By Mark Kelly


Chat Generative Pretrained Transformer, commonly known as ChatGPT, is an advanced chatbot and generative language tool inaugurated by OpenAI in November 2022.

1. Understanding ChatGPT: What is it and how does it operate?

Chat Generative Pretrained Transformer, commonly known as ChatGPT, is an advanced chatbot and generative language tool inaugurated by OpenAI in November 2022. Given an initial phrase or “prompt,” the ChatGPT models calculate the most likely sequence of letters or words. Built upon OpenAI’s GPT-3 family of vast language models, ChatGPT allows interaction with a model via a conversational user interface. The system was trained using 300 billion words sourced from books, internet texts, Wikipedia articles, and code libraries, and then refined with human feedback. In January 2023, Microsoft introduced Azure OpenAI Services, encompassing ChatGPT, additional language models, and supplementary enterprise services. It’s crucial for enterprise planners to differentiate between OpenAI’s ChatGPT and Azure OpenAI Service, which, while still evolving, offers promising enterprise operational features.

2. The Role of ChatGPT in the Enterprise:

How will it be utilized? ChatGPT and similar foundation models will serve as tools in conjunction with numerous other AI and hyperautomation innovations. They will be part of architected solutions that automate and augment humans or machines, and autonomously carry out business and IT processes. As generative AI finds its place alongside existing work approaches, ChatGPT and other competitors will be deployed to replace, recalibrate, and redefine various activities and tasks that are part of many job roles.

3. Use Cases of ChatGPT:

What are its primary applications? ChatGPT can generate and enhance prose and code development, summarize long-form texts, classify content, answer questions, and translate and convert languages (including programming languages).

4. ROI of ChatGPT:

What is its value? The ROI of ChatGPT hinges on the use case. For augmented scenarios, these tools can save time for writers and programmers, but such time savings may not necessarily benefit employers. Users should maintain realistic expectations about the use cases and the value they aim to derive, especially given that the service as-is has significant limitations, such as reliability issues. The generated text or code might be inaccurate or biased, necessitating human validation, which could offset the initial time savings. It is essential to link ChatGPT use cases to KPIs and ensure the project enhances operational efficiency, generates new revenue, or improves experiences.

5. Cost of ChatGPT:

How much is it? The 3.5 version of ChatGPT, is free of charge. OpenAI recently announced the launch of the ChatGpt 4.0 subscription plan for at $20 a month. ChatGPT will also be part of the Microsoft Azure OpenAI Service soon, but the pricing is currently being introduced. It’s plausible that substantial elements will be bundled with different Microsoft 365 software subscriptions.

6. Direct customer interaction with ChatGPT::

Should it be done? Generally, no – providing ChatGPT-powered experiences directly to customers is considered too high risk for most use cases at the present time, except in rare cases perhaps related to gaming or entertainment, where the correctness or impartiality of the content may not be under rigorous scrutiny.

7. Job Replacement by ChatGPT:

Will it happen? Initially, ChatGPT will primarily enhance specific activities or tasks rather than replace whole jobs. Future iterations of ChatGPT, along with other tools and their combinations, will likely progress beyond augmentation and start executing targeted activities or tasks independently. This process will necessitate testing, quality control, guardrails, and governance.

8. Impact of ChatGPT on the Enterprise Workforce:

What can we expect? As stated in response to question 2, ChatGPT will be one among various tools, including other hyperautomation and AI innovations, incorporated in architected solutions that automate, augment humans or machines, or autonomously conduct business or IT processes. It will replace, recalibrate, and redefine the activities and tasks that constitute many job roles.

9. Magnitude of Workforce Impact

What is the extent? There will be creation of new jobs and the redefinition of others. The net change in the workforce will considerably fluctuate based on factors such as industry, location, and the size and offerings (products or services) of the enterprise. However, it’s evident that tools like ChatGPT (or competitors), hyper-automation, and AI innovations will target repetitive, high-volume tasks with an emphasis on efficiency, such as reducing cycle time, boosting productivity, and enhancing quality control (reducing error rates), among others.

10. Future Prediction for the Enterprise:

Is it? No. Despite the impressive capabilities of ChatGPT and related large language models (LLMs) or foundation models, they cannot comprehend, learn, or undertake any intellectual task that humans can. ChatGPT is a type of reinforcement learning approach. While enhanced with human feedback, it fundamentally remains a machine learning construct and lacks the generalization attributes provided by symbolic techniques.

11. ChatGPT as Artificial General Intelligence:

What’s the forecast? By 2026, over 100 million humans will collaborate with robot colleagues (synthetic virtual colleagues) for enterprise work. This will not solely be powered by ChatGPT (or competitors); it will involve several other technologies and solutions.

12. Is ChatGPT a New AI Paradigm?

ChatGPT is more of an evolution of ongoing trends than a new paradigm. Its underlying model is based on transformer neural networks, which have been foundational for over five years, including in vendor applications. Nevertheless, ChatGPT introduces some new elements to those foundation models, such as conversational and short-term memory layers and massive human-in-the-loop feedback (reinforcement learning) for training. The engineering employed to make the model available for mass consumption is also novel, requiring extensive computational resources and model-serving architecture.

