---
id: "framework-ai-competence-skills"
type: "framework"
source_timestamps: ["§ Build AI competence."]
tags: ["skill-taxonomy", "ai-collaboration", "decision-making"]
related: ["concept-problem-framing", "claim-ai-competence-gap"]
steps: ["\\\"Problem framing: Querying Gen AI tools using clear", "well-structured prompts to arrive at desired results quickly.\\\"", "\\\"Collaborative problem solving: Knowing when to trust the Gen AI tool", "when to challenge its outputs", "and how to interpret its data critically.\\\"", "AI-enabled decision-making: Synthesizing Gen AI-powered insights with human judgment to aid and finalize decision-making."]
speakers: ["Sagar Goel", "Shubhankar Sohoni", "Lisa Krayer"]
sources: ["reskilling"]
sourceVaultSlug: "hbr-seg-reskilling"
originDay: 10
articleStem: "hbr-cl-86-genai-transform-l-and-d"
sourceUrl: "https://hbr.org/2025/09/how-gen-ai-could-transform-learning-and-development"
sourceTitle: "How Gen AI Could Transform Learning and Development"
---
# The AI Competence Skillset

## Framework: The AI Competence Skillset

To collaborate effectively with generative AI and move beyond basic tool adoption, employees must develop a specific, nuanced set of human skills. The authors are explicit that **traditional 'Gen AI 101' workshops fail to teach these contextual skills** — which is why the [[claim-ai-competence-gap]] persists and why [[action-shift-ai-training-focus]] calls for in-platform, contextual practice instead.

### The three pillars
1. **Problem framing** — Querying Gen AI tools using clear, well-structured prompts to arrive at desired results quickly. (This is the first step of problem-solving; see the dedicated note [[concept-problem-framing]].)
2. **Collaborative problem solving** — Knowing **when to trust** the Gen AI tool, **when to challenge** its outputs, and **how to interpret** its data critically.
3. **AI-enabled decision-making** — **Synthesizing** Gen AI-powered insights **with human judgment** to aid and finalize decisions.

Note the progression: from *directing* the tool (1) → *interrogating* the tool (2) → *integrating* the tool into human judgment (3).

**Enrichment / verification:** This triad is **strongly aligned with current 'AI fluency / AI literacy' discourse.** BCG's value-creation work stresses matching tasks to AI's competence frontier (pillar 1), human responsibility for critical evaluation (pillar 2), and human ownership of final decisions (pillar 3). Brookings adds that AI learners must engage in metacognition, self-explanation, and critical thinking. The framework is well supported conceptually.