accessibilityperformanceAI

Accessibility & Performance Checklist for AI‑Heavy Landing Pages

ccompose

2026-02-01

10 min read

A focused 2026 checklist to keep AI demos usable, inclusive, and fast—progressive enhancement, lazy model loads, ARIA tips, low‑bandwidth fallbacks, and KPIs.

Make AI demos usable and fast: an accessibility & performance checklist for 2026

Hook: You built an impressive AI demo—but visitors drop off before the model loads, screen‑reader users can’t follow streamed responses, and your conversion rate tanks. In 2026, AI features are expected; they shouldn't punish accessibility or page speed. This checklist helps creators and publishers ship AI‑heavy landing pages that stay fast, inclusive, and measurable.

Executive summary (most important first)

Ship an AI demo that works for everyone by combining two principles: progressive enhancement and graceful degradation. Start with a fast, accessible HTML baseline (server‑rendered copy, forms, sample outputs), then layer on AI interactivity that loads only when needed (lazy model loads, dynamic imports, web workers, or on-device inference). Measure the experience end‑to‑end with both synthetic and RUM KPIs: Core Web Vitals + model init latency + streaming token latency + bytes transferred.

Why this matters in 2026

Late 2025 and early 2026 accelerated two trends that change how demos should be built:

Local & edge AI is mainstream — browsers and devices (including mobile browsers with local AI and hobbyist hardware like Raspberry Pi HATs) now support on‑device inference, which reduces round‑trip latency but changes payload patterns.
Browsers expose new APIs (WebGPU, improved WebAssembly SIMD) that let teams run compact models client‑side, while networks remain variable worldwide — low‑bandwidth fallbacks are still essential.

So your strategy must balance: small initial payloads, optional client models, server fallback, and inclusive ARIA-driven experiences for assistive tech.

Checklist overview — fast scan

Baseline HTML first: server‑rendered text + forms + static sample outputs.
Progressive enhancement: enable AI features after user intent or idle time.
Lazy model loads: dynamic imports, web workers, and on‑demand downloads.
Low‑bandwidth fallbacks: smaller models, server inference, or canned responses.
ARIA & focus: live regions, role attributes, keyboard focus management.
KPIs: Core Web Vitals + AI KPIs (model init, TTFToken, bytes model).

1. Progressive enhancement: structure and flow

Progressive enhancement keeps the core experience available immediately. The AI interaction should be an upgrade, not a requirement.

Minimum viable baseline (deliver immediately)

Server‑render the landing copy, headings, and a static example of the AI output for the user to read immediately.
Provide a standard HTML form for input with proper labels (no JS required to submit).
Include clear fallback messaging: "If your browser can’t run the demo, see example results below."

Enhancement triggers (load AI only when it matters)

On user intent: click the demo button or focus the input (fastest and most respectful of bandwidth).
On hover or long‑press for desktop to prefetch small assets (heuristic: only prefetch if likely to engage).
On idle (requestIdleCallback) after primary content is interactive.

// example: on-demand JS loader
const loadAI = () => import('./ai-widget.js');
input.addEventListener('focus', () => loadAI().then(initAIWidget));

2. Lazy model loads — patterns that work

Loading large model files at page load kills conversions. Use these patterns to defer and shrink model delivery.

Pattern 1 — Dynamic import + web worker

Keep the main thread responsive by loading model code into a worker only when needed.

// main thread
button.addEventListener('click', async () => {
  button.disabled = true;
  const worker = new Worker('/workers/ai-worker.js', { type: 'module' });
  worker.postMessage({ cmd: 'init' });
  worker.onmessage = e => handleWorkerEvent(e.data);
});

// worker: fetch small model shard or WASM runtime

Pattern 2 — Model selection & downgrades

Detect network and device capability and choose a model accordingly:

High bandwidth + desktop → medium model (better quality)
Low bandwidth or mobile → tiny model or server fallback

// capability heuristic
const network = navigator.connection && navigator.connection.effectiveType;
const isSlow = network === '2g' || network === 'slow-2g' || navigator.deviceMemory < 2;
// choose modelURL based on isSlow

Pattern 3 — Stream and render partial results

Start showing tokens or partial answers as soon as they arrive to reduce perceived latency. Use server streaming (SSE/Fetch streams/WebSocket) or on‑device streaming. These real-time patterns are related to how teams build live collaboration and streaming UIs (collaborative live visual authoring).

