Despite all the promises of how artificial intelligence (AI) will transform businesses, the uncertainty that many executives feel about their AI initiatives remains in stark contrast to this rosy picture. Sure, AI in the enterprise is here to stay, but the path to fully realizing its potential—and return on investment—can be less certain.

Consider that a recent TechPro survey found that 53 percent of tech leaders said it’s not clear what to expect from current AI projects. At the same time, 47 percent said they were concerned that a lack of skills in AI and machine learning (ML) was a barrier to successful implementation and support. Other challenges cited by survey respondents included lack of knowledge in the C-Suite, unexpected project delays and increased costs, and poor vendor support.

Sound familiar?

Below we offer some guidance on how to ensure you’re getting the largest ROI on your AI projects, including best practices for successful initiatives and the right questions to ask along the way.

ZDNet’s Mary Shacklett recently tackled the topic in an article headlined, The True Costs and ROI of Implementing AI in the Enterprise. The article calls out two critical factors for ensuring your company gets value from its AI efforts. First, establishing a business use case is a must. This includes working with consultants and collaborative vendors to develop solutions, while carrying out proof-of-concepts in the process. The second factor for success centers on metrics and “justifying your investment,” according to the article. Today, as AI begins making a broader enterprise-wide impact, companies need to look at the role it plays on entire workflows or processes. Applying AI to only one part of a process, while others remain unchanged will “throttle the workflow” and diminish returns. Think bigger and look at AI’s potential to impact the business end-to-end.

A recent KDnuggets post on monetizing data also offers some useful tips, including a list of more than 20 questions to ask when deploying AL/ML. The post provides a comprehensive checklist for driving business value with AI/ML, giving great insight on all the things data scientists, and companies in general, need to think about when implementing AI. It also affirms how executives need to be involved to support adoption and success.

Here are some questions to consider when measuring the success of your AI initiatives:  

  • Have you agreed with Finance on how the “monetize” part of your AI initiative will be measured?
  • Have you thought through the operational impact, and do you have the full cooperation of line level managers?
  • Do you have active visible executive sponsorship over IT and Change Management?
  • Does your app have a compelling name that the CEO has already mentioned to his direct reports…in a good way (this is important)?
  • Is organizational adoption strong enough that the app will have a life after the current executive sponsor has moved on?

Make sure your AI initiatives are designed to deliver value over the long term and be sure to understand the roles of not just executives but also data scientists and employees in your company’s overall success.

You can read the complete KDnuggets post here for more information.

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