How do you measure the ROI of machine learning initiatives in your organization?

Defining goals and metrics for ML initiatives is crucial to evaluate their effectiveness. By establishing smart criteria, you ensure that your measurements are actionable and result-oriented. Consider both quantitative and qualitative metrics, reflecting the diverse benefits ML can offer. Regularly track and review your metrics, adjusting as needed to maintain alignment with evolving business priorities. By consistently monitoring progress, you can optimize the performance of your ML initiatives, driving better decision-making and maximizing ROI.

This process integrates machine learning with routine enterprise operations, which normally go through this type of planning to prepare a business case. This will help familiarize the concept of applied machine learning with others in the organization, who might be less familiar with the technology. In addition to being a great mechanism with which to socialize machine learning with your team, you also benefit from planning before jumping into the arduous exercise of gathering data.

From the beginning, consider your machine learning project through its entire lifecycle. This means planning to handle data drift, new use cases, training etc. This allows for a more complete understanding of how your costs are impacted. In terms of benefits: remember that today's gains might wane or even disappear in the future for any number of reasons e.g. new customer profiles, the quality problem disappears due to new feeds or manufacturing processes. How do you encompass both categories of future-looking cases above? Scenario analysis.

Measuring the ROI of your ML initiatives is crucial to evaluating their success and guiding future investments in machine learning. However, it's essential to consider both tangible and intangible benefits when calculating ROI. Tangible benefits may include increased revenue, reduced costs, or enhanced productivity, while intangible benefits could encompass improved customer satisfaction, better decision-making, or a more competitive market position. You should also factor in the total costs involved, which may include data acquisition and cleaning, infrastructure setup, algorithm development, and talent acquisition or training. Additionally, keep in mind that the ROI may change over time as the ML model improves and adapts.

Calculating your ROI - or any other financial metric for your project - enables your proposed work to be compared against others within the same enterprise, business unit etc. to compete for capital dollars. Remember that at the end of the day ML is a way for companies to achieve their stated objective, which is maximizing profits and eventually shareholder returns.

As mentioned earlier, scenario analysis enables a structured assessment of current and projected future inbound and outbound cash flows. The time value of money requires assumption of a cost of capital, so consider your borrowing costs and/or alternate uses for the capital that you might have if it were not for the machine learning project.

By generating multiple NPVs and IRRs for various scenarios and presenting them effectively e.g. through a tornado chart, you can translate the technical complexity of data gathering and machine learning into financial metrics that stakeholders can understand and respond to. This can direct your efforts as you try to get this project off the ground and informs your organization's financial authorities as they aim to make the right decision with the company's capital.