Cognizant launches AI training data services to rugby-tackle data debt

The irony of data’s role today is undeniable. While enterprise leaders acknowledge data as the ‘new oil,’ only a third of enterprises express confidence in their data’s ability to support strategic objectives. Enterprises confirm lack of data readiness as the number one obstacle to enterprise-wide AI adoption, outpacing talent shortages, budget constraints, and unclear ROI. Yet rather than treating data readiness as a prerequisite to AI, leading enterprises now flip the script, i.e., leveraging AI to unlock data’s massive value.

In this paradox lies immense opportunity. Data—when properly curated and aligned with AI strategies—opens pathways to monetize, optimize, and innovate. Companies such as Netflix and UnitedHealth’s Optum demonstrate that the strategic use of well-managed data yields tangible outcomes, including increased user engagement and improved patient care. Cognizant’s new offering is precisely targeted toward this shift.

Cognizant’s pivot: AI training data services to tackle data debt

Cognizant recently unveiled its AI Training Data Services, consolidating 10 years of industry-specific AI, domain-specific business processes, data expertise, and engineering experience into an integrated offering (see Exhibit 1).

Exhibit 1: Cognizant’s AI training data services framework outlines a comprehensive approach to unlocking data value for enterprise clients

Source: Cognizant Technology Solutions, 2025

While these capabilities, encompassing structured data, synthetic data creation, and human-in-the-loop processes, are not entirely unique, Cognizant has structured them within a robust advisory framework, placing industry knowledge at the core. Its sizable footprint, consisting of over 10,000 associates and 12 delivery centers, generating $150M+ annually in related services, underscores Cognizant’s maturity in this space. Essentially, as illustrated in Exhibit 2, Cognizant is trying to keep it simple by addressing the why, what, and how of the market.

Exhibit 2: Cognizant’s AI training data services hit the nub of the industry

Source: HFS Research, 2025

Unlike narrowly focused tech platforms, Cognizant’s differentiation is predicated on ‘data and AI training’ domain expertise, industry-specific solutions, and advisory support. Strategic partnerships with specialists such as LangChain, Datadog, and Hive offer comprehensive technical capabilities, including responsible AI frameworks and data observability that enhance trust, governance, and compliance.

While Cognizant’s AI Training Data Services hold clear promise, enterprise leaders must recognize the broader competitive landscape. Technology-first providers, such as Scale AI, and niche specialists continue to offer compelling alternatives. Moreover, success will ultimately depend on Cognizant’s ability to demonstrate quantifiable outcomes beyond advisory expertise and partnerships. Enterprises should scrutinize Cognizant’s approach to outcome measurement, scalability in complex data environments, and the ability to iterate and adapt across industries rapidly.

If past performance in the data field is anything to go by, Cognizant’s track record is impressive

Consider Cognizant’s work with autonomous vehicle systems: The firm has curated an extensive annotation and AI taxonomy for an automotive major that powers EV vehicles’ optimal performance, significantly enhancing accuracy and reducing operational risks.

Its partnership with one of the largest online video sharing platforms is equally compelling. Using contextual engineering and human-in-the-loop processes, Cognizant helped verify content creators efficiently, driving quicker monetization and significantly reducing manual effort. Similar innovations around AI-based playlist generation for music streaming improved content personalization and user engagement.

Enterprises scouring for AI solutions to tackle their data debt must take these immediate actions

• Prioritize iterative AI-driven data improvements: Adopt services explicitly designed to refine data quality progressively and demonstrate rapid incremental value.
• Initiate high-impact pilots: To validate the immediate potential of these services, begin with practical industry-specific applications, such as autonomous systems, digital content moderation, or personalization use cases.
• Evaluate ecosystem strength: Critically consider providers’ strategic partnerships and alliances, ensuring they align with your specific operational needs and scalability requirements.

The Bottom Line: Enterprises cannot afford to chase data perfection endlessly. Practical AI-driven services present a realistic pathway to improve data readiness and measure business outcomes immediately.

Enterprise leaders must urgently reconsider the entrenched notion that pristine data is essential before leveraging AI. Instead, adopting targeted, AI-driven training data services enables enterprises to rapidly refine their existing data assets, extracting immediate business value and identifying new revenue streams.