Playful Approaches Improve Corporate AI Adoption Rates

Forbes reports that many enterprises continue to invest large sums in AI consulting, tools, and workshops while seeing limited employee uptake, citing an MIT NANDA report, "The GenAI Divide: State of AI in Business 2025," that documents a divide between a small set of organizations capturing value from generative AI and a larger group whose initiatives stall. The Forbes profile by Veronica Zin highlights performers Mike Jacobson and Sami Kriegstein Jacobson, who created an interactive show called It Writes Itself and a related workshop; Forbes reports their New York event attracted 60 attendees. Forbes quotes Mike Jacobson: "Adoption is holding companies back." Forbes also notes the duo brought their act to SXSW 2026, per the article.

Industry-pattern observations: adoption bottlenecks for enterprise AI frequently reflect user experience, onboarding friction, and social acceptance rather than model capability alone. Companies rolling out generative-AI tools often confront low engagement because real work practices and incentives are not aligned with new interfaces. Techniques that lower cognitive friction-microlearning, contextual templates, and hands-on play-regularly improve feature discovery and retention in software rollouts.

The Forbes piece frames a practical alternative to conventional workshops: experiential, playful events that let people test AI in low-stakes, social settings. For product teams and adoption managers, this aligns with broader UX evidence showing that active, social learning increases sustained usage. For ML practitioners, the implication is that deployment success depends as much on workflows, prompts, and default templates as on model selection or fine-tuning.

• •Attendance and repeat-engagement metrics from experiential programs versus standard training cohorts, as a measurable adoption signal.
• •Whether case studies surface quantifiable productivity lifts tied to play-first onboarding versus standard change-management programs.
• •Third-party evaluations (reports, surveys) that track adoption drivers across industry verticals to validate MIT NANDA's divide.

Editorial analysis: Overall, the Forbes story underscores a recurring theme in enterprise AI adoption: improving uptake typically requires deliberate experience design and cultural approaches, not only technical investment. Practitioners should treat adoption as a product-design problem and instrument interventions to measure real user behavior rather than relying solely on training headcount or spend figures.