Long-form thinking on AI delivery

The Stride blog covers the working reality of AI-native software delivery: the workflows, prompt patterns, and integration architectures that change when AI participates as a first-class collaborator rather than a sidebar assistant. Most posts are 8–15 minute reads, written for senior engineers and engineering leaders who are past the “is AI useful?” question and into the “how do we actually wire it in?” question.

Three clusters get the deepest coverage. The first is AI in the delivery loop: what AI does well in acceptance criteria, Gherkin, test case generation, code review, and sprint planning; what it gets wrong; and the prompt patterns that move the success rate from 40% to 85%. The second is tool migrations: replacing Jira, leaving Confluence, escaping spreadsheet process mining. Migration posts cover the 30-day playbook, the gotchas vendor docs skip, and the cost math leaders actually need. The third is the connected delivery graph: the thesis that every artefact in software delivery (PRD, ADR, story, test, deploy) should be a typed node with explicit links, and that AI quality rises sharply when it can traverse that graph instead of guessing.

Editorial discipline: we link to primary sources (peer-reviewed research, RFC standards, vendor pricing pages) rather than other listicles. We disclose where our own product is the comparison point. We don't publish quarterly “here are AI updates” posts; new entries land when the topic is worth a defended position, not on a content calendar. Each post is dated and updated dates are surfaced explicitly so freshness is auditable.

For procurement-stage research, the comparison library has the head-to-head detail. For definitional reference, the glossary defines every term we work with crisply. For deeper guides, Learn has the multi-article hubs on sprint planning, code review, and test management.