To Build Or Buy, That Is The Question

The race to AI has left many companies’ heads spinning, with some companies putting little thought into the longer-range implications of their investments. For instance, at the Creotopy launch a few weeks ago, one couldn’t help but think they were taking the same boil-the-ocean approach as the failed WebVan from the first DotCom boom: “Do everything, excel at nothing.” WebVan’s focus on building an end-to-end vertically integrated solution, rather than combining with existing grocery chains or leveraging third-party logistics providers, created unnecessary complexity and overhead. Ultimately, their rapid expansion and complexity led to their demise.

A more measured approach to AI can deliver better results, but how can that be done? One way is to integrate with best-in-class solutions that plug into what you are already doing. This will give you the resources to hone and polish what you do best with the flexibility that having partners can provide you. When a better solution comes along, which I can guarantee with 100% certainty it will, you can switch it up.

Apple provides a prime example of this strategy. The company focuses on delivering top-notch hardware, software, and services, including the App Store. The App Store serves as a marketplace where developers can showcase their apps. The market, driven by user preferences and feedback, determines which apps are the best, leading to their success. It would be folly for Apple to try reproducing every app in its store. Few would be great, and their end users would suffer. 

So, what is a good framework for making this decision between building and buying?

1. Identify Core Competencies: Companies should identify their core competencies and strategic priorities. 
2. Assess Internal Capabilities: Companies should assess their internal expertise, resources, and capacity to develop and maintain AI solutions. Building AI capabilities in-house requires a significant investment in talent, technology, infrastructure, and ongoing training.
3. Evaluate Cost and Time: Developing AI solutions internally can be time-consuming and expensive, especially for companies without prior experience in AI. Consider the cost-benefit analysis of building versus buying AI solutions.
4. Risk Management: Consider the risks associated with in-house AI development, such as talent acquisition and retention, technology obsolescence, scalability challenges, and regulatory compliance. Partnering with established AI providers can mitigate some of these risks.
5. Focus on Innovation: AI rapidly evolves with continuous advancements in algorithms, models, and applications. Companies focusing on their core competencies may prefer partnering with AI experts to leverage the latest innovations and stay competitive.
6. Collaborate with Partners: Collaborating with AI-focused partners, such as AI software vendors, research institutions, or consulting firms, allows companies to leverage external expertise, access specialized AI tools and resources, accelerate time-to-market and reduce development risks and costs.

Ultimately, the decision to develop AI capabilities internally or collaborate with partners depends on factors such as the company’s strategic goals, existing capabilities, budget, risk tolerance, and the complexity of the required AI solutions.