Presented by AudioEye
While most organizations recognize the importance of accessibility from a theoretical angle, a stark gap exists between that awareness and actual execution. Companies can’t just give a nod to accessibility — and it can’t just be a nice-to-have. The chasm between knowing and doing is not only exposing businesses to significant legal risk, it’s also costing them actual business and growth opportunities.
According to AudioEye’s newly released 2026 Accessibility Advantage Report, 59% of business leaders say their organization would face legal risk due to accessibility failure if audited today, and more than half have already encountered accessibility-related lawsuits or threats. That’s unsurprising, because today the average web page still contains 297 accessibility issues, based on an analysis of over 15,000 websites in AudioEye’s 2025 Digital Accessibility Index.
The report, which surveyed more than 400 business leaders across the C-suite, VPs, and directors, reveals that organizations understand accessibility matters, but most lack the systems, expertise, and operational infrastructure to deliver it consistently, says Chad Sollis, CMO at AudioEye.
“What the data makes clear is that accessibility hasn’t stalled because people don’t care,” Sollis says. “It’s stalled because fragmented ownership and reactive workflows make it hard to sustain as digital experiences evolve. Leaders know accessibility matters, but their organizations aren’t set up to deliver it consistently.”
With regulations like the European Accessibility Act now in effect and enforcement intensifying globally, the benefits extend far beyond avoiding lawsuits. Over half of leaders now cite accessibility as a business growth opportunity, recognizing that accessible digital experiences drive better user outcomes across the board.
“Organizations that treat accessibility purely as a compliance exercise miss the opportunity to improve performance, reach new audiences, and build stronger digital experiences for everyone,” Sollis says. “Accessibility is a growth lever hiding in plain sight.”
In fact, accessible design doesn’t just serve users with disabilities; it creates faster, more intuitive experiences for everyone. Organizations leading in accessibility are seeing it as a performance multiplier that:
• improves site discoverability through better structure and cleaner code
• reduces friction in the customer journey
• strengthens brand loyalty by demonstrating inclusion in action
“The leaders making the smartest decisions aren’t asking, ‘What’s the fastest fix?’” Sollis adds. “They’re asking, ‘What gives us durable protection while improving experience?’”
Despite widespread recognition of accessibility’s importance, implementation remains inconsistent. The report identifies what AudioEye calls “The Yet Problem,” or the gap between good intentions and actual execution.
While many business leaders say they actively champion accessibility, the same percentage cite low budgets and limited expertise as barriers. Developers, designers, and content creators want to build accessible experiences. But when accessibility isn’t integrated into their everyday tools and processes, it creates additional complexity — with extra steps, extra time, and extra cost added to already heavy workloads and tight deadlines.
The result is what the report calls “patchwork accessibility,” or programs that appear compliant on paper but fail users in practice. Many organizations treat accessibility as a project to complete rather than a practice to maintain, pursuing compliance milestones or quick fixes without building sustainable systems.
“Accessibility doesn’t fail because companies aren’t trying; it fails because it’s treated as a single-layer problem,” Sollis says. “Real accessibility spans code, content, design, and ongoing change.”
This pattern reveals a fundamental truth: accessibility is failing because the systems supporting it weren’t built for the people doing the work. Until accessibility is easier to design, build, and track alongside other priorities, it will continue to be deprioritized.
Even when leaders secure better tools and a larger budget, progress often stalls because of the misconception that accessibility must be tackled entirely in-house. AudioEye calls this “the in-house illusion,” or the assumption that internal responsibility automatically translates to organizational ability.
“There’s a growing gap between ownership and capability,” Sollis explains. “Managing accessibility within the company can create the illusion of control, but without the right expertise and support, progress often stalls.”
In fact, while nearly half of organizations manage accessibility with their own teams, 50% admit those teams lack internal accessibility expertise, and 43% cite competing priorities as major barriers. Only 47% describe their programs as proactive, while the rest operate reactively or meet only bare minimums.
The illusion persists because many organizations equate ownership with control, and control with efficiency. In reality, accessibility is a specialized, evolving discipline.
