A central digital experience team at a large public research university is responsible for a sprawling website: thousands of pages across hundreds of departments and subdomains. Content requests arrive faster than a fixed-size team can process them. Keeping pages accurate, accessible, and on brand at that scale depended on manual effort and constant coordination, and the queue never really emptied.
The friction lived in the gap between a request and a published page. Every change request sat in a queue waiting to be handled by hand in the CMS, under constant SLA pressure. Accessibility and broken-link issues were flagged by a scanner but remediated manually, so they piled up faster than the team could clear them.
Demand kept climbing, including new work driven by AI search and SEO, with no additional headcount to absorb it.
faster bulk content updates
faster ticket cycle times
acceptance test pass rate
Once the university piloted Gradial, agents began running change tickets directly in the CMS, with authors validating and publishing. Ticket cycle times dropped about 35%, the backlog started clearing, and authors were freed for higher-value work.
Just as important, accessibility, brand, and QA checks now run automatically on every change, with an audit trail, so moving faster did not mean giving up governance.
We were drowning in change tickets, and AI search and SEO work kept adding more. Gradial cleared the backlog and made accessibility automatic, without adding people.
Digital Experience lead
A large public research university
The clearest evidence came from user acceptance testing: 29 real-world use cases across six testers, 105 total executions, and a 91.4% pass rate. The standout was a multi-item content update that took 30 minutes by hand and finished in about four and a half minutes with Gradial, an 85% reduction. Text updates dropped from 15 minutes to under four. Across every use case tested, the same pattern held.
Measured time savings included a 6.6x efficiency gain on multi-item updates, 4.1x on text updates, 2.6x on article tagging, 2.2x on link updates, 1.9x on image updates and PDF replacements, and 1.7x on new page creation.
Gradial worked directly inside the CMS the team already ran, with no platform migration. Governance was built in, so accessibility, brand, and QA checks ran on every change rather than being sampled occasionally. And it connected to the systems the team already used for ticketing and accessibility scanning, fitting the existing stack instead of replacing it.
This was not a generic AI tool bolted onto the work. It was built by people who understand content operations inside large, complex organizations.
The university sees the upside extending well beyond clearing a backlog. Next is more of the content supply chain: making pages ready for AI search, running migrations, and keeping accessibility current continuously.
The bigger shift is going from reactive cleanup to a website that stays accurate and findable for both students and the AI models that now shape how they choose a school. Explore the platform to see how Gradial helps enterprise teams turn direction into governed execution.
