Implement a Formal AI Literacy Framework for All Technical Roles
Implement a formal, multi-level AI literacy framework to build durable skills across the entire organization. AI literacy is a new core competency that is not limited to engineers. It emphasizes critical thinking, ethical reasoning, and the ability to evaluate AI outputs, which are essential skills to mitigate bias, reduce privacy risks, and build resilient, trust-based AI workflows.
Adopt and deploy a structured AI literacy framework to build foundational competence in all technical staff. This framework should define clear competencies across multiple levels, from "Understand" (basic concepts) and "Use" (prompting) to "Analyze & Evaluate" (critical/ethical reflection) and "Create" (building models).
AI literacy is a strategic imperative. Organizations that fail to build it "face bias, privacy risks, and poor decision-making," while organizations that embrace it "achieve agility, innovation, and accountability". This training is not about a specific tool, which will quickly become outdated. Instead, it focuses on "durable skills" like "evaluating outputs, framing problems, and balancing human and machine judgment".
This recommendation should be implemented immediately as part of any AI adoption initiative. It is a foundational prerequisite for all other recommendations. Apply Level 1 for all employees, including non-technical staff, to establish a common vocabulary. Apply Level 2 as part of the onboarding for any developer AI tool (e.g., Copilot). Apply Level 3 for any engineer or manager responsible for code review, architecture, or team leadership. Apply Level 4 when building an internal AI platform or ML team.
Adopt the four-level pyramid framework for AI literacy, which scaffolds learning from novice to expert: Level 1: Understand AI - Goal: Cover basic terms and concepts. Competencies: Define AI, ML, LLM. Recognize benefits and limitations. Identify different AI types. Understand the role of humans in programming and tuning AI. Level 2: Use and Apply AI - Goal: Achieve fluency in using generative AI tools. Competencies: Utilize prompt engineering techniques. Iterate and collaboratively refine AI outputs. Review content for "hallucinations," incorrect reasoning, and bias. Level 3: Analyze and Evaluate AI - Goal: Critically reflect on AI's broader context and implications. Competencies: Analyze ethical considerations (privacy, bias, labor, environment). Critique AI tools and outcomes. Understand how AI's lack of context can lead to insecure code. This level is the core requirement for enabling the ai-behavior/trust-but-verify-triage workflow. Level 4: Create AI - Goal: Engage with AI as a creator. Competencies: Build on open APIs. Leverage AI to develop new systems. Propose and build new AI models.
Workflows that implement or support this recommendation.
- AI Literacy White Paper | The Learning and Development Initiative - https://ldi.njit.edu/ai-literacy-white-paper
AI literacy emphasizes critical thinking, ethical reasoning, and the ability to evaluate AI outputs. - A Framework for AI Literacy | EDUCAUSE Review - https://er.educause.edu/articles/2024/6/a-framework-for-ai-literacy
Four-level pyramid framework for AI literacy: Understand, Use, Analyze & Evaluate, Create. - Understanding Security Risks in AI-Generated Code | CSA - https://cloudsecurityalliance.org/blog/2025/07/09/understanding-security-risks-in-ai-generated-code
AI models are "unaware of the risk model behind the code" and optimize for the "shortest path to a passing result," not for security.
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Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.