Understanding the Implementation of AI in Business

Understanding the Implementation of AI in Business

The new wave of AI has been a significant trend in recent years, as AI has rapidly advanced. If we take a peek through Google Trends, we will see that searches related to AI have surged in the past 2-3 years. Additionally, numerous AI-related tools have emerged.

Examples of popular AI tools currently trending include:

  • GPT-4 A chatbot that assists in writing articles, letters, code, and answering questions we may have.

  • MidJourney Used for generating images from text descriptions. Simply provide a description, and it will produce beautiful images ready for use. 

  • Spacely AI Capable of helping design and decorate your room. Just upload a photo of your room, and the AI will assist with the design.

  • Copilot in Microsoft Team Meeting AI that helps take notes and summarize meetings in real-time during the meeting.


Currently, companies are encountering issues with employee work, such as incorrect data entry, which can lead to errors in the database. When this data is used to create dashboards or for further analysis, it can require more time to clean the data than necessary. This is where AI becomes a beacon of hope, as it can help address these problems by alerting users to incorrect data types or formats. In this article, we will explore various perspectives on the challenges of implementing AI in business.

  1. Limitations of AI.
    In the GPT-4 model, the training data is limited by a “cut-off date,” meaning the model is trained only on data available up to that specific point. As a result, the information processed by the AI is not the most recent and is not updated in real-time.

    Examples of AI limitations:

    – Rapidly changing stock market news prevents AI from providing accurate and current market or financial analysis.
  2. Accuracy of AI and the Impact of Data Inaccuracies.
    AI models learn from data, much like a child learning from a teacher. If the training data is incorrect or incomplete, it can lead to errors.

    Examples of AI accuracy and the impact of data inaccuracies:

    – Writing emails using AI without checking them before sending can result in miscommunication and inaccurate content, which can undermine our credibility at work.
    – Using AI to assist in diagnosing patient illnesses can lead to incorrect or inaccurate diagnoses, potentially resulting in improper treatment and negatively affecting the patient’s health.

  3. Security
    In the development of models like Chat GPT or MidJourney, the data inputted into Large Language Models (LLMs) systems may be at risk of being exposed to the public. If sensitive or confidential organizational data is inadvertently shared, it can have significant repercussions on the business. Guidelines for maintaining security when using AI include various measures. Following recent news about Microsoft, they have introduced a new AI model called Microsoft Co-Pilot, which helps maintain confidentiality by ensuring that others cannot see the data we provide to the AI. Additionally, Microsoft pledges not to use this confidential information for further model training. 

  4. AI can help alleviate workload in many ways.
    However, in several instances, companies have faced challenges when implementing AI into their operations. For example, automating text messages to invite customers to visit a branch may seem efficient, but if employees are not notified, it can lead to an influx of customers, resulting in slower service. Similarly, when employees use Microsoft Co-Pilot, it’s essential to ensure that its use genuinely facilitates work rather than complicating it further. Therefore, before integrating AI into operations, it’s crucial to verify that its implementation genuinely facilitates work rather than making it more challenging than before.
  5. We believe this has been a persistent issue across all eras.
    From navigating Call Center menus by pressing 1, 3, 2, and then waiting on hold, which people dislike, to the current trend where many companies reduce customer service staff by implementing Chatbots. Despite these advancements, AI often fails to communicate effectively. Another example is McDonald’s ordering machines, which are more difficult to use than ordering at the counter, leading to situations where employees have to assist customers, standing by in confusion. Therefore, before implementing any AI, it’s crucial to consider its impact on customer experience and whether it might cause customers to become frustrated or dissatisfied.
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