The Future of Alt Text and AI: What You Need to Know

Understanding the Importance of Alt Text in AI

Alt text is crucial for making digital content accessible. It provides descriptions of images for users who rely on screen readers. As AI evolves, so does the way we generate and utilise alt text. This post explores the latest developments and their implications for your website.

What’s New in Alt Text + AI (2024–2025 Highlights)

Below is a roundup of recent developments, research, and evolving best practices around alt text and AI (and what to watch out for).

1. Smarter Alt-Text Generation Models / Methods

• A brand-new method called MCM-DPO (Multi-faceted Cross-Modal Direct Preference Optimisation) offers a way to train alt-text models that better capture what users prefer, not just what matches ground truth labels. It can look across visual, textual, and cross-modal preference dimensions. The authors show it outperforms older supervised methods.

• For chart images and data visualisation, there’s been progress in combining retrieval (finding similar charts) and suggestion UIs to help humans write better alt text. The idea is AI + human in the loop, especially for technical visuals.

• In the data science and notebook context, there’s MatplotAlt, a Python library that helps automatically generate alt text for Matplotlib figures (using heuristics and LLM prompts). It shows both promise and challenges (e.g., factual accuracy).

• In publishing and e-books, projects like AltGen are integrating vision models and language models to generate contextually aware alt text for images in EPUBs. The approach threads text around the images to improve accuracy.

  
  

These are signs that system-level tools are getting better, but they’re not perfect yet.

2. AI as a First Draft, Not a Replacement

Many experts now emphasise that AI-generated alt text should be treated as a draft or assistive layer, not something to publish without review.

Why?

AI can miss context, misinterpret subtle visual cues, or omit what’s most meaningful for a page. Human oversight (especially by someone who knows the content) remains essential.

3. Alt Text is Becoming More Strategically Important for Search / AI Discovery

• Alt text now plays a role beyond accessibility; it's part of how AI-powered search and answer engines interpret and index images. Some tools or articles are calling it a key lever for “AI-aware SEO.”

• The idea is that if a generative search engine or AI answer system is summarising a page, well-written alt text helps supply clearer signals about the image’s content and how it ties into the page.

  
  

In short, alt text is no longer just a compliance checkbox; it contributes to how your visuals “talk” to AI systems.

4. Real-World Deployments & Platform Moves

• TikTok is rolling out AI-generated alt text for images that users haven’t manually described, but creators can override or add their own text to improve accuracy.

• In accessibility and web tech circles, there’s growing discussion (and some caution) about AI-only solutions. Blind users and advocates have flagged cases where AI mis-descriptions caused confusion or misleading information.

• In “digital collection” or archive settings, institutions are experimenting with AI to auto-generate alt text at scale but emphasising the need for fallback review and control.

  
  

5. New Challenges & Risks to Watch

• Ambiguity & context sensitivity: Images often require understanding layout, labels, or surrounding content, which generic vision models may mis-handle.

• Errors and misinterpretation: AI can confidently produce wrong or nonsensical alt text, which is dangerous in accessibility contexts.

• Standards and consistency issues: There’s variance in how “good” alt text is defined, especially across domains (news, data visuals, marketing, charts).

• Legal and compliance risk: If a site relies purely on AI without oversight and ends up with misleading alt text, it may fall short of accessibility regulation expectations.

  
  

What This Means for Our Work (and For You)

Given all that, here’s how we’ll integrate these insights, and how you’ll benefit:

1. AI-assisted alt text, human-reviewed

   We’ll use AI tools (internally or via partner tools) to generate alt text drafts at scale, but always include a step of human review, ideally by someone familiar with the content/context.

   
   

2. Prioritise “meaningful over verbose”

   We’ll focus on alt text that captures what matters in the image (not just everything). Especially for charts, infographics, product shots, etc., we’ll tune based on what you want your audience to “see.”

   
   

3. Structured workflows & templates

   To maintain consistency, we’ll create guidelines and templates (e.g., for icons, decorative images, feature visuals) so everyone (writers, designers, devs) knows what good alt text looks like.

   
   

4. SEO / AI strategy integration

   We’ll treat alt text as part of your broader content and SEO strategy (not a separate afterthought). We’ll examine how alt text aligns with page topic, keywords, and how AI search and answer systems might surface your visual content.

   
   

5. Monitoring & audits

   Periodically, we’ll run audits (manual and automated) to check for missing, weak, or inaccurate alt text. As better alt generation models emerge, we’ll test them and update our approach.

   
   

6. Risk mitigation and oversight

   Because AI is fallible, we’ll use fallback checks and flags (for “low confidence” alt text) so we don’t publish weak or incorrect text unknowingly.