General

Accessibility Work and Its Impact on Future AI

Tracy Lee
5 min read
This article was written over 18 months ago and may contain information that is out of date. Some content may still be relevant, but please refer to official documentation for the latest information.

If you have ever had trouble reading something due to the size or color of font used, or had a hard time hearing the person next to you at a loud concert, then you have had the same accessibility experience many with disabilities deal with every day.

Those with sight, hearing, and motion impairments have a harder time getting access to information on the web. The main problem is most websites do not design with these use cases in mind.

There are approximately 1 billion people around the world that have some form of a disability according to the World Health Organization. What this means is as high as 15% of the world’s population are unable to effectively use a large number of websites on the web.

How can we ensure all users have equal access to content? How can developers, as they create websites, express themselves inclusively on the web?

Accessibility work, also known as a11y, strives to improve the web experience for those with disabilities in an effort to ensure web content and experiences are available and equal for everyone.

Rob Dodson, a Developer Advocate for Chrome at Google, is one of the advocates working to better web accessibility.

Fundamentally, accessibility is defined as a person’s ability to be able to access something, whether that be information, physical access, or even the ability to work comfortably. When we speak of accessibility for the web, we usually mean technical access, which includes the ability to read a web page, book a plane ticket online, or order food from a website. The goal for accessibility work is to help people interact with their environment and remove the impediments a person may experience in doing so.

It is easy for those without disabilities or impairments to take things like vision for granted. If you are able to effectively see your computer screen and consume information, you don’t think about how your experience might be impacted if you could not see the website you are viewing.

Most accessibility problems stem from user experience design, and can even be as simple as color contrast on a website. If text on a page has low contrast with the background color, it’s not just an access issue but also a usability issue; anyone viewing the page will have a hard time seeing the content clearly.

User efficiency also comes in to play when designing websites for accessibility. When considering the experience for users with motor impairments, or persons with a limited range of motion, it becomes more apparent as to why we need to be considering accessibility. These users may only be able to move a finger to activate a switch device, or use a similar sip and puff device to navigate the page. For these users, the ability to skip a menu and use the site with less button clicks becomes fundamental for efficient navigation.

But we must look further than the simple goal of helping everyone have equal access to the web. Accessibility is one of the key pieces in building the future of the web. If we look to many of the cutting edge products today, we begin to realize that many such as Amazon Alexa or Google Home began as accessibility technology. As AI becomes more integrated into our everyday lives, we often take for granted the text to speech capabilities we rely on was once only used for a calculator for the blind.

In following this concept of how accessibility work accelerates advancement of technology, we must take note of the significance of semantics when developing websites. Adding semantics to a web page will not only improve the experience for visually impaired or blind users today by improving the ability for screen and braille readers to work effectively, but it may also help lay a foundation for how future AIs understand and interact with our pages. As we build AIs, we must train them and teach them what it means to be a web page, and this comes with adding semantics.

Voice interactions, a technology increasingly relied on for communicating with digital assistants and AIs, are also an assistive technology. Voice interaction allows users with motor or vision impairments to access content and perform certain actions more easily than having to directly manipulate a switch device or screen reader. And much like voice interaction technology may help a blind person navigate the world around them they cannot see, it also can help tell users where to go in a foreign country where they cannot read or understand the native language.

Soon, AI will be able to help us around our homes. Ideas like this are being beta tested today. If Amazon Alexa were able to reliably help a user fix a toilet or install plumbing while the user was working on a project, we will have successfully leaped into the future. With more semantic content available for AI to consume, enablement of this type of assistance could be possible faster.

While developers work harder to understand the importance of accessibility, bodies such as WAI-ARIA (Web Accessibility Initiative — Accessible Rich Internet Applications) and WCAG 2.0 (Web Content Accessibility Guidelines) are working on improving web standards. Currently tools like aXe and WAVE can be used today to help audit a site to see how well it meets these standards.

Though still in the standardizing phase, there is hope in early accessibility work in JavaScript that will hopefully have a large impact in the accessibility community. Items like the inert attribute for HTML will make it easier to build accessibility modal dialogs. Focus-ring and evangelizing the importance of, is key for users with motor impairments.

