What are the two main options for AI adoption?

• A. Purchase commercial off-the-shelf (COTS) software or develop software in-house
• B. Use a cloud service only
• C. Hire consultants to build algorithms
• D. Open-source tools only

Answer: (A)

Two broad paths for adopting AI are to buy a ready-made AI solution or to develop a custom solution in-house.

Buying a commercial off-the-shelf (COTS) AI software means selecting a product that comes with pre-built models, interfaces, and support. This can speed up deployment, reduce the need for deep in-house data science expertise, and provide vendor-backed updates. However, it may offer limited customization, potential vendor lock-in, and less direct control over data handling and how the models operate.

Building in-house gives you full control to tailor models to your specific processes, data, and security requirements, which can yield a unique competitive edge. It also allows you to incrementally develop capabilities and integrate deeply with existing systems. The trade-offs are higher upfront investment, longer time-to-value, ongoing maintenance, and the need for skilled data scientists and strong governance.

The other options described—using cloud services only, hiring consultants to build algorithms, or relying exclusively on open-source tools—are not the two main adoption paths. They can be part of either approach (for example, a COTS deployment in the cloud, or in-house work that uses open-source tools), but they don’t define the two foundational choices for how an organization typically adopts AI.