Why You Need to Build AI Literacy Now — And How to Do It

Stakeholders — regardless of their support for AI — often misunderstand or underestimate the importance of a robust AI literacy program. To convince AI stakeholders that successful AI implementations require a variety of skills, uses and timelines, highlight and share potential benefits, including: 

• Strategic impact of AI: To realize AI’s potential as a competitive differentiator, senior leaders must possess enough AI literacy to align use cases with business strategies.

• AI value creation: AI business opportunities arise with an understanding of AI’s strengths and weaknesses across technical and nontechnical business stakeholders.

• Democratization of AI: Effective and responsible use of AI promotes the smart spread of the technology.

• Effective use of AI: Skillful processing of AI inputs and outputs improves the organization’s ability to incorporate the technology into regular workflows.

Tie these benefits to AI literacy through a “learning-to-earning” value proposition that highlights how learning activities lead to target business outcomes and adapt to support changing business needs. 

Not every role in the organization requires the same level or type of AI understanding.

AI literacy can be split into four categories:

• Foundations, including important concepts, main applications, techniques, methods and practices

• Value, including use cases, benefits, costs, domain expertise, evaluation, change management and culture

• Engineering, including design, buy-build decisions, data preparation, model selection/fine-tuning/training, validation, deployment, monitoring, operations, algorithm and model types, data management and engineering and analytics

• Governance, including regulations, policies, ethics, societal concerns, risk management, trust, transparency, explainability and data governance

How much of each category a particular role requires varies. For example, finance leaders need a minimal understanding of AI engineering, but a strong understanding of AI value, whereas engineers and operators need a basic understanding of AI value, but a solid understanding of AI engineering. 

Conduct an initial gap analysis of current and required AI literacy levels with a cross-functional team to get started, and repeat the exercise regularly as AI literacy evolves. 

Outcome-driven agile learning uses short bursts of on-the-job learning to directly enable desired outcomes while also dynamically adjusting to changing needs. Use three methods to create learning pathways to deliver desired business results: 

• Formal learning through courses augmented with just-in-time, quick-to-consume content, such as short videos

• Social learning via communities of practice, centers of excellence and coaching

• On-the-job experiential learning via opportunities to apply new AI literacy skills in AI experiments and in support of AI initiative implementations

This continuous cycle of learning enables AI literacy programs to keep pace with technology, adjust for simple concepts and deep proficiency, and is scalable and applicable for anyone in the organization.