When the IT Platform Is Ready but the Team Isn’t
Many organizations invest heavily in cloud platforms, modern data infrastructure, and artificial intelligence technologies, hoping these innovations will boost business performance. However, numerous companies still face challenges in translating these investments into tangible results.
AI projects often get bogged down in lengthy planning phases, with promising prototypes failing to reach end users. Teams frequently delay discovering whether a solution tackles a real business problem.
The underlying issue often lies not in the technology itself but in outdated operating models. Businesses continue to follow traditional software development processes characterized by extensive planning and requirement gathering before implementation. Although these practices are crucial for established production systems, they can hinder the quick experimentation needed to assess technologies like large language models, machine learning, and advanced retrieval systems.