How do you approach build versus buy decisions for AI and ML capabilities? What factors influence your recommendation?

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Accepted Answer

The build versus buy decision for AI capabilities requires balancing speed to market, competitive differentiation, total cost of ownership, and organizational capabilities.

When evaluating whether to buy, I consider several factors. Buying makes sense for commodity capabilities where speed matters and differentiation is not the goal. Standard use cases like document processing, sentiment analysis, or generic recommendation engines often have mature vendor solutions. Buying is faster, typically delivering value in weeks rather than months. It also provides predictable costs and shifts maintenance burden to the vendor. However, buying comes with limitations. Vendor solutions may not fit unique business requirements, and organizations become dependent on external roadmaps.

When evaluating whether to build, the calculus changes. Building is appropriate when the AI capability is core to competitive advantage. If your ML models directly differentiate your product or create barriers to entry, owning that intellectual property matters. Building also makes sense when your requirements are truly unique and no vendor solution adequately addresses your needs. Additionally, building offers more control over data privacy, model behavior, and integration with existing systems.

In practice, most organizations end up with a hybrid approach. They buy platforms and infrastructure while building custom models for differentiated use cases. For example, an organization might use a cloud provider's ML platform for compute and tooling, purchase pre-trained foundation models, but build custom fine-tuned models for their specific domain.

Key factors I evaluate include time to value, where buying wins for standard use cases. I assess strategic importance, where building wins for core differentiators. I examine internal capabilities, since building requires specialized talent that is expensive and scarce. I consider total cost of ownership over a multi-year horizon, as building often has higher upfront costs but lower ongoing costs at scale. Finally, I evaluate vendor ecosystem maturity, since immature markets favor building while mature markets favor buying.

Gartner research suggests that by 2028, over 50 percent of enterprises that built AI models from scratch will abandon those efforts due to costs, complexity, and technical debt. This reinforces the importance of being selective about where to invest in custom development.