13. Multilingual Capabilities of ChatGPT:

What can it do? ChatGPT was trained on a multilingual corpus, enabling it to respond to inputs and generate outputs in several languages. Gartner has informally observed that ChatGPT performs comparably to the leading commercial machine translation model for English to Spanish, but is not as proficient for other official UN languages (Arabic, Chinese, French, and Russian). ChatGPT’s translation is slower than commercial engines. The use of GPT-3 for translation should be evaluated on a case-by-case basis.

14. Different uses of ChatGPT:

What are they? ChatGPT can be utilized in four different ways:

  1. As-is: By entering prompts and receiving results via the web-based interface. This is the most popular usage approach currently.

    Prompt Engineering without APIs: This involves using a service like ChatGPT alongside other technologies as part of a workflow, which can be carried out manually or by using screen scrape and robotic process automation (RPA) technologies.

    Prompt Engineering using APIs: This model is not yet available but expected in the first half of 2023. Although there are solutions on Github enabling an API wrapper around ChatGPT, these are not recommended for production builds or scale, and they are not supported by OpenAI.

    Custom Build: Creating a custom build of the core GPT2/GPT3 model for a bespoke implementation is possible, but it wouldn’t have the conversational interaction.

15. Current Limitations of ChatGPT:

What are they? ChatGPT’s training only covers data up until 2021, which limits its recency. It cannot provide the sources of the information used to generate its answers and lacks explainability. The reliability of the model is dependent on its (unknown) underlying sources, which can sometimes be erroneous or inconsistent. While it can generate language and code, it cannot create images. Currently, there is no supported API available. You cannot train ChatGPT on your own knowledge bases. Despite seeming to perform complex tasks, ChatGPT only makes predictions without understanding the underlying concepts. It does not provide data privacy assurances. Although recent updates have improved its ability to handle mathematical queries, it still cannot be relied on for computation.

16. Using Your Own Data with ChatGPT:

Is it possible? At present, you can use your own data only for providing prompts to ChatGPT, but not for training or fine-tuning it. If you’re using ChatGPT as-is, you can include your own data and content with your questions, like pasting in software code for ChatGPT to debug, or inputting text for it to summarize. But currently, you can’t add your own industry or domain knowledge data to train or fine-tune ChatGPT, although this functionality is expected to be available in the Azure service in 2023. As an alternative, you can use the GPT2/3 engines without the ChatGPT conversational interface or additions and use transfer learning to train your own version of the model, but it would not result in the same type of model as ChatGPT.

17. Personalizing Content with ChatGPT:

Can it be done? While you cannot personalize the user experience (UX) of ChatGPT, users can influence the generative output via their prompts, such as by requesting that the generated content adhere to a certain writing style or educational level. The Azure OpenAI ChatGPT service is likely to add APIs in the future, which will likely make it possible to intercept the input and output and handle the user experience with a different interface.

18. Building or Integrating ChatGPT into Other Systems:

Is it possible? Yes, it is possible to use ChatGPT in the building of or integration into other systems. Currently, it is more suitable to construct augmented approaches that support various roles.

19. New Features for ChatGPT:

Is it? No, ChatGPT is not a threat to search; rather, it complements it. While ChatGPT generates answers, search is more focused on artifact discovery like finding a particular document or sentence. Many search and insight engine vendors have been using base GPT technology as part of their AI techniques for some time. It is predicted that over time, discovery methods like search will evolve to use foundation models in conjunction with existing approaches.

20. ChatGPT Replacing or Threatening Search:

What are they? ChatGPT is not a static service. For instance, it was recently improved to handle mathematical prompts more effectively. Microsoft might use the Azure OpenAI ChatGPT service to complement Bing search in 2023. Furthermore, it is expected that more formal API offerings will be added to the service. Recently, updates have been rolled out to Microsoft Teams Premium, utilizing the Azure OpenAI ChatGPT core model of GPT3.5.

21. Competitors of ChatGPT:

Who are they? Yes, ChatGPT does have competitors. Several smaller vendors have utilized large language models, similar to ChatGPT, to deliver specific task usage. However, many of the larger technology vendors have not yet commercialized their offerings. It is expected that competitors such as Baidu, IBM, and Google will enter the market in the first half of 2023. For instance, Google announced its own offering, Bard, on February 6, 2023.

22. Markets Around ChatGPT:

What are they? The biggest evolution will be in creating bespoke variants of models like GPT, where system integrators and vendors support end users to input their own knowledge bases via transfer learning. More corpora management and prompt engineering services and tools are expected to emerge in 2023, as well as tools for fact-checking and generated text detection. Vendors are likely to differentiate their products through task-specific fine-tuning of their models and by introducing tools to mitigate risks related to the explainability, reliability, fairness, security, and transparency of generated content.

