Retail businesses play a crucial role in the economy of the country. Nowadays, many organizations are using AI technology to develop their businesses, such as demand forecasting, product recommendations that meet individual customer needs, product assortment, data analytics to increase the efficiency of the supply chain, and managing other areas of their business. All of these benefits provide business owners with the opportunity to create campaigns to stimulate sales, generate profits, and reduce unwanted costs. These benefits have led to the increasing use of AI technology by business owners. However, in addition to the benefits that can be gained, businesses also face challenges that may arise during various processes, particularly with data quality. In this article, we would like to invite you to take a look at the challenges in the use of AI that businesses might face in order to prepare and cope with them.
Many people may have heard the saying 'Data is the new oil', meaning that data is a valuable asset, especially in highly competitive retail businesses. Creating a competitive advantage in business operations often comes from data analysis. Some people may understand that the more data there is, the better solutions can be created from it. This understanding is partially true, but sometimes having too much data can also affect processing efficiency (Debilitated by Data). Especially nowadays, when the amount and variety of data have increased, including online social news trends, daily transactions, or data collected from various sensors. Moreover, the size of data may be larger than traditional data storage technologies can handle. Therefore, we should deal with this challenge by organizing data systems appropriately to increase processing speed and control the cost of data storage.
The next challenge is that the data analysis system requires collecting data with multiple characteristics, such as:
Accuracy: Collected data may have errors.
Currency: Timely data is crucial for addressing ever-changing customer demands.
Continuity: Regular collection improves efficiency in analysis and processing.
Relevance: Data must align with business requirements for effective strategy development.
When considering these challenges, many organizations may realize the challenges of using data to create AI models. This is a good starting point for business owners to begin
collecting and organizing data before moving on to the next step of using AI to analyze and solve business problems.
Another important challenge of using AI in retail businesses is the Personal Data Protection Act (PDPA), which requires many organizations to change their practices to comply with PDPA standards, especially regarding the collection of customers' personal data and data of employees or other individuals. PDPA is not a law that prohibits the access to or the use of data, but rather a law that aims to establish standards for organizations in terms of data collection and usage with integrity. This is a challenge for organizations to adjust and practice rigorously to prevent data breaches or inappropriate uses. Any mistakes could have a negative impact on the organization's image and customer confidence in the organization.
These challenges are just some of the things that business owners have to face. However, compared to the benefits that technology can bring to retail businesses, these challenges can be relatively small. All that is needed is to prepare the necessary resources both human and technology, continuously adapt to new things, or consult with leading technology solution experts to help take care of these issues.