Why data reliability is the new AI battleground?

The agentic revolution: Data as the cornerstone

Sam was quick to pivot the conversation from the buzz of AI models to what he sees as the true competitive differentiator: high-quality data. Sam stated, "The agentic revolution is here... but to get the maximum use out of these new AI and agentic capabilities, it depends on high-quality data." He noted that while a handful of large companies are focused on building world-class models, every individual enterprise’s true competitive edge lies in its own proprietary data. The current challenge, he explains, is the struggle to move beyond simple proofs of concept (POCs), emphasizing that, "As soon as you scale anything to any meaningful size, quality, trustworthiness, [and] feasibility is a fundamental requirement that many large companies are missing."

According to Sam, this realization is driving a "huge shift" in investment priorities. Executive and investment committees are realizing it’s not just about implementing one AI use case after another, but about establishing a robust data foundation, complete with the right levels of master data management, data governance, and assigned data owners and data stewards. These roles are essential for taking responsibility for data assets and, increasingly, data products, which are fed into the algorithm. Sam cautioned that, "If that data is not reliable and if it's not correct, even the best model in the world... is going to take an incorrect action or incorrect decision."

Escaping the Proof-of-Concept trap and the three layers of scale

The difficulty many enterprises face in moving pilot projects to full-scale production stems from a fundamental design flaw, according to the Lingaro leader. Sam asserted that even when designing a proof of concept, one has to design for scalability because a small use case alone will not make a meaningful difference to the company at scale. Successful scaling, he argued, depends on three critical layers working in concert. First, there must be an understanding of the broader business process impact of the AI solution.

Second, the architecture must be correctly designed, which increasingly means fusing a centralized architecture with a distributed architecture to combine centrally held data with real-time data from the edge. This requires a fundamental reworking of hybrid architectures. Finally, and most importantly, it's the reliability of the data that is fed in. The most advanced companies adopting AI at scale have the right level of data governance, with Chief Data Officers setting the rules, and dedicated data owners to curate essential data components. Sam stressed that until a unified owner is managing and curating data sets, ensuring consistency across multiple systems, "you'll never be able to feed a consistent data into the redesigned architecture."

The CIO's new mandate: From order taker to business transformer

The pressure to deliver measurable ROI on Generative AI has profoundly changed the role of the Chief Information Officer (CIO). Sam, a former CIO himself, emphasized that the job has evolved from being an "order taker" to a strategic partner. "Today, the board, the CEO is coming to the CIO and saying, how can you help us transform the business?"

The biggest barrier, he observed, is often whether the tech function has a fundamental understanding of what the business does, how the business runs. He pointed out that this signifies that, "This world of tech and business is collapsing really, really quickly." The successful leader of tomorrow will be the one who understands both business and technology and how they fundamentally work together. This convergence is turning the CIO seat into a more empowered and influential position at the executive table, predicated on understanding how the business can operate better.

Partnership, In-sourcing, and Global Reach

On its approach to working with India's burgeoning Global Capability Centers (GCCs), Lingaro deliberately positions itself away from the traditional outsourcing model. Sam explained, "Our business model is much more strongly aligned with working in a very close partnership with the business to build their internal capability." By focusing on this "in-source" capability building, Lingaro aims to sit alongside GCCs, bringing its global talent and data expertise to help enterprises scale their internal teams for future success.

To meet its projected 30% year-on-year growth in the Indian market, Lingaro is focusing its investments in several ways. Firstly, it prioritizes strong partnerships with hyperscalers and data product companies to build solutions. Secondly, it employs a strategy of having virtually located employees to tap into the "best data talent in the country, wherever they’re located," whether in Bangalore, Delhi, or Pune, offering them compelling global projects.

Finally, it's investing heavily in its market presence, to build the Lingaro brand within India's world-class technology ecosystem. Sam added that providing global scale while ensuring local market relevance is achieved by both having a distributed global presence (in India, Poland, the Philippines, and Mexico) and by cultivating deep domain knowledge, understanding concepts like price elasticity and revenue growth management within specific sectors like CPG, which he considers Lingaro's "secret sauce."

Attracting and retaining the best data talent

Addressing the global skill shortage in AI and data, Sam acknowledged that talent is the ultimate challenge for service providers. Lingaro's low attrition rate, he believes, is a result of focusing on a specialized niche and offering a compelling value proposition to senior experts. The company focuses on the niche of data and the "agentic evolution," which is a highly attractive space right now. Lingaro also provides a high degree of flexibility for employees to work from wherever they want, promoting a strong work-life balance alongside high expectations. Crucially, it offers "some of the most complex, most exciting projects to work on with some of the best brands from around the world," ingredients that help attract and retain the best talent.

Looking ahead to 2026, Sam concluded by summarizing his top priorities through the dual lens of employees and clients. The quality of services depends on talent, so the focus will remain on attracting the right talent, empowering them, and equipping them with the necessary skills. For the market, the priority is to spread the message that Lingaro can combine the agentic workflow with high-quality data to deliver reliable business actions, aiming to convert more prospective companies into clients.