To learn more about accessibility, you can check out a11ycasts, a YouTube show Rob produces focused on accessibility and his free accessibility udacity course.

About the author

Tracy Lee

Tracy Lee

CEO, This Dot Labs

Partner, This Dot Google Developer Expert (Angular, TC39, Web) RxJS Core Team & Lead for RxJS Learning Team Contributor to Angular, RxJS, EmberJS __Key Strengths:__ - Teaching developer relations strategies - Influencer marketing - Developing brands - Community strategies and maintenance - Creating product launch strategies - Leading marketing operations efforts & standards - Effective conference presence & speaking strategies

Keep reading

View all posts →

This Dot AI Field Notes - Anatomy of a Coding Harness

A coding agent is not magic, it’s a loop. We call this a harness. The harness is a deterministic layer of code that wraps an LLM....

1 min
AI

AI Is Speeding Up Development. But Where Are the New Bottlenecks?

AI is accelerating development, but it’s also exposing everything else that’s broken. At the Leadership Exchange, leaders unpacked how AI is reshaping the SDLC and what organizations need to address beyond just coding to make adoption successful. Moderated by Rob Ocel, VP of Innovation at This Dot Labs, the panel featured Itai Gerchikov at Anthropic and Harald Kirschner, Principal Product Manager for GitHub Copilot & VS Code at Microsoft. Panelists explored the current state of AI adoption across the software development lifecycle and shared practical insights into how organizations can effectively integrate AI tools. Panelists discussed how companies are investing in AI tools, skills, and managed competency programs to support developers. While AI can dramatically accelerate coding, the panel emphasized that adoption affects every stage of the SDLC. Bottlenecks now appear in testing, DevOps, product delivery, and marketing as AI speeds up development. Organizations that address technical debt and process inefficiencies are better positioned to extract maximum value from AI tools. The conversation also focused on opportunities and risks. Security, governance, and workforce education were highlighted as critical factors for adoption. Panelists stressed that AI initiatives should be aligned with broader business goals rather than pursued in isolation. They noted that companies experimenting at the cutting edge need to consider organizational readiness just as carefully as technical capabilities. Panelists also explored how leading organizations are navigating the early stages of adoption. Those ahead of the curve are using structured experimentation, prioritizing process improvements, and continuously evaluating outcomes to refine their AI strategies. Learning from these early adopters allows other organizations to anticipate emerging trends and prepare for the next phase of AI adoption rather than simply replicating past approaches. Key Takeaways Investing in AI skills and tools should be done thoughtfully, with clear alignment to business objectives. Examining the full SDLC helps identify bottlenecks that AI may accelerate or expose. Organizations can gain a competitive advantage by learning from early adopters and planning for where AI adoption is heading. AI adoption is not just a technical initiative; it is a strategic transformation that requires attention to people, process, and technology. Organizations that balance innovation with operational discipline will be best positioned to capture the full potential of AI across the software lifecycle. Seeing similar challenges in your own SDLC? Let’s compare notes. Join us at an upcoming Leadership Exchange or reach out to continue the conversation. Tracy can be reached at tlee@thisdot.co....

