AI Taxonomy

Response: In today’s volatile, uncertain, complex, and ambiguous (VUCA) marketplace, organizations must adapt to rapid technological shifts, fluid economics, and evolving stakeholder expectations by building durable learning capacity and AI fluency. Edmondson’s Taxonomy for AI Adoption provides a stepwise path—moving leaders and teams from basic awareness to strategic, leadership-driven transformation—so AI becomes a responsible, value-creating capability rather than a risky bolt-on. The approach centers on combining technical proficiency with humanistic skills (ethics, change navigation, critical evaluation) to ensure AI augments people and strengthens institutional trust. Recent evidence underscores the urgency: the LinkedIn Learning Workplace Learning Report 2025 elevates a culture of learning across technical and human skills as a strategic imperative for organizational sustainability. Likewise, The Conference Board (2024) stresses “future-proofing” with stackable credentials and ongoing upskilling to meet AI-era demands. Together, these sources validate the taxonomy’s focus on phased maturity, workforce readiness, and measurable performance gains.

Response: Level 1 builds organization-wide awareness of AI’s benefits, limitations, and responsible use, creating shared language and expectations across roles. The goal is not technical mastery but clarity about where AI fits, what risks exist, and why literacy matters for every function. The World Economic Forum (2024) notes that employees already using AI still require structured training for safe, effective deployment—awareness alone is necessary but insufficient. McKinsey’s Superagency in the Workplace (2025) finds that only 1 percent of companies consider themselves truly mature, revealing readiness gaps that begin at the awareness stage. By mapping workflows to AI touchpoints such as data privacy, bias mitigation, and audit trails, organizations establish ethical baselines and trust. Clear messaging at this stage reduces fear, aligns stakeholders, and prepares teams to advance into structured application.

Response: Level 2 shifts from awareness to controlled, practical use of AI in daily tasks—customer triage, document summarization, and quality control. Teams pilot tools with guardrails, role clarity, and measurable outcomes like time savings or error reduction. Accenture (2024) reports that 74 percent of organizations saw AI investments meet or exceed expectations, highlighting the value of intentional rollout (Accenture 2024). The WEF and PwC (2024) document similar results where early adopters used AI for augmentation and productivity gains. Effective early application depends on coupling use cases with ethical training, privacy checks, and human-in-the-loop oversight. Early wins create internal credibility and momentum for analytical maturity in Level 3.

Response: Level 3 emphasizes scrutiny of AI outputs—accuracy, fairness, and policy alignment—so insights remain reliable. Organizations strengthen data governance and audit workflows, ensuring results meet ethical and strategic thresholds. McKinsey’s State of AI in Early 2024 found 65 percent of firms use generative AI regularly yet fewer than half mitigate error, proving why critical evaluation skills are essential. The WEF AI Governance Trends Report 2024 identifies emerging risks—bias, security, and reputation—underscoring the importance of robust governance. Model cards, bias testing, and audit logs turn experimentation into accountable practice and prepare teams for integrative synthesis in Level 4.

Response: Level 4 leverages AI for creativity and innovation by connecting analytical insight with design thinking and stakeholder empathy. Teams combine automation with human expertise to prototype services, improve decision-making, and personalize engagement. As Gebreamlak (2025) writes in SHRM, every sector must align learning and development to evolving workforce demands using AI to amplify—not replace—human capability. This synthesis transforms AI from a support tool into an innovation driver while maintaining ethical and operational oversight. The result is a creative, evidence-based culture that links learning with sustainable growth and mission alignment.

Response: Level 5 institutionalizes AI by embedding it into planning, budgeting, and performance systems. Organizations at this stage build enterprise-wide integration roadmaps, role realignment, and continuous feedback loops. Ali et al. (2024) found that AI adoption increased innovation and decision accuracy in high-tech enterprises (Ali et al., 2024). Governance frameworks now link AI risk management with regulatory and talent strategies, ensuring sustainability. Leaders define KPIs and success metrics to measure productivity, trust, and impact. The organization emerges as a learning ecosystem capable of adapting to shifting technologies and market conditions.

Response: Level 6 elevates AI to a strategic enterprise asset guided by visionary leadership and ethical governance. Executives embed AI into mission and values, linking technology to human development and equitable impact. Fountaine, McCarthy, and Saleh (2019) emphasize that culture—not technology—is the hardest part of transformation (HBR 2019). Leaders cultivate psychological safety so teams experiment and learn without fear while maintaining transparency and accountability. Continuous learning ecosystems and microcredentials keep skills current as models and markets evolve. At this level, innovation and responsibility co-exist to drive sustainable performance and public trust.

Response: In today’s volatile, uncertain, complex, and ambiguous (VUCA) global marketplace, organizations must navigate rapid technological advancements, shifting economic landscapes, and evolving consumer expectations. In addition to the non-credit AI Literacy Microcredential, NJIT’s Learning and Development Initiative (LDI) offers Edmondson’s Taxonomy of AI Adoption. This framework provides a structured, progressive path to integrate artificial intelligence effectively, ensuring both short-term operational efficiency and long-term strategic growth. By advancing from fundamental awareness to leadership-driven visionary thinking, the taxonomy makes AI adoption a transformational—not merely technical—shift. With AI influencing decision-making, automation, and innovation, following this taxonomy enables organizations to remain competitive, adaptive, and forward-thinking. It stands as both a curriculum model and a strategic guide for sustained readiness.

Response: The LinkedIn Learning Future of Work Report (2025) identifies “a strong culture of learning around both technical and uniquely human skills” as the strategic imperative for sustainable organizations. Leaders must champion ongoing upskilling to expand their own and employees’ competence as a competitive advantage. The Conference Board (2024) similarly urges organizations to future-proof their workforces for the AI era by expanding training access, stackable credentials, and business-aligned learning models. Together, these findings affirm that human development is inseparable from technological innovation. Edmondson’s Taxonomy of AI Adoption serves as a blueprint for future-focused organizations of any size or sector dedicated to sustainable growth through responsible AI integration. To download a PDF of this page, click here.

Response: Edmondson’s Taxonomy for AI Adoption offers a comprehensive blueprint for sequencing AI journeys—awareness, application, evaluation, synthesis, adaptation, and leadership—to unlock long-term value. It turns AI integration into an evolving capability aligned with strategy and ethics. The Conference Board (2024) warns that failing to act invites organizational stagnation and talent loss, making AI learning and change management imperatives. By connecting non-credit learning (e.g., microcredentials and digital badges) with enterprise processes, organizations build resilient, ethical workforces. As learning cultures deepen, trust and performance reinforce each other—delivering sustainable innovation and competitive advantage.