Without cross-functional expertise and external guidance, well-intentioned teams end up doing more work for less impact and more cost. True ownership doesn’t mean doing everything yourself, but knowing where to partner, automate, and delegate.
The organizations advancing fastest are rethinking ownership altogether, treating accessibility as a system to orchestrate rather than a silo to control.
The report’s findings point toward a clear path forward: organizations must move accessibility from aspiration to operational habit. This requires giving teams what they need to implement, maintain, and measure accessibility efficiently.
Leading companies are building scalable systems that make accessibility part of everyday work. Plus, they’re elevating it from a compliance cost to a growth opportunity, in order to secure adequate budget and internal resources. And they’re quantifying the impact of the work, to demonstrate that accessibility improvements drive traffic, reduce abandonment, and expand total addressable market.
Most importantly, they’re recognizing that sustainability often requires partnership.
“The organizations making the most progress are the ones treating accessibility as an always-on system rather than a one-time project,” Sollis says. “That means using automation to handle scale, pairing it with expert review for complex, high-risk issues, and backing it all with protection that actually holds up when legal claims arise.”
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Recent reports about AI project failure rates have raised uncomfortable questions for organizations investing heavily in AI. Much of the discussion has focused on technical factors like model accuracy and data quality, but after watching dozens of AI initiatives launch, I’ve noticed that the biggest opportunities for improvement are often cultural, not technical.
Internal projects that struggle tend to share common issues. For example, engineering teams build models that product managers don’t know how to use. Data scientists build prototypes that operations teams struggle to maintain. And AI applications sit unused because the people they were built for weren’t involved in deciding what “useful” really meant.
In contrast, organizations that achieve meaningful value with AI have figured out how to create the right kind of collaboration across departments, and established shared accountability for outcomes. The technology matters, but the organizational readiness matters just as much.
Here are three practices I’ve observed that address the cultural and organizational barriers that can impede AI success.
When only engineers understand how an AI system works and what it’s capable of, collaboration breaks down. Product managers can’t evaluate trade-offs they don’t understand. Designers can’t create interfaces for capabilities they can’t articulate. Analysts can’t validate outputs they can’t interpret.
The solution isn’t making everyone a data scientist. It’s helping each role understand how AI applies to their specific work. Product managers need to grasp what kinds of generated content, predictions or recommendations are realistic given available data. Designers need to understand what the AI can actually do so they can design features users will find useful. Analysts need to know which AI outputs require human validation versus which can be trusted.
When teams share this working vocabulary, AI stops being something that happens in the engineering department and becomes a tool the entire organization can use effectively.
The second challenge involves knowing where AI can act on its own versus where human approval is required. Many organizations default to extremes, either bottlenecking every AI decision through human review, or letting AI systems operate without guardrails.
What’s needed is a clear framework that defines where and how AI can act autonomously. This means establishing rules upfront: Can AI approve routine configuration changes? Can it recommend schema updates but not implement them? Can it deploy code to staging environments but not production?
These rules should include three elements: auditability (can you trace how the AI reached its decision?), reproducibility (can you recreate the decision path?), and observability (can teams monitor AI behavior as it happens?). Without this framework, you either slow down to the point where AI provides no advantage, or you create systems making decisions nobody can explain or control.
The third step is codifying how different teams actually work with AI systems. When every department develops its own approach, you get inconsistent results and redundant effort.
Cross-functional playbooks work best when teams develop them together rather than having them imposed from above. These playbooks answer concrete questions like: How do we test AI recommendations before putting them into production? What’s our fallback procedure when an automated deployment fails – does it hand off to human operators or try a different approach first? Who needs to be involved when we override an AI decision? How do we incorporate feedback to improve the system?
The goal isn’t to add bureaucracy. It’s ensuring everyone understands how AI fits into their existing work, and what to do when results don’t match expectations.
Technical excellence in AI remains important, but enterprises that over-index on model performance while ignoring organizational factors are setting themselves up for avoidable challenges. The successful AI deployments I’ve seen treat cultural transformation and workflows just as seriously as technical implementation.
The question isn’t whether your AI technology is sophisticated enough. It’s whether your organization is ready to work with it.
Adi Polak is director for advocacy and developer experience engineering at Confluent.
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