Measure ROI of AI
- 83% of companies claim AI is a top priority in their business plans.
- 64% of businesses expect AI to increase productivity.
Expectations are AI is a game changer. But how do know unless you measure ROI of AI?
Consider this. First, AI is not free. Second, costs vary widely. The software system alone is estimated to run a range from $900 (for third party solutions) to $500,000 for custom solutions. Then, there are the manpower costs to set it up, run and maintain it. AI may be a game changer, but it is also an on-going investment.
Moreover, for any area with this much hype, expect a bubble. One with winners and losers. Those who get in at the right time and those who get burned.
To make sure your business comes out a top, a plan to measure ROI of AI is your most effective way to ensure success. Here are 3 important ways.
#1. System Performance. How good is the AI system at doing its job?
A technical assessment can be done based on measurements currently available for machine learning. After all, if the AI system doesn’t measure up, neither will the results. Make sure to have this set up with your AI software. Measurements include:
- Accuracy: The proportion of correct predictions out of the total predictions.
- Precision: The proportion of true positive predictions out of the total positive predictions (precision = true positives / (true positives + false positives)).
- Recall (Sensitivity or True Positive Rate): The proportion of true positive predictions out of the total actual positive instances (recall = true positives / (true positives + false negatives)).
- F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics (F1 score = 2 * ((precision * recall) / (precision + recall))).
- Additional machine learning evaluations such as ROC curve, AUC, MSE, MAE, RMSE, R-squared and others may also come into use.
#2. Usability. What is the customer experience when you measure ROI of AI?
How do customers and prospects react? Some of the usability metrics to use are:
- Completion Rate: The percentage of people who complete a desired action based on the total number who had the opportunity.
- Success Rate: The percentage of success among a number of attempts to perform a procedure or task.
- Task Time: The time it takes to complete a desired task.
- Error Rate: The percentage of processing errors for a specific task or action.
- Satisfaction Score: A metric from a satisfaction survey that measures how happy a customer is with a specific product.
- Net Promoter Score (NPS): On a scale from 0 to 10, how likely are you to recommend this product/company to a friend or colleague.
You can collect user feedback, such as surveys, interviews, or reviews, to understand the user needs, preferences, and pain points.
#3. Impact. Does AI achieve its business goals?
Here’s where the rubber meets the road. Did AI enable the business to generate the revenue and/or profits, and productivity increases intended? Key measurement in this area include:
- Conversion Rate: The percentage of users who completed a desired action.
- Retention Rate: The percentage of customers who continue paying for a product over a given time frame.
- Customer Lifetime Value (CLV): A prediction of the net profit attributed to the future relationship with a customer.
- Churn Rate: (Lost Customers ÷ Total Customers at the Start of Time Period) x 100.
- ROI (Return on Investment): (Gross Return – Cost of Investment) ÷ Cost of Investment
Management, the C-Suite, and potential investors, of course, focus on the area. However, to know whether your AI investment is working or not working and why, measurements from the other areas provide the insights. Measurements are your guide, but improvements with AI occur with consistent attention and action.
Do you believe AI is a game-changer? Will there also be a bubble? Does having a measurement plan to measure the ROI of AI represents the best way to ensure success? Do these 3 ways makes sense to you?
