Buying vs. Building AI Models
In the dynamic realm of artificial intelligence, a pivotal question confronts businesses: to buy or to build AI models? According to Thomas Malone, founding director of MIT’s Center for Collective Intelligence, “It comes down to the key question: how strategic and unique to your company are your applications of AI likely to be?” (Forbes)
To answer this key question, consider the following questions first to understand the circumstances of your company better: What are the business needs? What outcomes are expected from incorporating AI? Are there enough talents either in-house or external who can make it happen? After these are clear and fulfilled, building an AI model is better than buying existing AI models because of its advantages in the following factors:
Customization: Building your own AI model allows for customization that achieves exactly what the business wants and thus contributes to more business values. This can be particularly important if the problem domain is unique or if the available off-the-shelf solutions don't fully address the company's requirements.
Competitive advantage: Developing proprietary AI models can provide a competitive advantage by creating unique capabilities that are not readily available to competitors.
Cost efficiency: While the initial development costs of building AI models can be higher, over the long term, the cost might be lower than ongoing licensing fees for third-party solutions.
Consider a SaaS subscription company, for example, focused on increasing customer acquisition rates as a core business need. One potential approach is to construct an automated system that assists the sales team in identifying free-tier users likely to convert to paying members when approached by a sales representative. Such a system demands a high degree of customization to precisely meet the company's requirements. Additionally, it bolsters competitive advantage by elevating conversion rates within a more targeted audience. In comparison to purchasing existing AI solutions, building an in-house AI model allows for flexibility and iterative improvements in response to evolving sales strategies. Consequently, the AI model becomes increasingly attuned to the company's specific data and needs, resulting in a superior return on investment over time.
In the case of a fintech startup issuing digital credit cards, security, particularly fraud detection, assumes paramount importance, impacting the company's valuation. Developing a proprietary AI model is better aligned with the need for customization and creates a substantial competitive advantage by safeguarding the security system within the company's walls. This investment not only fortifies security but also enhances the company's valuation, resulting in a significantly higher return on investment compared to third-party AI solutions.
However, for standardized and operationally vital functions like finance and accounting, human resources, and SEO, acquiring well-established AI solutions is preferable due to their limited customization requirements, minimal impact on competitive advantage, and superior cost efficiency.
Overall, the decision to build or buy AI models hinges on the company's strategic prerequisites. This choice lays the groundwork for future innovation and prosperity, signifying the transformative role of AI in shaping the business landscape.
