Image recognition can describe visible objects but miss why an image matters in a document. A chart caption that ignores trend direction may be technically fluent and still useless to a screen-reader user.
This is a decision and operations guide for editorial, design and accessibility reviewers. It does not assume that a particular chatbot, enterprise license, API integration or regional service feature exists in every market. The central question is whether the activity can be governed as a business process with evidence, a responsible reviewer and an effective non-AI fallback.
Decide what the system is and is not allowed to do
Decide first whether the image is informative, functional, complex or decorative. The surrounding page text may make redundant alt text less helpful than an empty alt attribute for decoration.
Write a small authorization statement before testing: which source records may be processed, which staff may see the draft, which decisions remain with a person, and which changes require a separate sign-off. Record an exception state such as “insufficient evidence” or “requires review.” The absence of an answer is not a permission to improvise one.
Implementation: four controlled stages
Define the source and owner
Create a human purpose statement for each meaningful image, distinguishing editorial evidence from purely visual styling.
Create a small input manifest with the file or record identifier, revision date, data sensitivity and legal or contractual authority. If a key field is unavailable, mark it unknown rather than allowing a model to infer it.
Build the constrained operation
Draft candidate text using the image and surrounding context, without inventing identities, emotional states or unseen measurements.
Produce an inspectable interim artifact: a table, tracked change, draft reply or exception list. Avoid irreversible system writes at this stage; both raw evidence and machine-generated suggestions should be visible to the reviewer.
Review material exceptions
Verify factual details against the original source; provide extended text or a data table for complex charts where necessary.
Use negative tests as well as clean examples. Include missing attachments, contradicting documents, outdated policies, unexpected locales and requests outside the user’s authorization. Capture the reasons for correction.
Release, monitor and revise
Test with a screen reader and human accessibility reviewer; store approval alongside the image asset and revisit when the content changes.
Have the designated business owner approve the final work product before sending, publishing, scoring an individual or amending a source-of-truth record. Preserve only the records justified by the retention schedule.
Worked scenario, with no invented performance claims
A line graph compares quarterly shipping complaints. The alt text names the variables and broad trend, while a nearby accessible table gives exact values.
To turn this illustrative scenario into a pilot, an organisation would select permitted historical examples, define acceptance criteria before seeing results and compare the complete workflow against the current baseline. The evaluation should count staff corrections, escalations and downstream rework, not just generation time. No independent hands-on test or success percentage is asserted here.
Failure modes and human escalation
Models can infer demographics or product specifications from appearances without evidence. A generic “image of chart” wastes the opportunity to communicate meaning.
A human approval gate is meaningful only if the reviewer sees the source of the decision, understands the scope of responsibility and has the authority to reject or correct the output. Escalation should be specific: identify a role, the evidence needed, a time limit where relevant and the safe state of the workflow while the issue remains open. Confidence scores alone are not approval.
Measurement and total cost of ownership
Track missing meaningful alt text; unsupported image claims; screen-reader navigation failures; complex chart alternatives. Define each denominator and review period beforehand; separate common cases from high-impact exceptions. Report missed cases and abstentions alongside accepted work. Monitor differences between teams, customer groups or languages if these affect the task.
A practical net-benefit estimate is: (eligible cases × verified reduction in full end-to-end labor minutes × fully loaded cost per minute) − (service costs + deployment engineering + human review + security/legal work + training + incidents + ongoing maintenance). This is a method, not a claim of realized savings. Reassigned minutes are not automatically new revenue, a reduced payroll cost, or an independently proven productivity gain.
Limits, alternatives and regional applicability
For simple decorative graphics, a correctly empty alt value and proper document structure are preferable to an AI-generated caption.
WCAG 2.2 is a W3C Recommendation; the applicable legal accessibility obligation and required conformance level depend on jurisdiction and context.
The user-facing service name does not establish the applicable data policy. Check vendor terms for the exact product, tenancy, features, region and date. Price, billing units, free tiers, training permissions and model identifiers are intentionally not guessed. Procurement should use a documented AI 10 price snapshot; architecture and autonomous tool permissions belong to AI 07, while detailed model capability claims belong to AI 02.
What the first-party sources actually establish
The W3C published WCAG 2.2 as a Recommendation on 5 October 2023, adding nine success criteria over WCAG 2.1.
NIST published AI Risk Management Framework 1.0 in January 2023 as voluntary guidance.
NIST published its AI RMF Generative AI Profile in July 2024 as a companion resource to the original framework.
The institutional sources above support the identified governance context, applicable standards and dates. They do not validate the performance of this proposed workflow in a particular organisation. All proposed operating steps are original editorial guidance and should be tested against actual business evidence and local legal requirements.
Sources & Further Reading
- Web Content Accessibility Guidelines (WCAG) 2.2 is a Recommendation — W3C. 2023-10-05; scope: global accessibility standard. Source checked 2026-10-25.
- AI Risk Management Framework 1.0 (January 26, 2023) — NIST. 2023-01-26; scope: global: voluntary guidance. Source checked 2026-10-25.
- AI RMF Generative AI Profile (July 26, 2024) — NIST. 2024-07-26; scope: global: voluntary guidance. Source checked 2026-10-25.