As organizations rush to implement generative AI across their entire operations, executive decision-makers find themselves standing at a critical crossroads. As I've observed through countless conversations with business leaders and technical stakeholders over the past several months, nearly every enterprise is wrestling with the same fundamental question: Should we build our proprietary generative AI solution or buy one from the growing marketplace of vendors? This seemingly straightforward strategic decision has become the tech equivalent of Shakespeare's "to be or not to be"—a question that often leads to circular debates and decision paralysis.
In boardrooms across industries, the CTO presents their case for building in-house: "We have the engineering talent. We can create something perfectly aligned with our complex workflows. We'll maintain complete ownership of our AI stack." These arguments carry significant weight in executive discussions where autonomy and competitive differentiation are prized.
On the other hand, some advocate for buying: "Time-to-market is critical. We lack the specialized AI expertise. We need a proven solution that won't drain our resources." Fast Company notes that this buy-oriented approach aligns with conventional enterprise IT strategies: acquire, integrate, and deploy.
Both perspectives have valid points, but the reality of implementing generative AI is considerably more nuanced. The traditional build-or-buy paradigm oversimplifies the complexities of enterprise AI deployment.
Building a generative AI platform isn't just about hiring skilled developers and data scientists. It requires deep expertise in transformer architectures, retrieval augmentation generation (RAG), vector embeddings, and infrastructure.
Choosing to build is a perpetual commitment. Unlike traditional software, where maintenance might be 20% to 30% of the cost, generative AI systems require continuous updates as models, best practices, and security requirements evolve rapidly. This burden grows exponentially, especially when scaling from proof-of-concept to enterprise-wide deployment.
Even technically sophisticated organizations rarely possess all necessary capabilities in-house. The technical debt extends beyond initial development, creating ongoing complexity. Building means committing to indefinite investment in the technology stack with no end to maintenance costs in sight.
For executives, the key question isn't just technical feasibility, but also strategic resource allocation. Consider building when ALL of these conditions are met:
The apparent simplicity of initial proof-of-concepts can be misleading. Creating a basic chatbot with a few documents is relatively easy, leading organizations to underestimate the complexity of enterprise-grade implementations.
Challenges arise when scaling from hundreds of documents to thousands (or hundreds of thousands) of documents, such as:
Scaling requires far more effort than initial implementation, with ongoing maintenance costs growing alongside usage. This scaling reality is rarely reflected in initial cost projections or timelines, leading many companies to fail at production-scale AI.
According to McKinsey, “It is relatively easy to build gee-whiz genAI pilots, but turning them into at-scale capabilities is another story. The difficulty in making that leap goes a long way to explaining why just 11% of companies have adopted genAI at scale.”
On the other side of the equation, the seemingly straightforward "buy" option comes with significant strategic constraints for executive decision-makers. When you purchase an off-the-shelf solution, you're effectively betting on a vendor's vision aligning with your enterprise needs—both present and future.
More concerning from a technical perspective is the unique nature of vendor lock-in within the generative AI ecosystem. Unlike previous technology waves where standards enabled interoperability (such as SAML for identity management or hybrid infrastructure for cloud deployments), most generative AI applications are inherently model-dependent. As one industry analysis bluntly states: "There's almost no such thing as an LLM-agnostic application... by definition, you are 'locked in'—you can't build an app where you can easily swap one model for another with no re-work." This creates a strategic dependency that's particularly problematic in a field evolving as rapidly as generative AI, where today's leading model may be outperformed in mere months.
For those evaluating this path, the critical question isn't “is it worth the money?” It’s much more for that. Therefore, the decision to buy should only occur when ALL of these conditions are met:
Forward-thinking enterprises are adopting a composable approach, blending building and buying. Monika Sinha, Gartner's VP, Analyst CIO Research, stated, “Traditional business thinking views change as risk, while composable thinking is the means to master the risk of accelerating change and to create new business value.”
This hybrid strategy leverages pre-built components for rapid deployment while maintaining the flexibility to customize critical elements for competitive differentiation. Technically, this means designing systems with modular components that can be easily updated.
A hybrid approach also preserves critical thinking capabilities by maintaining human expertise in strategic areas while leveraging AI for efficiency. In practice, this approach enables organizations to:
A well-designed hybrid architecture allows for component-level flexibility across the entire AI stack in areas such as:
The consequences of getting this decision wrong are substantial. Recently, I heard a story about an organization that had invested nine months into building an internal generative AI solution from scratch. The result was particularly sobering: When it was finally tested against production data, the system failed completely—rendering almost a year of intensive development effectively worthless.
This case illustrates why executive decision-makers must prioritize not just "time to value" but also "time to iteration." The ability to test hypotheses quickly, learn from failures, and adapt is perhaps the most critical success factor.
Executives must consider deployment architecture—a decision with significant technical and compliance implications. Flexibility in deployment options is crucial, especially for regulated industries and those with data sovereignty concerns. A truly flexible solution should support:
This flexibility is rare, as many vendors force customers into their own cloud infrastructure. For executives concerned with data governance, security, and operational integration, deployment flexibility should be considered a non-negotiable.
Since I work at Yurts, people frequently ask me how Yurts fits into this whole paradigm. Does choosing Yurts mean I’ve decided to buy? Or that I've chosen the hybrid approach? The answer is yes to both. Yurts gives you a fully featured, end-to-end generative AI platform that’s ready to use out of the box. All the components needed to deploy at scale are included such as integrations with common applications, analytics, a white labeled front-end, a model hub, and more.
But here’s where Yurts stands apart: The entire architecture is modular and pluggable. So yes, you can be up and running on day one—but you can also keep customizing it as your needs evolve. Want to swap in a new open-source model? Go for it. Integrate with Bedrock? Absolutely. Test out your latest jargon extractor? Knock yourself out.
You can think of Yurts as the Ship of Theseus if the Ship of Theseus were a generative AI platform. It starts off whole and complete, but it can change over time to fit you. And after enough tweaks, you might find yourself wondering: Is this even the same AI anymore?
The generative AI implementation strategy should move beyond the traditional build-vs-buy framework. Combining both approaches—building for unique value and buying for rapid deployment—is the most prudent path.
When asked about the build-or-buy decision, decision-makers should respond with: "We're pursuing a hybrid strategy that gives us immediate capabilities while preserving long-term flexibility." In generative AI, the best answer is often: "Both, strategically integrated."
By embracing architectural flexibility and rejecting false dichotomies, organizations can achieve sustained success in harnessing generative AI, regardless of which technologies or models lead the field. Request a demo to learn about how you can integrate Yurts into your operations.
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