In the rapidly evolving landscape of artificial intelligence (AI), enterprises face a pivotal choice when it comes to generative AI: should they build a custom platform internally or opt for a prepackaged solution offered by an AI vendor as a cloud service? This decision is pivotal due to the profound impact generative AI can have on an organization’s operations.
Total control through customization
One compelling argument for building a generative AI platform from scratch is the level of control it provides to enterprises. When developing their own AI technology, organizations can tailor every aspect to align precisely with their unique requirements. This bespoke approach ensures compliance with specific workflows and delivers a user experience tailored to their exact needs. Importantly, this approach can be implemented on various platforms, including public, private, or traditional ones, making it adaptable to the organization’s existing infrastructure.
However, it’s crucial to acknowledge the concerns that come with complete customization. As generative AI systems become increasingly integral to business processes, organizations worry that without full control over all features and functions, they risk the system not delivering its intended value. If a purchased AI platform takes a different direction or ceases to exist, they could be left with a failed system and a compromised business.
More resources required for DIY
Building a complex generative AI platform demands a team of experts with specialized knowledge in AI and data science. Finding and retaining such talent can be challenging, leading to increased complexity and the need to hire expensive personnel. Some enterprises are resorting to innovative approaches, like recruiting directly from technical universities before graduates enter the job market, to overcome the talent shortage.
Many organizations are finding it challenging to assemble the right team and are consequently facing delays in their projects or choosing to buy a ready-made system rather than building one from scratch.
The value of buying
On the flip side, purchasing a generative AI system offers rapid deployment and immediate functionality. Prebuilt solutions enable quick implementation, resulting in accelerated time to market. Additionally, buying a generative AI service ensures ongoing support, regular updates, and improvements. In contrast, the DIY approach often leaves organizations to fend for themselves, which can be daunting when dealing with complex AI systems.
An apt analogy is comparing the cost and effort of building and maintaining a custom database versus buying one from a specialized vendor. While AI systems are more intricate and multifaceted, the fundamental concept remains the same.
Weighing the factors
When confronted with the decision of building or buying a generative AI platform, organizations must carefully weigh the pros and cons. The cost of building generative AI internally can be substantial, whereas off-the-shelf solutions offer practicality and cost-effectiveness. Building from scratch necessitates assembling a proficient team, whereas purchasing a prebuilt solution grants access to the vendor’s expertise, transferring risk and cost to the provider.
Customization and control over the technical process are the primary advantages of the build approach, allowing organizations to implement compliance measures and exact functionality from the outset. However, this path can lead to numerous iterations and time-intensive development, along with the critical need for in-house support and maintenance.
The decision to build or buy a generative AI platform is a critical one, with far-reaching implications for organizations. The choice hinges on factors like the need for complete customization, the availability of talent, budget constraints, and the strategic importance of generative AI in the future. A careful assessment of these factors is essential to ensure that the chosen approach aligns with the organization’s goals and leads to a successful implementation.
As the AI landscape continues to evolve, enterprises must make informed decisions that position them for success in an increasingly competitive and AI-driven world. While there is no one-size-fits-all answer, understanding the trade-offs between building and buying generative AI is paramount in navigating this transformative technology landscape. The stakes are high, and the impact of this decision can determine whether a business thrives or faces challenges in the years to come.