3. Low‑bandwidth fallbacks

Global audiences often use limited mobile data. Provide alternatives that preserve utility while cutting bytes.

Static example outputs — always available in the HTML so users get value without model load.
Small quantized models — offer a “Lite” demo that uses a tiny quantized model under 2–10 MB (local-first appliances and small quantized runtimes are a fit here).
Server compute switch — automatically send inference to the server when client conditions are poor.
Bandwidth pref — a toggle letting users choose “Low data mode” (tie this to product-level audits that strip the fat from your payloads).

Practical low‑bandwidth checklist

Is the initial page shell & sample output under 200 KB (HTML+CSS)?
Do we detect poor connections and switch to server inference?
Do we offer a tiny model option and document expected quality tradeoffs?
Are images lazy and compressed (AVIF/WebP, responsive srcset)?

4. Accessibility — ARIA, focus, and streamed content

AI demos are interactive, asynchronous, and often stream responses — all are accessibility hazards if not handled properly. Use ARIA patterns to keep screen‑reader users in the loop.

Key ARIA practices

aria-live regions for streamed responses. Use aria-live="polite" for non‑urgent updates and aria-live="assertive" sparingly (only for critical info).
aria-busy while waiting for model init or network responses.
role="status" for short status messages; role="alert" for errors.
Ensure interactive elements are keyboard focusable and have visible focus styles.
Announce progress percentage or token count for long operations so assistive tech users know how long to wait.

<div id="ai-output" aria-live="polite" aria-atomic="false">
  <p>Example output shown here. When the demo runs, partial results will appear below.</p>
</div>

// When starting async work:
aiOutput.setAttribute('aria-busy', 'true');
// on update:
aiOutput.textContent += '\n' + token;
// when done:
aiOutput.removeAttribute('aria-busy');

Focus management and form labels

Move keyboard focus to the result container when an answer is ready (announce with aria-live so screen readers read it).
Make every input have an explicit <label> and aria-describedby for helper text.
Ensure error messages are linked with aria-invalid="true" and aria-describedby.

Testing checklist

Test with NVDA, VoiceOver, and TalkBack on both desktop and mobile.
Verify streaming tokens are announced in a usable way — consider batching tokens into sentences for screen readers.
Verify keyboard tab order and that interactive controls are reachable without a mouse.

5. Performance checklist — what to measure and targets

Measure both standard web metrics and AI-specific KPIs. Use synthetic tests (Lighthouse, WebPageTest) and RUM (Real User Monitoring).

Core web KPIs (2026 targets)

LCP (Largest Contentful Paint): < 2.5s on 4G for landing shell
CLS (Cumulative Layout Shift): < 0.1
INP (Interaction to Next Paint): < 200 ms
TTFB: < 800 ms (for initial HTML)

AI demo specific KPIs

Model init time — time between user intent and model ready: target < 2s (desktop), < 5s (mobile) for on‑device models; server init should be < 1s for warmed containers. Monitor these with an observability playbook (observability & cost control).
First token latency (TTFT) — first meaningful token: < 300ms ideally for streaming server responses; < 800ms for on‑device cold starts.
Average token throughput — tokens/sec after first token (higher is better; measure to set expectations).
Model payload — initial bytes to download for model: target < 2–10 MB for tiny client models; document tradeoffs.
Bytes transferred — measure total additional bytes when AI features load; aim to keep incremental load < 1 MB when possible.

Monitoring & alerts

Use RUM to capture model init times and token latencies per user region and network type (observability & cost control covers monitoring playbooks).
Create synthetic checks that run under Slow‑4G and 3G to validate fallbacks.
Alert when model init p95 exceeds your threshold or model payload grows beyond budget.

6. UX & conversion best practices (don’t forget CRO)

Performance and accessibility influence conversions. Here are tactical UX rules that help both conversion and inclusivity.

Always show an example result near the CTA so users understand the output before waiting for a demo.
Use microcopy to set expectations: e.g., "Lite mode: smaller model, faster results".
Provide a progress UI with clear affordances (spinner + percent + estimated time).
Offer an explicit download/print of results for users on flaky connections.
Use A/B tests to measure how lazy loading strategies affect conversions (e.g., immediate prefetch vs on‑click load).

