When deciding whether to build or buy your own artificial intelligence (AI) tools, the typical considerations of cost and efficiency become almost irrelevant. The issues surrounding AI are far more complex, involving variables that are more gray than black and white.
One reason for the complexity is the fact that AI talent is scarce and expensive. Organizations lucky enough to have in-house talent willing to learn AI skills will need to factor in the long learning curve typically required to get up to speed with AI. On the other hand, an outside vendor with highly sought-after AI skills will likely lack the business expertise and specialized knowledge that your solution may require.
Another key issue when deciding whether to buy or build AI tools is data, the core of any successful AI initiative. The way your data is captured, stored, and accessed could limit not only the type of solutions available to you but also the available specialists who can unleash your data’s power. Security is another issue to consider. If you have unique proprietary data, you may inadvertently create a security breach by outsourcing AI.
The decision about whether to build your own AI tools or outsource must be deliberated over individually because each instance is unique. Here are a few guidelines that can help lead you to the right decision.
Questions of Secrecy and Exclusivity
As alluded to earlier, security is an essential factor in determining how to handle your AI project. If your data or initiative includes information that has a relatively low security risk, you can consider outsourcing without the gamble of exposing your project to a security breach. Proprietary data, however, is not the only consideration. If you are creating models with your AI initiative that will give you a clear competitive advantage, consider how sharing this information with outside vendors could compromise your competitive position.
How Expensive Is Efficiency?
A quick evaluation of costs shows that a boxed solution is always less expensive, and it’s likely the quickest path toward implementation. Although this one-size-fits-all solution may bring efficiency, it lacks customization. Boxes come with a price. The cost is based on many factors, including the following:
- Data. Is the data formatted in a compatible way? Will you need to spend energy reformatting data to match the specifications of the prepackaged solution? Does the program offer customization tools? How much effort will this require? Does your team have the skills required?
- Infrastructure. Is the solution compatible with your existing IT infrastructure? Will you be able to run the processes of the solution with your existing tools? Is it compatible, or will it require expensive and lengthy customization protocols to match data and processes you are currently using?
- Adaptability. Will you be locked in? Is the solution scalable? Will you be limited to a solution that lacks the flexibility you will need down the road? Is the solution missing controls and features that you don’t need now but may need later? Is the solution too complicated to be valuable? Will employees need to learn a new language and skills that could delay implementation?
As you can see, the decision of whether to build or buy is not a simple one. Regardless of which solution you choose, one key takeaway is clear: all companies should begin thinking about how to strengthen their competitive advantage in AI by looking for ways to build their internal talent to stay competitive with the evolving advantages of AI technology.