Solving the AI Buy or Build dilemma for tech leaders

Buy vs. Build Dilemma for Tech Leaders R1hbapm
Shine: a podcast by Star_Episode 2.20

Companies are increasingly exploring how AI can improve their operations, but they face a critical decision: should they build their own custom AI solution from scratch or buy a pre-built one from a vendor?

In this Star Shine podcast limited series on AI, Star CTO and co-founder Sergii Gorpynich and host Cherry Ye, Head of Communications, explore:

  • The Buy vs. Build dilemma in the context of AI solutions and an overview of the Big Three solution providers (OpenAI, Google, Anthropic) and open-source options.
  • A deep dive into open-source models and their advantages.
  • How tech leaders can make the right decisions based on their businesses' readiness for AI adoption and cost-benefit analysis.
  • The importance of talent development in AI transformation and the importance of experimentation.

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Sergii outlined three primary approaches to adopting AI solutions for businesses. The first option is to buy ready-made solutions from established AI providers. This method is quick and convenient but often comes at a higher cost and offers limited customization. Alternatively, businesses can choose to build their custom AI solutions. This approach allows for maximum customization and control over the technology but requires substantial resources, including time, money, and technical expertise.

The third option Sergii discussed involves fine-tuning open-source AI models to meet specific business needs. This method strikes a balance by being more cost-effective than purchasing proprietary solutions and faster than building a system from scratch, while still allowing for a degree of customization. Sergii believes making the right strategic choice among these options depends on various factors of your organization. Technology leaders must consider the level of specialization needed – whether a general-purpose AI suffices or a bespoke solution is required for particular tasks. The total cost of ownership, which includes not only the initial price but also costs related to integration, development, operations and transitions, is crucial. Additionally, infrastructure capabilities and data security concerns, such as data privacy or internet connectivity issues, especially for cloud-based solutions, are essential considerations in deciding the best AI adoption strategy.

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