Build vs Buy in AI: A Framework for Decision Making

The false dichotomy

"Should we build or buy?" is the wrong question. The real question is: "What parts should we build, what parts should we buy, and what parts should we wait on?"

Almost no organization should build everything from scratch. Almost no organization should buy everything off-the-shelf. The art is in knowing which is which.

When to buy

Buy when the problem is well-understood and common across industries.

Examples:

• Document processing and OCR
• Basic sentiment analysis
• Speech-to-text transcription
• Standard chatbot functionality
• Image classification for common objects

These are solved problems. Hundreds of companies have built solutions. The technology is mature, the vendors are established, and you're unlikely to gain competitive advantage by building your own.

When to build

Build when the solution is core to your competitive advantage or requires deep integration with proprietary data and processes.

Examples:

• Customer behavior prediction using your unique data
• Product recommendations based on proprietary understanding
• Risk models specific to your industry and customer base
• Automation of processes unique to your operations

If the AI system is going to be a source of competitive differentiation, you probably need to build it.

The hybrid reality

Most real-world AI implementations are hybrid:

Buy the foundation, build the application. Use pre-trained models and cloud AI services for the heavy lifting, but build the application layer that integrates with your specific data and workflows.

Start with buy, migrate to build. Prove the concept with an off-the-shelf solution, then build custom when you understand the requirements better.

The most common mistake

The most common mistake is building commodity AI to prove you can, while buying solutions for the problems that actually differentiate your business.

This is backwards. Build where it matters. Buy where it doesn't.