The operational companion to a serious AI SEO practice: checklists to verify, playbooks to execute, templates to paste, briefings to understand, and news to stay current.
Upstream of every checklist and playbook sits a set of decisions only your organization can make: what an AI SEO program should cover, who owns it, what to build first, how to measure it, and what not to fund. Theprogram design guides walk those decisions in order — and name the myths worth striking from the roadmap. When the decisions land, AI SEO Projects turns them into a chartered selection: twenty-five projects across five topical areas.
Schema, product feeds, and crawler access are the machine-readable layer AI engines check your facts against — not your marketing copy. Get PIM-driven JSON-LD, feed cadence, or crawler policy wrong, and answers about your products get diluted or invented.
AI answers come from an entity graph, not a keyword index, and brand mentions now correlate with AI visibility far more than backlinks do. This area covers your entity home, Wikidata anchoring, earned media, and disambiguating your brand from generic-word collisions.
Answer-first, human-led content still earns citations — quotations, statistics, and cited sources each move visibility, while scaled AI content risks an enforcement penalty that can cut traffic outright. This area covers editorial operations, answer-first templates, and refresh cycles tied to your product calendar.
Cited sources turn over 40–60% month to month, and most brands are only partially tracking who gets cited and why. This area covers multi-surface share-of-voice, spec-hallucination monitoring, and prompt corpora built to keep the picture current.
Marketplace assistants, the Bing/Copilot stack, and zero-click AI answers are now distinct disciplines with their own signals and timelines. This area covers marketplace listing optimization, the Bing/Copilot presence, and market-tier sequencing for international rollout.