Why accessibility is one of the most important AI procurement decisions you can make

An AI product can appear convincing in a demonstration yet become an expensive problem once deployed in live service. According to AbilityNet, the gap between promise and reality often emerges only after a contract is signed, integrations are built, and real users begin interacting with the system. At that point, organisations discover that a chatbot is difficult to navigate with a screen reader, an AI assistant cannot be controlled reliably by voice, generated answers lack structure some people need, or a decision cannot be understood and challenged. What begins as an accessibility failure can quickly escalate into delivery delays, customer complaints, governance concerns and costly remediation programmes.

For large organisations, inaccessible AI is fundamentally a procurement and assurance problem. AbilityNet notes that the decisions determining whether AI will work for disabled people are increasingly made before the technology is purchased. Traditional software procurement usually assesses a relatively defined product, but AI services may combine models, infrastructure providers, third-party components, data sources, APIs and interfaces, any of which can change after deployment. Buyers must therefore assess not only how a product works on the day of evaluation, but also how the service will be governed as models, features and suppliers change, and who is responsible when an update introduces a barrier or an output becomes harder to explain.

UK Government guidance on AI procurement already emphasises AI-specific criteria, supplier evaluation and responsible deployment, with accessibility needing to be addressed well before testing. The article outlines five questions buyers should ask before signing: whether everyone can use critical journeys with assistive technologies; whether important outputs can be understood and challenged; what model, interface and workflow changes require retesting after launch; who owns a failure end to end across supplier, procurement, technology, risk and accessibility teams; and what evidence will be available throughout the contract for known issues, testing results and incident reporting.

Regulatory pressure is also increasing. Article 14 of the EU AI Act requires high-risk AI systems to support effective human oversight, including the ability to understand limitations and intervene. AbilityNet argues that this oversight is weakened if disabled customers, employees or reviewers cannot use the system, interpret its output or reach a human route when needed. For sectors such as financial services, the UK Government's Financial Services AI Adoption Plan describes responsible adoption at pace across fraud detection, operations and risk management, making early assurance even more critical.

To reduce risk, organisations should put measurable accessibility requirements into procurement contracts, review the highest-risk journeys before wider rollout, bring procurement, AI leadership, risk, legal, technology and accessibility expertise together early, test with diverse users in realistic conditions, and create triggers for reassessment when the system changes. AbilityNet says that addressing accessibility before purchase gives an organisation more freedom to choose a different supplier, strengthen contractual requirements or delay scaling a proposed use of AI until it is ready.