A lot of people would be surprised at the extent to which AI is inherently domain-specific -- at times even organization-specific -- and definitions of terms can vary greatly. For example, the word "short" is a positive attribute if you're talking about the checkout queue in your retail store, but negative if you're talking about the battery life of your new mobile phone -- and entirely different in the context of financial trading. While many organizations may try out the latest and greatest general-purpose algorithms available, even the best AI will fail without the proper context and data tuning for your specific scenarios. The other challenge with AI is that it needs to be deployed and embedded into your existing business processes. We call this "the last mile of AI" problem, and it requires significant buy-in from your stakeholders and alignment on the outcome(s) to be successful.

To help curb AI sprawl and make smarter decisions about implementation, organizations should devote more thought to defining the value they are hoping to get from implementing AI solutions. With AI, teams often lean first toward the build option, believing it gives them the most control over what will hopefully prove to be a competitive differentiator. More often than not, however, buy or partner makes more sense. Small businesses, for example, usually use APIs to access data from companies such as Visa on credit card transactions, data that can be used in AI solutions. If the needed data or expertise do not exist internally, companies need to determine who they can partner with to obtain them relatively quickly.