The Buy vs Build Decision in AI

In 2023, the market for AI technology stood at USD 200 billion and the numbers are speculated to hit USD 1.8 trillion by 2030. These numbers showcase the aggressive adoption of AI technology by companies worldwide. With the massive adoption, AI companies are rolling out tools to cater to diverse needs. However, companies are confounded by the question of whether to build or buy AI tools for their operations. In this blog, we aim to help you get the answer to this question and make an informed decision.

Skill Accessibility

Although highly versatile, many AI tools fail to cater to niche business strategies of specific use cases. A solid team including data scientists, engineers, and analysts makes it possible to build customized AI solutions. If applying a GenAI solution to your unique case is the goal, building an AI solution would be more effective. However, when developing these solutions, project leads must consider the entire lifecycle of the products and compare them with their teams’ capabilities.

If your company doesn’t have the budget for the right team and does not require a high level of industry expertise, out-of-the-box AI solutions are more viable. High-quality AI solutions can address common use problems in many industries.

The End Goal of the AI Solution

Whether companies build or buy AI solutions, they have to shed a considerable amount of money. Before making the decision, it is crucial to set realistic expectations about the effect of these technologies on day-to-day operations. Work with an industry expert to understand how a certain AI-backed technology can add value to your existing business and how it fits the bigger picture.

Outline all the business questions that you think the solution may be able to answer and further evaluate based on them. Setting realistic expectations during decision-making ensures that the investment aligns with the business strategy.

Timeframe for the Solution

Companies that are working with restricted timelines would benefit from building AI solutions. However, these solutions are built to address standard problems and may not be able to cater to industry-specific needs. On the other hand, building a tool requires multiple steps including designing, developing, and deploying. This means companies will have to wait longer to use the custom-built solutions. How quickly you want to utilize, and your use case defines whether to buy or build AI solutions.

Total Cost of Ownership

Buying an out-of-the-box solution from the market will cost considerably less than building a custom solution. Ensure to do your research about the software that already exists. Understand the strengths and limitations of your existing team. Analyze the long-term resources that will go towards the maintenance of the solutions. Will the solution be a key differentiator in giving you a competitive edge? Does the functionality already exist in a tool? Eventually, the overall costs should make sense from both people and technology prospects.

Adherence to Compliance

AI regulations vary based on industries and regions. Industries such as government, healthcare, and finance are highly regulated. These industries primarily collect a lot of confidential data from the consumer that needs to be strictly protected. Therefore, it is more efficient to build custom solutions and ensure that the development aligns with the guidelines and regulations.

Conclusion

With AI at the forefront of every solution, we can expect more customized AI solutions in coming times. We are yet to witness whether they will be able to address industry-specific use cases. But for the time being, companies can develop customized AI solutions to cater to their operational needs. The above factors can help companies evaluate their needs against the available resources and navigate them to the right solutions.