Artificial Intelligence (AI) is revolutionizing decision-making processes at a rapid pace. Every organization is faced with a critical choice: should they develop a custom AI platform in-house or purchase a prepackaged solution from an AI vendor, typically delivered as a cloud service.
The current trends and opportunities seem to favor the DIY approach. This may come as a surprise, but the reasons behind this shift are quite intriguing and might prompt a reassessment of your enterprise’s genAI strategy.
Tailored Solutions and Flexibility
Creating a generative AI platform from the ground up provides a company with complete control over its features and functionalities. This allows the AI technology to be precisely tailored to meet the organization’s specific needs, ensuring alignment with unique workflows and delivering a personalized user experience. It’s important to note that DIY generative AI can be implemented on various platforms, including public, private, or traditional ones. Currently, the focus is on utilizing specific genAI technology, particularly open source, either on-premises or on a public cloud.
Natural language interactions offer a more human-centric approach to streamlining business processes. However, there are concerns that these systems might become integral to the business quickly. Without full control over all aspects of the platform, there’s a risk of not fully realizing its value. If a purchased AI platform with advanced features suddenly changes direction or becomes obsolete, the organization could be left with a failed system and business.
Investment, Expertise, and Challenges
Developing a sophisticated generative AI platform requires a team of experts with specialized skills, which can be challenging to assemble due to the scarcity of talent in this field. Data scientists and AI engineers need to collaborate with platform engineers, both in cloud and non-cloud environments, to create customized genAI solutions tailored to the company’s exact requirements.
This complexity often leads to the need to recruit expensive talent. I recently heard of a Chief Information Officer resorting to recruiting talent directly from technical universities before they even enter the job market, showcasing the innovative yet concerning measures some companies are taking to address this talent shortage.
Many enterprises are struggling to find the right people for their projects, leading to delays or decisions to purchase a system instead of building one from scratch.
Benefits of Purchasing
Acquiring a prebuilt system offers rapid deployment and immediate functionality. These off-the-shelf solutions enable quick implementation, providing instant value and accelerating time-to-market. Moreover, buying a generative AI service ensures continuous support, updates, and enhancements. While the DIY approach may offer some assistance in certain areas, building a platform mostly leaves you on your own.
Consider the analogy of building and maintaining your database versus purchasing one from a vendor. Although AI systems are more intricate and multifaceted, the comparison is apt.
The decision to build or buy largely depends on the need for a customized solution tailored to the business’s specific requirements. Investing in building a platform entails additional costs, time, and risks, with the promise of complete control over the core system. For many organizations, the strategic use of genAI could make or break their business in the coming years, signifying high stakes.
Consider All Factors
When weighing the options between building and buying a generative AI platform, it’s essential to evaluate all the pros and cons. Building genAI internally can incur significant costs, whereas off-the-shelf solutions offer convenience and cost-effectiveness. Developing an AI solution in-house requires assembling a proficient team, while a prepackaged solution grants access to the expertise of the AI vendor who developed the system.
Creating AI solutions from scratch allows for complete customization and control over the technical process, enabling the integration of compliance measures and specific functionalities from the outset. However, the customization process often involves numerous iterations and time-intensive development. In-house genAI solutions also require ongoing support and maintenance. If the value derived from a DIY approach doesn’t justify the investment, consider purchasing a solution to mitigate risks, save time, and reduce costs.
Ultimately, the decisions made regarding generative AI platforms could significantly impact a business’s success or failure. Whether to build or buy depends on various factors, including talent availability, budget constraints, and the strategic importance of AI in the industry. It’s crucial to carefully assess these factors before making a decision to ensure the long-term viability of the business.
