Artificial Intelligence and machine learning have made it possible for organizations around the world to improve processes and optimize efficiency. However, it has growing limitations with its ability to differentiate correlation and causation.
That means we think we’re being data-driven, but we’re actually jumping to the wrong conclusions.
The impact of this can be comical like the graph below. It shows that the trend line of revenue generated by arcades maps closely to computer science doctorates awarded in the US. So if we want more computer scientists, we just need to get more people to spend money at arcades. Right?
To make human-level data-driven decisions, we need an innovative approach to analytics that separates correlation from causation. Luckily, it’s not only already here and picking up speed…it’s trending.
All Aboard the Causal AI Hype Train, It’s a Bright Future Ahead
Gartner released their 2022 Gartner Hype Cycle of emerging technology and Causal Artificial Intelligence (Causal AI) was featured as a key innovation that will transform the world within the next ten years.
Dealtale is in good company driving this train alongside companies like McKinsey and Microsoft. Across industries, we’re breaking open the black box that predictive analytics lives in to make data insights actually actionable.
As Gartner defines it:
“Causal artificial intelligence (AI) identifies and utilizes cause-and-effect relationships to go beyond correlation-based predictive models and toward AI systems that can prescribe actions more effectively and act more autonomously.”
While a machine learning algorithm can provide insights with predicted outcomes, it can’t tell you how different decisions will change the results.
A common example Forbes outlined in a recent article on Causal AI is geared towards retail marketers who need to run an offer, but can only select one cohort to run the promotion for. A predictive model might tell them that the highest likelihood to buy would be loyal shoppers. So the marketer might send the promotion to them, see the big return on sales, and think “Data-driven success!”
But wait. Wouldn’t those loyal customers buy already without incentive?
To truly measure the increased gross margins, you would want to do an A/B test or leverage a causal algorithm.
Causal AI Turns Predictive Insights Into Prescriptive Actions
Causal AI algorithms move beyond predicting who would be the most likely to buy, they guide on the most effective way to make it happen. When marketers are looking at a campaign and considering how they should promote through ads, email, or mail, they want more than just a spreadsheet—they want prescriptive suggestions they can trust.
*An excerpt from KDD poster “ML Prescriptive Canvas for Optimizing Business Outcomes” by Gerben Oostra.
According to Forbes, it’s the trust that Causal AI can generate that is driving the industry toward it at full speed; decisions are more transparent, results can be reproduced, and bias is eliminated. It brings the human ability to understand the larger context of casual and effect into the equation and delivers better business results.
Join Gartner Aboard the Casual AI Hype Train Today
Introducing Causal AI into your business will help make predictions more accurate so you can make faster, smarter decisions with predictable results. The good news is that you don’t have to wait.
Get ahead of the curve. Dealtale’s Causal AI is ready and waiting for you. It is out-of-the-box, no code, and is easy to implement. Check out our free two-week trial.