Why AI literacy matters and key insights for achieving it

Why AI literacy matters

AI’s rapid advancement presents enormous opportunities, yet many organizations struggle to fully unlock its potential. As with previous digital transformations, investments often favor technology over the critical organizational, cultural and knowledge-related shifts needed for success. With AI’s accelerating evolution and widespread accessibility, organizations must address both fear and uncertainty while empowering employees to leverage AI responsibly within their roles, processes and business strategies.

AI literacy serves as a foundational change management tool, accelerating business outcomes while managing risks. There is a direct link between technology literacy, a culture of continuous learning and business success. On the risk side, regulations are catching up, with the European AI Act, for example, requiring organizations to ensure AI literacy among employees using or deploying AI systems.

Who needs AI literacy?

Organizations should start by identifying who within their workforce requires AI literacy. At CGI, our position is clear—AI literacy is essential for everyone, ensuring AI is robust, ethical and trustworthy in both usage and development.

AI literacy should take a holistic approach that includes the following:

• Foundational and regulatory understanding for all employees
• Specialized training for those working directly with AI applications
• Opportunities for hands-on learning aligned with employees’ roles and responsibilities

A few awareness courses are not enough. AI literacy must be role-based, contextualized and actionable.

How AI systems differ from conventional software systems

AI systems operate fundamentally differently from traditional software. Unlike conventional systems that follow deterministic logic, AI outcomes often depend on statistical models, reference data and prediction logic. Results may even vary for the same input, requiring users to understand and assess AI-generated outputs critically.

To ensure safe and effective AI use, employees must learn to interpret outputs using explainable AI (XAI)—techniques that clarify how AI systems function and respond to changes in input data or model parameters. This transparency is key to trustworthy AI adoption.

Taking a role-based approach to AI literacy

A role-based, multi-faceted AI literacy strategy is crucial because what it means to be AI-literate varies based on role, experience and industry context.

Key components of a role-based AI literacy framework include the following:

• General AI literacy for all employees: Basics of AI functioning, responsible AI use and relevant regulations
• Role-specific learning paths: Tailored training for personas (e.g., developers, compliance teams, business leaders)
• Technical expertise development: Deep dives into AI architectures, risk mitigation and compliance requirements

For example, the EU AI Act mandates varying levels of AI literacy based on role. High-risk AI applications require in-depth knowledge for system operators and compliance teams, while AI developers must understand regulatory frameworks, monitoring architectures and domain-specific risks.

Shifting the focus to AI’s benefits and practical applications

One common pitfall organizations face is focusing AI literacy efforts too heavily on compliance and risk, without emphasizing AI’s practical benefits. While regulatory knowledge is critical, an overemphasis on restrictions—as seen with GDPR—can create uncertainty, reducing AI adoption.

Here are some recommendations for making AI literacy practical and engaging:

• Provide real-world use cases showing how AI enhances productivity, creativity and decision-making
• Offer hands-on learning with AI assistants in everyday work environments
• Develop tailored training for generative AI applications; for example, we offer an AI@Work “Use of Intelligent Chatbots” program
• Encourage employees to identify AI-driven improvements in their workflows

For developers, AI literacy must go beyond general AI knowledge to include technical training on architectures like RAG pipelines, agentic AI and knowledge graphs. Hands-on projects, such as AI-powered documentation automation or AI-assisted coding, can further accelerate adoption.

Building an effective AI literacy governance framework

AI literacy must be continuously refined to align with an organization’s strategic goals, workforce needs and evolving regulations. A well-defined governance framework ensures AI training remains relevant, accessible and effective.

For example, here are the key components of CGI’s AI Literacy Governance Model:

1. Define AI literacy objectives and principles: Establish guidelines aligned with responsible AI use and business goals
2. Develop a governance structure: Create role-specific training, information channels and best practices for AI adoption
3. Foster continuous learning: Use formal training, cross-functional communities and feedback loops to improve AI literacy programs

AI literacy must be visible, accessible and continuously reinforced. Organizations should invest in structured programs while also cultivating an active learning culture.

We want to thank our colleagues Helen Fang and Jeremie Puget for their contributions to this blog. We also welcome the opportunity to speak with you about your internal AI training needs.

CGI has recognized experience in developing these programs, having received Skillsoft’s 2024 "Program of the Year" and "Impact" awards, and being selected as one of eight companies to present AI literacy best practices to the European Commission and AI Office in 2024.

Learn more about our approach via our AI Literacy page.