AI solutions are piloted effectively when they are tested under real-world conditions based on clear objectives and success criteria. A pilot project serves to specifically validate benefits, feasibility, and risks, rather than for the non-binding testing of technology.
The starting point is a clearly defined use case. It must be established which process is being tested, which problem is to be solved, and how success will be measured—such as time savings, quality improvements, or reduced manual effort. This clarity ideally emerges during an AI potential analysis and prevents pilot projects from expanding beyond their scope.
The pilot should take place close to day-to-day operations. AI solutions must be tested with real data, actual users, and existing systems to enable reliable conclusions for future deployment. Isolated demos or purely technical tests usually do not provide a sufficient foundation. Particularly with custom AI agents, early system integration is critical.
Equally important is clear project governance. Pilots require fixed timeframes, defined responsibilities, and regular reviews. Iterative approaches make sense but must be structured—a core component of AI implementation & project management.
The end result is a deliberate decision: scale, adapt, or stop. All three outcomes are valid as long as they are based on clear insights and embedded in an overarching AI strategy development.
