Designing for Limitations: How TCS Is Using AI to Make Accessibility a Competitive Edge

For decades, accessibility lived in the same bucket as most compliance work: bolted on after the product was built, driven by regulation, and measured by whether complaints stopped coming in. In a recent interview, Constellation analyst Holger Mueller sat down with Shashi Bhushan and Charudatta Jadhav of TCS to make the case that this era is ending, and that AI is the reason the shift is happening now rather than another decade from now.

From Compliance to Competitive Advantage

The starting number in the conversation is hard to ignore: one out of six people globally lives with a disability. For years, that population was treated as a niche segment with limited purchasing power, worth accommodating for legal and reputational reasons but not worth building a strategy around. That calculation is changing. Bhushan pointed to an estimated $1 trillion in aggregate purchasing power among people with disabilities globally, along with anecdotal evidence that companies that strategically serve this segment see substantially higher revenue outcomes.

The panel drew a direct comparison to how organizations now treat cybersecurity spending: initially driven by fear and regulation, but increasingly justified on business grounds. Accessibility, they argued, is on a similar trajectory, just a few years behind. As Bhushan put it, once the realization sets in that this is a one-out-of-six customer base rather than an act of goodwill, board conversations shift from good intentions to competitive strategy.

Designing for Limitations, Not Personas

One of the more interesting reframes in the conversation was TCS's move away from traditional persona-based accessibility design. The conventional approach starts with a defined disability persona and designs around its known requirements, useful, but inherently reactive and limited to whatever personas were considered upfront.

TCS's alternative, which Jadhav called "designing for limitations," starts instead from the limitation itself: sensory, environmental, or ecosystem-level constraints. Rather than asking "how do we accommodate this persona," the question becomes "how do we solve for this class of limitation." Jadhav noted that this approach tends to produce a wider canvas for innovation, and frequently ends up improving the experience for people outside the original target group entirely, a pattern accessibility work has produced repeatedly produced: features built for a specific limitation often end up mainstream. Screen readers, originally built for visually impaired users, are a direct ancestor of the voice assistants now sitting on millions of kitchen counters.

Where AI Changes the Equation

The panel's most forward-looking material centered on what Jadhav called "egocentric models," AI systems trained specifically on accessibility-related data to understand how people with different sensory or physical constraints actually perceive and navigate the world. Unlike general-purpose large language models trained broadly on the internet, these models are purpose-built to understand, for example, how a visually impaired person constructs a mental map of a physical or digital space.

Jadhav extended this into a compelling long-term vision: an AI-powered "digital companion" that accompanies a person across every experience, banking, retail, travel, and beyond, retaining memory of preferences, context, and prior interactions throughout that person's life. Rather than solving accessibility one interaction at a time, the goal becomes eliminating the friction of the limitation itself from the equation entirely. Bhushan, drawing on his own lived experience navigating vision-related accessibility challenges, described the shift plainly: the goal is a world where technology adapts to the human, rather than the human adapting to the technology.

The Road Ahead

Looking toward 2030, the panel pointed to two converging forces: AI's growing ability to directly enhance human capabilities, and its growing ability to build genuinely equitable systems around those capabilities. Their closing framing captured the throughline of the conversation well: enterprises built around accessibility in the AI era will need to be, in Bhushan's words, SMART, sustainable, meaningful, accessible, resilient, and trusted, with technology functioning as an invisible enabler rather than a visible workaround.

The larger argument running through the discussion isn't really about accessibility as a category. It's about where the next decade of competitive differentiation in enterprise technology is likely to come from, and the panel makes a clear case that the answer includes a market segment most organizations have historically underpriced.