Driving ROI from AI

2 min read
May 4, 2023 9:37:43 AM

Maximising ROI through Machine Learning in Business Operations
As businesses look towards implementing machine learning (ML), focusing on return on investment (ROI) from model development and maintenance is critical. This ROI is primarily derived from two key business objectives: increasing revenue and improving efficiency.

Identifying and Quantifying Use Cases
The journey begins with identifying a use case and moving into the ideation stage. Here, certain aspects need to be quantified. For instance, if the model is designed to improve the stock management process, you must first measure the existing system's performance. Consider the current forecasting process's accuracy, how often it leads to product unavailability or waste, and the associated costs.

An understanding of your existing system’s performance forms a benchmark against which the performance of your new ML-driven system can be evaluated. This lets you see how much you could save by improving forecast accuracy and reducing lost business or wasted stock.

Prototype Models and Business Value
Using machine learning, data scientists can build prototype models to determine potential accuracy gains. This offers a rough estimate of the actual value ML can bring to your business.

Moreover, while not easily quantifiable, the potential reputation boost as a more reliable supplier and a waste-conscious company should not be overlooked in the decision-making process.

Applying Machine Learning to Increase Revenue
Growing the customer base is an obvious path to revenue growth. However, revenue growth can also be created by enhancing how existing customers are targeted. Often, businesses send promotional information to existing customers through communication channels such as email or phone calls. Without ML’s predictive capabilities, such methods risk wasting valuable time or annoying customers with irrelevant spam.

When quantifying gains in response to promotional material, it's easier to assess the expected efficiency of the process. Consider the time marketing teams take to create an effective list of products and target customers. How much faster could this process be completed with an ML algorithm? This time-saving aspect can provide the motivation needed to embark on the ML journey.

Machine learning can help identify products customers are likely interested in and the best communication channel to get their initial engagement, resulting in more effective marketing campaigns, wider sales, and significant time savings.

Improving Efficiency with Machine Learning
Reducing costs is as crucial to growing your bottom line as increasing revenue. In manufacturing, understanding when and how a machine is likely to fail can significantly improve the overall efficiency of the process. With sensor data on machinery and the environment in which these machines operate, along with historical data of past failures, ML models can enhance the maintenance schedules of manufacturing plants, increasing uptime, and ensuring adequate stock of replacement parts.

Quantifying these gains should be based on machine failure and downtime costs. By comparing the expected reduction in downtime due to predictive maintenance, you can get an idea of the possible gains.

The Bottom Line
Understanding the ROI for implementing a Machine Learning solution is essential when assessing its usefulness. Sometimes, this can be straightforward; at other times, it may require a system implementation to see its performance. In such scenarios, creating pilot groups to A/B test a new method over a period of time may be the best approach.

Regardless of the complexity, understanding where theoretical gains are expected to come from should be clear. Always remember to validate an appealing idea against its potential practical outcomes.

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