognitive bias or familiarity with certain solutions. Detecting these problems early helps prevent delays and additional costs in later project phases.

Another relevant area is stress testing of design ideas. AI can support the analysis of human-generated technical solutions by highlighting potential issues that do not always emerge during early design stages. It does not decide in place of the engineer, but it broadens the field of observation.

An Ongoing Journey, Not a Final Outcome

The integration of AI in business—and particularly in a technical engineering firm like CHORA—is not a completed process. It is an ongoing journey, driven both by the rapid evolution of the technology itself and by our continuous effort to explore new ways to improve performance, quality, and time management.

We have learned that AI can deliver tangible results even in complex environments, but not immediately. Processes are not always streamlined, and changing established approaches requires time, dialogue, and a rethinking of daily work practices. For this reason, we continue to view artificial intelligence as a tool to be integrated thoughtfully, keeping human control at the center and aiming for real improvement rather than superficial innovation.