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Building vs. Buying: Navigating the Generative AI Landscape

The choice between building and buying generative AI systems hinges on a multi-faceted decision-making process.

The adoption of generative AI, whether for text, images, videos, or a combination of these, is no longer a matter of “if” but “when” for organizations. The interest in generative AI has surged in recent times, driven by its potential to revolutionize various aspects of business operations. However, as organizations explore this transformative technology, they face the challenging decision of whether to build their generative AI systems from the ground up or buy pre-existing solutions. This choice is complicated by the myriad options available for refining and customizing purchased services and the significant effort required to turn a bought or built system into a reliable and responsible part of the organization’s workflow.

The Three Approaches: Taking, Shaping, and Making

The Taker Approach: In the “taking” model, organizations consume generative AI through APIs or other applications, such as ChatGPT or GitHub Copilot. This approach is suitable when adopting generative AI features and capabilities in existing applications is crucial for maintaining competitiveness. These readily available features can help organizations accelerate their workflow and provide new functionality, but they may not offer extensive customization.

The Shaper Approach: Shaping generative AI models involves leveraging existing foundational models available off the shelf. However, these models are retrained using an organization’s own data to enhance accuracy and relevance. Shaping is particularly effective in reducing the “hallucination” problem commonly associated with generative AI. By recalibrating models to their specific needs, organizations can improve the quality of results.

The Maker Approach: Building generative AI models from scratch is the most complex and resource-intensive approach. It’s typically reserved for organizations with a high level of expertise in designing and building large language models (LLMs). While this approach allows for complete customization, it also comes with substantial investments in data collection, infrastructure, and expertise.

Making the Decision

The choice between building and buying generative AI systems hinges on a multi-faceted decision-making process. Organizations must carefully assess their unique capabilities, goals, and available resources to determine the most suitable path forward. Whether opting to purchase generative AI for rapid deployment, reduced development effort, and reliable support or embark on the complex journey of building a fully customized solution, the decision should align with an organization’s strategic objectives. As the technology continues to evolve, organizations should remain flexible and open to revisiting their decisions, ensuring they stay competitive and leverage generative AI to its fullest potential.

The Importance of Responsibly Using Generative AI

Regardless of whether organizations take, shape, or make generative AI, the responsible use of the technology remains a top priority. This includes addressing document privacy, authorization, governance, and data protection. Legal and compliance teams play an essential role in ensuring that generative AI aligns with regulations and company policies. As generative AI continues to evolve, organizations need to be prepared to revisit their build vs. buy decisions and adapt to new capabilities.

The adoption of generative AI represents a transformative shift in the way organizations operate. By carefully considering their specific needs, capabilities, and strategic objectives, organizations can harness the power of generative AI to enhance their competitiveness, streamline processes, and drive innovation. Whether building or buying, the ultimate goal is to leverage this advanced technology to thrive in an ever-evolving digital landscape.

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