23. Effect of ChatGPT on Current Natural Language Technologies (NLT):

Does it make them obsolete? No, ChatGPT does not render current natural language technologies obsolete. It intersects two markets in the NLT space: conversational AI and natural language generation. If your chatbot conducts transactional conversations and relies on your own knowledge body, then ChatGPT won’t replace it. At present, ChatGPT serves as a broadly useful, general-purpose conversational tool, not a single-API solution for NLT. Within a workflow, ChatGPT and GPT technologies have a role. It might be possible to use the technology within NLT systems, like generating synonyms, utterances, and responses. It’s advisable to check with your current vendors to understand how they are utilizing generative technologies like ChatGPT.

24. Security of ChatGPT for Staff Usage:

ChatGPT should be used with the same caution as public online platforms. Employees should avoid sharing sensitive personal, company, or client information. While there aren’t currently clear assurances of privacy or confidentiality, Microsoft plans to introduce privacy assurances for its Azure OpenAI ChatGPT service, just like its other software services.

25. Content Filtering in ChatGPT

ChatGPT has a toxic content filter for both inputs and outputs. However, due to the nuanced and contextually dependent nature of this task, users should not completely rely on the model’s output for compliance or risk management, and should ensure human oversight of inputs and outputs.

26. Risk of Misuse of ChatGPT by Bad Actors

There’s a valid concern that bad actors may misuse ChatGPT to generate false information, create convincing phishing emails, or even generate malicious code. The simplicity and widespread availability of ChatGPT heighten this risk. Users may be required to sign ethical usage agreements, but these could be hard to enforce.

27. Who Can View Conversations with ChatGPT?

ChatGPT service providers, currently OpenAI and soon Microsoft, can review conversations to improve their systems and ensure compliance with their policies. There are no assurances regarding other parties who might access the posted information. The Azure version of the service is expected to follow existing Azure OpenAI services in this respect.

28. Conversations with ChatGPT Used for Training

Yes, conversations may be used for training and could be reviewed by trainers. It is not currently possible to delete specific prompts, so users need to be careful about what they share. While it is possible to delete an account, this action won’t erase the training data.

29. Biases in ChatGPT

ChatGPT’s fine-tuning is aligned to the trainers’ preferences rather than verified facts, leading to plausible but potentially unreliable outputs. Bias may be present in the large datasets used to train the GPT-3 model. Despite OpenAI’s efforts to minimize bias, there have been known instances of it surfacing.

30. Regulatory Risks Regarding Training Data Content Ownership

There are concerns about the ownership of data and intellectual property rights with respect to content used to train GPT-3 and ChatGPT. As of now, there is no clarity on this matter, posing a risk to OpenAI and the further usage of ChatGPT.

31. Detecting ChatGPT-Generated Content

There’s currently no reliable method to detect whether content was generated by ChatGPT or a human. Some tools have attempted to do this, but with poor results so far.

32. Implementing a Company Policy on ChatGPT

It is advisable to establish a policy as knowledge workers might already be using ChatGPT for various tasks. An outright block might result in covert “shadow” ChatGPT usage, giving organizations a false sense of compliance. Employees should treat information posted through ChatGPT as if it were public. Organizations should monitor usage, encourage innovation, but ensure ChatGPT is used responsibly and never unfiltered with customers and partners.


The provided list of resources highlights the various sources from which information about ChatGPT and related AI technologies has been drawn for the purpose of the FAQ. These resources, provided by the creators and hosts of the technology, offer insights into the development, deployment, and implications of using AI models like ChatGPT. Here’s a brief summary of what these resources entail:

ChatGPT and GPT Board Reference Presentation: A deep dive into the workings, application, and impact of GPT and ChatGPT models.

Innovation Insight for ML-Powered Coding Assistants: A resource providing information on how machine learning is being used to facilitate coding, discussing AI models like GPT-3’s code-generation capabilities.

ChatGPT: Optimizing Language Models for Dialogue, OpenAI: A detailed explanation of the development and optimization process of the ChatGPT model, published by OpenAI.

Azure OpenAI Service, Microsoft: Information on Microsoft’s Azure OpenAI service, including its features, capabilities, and use cases.

Introducing ChatGPT Plus, OpenAI: An announcement by OpenAI introducing the enhanced features and capabilities of the ChatGPT Plus model.

General Availability of Azure OpenAI Service Expands Access to Large, Advanced AI Models With Added Enterprise Benefits, Microsoft: A release by Microsoft announcing the broader availability of their Azure OpenAI service, with an emphasis on its benefits for enterprises.

Microsoft Teams Premium: Cut Costs and Add AI-Powered Productivity, Microsoft: A resource detailing the integration of AI technologies into Microsoft Teams for enhanced productivity and efficiency.

Data, Privacy, and Security for Azure OpenAI Service, Microsoft: Information provided by Microsoft regarding data handling, privacy measures, and security features within their Azure OpenAI service.