Buy vs Build: navigating AI solutions

As technology leaders, we face the critical decision of whether to buy or build solutions, a choice that impacts both immediate capabilities and long-term competitive advantage. In today's digital landscape, where business models are increasingly defined by software and AI, making the right decision is crucial.

On the buy side, providers like OpenAI, Google and Anthropic with their GPT, Gemini and Claude model families offer many out-of-the-box options. AI-powered solutions built with these foundational model providers require less initial capital investment and allow for rapid integration, making them ideal for quick prototyping or proof of concept. However, as your AI solutions scale, the focus inevitably shifts to the total cost of ownership. Developing tailored applied AI solutions, via fine-tuning open-source language models, can offer lower costs and better alignment with specific business needs. Fine-tuned open-source language models oftentimes also provide a quality exceeding foundational models, as measured by the specific business applications. 

Here are some of the practical steps you can take to assist with your decision-making:

• Assess operational realities: Evaluate your existing infrastructure, organizational capabilities and technical expertise to determine your AI readiness and find the most sensible path forward.
• Consider your long-term goals: The technological investments you make today will shape your future. Don't get blindsided by short-term gains; align your decisions with your long-term strategic objectives.
• Evaluate total cost of ownership: Consider maintenance, scalability and operational expenses, and think about the financial impact should a provider increase its pricing.
• Define your business-specific benchmarks: Clearly understand your definition of quality and compare different options against an objective set of criteria.
• Experiment and iterate: Start with a buy-type of a solution for quick prototyping and experimentation, and then use the learnings to vet your thinking and decisions for the long run.

A common misconception is that building AI solutions, as opposed to buying, is always prohibitively expensive. While initial development costs can be higher, the long-term savings and competitive advantages often outweigh these initial investments. The democratization of AI tools has made building custom solutions more accessible.

Overall, the Buy vs Build decision requires careful consideration of your company's capabilities, goals and costs. I explore how Endgame Thinking can help you navigate this complexity in this month’s newsletter. 

Remember, the AI landscape is an ongoing arms race. New tools and solutions will always emerge, so staying agile and informed is key. While pre-built solutions offer ease of use, consider potential issues like vendor lock-in and limited customization. Building custom AI offers ultimate control but requires substantial talent and resources, and there's a risk that your solution may become outdated if you don't stay ahead of the curve. No matter which path you choose, continuous learning and adaptation will be essential for success.

Thanks for allowing me to be this month’s guest editor. Happy reading.