AI Learning Strategist
Pubblicato il 13-10-2026 - Talent Garden in La Spezia
About us
Talent Garden is a leading AI Transformation school in Europe, empowering individuals and organizations to thrive in the innovation ecosystem through diverse learning experiences. With online, offline, and hybrid courses, corporate programs and work and events spaces we offer an unparalleled vision to combine work life and learning.
Founded in Italy in 2011, Talent Garden has trained over 25,000 professionals in digital skills and business innovation, developed tailored corporate education programs for Fortune 500 companies, and built a network of 7 campuses in 3 countries, hosting over 4,500 innovators from startups, corporate labs, investors, and SMEs.
Today, our focus is on helping organizations adopt AI as a driver of business transformation, combining strategic advisory with hands‑on implementation.
The role
We are on the lookout for an AI Learning Strategist to join our Strategy team within the B2B Corporate Transformation business unit.
In this role, your mission will be to identify high-impact AI opportunities with our corporate clients,
shape practical AI-enabled solutions through discovery and rapid prototyping, and translate them into structured transformation programs and learning pathways ready for delivery.
Responsibilities
- Conduct discovery workshops with C-level stakeholders to identify business pain points and prioritize high-impact AI use cases.
- Design AI-enabled transformation initiatives and learning programs, translating strategic objectives into structured adoption paths.
- Build rapid proofs of concept and prototypes using GenAI tools, prompt engineering, and automation platforms (e.g. Microsoft Copilot, Claude, Google Gemini, OpenAI APIs, n8n, Make or similar tools).
- Analyze AS-IS process and workflows and design TO-BE AI-augmented processes to unlock automation, productivity gains, and improved operating models.
- Translate AI capabilities into clear business outcomes, connecting technologies such as LLMs, embeddings, or