Cutting Through the AI Hype
It’s been the hot topic in retail lately, but do you really know the ins and outs of AI and machine learning? Take this short quiz and see if you can spot the difference between vendor hype and mature, applied technology.
QUESTION 02
What is the relationship between machine learning and AI?
They are two separate entities, with no relationship to each other.
They are two definitions for the exact same thing.
AI can learn on its own, machine learning requires human intervention to adjust the parameters and models.
Machine learning is a subset of AI using statistical techniques that give computers ability to learn with new data without being explicitly programmed.
QUESTION 03
Which statement is true about the feasibility of AI in pricing?
It is hype that has yet to prove its practical application in retail.
2nd generation pricing solutions have been successfully used in retail pricing across all retail segments/industries and channels for over a decade.
It is new to retail but is showing promise.
It's success has only been shown for online retailers and it is still being tested in B&M retailers.
QUESTION 04
What is necessary for a market ready AI pricing solution?
The AI must have already learned from a lot of real world data.
The solution must have had the proper guidance to ensure it does the correct action when new inputs are provided.
It requires scientists and experts that have already solved the retail pricing problems for thousands of situations.
All of the Above.
QUESTION 05
Which statement is true about the AI within a pricing solution?
Solving pricing problems requires many different AI techniques and algorithms. There isn't a single AI technique used for all of pricing.
The same AI that is used in different industries (CPG, Finance) or for other areas of retail (supply chain, demand forecasting) can easily be applied to retail pricing because it learns from the inputs and applies the best answer.
The newer the AI the better the AI.
All of the Above.
QUESTION 06
Why is it challenging to create successful AI in pricing?
The AI toolset is very broad and experience is required to know what tool to use for which situation as well as experience to know how to use the tool.
AI requires a lot extensive real world data, experience and feedback loops and adjustments to ensure it is actually solving the problem as desired.
Everyday users have to be able to understand AI decisions, especially in Pricing. If they can't understand what it is trying to do, they will not accept it.
All of the Above.
QUESTION 07
What are some practical questions to ask with AI Pricing applications?
How do I know if the AI is trustworthy in its recommendations?
How do I know if the goals and objectives that the AI is driving towards are the right ones?
How are risks mitigated when the outcomes of the AI are uncertain?
All of the Above
QUESTION 08
What are some risks with AI pricing applications?
Open source tools are very prevalent in the market.
AI pricing has not been proven in B&M retailers and there isn't enough data to understand shoppers.
AI pricing that has only been tested with simulated data can make many assumptions that aren't true.
You have to give up control, because all AI pricing solutions make it difficult to understand the why it is making the decisions that it is.