Technical Context
I appreciate cases like this not for their glossy presentation, but for their practicality. The company gathered 42 people into 11 teams, gave them one day, and a solid tech stack: Claude, Gemini, Make, n8n, and everything needed to quickly build AI automation without months of bureaucracy.
The setup was sound: team captains proposed ideas in advance, a jury selected 12 out of 20, and teams were formed before the start. The pitch was at 10 AM, and the demos were at 6 PM. This is what I like most: not just talk about artificial intelligence implementation, but working prototypes for specific operational processes.
In reality, this wasn't a hackathon for the sake of it. It was a condensed discovery phase plus a quick validation of where LLMs genuinely reduce routine tasks versus where they only create an illusion of progress. If 11 teams produced at least prototypes in a day, it means the tasks were chosen correctly: narrow, clear, and with a quick ROI.
I want to highlight the cross-functionality. When a team includes not just developers but also operations staff, sales, marketing, analysts, and UX, the quality of the solution is usually higher. People who had never used Claude before suddenly began to understand how to break down a process, where a human-in-the-loop is needed, and where Make or n8n on top of a model is sufficient.
And yes, the joke about non-technical people feeling like programmers and pushing to production is very accurate. This is usually where I step in to slow the team down: a one-day prototype is excellent, but what follows requires access rights, a sandbox, integration audits, logging, and a proper AI architecture. Otherwise, automation quickly turns into a new source of chaos.
Impact on Business and Automation
The main figure here is singular: a potential saving of up to 10 person-months per month after the solutions are fully developed. For the company, this is no longer just 'interest in AI' but a very concrete reallocation of people from routine tasks to sales, product, or customer support.
The second benefit is just as important. In one day, non-technical employees stop being afraid of the tools and start formulating tasks better. After this, AI integration proceeds faster because engineers receive proper business scenarios instead of a vague 'work your magic' request.
Companies that know how to take such prototypes to production win. Those who stop at the demo day and then spend months forwarding slides to each other lose.
At my Nahornyi AI Lab, I solve this exact tricky part between the 'wow' effect and real value: selecting processes, ensuring secure assembly, handling integrations, and launching AI solutions for business without the circus of manual workarounds. If you have such ideas brewing within your company, you don't have to wait for the next hackathon—you can immediately see where building AI automation will yield the quickest and smoothest results.