AI can’t automate accessibility fixes, but it can make remediation a hell of a lot faster.
Let’s say you’re an experienced developer who is still fairly new to accessibility. You can go to ChatGPT or Google’s Gemini and instantly get some good intel on how to make fixes — an excellent use of AI technology.
But why stop there?
What if you had AI synced with the exact audit report you need to work from?
What if AI had all of the audit data — the specific data for each issue from the audit report — and was already pre-prompted with the perfect prompt to get the exact answer you need to make each fix?
That would save you / your team a few minutes per issue, right?
How many issues are in your report?
You see where this is headed: Efficiencyville, population You.
We have force multiplied accessibility project efficiency and effectiveness with our new digital accessibility platform, Accessibility Tracker.
AI isn’t the only feature, but we can certainly understand why the integrated AI is the showstopper garners all of the attention. For the rest of this post, we’ll explain step-by-step how to use Tracker AI to make remediating accessibility issues faster and easier.
| Benefit | What It Means | Real Impact |
|---|---|---|
| 2.5x Faster Project Completion | Tracker AI can realistically turn remediation from a 10-week projects into a 4-week projects through instant guidance, eliminated research time, and fewer validation rounds. | A 50-hour remediation project becomes a 20-hour project. Meet compliance deadlines faster and reduce project costs dramatically. |
| Save $195/Hour on Technical Support | AI answers the questions that typically require expensive consultant hours. Developers get instant code examples and implementation guidance without scheduling meetings. | A 200-issue project might have needed 20 hours of support 2 years ago. With Tracker, that can easily drop to 8 hours—saving $2,340 for one project. |
| Developers Learn While Fixing | Each issue fixed with AI guidance builds permanent accessibility proficiency. After 50 issues, developers intuitively start to pick up accessibility patterns. | Your team goes from zero accessibility knowledge to competent in weeks. Future audits find fewer issues because developers implement accessible patterns from the start. |
| Instant, Context-Specific Assistance | AI is pre-loaded with your exact audit data. No prompt engineering or copying and pasting needed—just click “Analyze with AI” and select from 5 specialized tools. | Developers stay in flow state instead of spending 3-10 minutes researching an unfamiliar issue. More fixes are made correctly the first time with fewer validation rounds. |
| No More Copy-Pasting Into ChatGPT | AI already has your audit data pre-loaded. No need to copy issue details, provide WCAG context, or explain your code. | Eliminate the 3+ minutes per issue spent copying audit details into ChatGPT and rewording prompts to get the right answer. That’s 5+ hours saved on a 100-issue project just from pre-configured AI. |
Table of Contents
Step-by-Step: Using AI to Fix Accessibility Issues
Step 1: Upload Your Accessibility Audit Report
Start with a real accessibility audit report in Excel spreadsheet format from Accessible.org or another provider.
Upload your Excel audit report directly to Accessibility Tracker. The platform automatically extracts all issue data, recommendations, and technical details.
Step 2: Navigate to a Specific Issue
From your project dashboard, view the complete list of accessibility issues. You can filter and sort by priority, WCAG criterion, or assigned team member. Click “View” on any issue to see all the detailed information extracted from your audit report:
- Issue location
- WCAG success criterion
- Issue description
- Applicable code
- Suggested fix
- WCAG success criterion
Step 3: Analyze the Issue with AI
Click “Analyze with AI” to access five pre-configured AI tools. Each tool serves a different purpose in the remediation process:
- Simplify and Explain translates technical WCAG language into plain English. Perfect for team members new to accessibility who need to understand what the issue actually means and why it matters.
- Detailed Technical Answer provides comprehensive code examples and implementation guidance. This is where developers get advanced code snippets to work from.
- Alternative Approaches offers different remediation methods when the standard fix conflicts with existing functionality or design requirements.
- WCAG Standards explains the success criterion in depth, helping your team understand not just how to fix the issue, but why it matters for users with disabilities.
- Custom Analysis lets you ask specific questions that don’t fit the other categories, providing flexibility for unique situations.
Step 4: Select an AI Tool
Choose the tool that matches what you need. If you’re a developer looking for code examples, select Detailed Technical Answer. If you’re a project manager trying to understand the issue’s impact, choose Simplify and Explain.
The AI generates its response using the pre-loaded audit data.
Step 5: Implement the Fix
Use the AI guidance to implement your fix. For code issues, you’ll have specific examples to work from. For content issues, you’ll understand the principles behind effective remediation.
The AI might provide multiple approaches or step-by-step implementation guidance. Copy relevant code snippets, adapt them to your specific implementation, and test the fix.
Step 6: Mark Status and Add Notes
Update the issue status to “Completed” once you’ve implemented the fix. Add notes about your implementation approach for future reference or validation.
Team members can see status updates in real-time, eliminating the back-and-forth of spreadsheet updates and email threads.
Step 7: Validation
Once marked as completed, the issue moves to validation. Auditors can validate fixes directly in the platform, with all context and implementation notes available. If additional work is needed, they can mark it as “Needs Work” with specific feedback.
Real Examples of AI-Assisted Fixes
Let’s say you encounter missing alternative text for images (WCAG 1.1.1). The Simplify and Explain tool breaks it down: “Screen reader users can’t understand what’s in your images because there’s no text description. When they encounter the image, their screen reader just says ‘image’ without any context about what it shows.”
The Detailed Technical Answer provides the exact implementation, showing how to add meaningful alt text to your specific image elements, with examples of good versus poor alt text for different types of images.
For a more complex issue like a keyboard trap, the AI explains that users can enter your dropdown menu but can’t escape it using standard keyboard navigation. It then provides JavaScript code to properly manage focus and ensure users can move through and exit the component.
Time and Cost Savings
Traditional remediation often requires extensive technical support hours at $195 per hour. A typical 100-issue project might need 20 hours of support for developer questions and implementation guidance.
With AI assistance built into the workflow, that same project might only need 8 hours of support. The AI handles routine questions, provides immediate code examples, and explains WCAG requirements without scheduling meetings or waiting for responses.
Beyond direct cost savings, there’s the efficiency gain. Developers don’t stop work to research issues. Project managers don’t coordinate multiple communication channels. Teams fix issues faster with fewer validation rounds.
Building Long-Term Capability
Each issue fixed with AI guidance builds your team’s accessibility knowledge. After working through 50 issues with AI assistance, developers start recognizing patterns. They understand keyboard navigation requirements. They know what makes alt text effective. They can spot accessibility issues before they reach production.
This experiential learning—fixing real issues in your actual codebase with immediate guidance—creates lasting expertise. Your next audit finds fewer issues because your team is implementing accessible patterns from the start.
Summary
Fixing accessibility issues with AI means using artificial intelligence to make your remediation process more efficient—not automated, but dramatically streamlined. By integrating AI directly into Accessibility Tracker, we’ve eliminated the friction points that slow down remediation projects.
The process is straightforward: upload your audit report, view any issue, click Analyze with AI, select the appropriate tool, and get instant guidance tailored to your specific issue. No prompt engineering, no context switching, no waiting for technical support.
This approach saves time, reduces costs, and builds lasting accessibility expertise within your team. Each issue fixed with AI assistance is knowledge gained, creating organizational capability that extends beyond the current project.
You can start remediating with AI now with a free plan at AccessibilityTracker.com.
