With the growing number of ready-to-use or semi-ready-to-use AI tools, buy or build AI questions are top of mind for many leaders. There’s a big push to start using AI immediately, but many leaders also understand that not all AI tools will fit their organization’s needs.

For example, one of our customers invested heavily in developing machine learning models internally, from scratch. However, after deployment, they were inundated with customer complaints due to inaccuracies in model output and needed a way out of this problem.

Our evaluation pointed to several significant problems that would require the company to rebuild models entirely. To prevent further setbacks and to contain costs, instead of investing more in in-house development, we helped them transition to a third-party solution, which outperformed their existing one while keeping costs within expectations.

There can be many such twists and turns when it comes to implementing AI. You may have budget limitations, need more data, or your company’s IT infrastructure may also be too “simple” to support the use of AI.

You may also be faced with privacy concerns, cost complications, and other important organization-specific challenges which could push you into either building from scratch or buying an off-the-shelf solution.

Therefore, the best strategy for integrating AI is highly context-dependent.

In this article, we’ll look at the different ways to integrate AI and the pros and limitations or things to consider for each. This will help you assess what’s best for your organization.