undefined

Abstract blue light streaks forming futuristic data tunnel with glowing particles

The AI Builder advantage starts at the foundation

June 03, 2026

Delivering AI at scale requires more than model capability. It requires the infrastructure knowledge, integration experience, organizational fluency and long-term client trust that comes from doing this work across thousands of enterprise environments over decades.

Visit our AI Builder webpage

<a rel="noopener noreferrer" href="https://www.cognizant.com/us/en/insights/insights-blog/why-ai-builders-are-essential-for-large-enterprises" target="_blank">The AI deployment market</a> is maturing fast, and the influx of new entrants is confirmation of how large and legitimate the opportunity has become. Model companies, including our partners at OpenAI and Anthropic, are branching out into delivery<a></a><a>. </a>Capital is flowing toward the conviction that the distance between AI capability and enterprise value <a rel="noopener noreferrer" href="https://www.cognizant.com/us/en/insights/insights-blog/bridge-to-ai-value-will-be-built-not-bought" target="_blank">is the most consequential gap</a> in technology right now. That gap will define the next era of enterprise technology. What the most forward-thinking enterprise leaders already understand is that delivering AI at scale inside complex organizations is a distinct discipline. It requires more than model capability—it requires the infrastructure knowledge, integration experience, organizational fluency and long-term client trust that comes from doing this work across thousands of enterprise environments over decades. <h3>The market is enormous, and the gap is significant</h3> According to our latest pulse poll, only 13% of enterprise leaders say their AI capabilities match their AI ambitions. That means nearly 9 in 10 large organizations need help deploying it in ways that produce measurable outcomes. Consider that the G2000 alone represents nearly $100 trillion in combined revenue and cost. This is one of the largest technology deployment challenges in history and, as such, one of the greatest opportunities the IT services sector has ever seen—potentially worth up to $6 trillion.1 With numbers like this, it's easy to understand why AI deployment is quickly becoming a must-have offering for companies across the tech sector.

We have seen this dynamic before, and <a rel="noopener noreferrer" target="_blank" href="https://www.cognizant.com/us/en/insights/insights-blog/it-services-firms-underwriting-outcomes">we are ready for the challenge</a>. When enterprises needed to migrate to the cloud, the work required an ecosystem of implementation partners, infrastructure specialists and change management experts operating simultaneously across thousands of client environments. The same was true when decades of accumulated legacy infrastructure needed modernization. Now, AI deployment is that moment—and the complexity is at least as high. In fact, the organizational change required is deeper, and the stakes are larger. <h3>From systems integrators to AI builders</h3> According to our research, when large organizations report measurable AI success, 65% of them trace back their results to solutions that are external, customized or partner-built. Only 17% say they come from internally developed tools. The organizations achieving strong outcomes are those working with partners that can own the full operating stack. This means not just the model but also the workflow, the data architecture and the accountability for what it produces. To meet that challenge, leading IT services firms have evolved from systems integrators into AI builders—creating the platforms, models and tooling required to deliver outcomes rather than implement off-the-shelf software. <a rel="noopener noreferrer" href="https://www.cognizant.com/us/en/insights/insights-blog/talent-architecture-for-ai-workforce" target="_blank">Talent is shifting</a> from traditional delivery pyramids toward interdisciplinary teams fluent in business domain, operations and technology simultaneously. <a rel="noopener noreferrer" href="https://www.cognizant.com/us/en/insights/insights-blog/new-value-model-for-enterprise-services" target="_blank">Delivery is moving</a> from labor-based models toward platforms and managed services accountable for results. And providers are increasingly underwriting operational outcomes at scale—something that cloud infrastructure, real-time data and agentic AI now make genuinely possible. What makes that evolution credible is what preceded it. Decades of systems integration work—modernizing legacy infrastructure, building the data architecture that enterprises run on, managing the integration fabric that connects it all—is the foundation that successful AI builds require. We have learned by doing, and our deep partnerships with frontier providers—like Open AI, Anthropic, Palantir and others—have been earned through years of collaborative work and delivery. When we deploy Palantir's Foundry and AIP within our healthcare operations at TriZetto or bring Anthropic's frontier models to bear through deep domain knowledge, we are not simply layering AI onto existing workflows—we are redesigning the entire foundation. <h3>Frontier-embedded delivery is the future of enterprise AI value creation</h3> Consider a financial services firm deploying an AI-driven credit risk model that needs to integrate with a core banking system that has been running since 1997. Finding a capable AI model is now the easy part. The harder work is everything else: data quality issues buried in legacy infrastructure, compliance requirements governing how model outputs can be used, change management required to shift how analysts manage their new workstreams, integration complexity spanning decades of technical history, and the organizational trust-building that ensures AI decisions can be explained, defended and accepted by the people and regulators they affect. That kind of challenge requires engineers embedded inside the client organization who understand the operational texture of the business, have earned the institutional trust to navigate genuine constraints, and can build solutions shaped by the data and context surrounding them. <h3>The multi-model advantage</h3> Enterprise AI strategy is not a single-vendor decision, and the ability to operate fluently across the ecosystem—rather than advocate for any one platform—is a useful skill learned by building every iteration of enterprise IT architecture over multiple decades. The organizations navigating this moment most effectively draw on different models and platforms for different use cases, building governance structures that make those investments coherent over time. Our partnerships with frontier providers are sustainable precisely because Cognizant's value lies in the integration layer: the data architecture, the workflow design and the operational accountability that make model capability usable at enterprise scale. That independence is what makes genuine advisory possible. <h3>The AI Builder's foundation</h3> The enterprises best positioned to capture AI's potential are those with the data infrastructure to feed models meaningfully, the integration fabric to connect AI outputs to operational workflows, and the governance structures to deploy AI responsibly. Building those foundations is the prerequisite. Owning the outcomes they enable is the opportunity. <a rel="noopener noreferrer" href="https://www.cognizant.com/us/en/insights/insights-blog/closing-gap-between-ai-infrastructure-investments-and-business-value-realization" target="_blank">The gap between enterprise AI ambition and capability</a> will not close through product launches or single-vendor bets. It closes through sustained organizational work that shifts accountability from project delivery to operational outcomes—and that requires a partner with the foundation, the fluency and the track record to stand behind the results. That is the AI builder's work, and the ones who do it best will define what enterprise success looks like in the decade ahead. 1 <a rel="noopener noreferrer" target="_blank" href="https://www.gartner.com/en/newsroom/press-releases/2026-02-03-gartner-forecasts-worldwide-it-spending-to-grow-10-point-8-percent-in-2026-totaling-6-point-15-trillion-dollars">\Gartner</a> estimates the 2026 IT Services Market at ~ 1.87 trillion. Roughly 11%-14% of enterprise revenues are spent to operate corporate functions alone, including labor. 11% of global Fortune 500 companies’ revenue in 2025 ($41T) translates to ~$4.5T. Together, IT services and operational spend would yield a market of over $6T.

Babak Hodjat