This post is part of a series designed to help AI-interested regulated industry leaders overcome challenges to successful AI design. For more information, download Top Challenges to Designing AI in Regulated Industries [And How to Overcome Them].
AI has incredible capabilities to improve efficiencies, streamline processes, and ultimately increase your organization’s revenue.
A strategy-first approach, however, is critical to ensuring your AI solution meets the guidelines of regulated industries. And, because AI models require significant resources, it’s important your model is positioned for the best possible chance of success.
To realize the full value of an AI solution, the strategy you develop must account for the solution itself and any actions your organization must take.
This post outlines three steps to developing a realistic AI strategy that will yield a positive ROI.
Defining a clear goal in your AI strategy allows you to measure the results of your AI solution properly and realistically. Goals vary based on use case and your organization’s level of AI maturity, but it’s important to involve the right people in the development process.
In a healthcare setting, for example, a team of data scientists, data engineers, and a hospital director may work together to set goals like:
Even if your solution falls short of reaching its goal, learning from failures can help you correct and improve the solution. These failures can also help you set and manage realistic expectations for the future.
While AI is a powerful tool, it cannot be used as a solution to every problem. AI usually works best when applied to specific use cases—like augmenting tasks or recognizing patterns. Projects that are too broadly defined often fail.
Narrowly defining the use case for your AI will make it easier to monitor performance and measure success. To achieve faster diagnostics, for example, your team could design an AI that identifies abnormalities in different imaging modalities.
An AI model alone will likely not solve your business problem—no matter how narrowly its use case is defined. What matters is the actions you take in conjunction with AI.
To reach a goal, you need methodologies. And applying methodologies requires action. Consider this example.
Stakeholders at a university set a goal to boost the graduation rate of students of color. They then designed and implemented an AI model to identify at-risk students.
Had they taken no further action, stakeholders would have been left with nothing but a list of names. And while the list may have been helpful, it wouldn’t have resulted in a positive ROI.
But, the university took their strategy one step further by hiring 40 academic advisors to assist the at-risk students identified on the list.
By accounting for goals, a specific use case, and associated actions, the university successfully strategized an AI model that ultimately achieved increased graduation rates.
Interested in learning more about the AI design challenges faced by regulated industry leaders? Globally renowned AI Strategist, Cal Al-Dhubaib, shares some of the top challenges he’s seen throughout his years as a data scientist—and how you can avoid them—in the complimentary resource, Top Challenges to Designing AI Solutions in Regulated Industries [And How To Overcome Them]. Click below to download the PDF.
Contributor: Nicole Ponstingle McCaffrey is the COO and AI Translator at Pandata.