Common shortfalls of using ChatGPT in a professional environment


ChatGPT is an advanced text-generating dialogue system that utilizes natural language processing (NLP) techniques. It is based on the Generative Pre-trained Transformer (GPT) architecture and has been trained on extensive conversational data from the internet. This powerful NLP model can perform various tasks, including translation, question answering, and text completion. ChatGPT is often a conversational AI solution in chatbots, virtual agents, and other conversational interfaces.

ChatGPT leverages machine learning algorithms to generate human-like responses based on user inputs. It operates on a neural network architecture called a Transformer. By training the model on large datasets containing text from websites, books, and articles, it learns the patterns and structure of language. This allows it to predict the next word in a sentence based on the preceding one.

When generating text, ChatGPT predicts subsequent words in a sentence or prompt to construct a complete response. An attention mechanism is also employed, enabling the model to selectively focus on specific input parts, resulting in more accurate and coherent responses. For conversational AI purposes, the model is often fine-tuned using a smaller dataset of conversational text to enhance its ability to generate human-like dialogue.

Common use cases for ChatGPT

ChatGPT finds applications for various end-user services in the information technology and digital workplace domain. Some common use cases include:

  • Virtual Assistants: ChatGPT can be utilized to develop virtual assistants capable of handling scheduling, email management, and customer service tasks.
  • Email Responders: ChatGPT can generate automated responses to common customer inquiries, streamlining communication and providing timely assistance.
  • Knowledge Base: ChatGPT can contribute to creating a knowledge base that answers frequently asked questions and provides information to users.
  • IT Service Desk: Automated assistance can be provided to users encountering IT-related issues, like password resets and account lockouts, using ChatGPT.
  • HR Assistance: ChatGPT can offer automated support to employees seeking assistance with HR-related matters, such as benefits and time-off requests.
  • Document Automation: ChatGPT can automate the generation of documents, such as contracts and reports, by utilizing predefined templates and prompts.
  • Language Translation: It can translate messages, emails, and documents, facilitating multilingual communication.
  • Meeting Summary: ChatGPT can generate summaries of key points discussed in meetings, aiding in progress tracking and ensuring everyone is on the same page.

Common shortfalls of using ChatGPT in a professional environment

  1. Quality of Generated Text: While ChatGPT is trained on extensive data, the quality of generated text may vary. It can sometimes produce responses that lack coherence or accuracy, leading to potential misunderstandings or misinformation.
  2. Bias in the Training Data: GPT-based models like ChatGPT learn from vast amounts of internet text, which can contain inherent biases. If not carefully addressed, these biases can be perpetuated in the model’s responses, potentially leading to biased or unfair outcomes.
  3. Lack of Contextual Understanding: ChatGPT may struggle to fully comprehend a conversation’s specific context or nuances. It may generate tangential responses or fail to address the user’s intended meaning, resulting in less useful or relevant information.
  4. Legal and Ethical Implications: The deployment of ChatGPT in enterprise services can raise legal and ethical concerns. Privacy and data protection laws, consent, and transparency must be carefully addressed to ensure compliance and prevent potential legal or reputational risks.
  5. High Computational Cost: GPT-based models like ChatGPT are computationally intensive, requiring powerful hardware and infrastructure to operate efficiently. Fine-tuning or running the model at scale may demand substantial computational resources, impacting operational costs.
  6. Limited Handling of Structured Data: While ChatGPT excels in natural language tasks, it may struggle with structured data handling. Tasks that require precise extraction or manipulation of structured information may be challenging for the model, requiring additional solutions or integration with specialized tools.
  7. Vulnerability to Adversarial Examples: GPT-based models, including ChatGPT, can be vulnerable to adversarial attacks. Adversaries can craft inputs designed to mislead or manipulate the model into generating incorrect or undesired responses, potentially posing security risks or enabling malicious activities.
  8. Lack of Explainability: GPT-based models operate on complex deep learning techniques, making it difficult to understand the model’s inner workings and decision-making process. Lack of explainability can hinder trust, transparency, and accountability, especially in sensitive professional applications.
  9. Absence of Service Level Agreements (SLAs): Currently, there are no predefined SLAs for enterprise usage of ChatGPT. This absence of formal agreements regarding performance, availability, and support can make it challenging to set realistic expectations or obtain necessary assurances for enterprise deployments.
  10. Security and Legal Approval: Since ChatGPT is an open-source model, ensuring security and obtaining legal approvals or agreements for customer data usage can present challenges. Organizations must carefully assess and address these considerations to maintain data protection and regulatory compliance.
  11. Development Effort and Maintenance: Implementing ChatGPT professionally requires dedicated teams and effort. Setting up, integrating via APIs, and customizing the model to align with specific customer environments can involve significant development and maintenance effort.
  12. Training and Learning: ChatGPT is trained on internet data until 2021. For specific enterprise (DWP) training, a large amount of data for supervised training and reinforced learning is needed, requiring continuous development effort. Additionally, updating the model with current information may necessitate regular retraining.
  13. Lack of Dedicated/Agreed Support: Obtaining specific support tailored to enterprise customer environments may be challenging in an open-source model. Organizations should be prepared to address potential support limitations or invest in dedicated resources to ensure smooth operations.