Thanos Athanasiadis

AI adoption is not (or at least shouldn’t be) driven by hype alone. When implemented properly, it maps very clearly to three classic business outcomes: efficiency, effectiveness, and innovation. These show up as automation, augmentation, and differentiation.​ Automation → Efficiency Automation is about taking manual, repetitive, error‑prone tasks and handing them to AI systems. Examples include customer support assistants, data entry, reporting, invoicing, lead qualification, or document...

Large language models are no longer exotic research toys; they sit underneath copilots, internal agents, and automations across most modern organizations. The question is no longer “Should we use LLMs?” but “Which kind of model fits our business and use case?”.

Commercial teams today have three main ways to use large language models (LLMs): chat interfaces, cloud APIs, and running open‑source models directly. Each path trades off speed, control, and cost.​