Build an effective AI strategy: Overcome four common adoption challenges

Build an informed AI strategy

Despite the surge in AI development, most initiatives currently stay in development. IDC “Survey results show that while AI/ML initiatives are steadily gaining traction with 31% of respondents saying they now have AI in production, most enterprises are still in an experimentation, evaluation/test, or prototyping phase. Of the 31% with AI in production, only one-third claim to have reached a mature state of adoption wherein the entire organization benefits from an enterprise-wide AI strategy.”

So why do so many companies that have otherwise efficient software practices, plenty of data, and skilled data science teams struggle to make AI work for them? Four core challenges across people, processes, technologies, and data are most often the culprit:

• Challenge 1:
  There’s an ML skills gap.
• Challenge 2:
  AI/ML is a multidisciplinary process. Variances in skills, tools, data, and processes often create obstacles.
• Challenge 3:
  Slow progress in deploying and maintaining models often undermines momentum, confidence, and support.
• Challenge 4:
  Steep compute and data requirements can create infeasible resource demands.