Resources

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,

Tourix is a digital marketing agency specializing in the tourism industry. Despite strong lead generation performance, they faced a recurring challenge: converting leads into clients efficiently, especially given the high cost of customer acquisition.​

Adopting AI isn’t about jumping on the latest trend: it’s about making smart, strategic decisions that drive measurable business value.

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.​