This post is provided by guest blogger, Suzanne M. Gillespie, graduate student at the University of St. Francis in Joliet, M.S. in Talent Development program.

In a recent article published in Training Magazine, Tulsiani (2026) argues that learning and development (L&D) must evolve beyond the efficient delivery of content toward a more intelligence-driven function that informs organizational decision-making. The article contends that traditional learning models, which emphasize course completion and participation metrics, are insufficient in environments characterized by rapid change, skills volatility, and increasing performance demands. Instead, learning intelligence integrates data, analytics, and artificial intelligence to generate insights about how learning influences behavior, capability development, and business outcomes. Tulsiani (2026) emphasizes that this transition is fundamentally conceptual rather than purely technological, requiring L&D professionals to adopt systems thinking, strengthen data literacy, and align learning strategies more closely with organizational priorities. By positioning learning as a source of actionable intelligence rather than a support service, the article reframes L&D as a strategic partner capable of enabling adaptability and sustained performance. Ultimately, the shift to learning intelligence is presented as essential for maintaining the relevance, credibility, and impact of learning functions in an increasingly complex and AI-enabled workplace.

Tulsiani (2026) offers a timely and compelling argument for repositioning learning and development as a strategic, intelligence-driven function. A key strength of the article lies in its clear articulation of why traditional delivery-focused metrics are insufficient in complex, AI-enabled workplaces. The emphasis on conceptual change—rather than technology alone—usefully challenges reductive, tool-centered approaches to learning transformation. However, the article remains largely aspirational, offering limited empirical evidence or concrete examples to substantiate the proposed shift. Tulsiani (2026) outlines what learning intelligence should achieve but provides less guidance on how organizations might operationalize this model or address common constraints such as data quality, governance, and capability gaps. As a result, the article is most effective as a strategic framing piece rather than a practical roadmap, inviting further research and applied case studies to strengthen its contribution.

Reference:

Tulsiani, R. (2026, February 11). The big shift: From learning delivery to learning intelligence. Training Magazine. Retrieved from https://trainingmag.com/the-big-shift-from-learning-delivery-to-learning-intelligence/