The best practices around determining whether your AI project will achieve a return for the business center around determining at the outset how the return on Investment will be measured.
The evidence shows it will be time well spent. An estimated 87% of data science projects never make it to the production stage, and 56% of global CEOs expect it to take three to five years to see any real ROI on AI investments, according to a recent account in Forbes.
Like any other technology Investment, business leaders need to define the specific goals of the AI projects, and commit to tracking it with benchmarks and key performance indicators, suggested author Mark Minevich, Advisor to Boston Consulting Group, venture capitalist and cognitive strategist. The company needs to think about the types of business problems that can be addressed with AI, so as not to set unrealistic expectations and not set the AI off in search of a business problem to solve.
Figuring out how to assign the people needed to help with the project is crucial. Some companies are using virtual teams where data scientists might work with an operations team two days a week. To break down the organization silos and allow various stakeholders to interact and collaborate, is a critical enabler of an AI project.
Employees need to be prepared. Investments in reskilling employees in AI need to be made, including for management in how to work in cross-functional teams across operations.
Every company engaged in AI projects is challenged to measure ROI. Author Minevich suggests focusing on what the project will save instead of potential revenue growth. “How much you invest in AI should be based on these saving forecasts and not revenue uplift,” he stated. That way, “If the deployment is not successful, the organization will have risked only what it expected to save, rather than risking what it expected to add in revenue.”
He also suggested knowing where the break-even point will be, when the cost savings of the project equals the investment.
