CrowdANALYTIX offers a variety of automated solutions. Among the solutions we provide, there is an AI-based product category classification and labeling automation solution [CrowdANALYTIX for Product Master Database] for EC retailers and distributors (hereafter, CAX PMD). The benefits of automating this process are myriad, among which are increased speed, increased accuracy, reduced costs and, of course, scalability. What challenges do companies face when implementing automation?
Automation cannot happen instantaneously
Every business wants a solution that can digitally transform their business on the fly and deliver immediate ROI. Unfortunately, however, it is not possible to achieve this simply by using AI. The above product data operations solution can achieve 90% automation within 6 weeks for companies with access to high quality data, but we still have to start by doing everything manually. must be During the six-week ramp-up for automation, the AI algorithms have time to learn their own taxonomies and clean up the data before taking over human tasks such as data validation. A human can start taking over from day one, but the AI will gradually take over over a period of weeks of learning.
output depends on input
The level of automation possible is different for each business and highly dependent on the quality of data available. If suppliers and manufacturers do not have good quality data, the level of automation available is limited. CAX PMD can handle data in almost any format, unorganized data, and large amounts of data. However, CAX PMD cannot generate non-existent information, such as the width of the window frame or the color of the rug, from scratch.
Similarly, the level of accuracy is highly dependent on the quality and quantity of data available. As we will see later, CrowdANALYTIX offers an accuracy of 90%, far better than human accuracy. However, if AI receives insufficient data (incomplete text, blurry PDF, damaged images, etc.) or insufficient quantity, the expanded product data generated may be incomplete, inaccurate, or becomes unclear.
Many early expected errors are part of the process
Just as full automation cannot be implemented instantaneously, AI accuracy cannot be perfected instantaneously. Humans starting the AI implementation process must learn each company's taxonomy and custom business rules in order to build the algorithms needed for automation. As many companies have already found out, humans are prone to error. Even if a human being pays close attention, the only thing that can be acquired correctly is That's it.
As the AI learns by processing more data and making adjustments based on feedback, errors will occur. However, unlike humans, AI can achieve an accuracy of about 90% under the right conditions, and in most cases even better than humans.
Since it is impossible to create an error-free system, it is difficult to ensure an accuracy of 90% or more. The accuracy you can expect is worth it alone.
Requires ongoing maintenance
Solutions like CAX PMD require maintenance after deployment. All intelligent systems must be tuned to achieve their goals. Because the input data is supposed to change over time, the accuracy of early AI algorithms can decrease over time. Unlike software, AI deals with fluctuating data in real time, so it will never be 100% consistent over time. Due to mergers and acquisitions, changing customer preferences and demands, many companies' taxonomies, definitions, and business rules also need to be adjusted over time.
For more detailed information, you can download the white paper "AI that automates product classification, product attribute extraction, and structuring" from the button below. Please take advantage of it.
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