Whether to implement an artificial intelligence strategy is no longer a question for middle market organizations. AI has rapidly evolved beyond a buzzword to become a critical element of business growth and overall success. However, to develop an effective AI approach, companies face another critical choice that will guide strategy and investments: whether to buy or build AI tools and applications.
With the number of AI solutions growing by the day, companies have the option of buying existing AI tools embedded in their existing technology stack or building a more customized AI solution geared toward specific objectives or metrics. Both are proven avenues for success, but the right choice for your organization depends on cost and time-to-market, as well as use case intentions, in-house talent capabilities and data foundations.
Always weigh the benefits of leveraging AI built into existing technologies versus building custom models, factoring in cost, security and data quality.
Below we detail key considerations your leadership team should weigh to assess which route—building AI tools in-house or using external ones—makes the most sense for your organization.
Real-world AI considerations and use cases from middle market data executives
Fireside chat: CDOs share real-world AI implementation challenges and recommendations
Tune in for lessons learned and the key to middle market AI adoption success
