Planning Remediation Capacity for the 2027 and 2028 Title II Dates

A remediation schedule depends on the institution's actual in-scope inventory and available capacity. A large backlog can exceed a team's time, but that does not prove manual remediation is impossible at every institution or that a particular AI product is required.

The DOJ Title II rule sets WCAG 2.1 Level AA requirements for covered state and local government web content and mobile apps, subject to the rule's scope and exceptions. The April 2026 IFR extends the dates to April 26, 2027 for public entities with a population of 50,000 or more and April 26, 2028 for smaller public entities and special district governments. Determine the institution's cohort using DOJ's population rules; enrollment and federal funding alone do not determine it. Existing accessibility obligations continue before those dates.

Establish the scope before estimating effort

Inventory current documents, duplicates, source files, ownership, student tasks and barriers. Assess exceptions under the actual rule rather than treating every historic file as an identical remediation obligation. Retiring genuinely unused content, improving authoring, obtaining accessible publisher material and rebuilding difficult sources can all change the work required.

A hypothetical capacity model

The following assumptions illustrate the calculation; they are not measured university data, vendor prices or Aelira performance:

• 2,000 unique documents need work.
• A representative trial finds an average of 1.5 staff hours per accepted document, including checks and corrections.
• Four staff members can each dedicate 20 hours a week to this program.

The workload is 3,000 hours. Available capacity is 80 hours a week, so the modeled backlog takes 37.5 working weeks before new content, leave, procurement delays and rework. If new material consumes 20 hours each week, backlog capacity falls to 60 hours and the modeled duration becomes 50 weeks.

Change the inputs and the conclusion changes. A smaller inventory or a simpler source-based workflow may fit the available staff. More difficult material or fewer staff hours may require additional resources, earlier prioritization or revised delivery plans.

Test automation within the model

Measure total accepted-output time: preparation, processing, review, correction, manual fallback and publishing. Include failed inputs and unavailable outputs. Compare the same acceptance standard across manual, automated and combined workflows. Faster job completion alone is not equivalent to accepted remediation.

The April 2026 IFR discusses limitations of automating remediation at scale. It does not mandate a specific hybrid architecture, require AI procurement, or approve an Aelira workflow.

Budget once for each cost

For in-house work, multiply measured staff hours by the institution's loaded hourly cost and add infrastructure, licenses, training and audit costs. For outsourced work, use the quoted scope and acceptance criteria; do not add an equivalent full internal remediation labor charge unless both costs actually occur.

Aelira Core is beta software. Supported workflows can apply eligible partial fixes and record outcomes, but some inputs need manual work or cannot produce a downloadable remediated file. Review the actual saved output, unresolved findings and content preservation before publication. Automated scores describe implemented checks rather than accessibility conformance. Inspect the public source and release documentation and evaluate a representative authorized sample before planning a rollout.

Bring the measured capacity gap, options and uncertainties to leadership. The useful procurement conclusion is the institution-specific resource decision supported by that evidence.

Further reading

These links provide additional context; each source should be assessed for its own scope.

AI-Assisted University Accessibility: Review, Evidence and Legal Scope

Reviewing AI-Generated Alt Text for Academic Figures

The Business Case for Accessibility Remediation in Higher Education