7. Implementation playbook — step-by-step

Quick playbook to deploy a compliant AI demo in a week.

Build the static baseline: server render landing copy, example output, and labelled form fields.
Integrate ARIA: add aria-live output region, aria-busy toggles, and labels.
Implement lazy loading: dynamic import of widget + worker-based model loader, triggered on focus/click.
Add network heuristics: choose tiny model or server fallback on slow connections.
Stream responses: implement server streaming or client tokenization to render incremental results.
Measure: instrument RUM for Core Web Vitals + model KPIs; run synthetic Slow-4G checks.
Iterate: A/B test preload strategies, update microcopy, and rebalance model size vs quality.

Developer snippet — aria-live + progressive reveal

<form id="demo-form">
  <label for="prompt">Enter prompt</label>
  <input id="prompt" name="prompt" />
  <button id="run" type="button">Run demo (loads model)</button>
</form>

<div id="result" aria-live="polite" aria-atomic="false">Sample output shown here.</div>

<script type="module">
const button = document.getElementById('run');
const result = document.getElementById('result');
button.addEventListener('click', async () => {
  result.setAttribute('aria-busy', 'true');
  const { initDemo } = await import('./ai-widget.js');
  const demo = await initDemo({ onToken: t => {
    result.textContent += t; // tokens appended
  }});
  result.removeAttribute('aria-busy');
  result.focus(); // move focus for screen readers
});
</script>

8. Real‑world examples & patterns (experience)

Two practical patterns we’ve used at compose.page and with partners in 2025–2026:

Edge-first demo: Small quantized model (~5MB) loaded into a web worker on user click, with server fallback when deviceMemory < 2. Served on a product launch page; increased demo engagement by 28% and reduced server costs.
Server-stream hybrid: Start with server streaming for the first answer (fast TTFT), then optionally download a tiny model for follow‑ups. Improved perceived latency and accessibility because the aria-live region received immediate partial text. This hybrid pattern overlaps with collaborative, edge-based authoring approaches (collaborative live visual authoring).

“Users should never wait for a model to appear to get value; show them an example and upgrade the experience in the background.”

9. A/B testing ideas to validate choices

Prefetch vs on‑click: measure conversion and bounce, segmented by network type. (Run this as part of a lightweight product audit to strip underused preloads.)
Tiny model vs server inference: measure model quality satisfaction and cost.
Different progress UI: spinner-only vs percent + ETA — measure abandonment during model init.

10. Checklist you can paste into PRs

Copy this short checklist into pull requests to ensure consistency across pages.

[ ] Baseline HTML with static example visible without JS (hardening local JS tooling)
[ ] Inputs have labels and aria-describedby where needed
[ ] Demo loads model only on user intent or idle
[ ] Model loader runs in a web worker or off main thread
[ ] Provide a low‑bandwidth toggle and server fallback
[ ] Results stream into aria-live region and toggle aria-busy
[ ] Synthetic tests: Lighthouse & Slow‑4G checks passing
[ ] RUM metrics capture model init time and token latency (feed into your observability stack)

Final thoughts & future predictions

By 2026, expect more demos running fully on device (WebGPU + WASM), and browser vendors to add richer hints for AI prefetching. That makes early investment in a progressive enhancement strategy even more valuable: you’ll be able to choose client or edge execution dynamically without reworking the UX. But networks remain uneven — low‑bandwidth fallbacks and ARIA patterns will keep your demos inclusive and high‑performing.

Actionable takeaways

Never block the page with model downloads; always ship a usable HTML baseline.
Load models lazily using workers or dynamic imports, triggered by user intent.
Detect connection/device to pick a model or fallback to server inference.
Use aria-live, aria-busy, and clear focus management for streamed responses.
Measure standard web vitals and AI KPIs (model init, first token latency, bytes transferred).

Call to action

Ready to ship AI demos that convert and remain accessible? Start with the PR checklist above. If you want a tailored audit, we can map your landing pages to this checklist, run Slow‑4G tests, and produce prioritized fixes that reduce model init time and improve accessibility. Get a free mini‑audit or download our one‑page checklist to share with your team.

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