Medium: When to use vs. avoid AI-generated content — 2026 guide

Jaspal Sandhu’s guide, published in February 2026, organizes the question of AI content use around the types of tasks where machine-generated drafts provide clear workflow benefit versus the types where they introduce credibility or quality risk. The framing is practical: the goal is to identify where AI reduces friction without reducing the value of the output.

The article identifies four categories where AI assistance is well-suited. High-volume, data-driven pages — product descriptions, meta tags, alt text — are candidates because the content is structural and the quality bar is consistency rather than originality. Research and ideation work — outlines, topic clusters, keyword expansion — benefits from AI’s ability to generate options quickly, leaving editorial judgment for the selection stage. Updating older content for freshness follows a similar logic: AI can identify gaps and suggest additions; a human confirms whether they are accurate. Technical summaries and FAQ sections can be drafted by AI and reviewed, rather than written from scratch.

Four categories where Sandhu recommends avoiding AI drafts are organized around the E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) that search systems use to evaluate content quality. YMYL (Your Money Your Life) topics — health, legal, financial — require demonstrable expertise and source citations that AI cannot reliably provide. Opinion pieces and thought leadership require original perspective that comes from lived experience or original analysis. Original research and case studies require firsthand data. Brand storytelling requires emotional authenticity that AI-generated text does not carry in the same way.

The article’s central recommendation is a “human-led, AI-supported” model: AI handles structurally defined tasks in the production pipeline, while human writers and editors contribute original insight, verify accuracy, and maintain voice. Raw AI drafts are rarely publication-ready; the work that makes them usable — fact-checking, tone adjustment, injection of specific examples — is where the meaningful editing happens.

The piece is addressed to content strategists, marketers, and SEO professionals who are making tooling decisions about their workflows. It is less applicable to editorial journalism contexts where the standards for sourcing and attribution require a different analysis.