6 mistakes teams make when scaling AI (and how to avoid them)

Zapier has fully integrated AI into its operations over the past few years, learning from both successes and challenges in the process. The company has identified six common mistakes in AI adoption, such as isolating AI in individual toolkits rather than integrating them into shared workflows, neglecting to establish ownership of AI projects, applying inconsistent levels of scrutiny to AI use cases, failing to clearly define AI and human decision-making boundaries, prioritizing AI adoption metrics over impact, and deploying AI without adequate policies or guardrails. To address these issues, Zapier emphasizes the importance of creating shared AI resources, assigning clear roles and responsibilities, tiering AI workflows by impact, and developing comprehensive AI policies. Additionally, Zapier's platform facilitates the sharing of AI workflows across teams, providing leadership with visibility and control, and ensuring secure scaling through robust security measures.

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