Amadeus Fire AG Deep Dive: Q&A

Building AI-Literate Organisations: A Strategic Framework

In this deep dive, Amadeus Fire Group’s Chief Operating Officer, Monika Wiederhold, outlines a comprehensive framework for building AI-literate organisations, addressing one of the most pressing structural challenges in today’s corporate landscape. However, organizations may face barriers such as resource constraints, resistance to change, or skill gaps that could hinder implementation. Recognizing these challenges upfront enables leaders to develop targeted strategies for overcoming them.

Wiederhold emphasizes that AI is no longer a specialised skill for data scientists but a shared responsibility across all levels, from supervisory boards to trainees. Recognizing this can foster a sense of collective purpose and motivate every employee to engage in AI learning quickly and continuously.

The Three-Layer Learning Model

Wiederhold introduces a structured, three-layer learning model that companies must adopt to remain competitive:

1. Horizontal Learning (AI fundamentals for everyone)

The foundational layer focuses on universal AI knowledge, including responsible AI use, prompting, AI basics, legal obligations, and workplace tools such as Microsoft Copilot. These skills must be mandatory and company-wide to create a shared understanding of AI’s applications and risks. Horizontal learning provides the organisational baseline that enables fast adoption and prevents knowledge silos.

2. Vertical Learning (AI skills tailored to each function)

Different functions require specialised AI training to enhance productivity and decision-making in their respective domains.

Examples include:

– Accounting: AI-supported financial workflows, automated reconciliation, and specialised certifications created within the Amadeus Fire Group.

– Marketing: Creative AI tools, generative content systems, text-to-image/video technologies, and performance optimisation.

– HR: Recruiting agents, interview support tools, talent analytics, and skills-matching agents.

This vertical layer ensures that AI is embedded in day-to-day business processes.

3. Continuous Micro-Learning (staying up-to-date with rapid AI evolution)

Given the pace of AI innovation, traditional annual or quarterly learning formats are insufficient. Wiederhold emphasises the need for high-frequency learning routines—daily or weekly micro-learning modules that update employees on new tools, techniques, regulations, and best practices. Monthly learning cycles are already too slow for AI’s development curve.

Operationalising the AI Learning Architecture

Once these three content layers are defined, organisations must address the critical question of how to operationalise them. Wiederhold highlights several structural enablers that determine whether AI learning can scale:

A Dedicated Digital Learning Platform

Companies need an integrated platform capable of:

– distributing learning content at scale,

– personalising training paths,

– adapting to different skill levels,

– surfacing relevant micro-learning content,

– enabling horizontal, vertical, and continuous learning simultaneously.

This platform becomes the organisational backbone for developing AI capabilities.

▶️ Other videos:

Elevator Pitch: https://seat11a.com/investor-relations-elevator-pitch/

Company Presentation: https://seat11a.com/investor-relations-company-presentation/

Deep Dive Presentation: https://seat11a.com/investor-relations-deep-dive/

Financial Results Presentation: https://seat11a.com/investor-relations-financial-results/

ESG Presentation: https://seat11a.com/investor-relations-esg/

T&C

This publication is intended solely for informational purposes and does not constitute investment advice. By using this website, you agree to our terms and conditions as outlined on www.seat11a.com/legal and www.seat11a.com/imprint.