Calypso Hernandez2 mins
AI AdoptionAILeadership ExchangeEngineering Leadership

Making AI Deliver: From Pilots to Measurable Business Impact

A lot of organizations have experimented with AI, but far fewer are seeing real business results. At the Leadership Exchange, this panel focused on what it actually takes to move beyond experimentation and turn AI into measurable ROI. Over the past few years, many organizations have experimented with AI, but the challenge today is translating experimentation into measurable business value. Moderated by Tracy Lee, CEO at This Dot Labs, panelists featured Dorren Schmitt, Vice President IT Strategy & Innovation at Allen Media Group, Greg Geodakyan, CTO at Client Command, and Elliott Fouts, CAIO & CTO at This Dot Labs. Panelists discussed how companies are moving from early AI experiments to initiatives that deliver real results. They began by examining how experimentation has evolved over the past year. While many organizations did not fully utilize AI experimentation budgets in 2025, 2026 is showing a shift toward more intentional investment. Structured budgets and clearly defined frameworks are enabling companies to explore AI strategically and identify initiatives with high potential impact. The conversation then turned to alignment and ROI. Panelists highlighted the importance of connecting AI projects to corporate strategy and leadership priorities. Ensuring that AI initiatives translate into operational efficiency, productivity gains, and measurable business impact is essential. Companies that successfully align AI efforts with organizational goals are better equipped to demonstrate tangible outcomes from their investments. Moving from pilots and proofs of concept to production was another major focus. Governance, prioritization, and workflow integration were cited as essential for scaling AI initiatives. One panelist shared that out of nine proofs of concept, eight successfully launched, resulting in improvements in quality and operational efficiency. Panelists also explored the future of AI within organizations, including the potential for agentic workflows and reduced human in the loop processes. New capabilities are emerging that extend beyond coding tasks, reshaping how teams collaborate and how work is structured across departments. Key Takeaways Structured experimentation and defined budgets allow organizations to explore AI strategically and safely. Alignment with business priorities is essential for translating AI capabilities into measurable outcomes. Governance and workflow integration are critical to moving AI initiatives from pilot stages to production deployment. Successfully leveraging AI requires a balance between experimentation, strategic alignment, and operational discipline. Organizations that approach AI as a structured, measurable initiative can capture meaningful results and unlock new opportunities for innovation. Curious how your organization can move from AI experimentation to real impact? Let’s talk. Reach out to continue the conversation or join us at an upcoming Leadership Exchange. Tracy can be reached at tlee@thisdot.co....

Calypso Hernandez2 mins
AI AdoptionAILeadership ExchangeEngineering Leadership

What does it actually look like to build software with AI today? Not in theory, but in practice.

What does it actually look like to build software with AI today? Not in theory, but in practice. At the Leadership Exchange, this was the question at the center of the Developer Panel, where leaders from across the industry unpacked what’s really changing inside engineering teams and what organizations need to do right now to keep up. The Developer Panel at the Leadership Exchange explored the cutting edge of AI in software engineering and examined what organizations should focus on today to prepare for the future. Moderated by Jeff Cross, Co Founder & CEO at Nx, the panel featured Victor Savkin, Cofounder & CTO at Nx, Alex Sover, Vice President of Engineering at OpenAP, Brent Zucker, Senior Director of Engineering at Visa, and Jonathan Fontanez, AI Engineering Lead at This Dot Labs. Panelists shared insights into how AI is transforming the software development lifecycle and how teams can adopt tools effectively while preparing for organizational change. Panelists discussed emerging workflows, including CI in the loop, agentic healing, and context engineering. They examined how validation, code reviews, and PRDs are evolving alongside AI capabilities and how teams are integrating external sources such as production traces to improve quality and reliability. The discussion also covered what the next generation of agentic tools might look like and how these capabilities will shape engineering practices in the near future. Adoption of AI comes with challenges. Teams often rely on plugins or extensions without foundational understanding, and individual contributors may fear displacement. Panelists emphasized that education, governance, and skill building are essential for teams to manage AI agents effectively while maintaining quality. They also highlighted the need to standardize workflows and ensure organizational alignment to fully leverage AI capabilities. The conversation extended beyond technical challenges to organizational implications. Panelists discussed how teams can avoid issues like Conway’s Law, manage distributed teams effectively, and evolve engineering practices alongside AI adoption. Leadership and management strategies play a crucial role in ensuring that AI integration delivers meaningful outcomes while maintaining efficiency and alignment with business objectives. Key Takeaways AI workflows require both technical and organizational preparation. Education, governance, and skill development are essential for successful implementation. Forward looking teams are rethinking validation, CI pipelines, and context management to fully leverage agentic AI. The discussion highlighted that adopting AI at the cutting edge is not just about new tools it is about rethinking processes, workflows, and organizational culture. Companies that embrace this holistic approach are most likely to succeed in leveraging AI to its full potential. Are you interested in more conversations like this? Message us for an invite to the next, or for a private discussion around these topics. Tracy can be reached at tlee@thisdot.co....

Calypso Hernandez2 mins
AI AdoptionAILeadership ExchangeEngineering Leadership