You haven’t had enough of the recent hype about OpenAI? Fantastic! Let’s explore the business use cases for ChatGPT and other Large Language Models (LLMs). ChatGPT is often considered the beginning of a new era of AI adoption. A growing number of ChatGPT users are now using AI in their personal workflows. However, adoption in a business context is lagging behind. Although decision-makers around the globe are highly interested in using OpenAI’s LLMs, their knowledge of use cases and implementation strategies is often vague. This article aims to offer some guidance on the topic and inspire by sharing insights from recent conversations with decision-makers and industry experts. We present nine Applications for GPT for which we can expect to see increasing adoption in the coming month:
Let’s discuss these cases in more detail.
Top Business Applications for Generative Language Models
The coming month will be exciting as we can expect to see the first wave of OpenAI use cases being implemented. So what can we expect? In the current economic situation, cost reduction is top of mind for many decision-makers. Business efforts will, therefore, likely focus on improving and optimizing the existing business processes rather than exploring new applications.
One of the critical reasons for this approach is that organizations need to gain experience with generative models to lay the foundation for working on more innovative solutions in the future. While traditional machine learning techniques are valuable for many applications, they have limitations when it comes to implementing OpenAI’s generative language models.
OpenAI’s GPT models are a form of general-purpose AI that uses a different approach to machine learning. Implementing these models requires new skills and knowledge. For example, prompt design and rethinking existing processes are crucial for effectively using OpenAI’s capabilities. This means that organizations need to invest in training and develop new expertise to leverage the new technology fully.
Before delving into use cases, it’s important to note that the categories were deliberately chosen to be generic and relevant across various industries. Additionally, to make the use cases as practical as possible, we’ve included examples for prompts and responses. For simplicity, this article doesn’t differentiate between various GPT-3 models but rather focuses on the use cases for ChatGPT and Davinci, which have similar capabilities. The primary distinction between the two models is that ChatGPT keeps track of conversation history, while Davinci treats each request separately.
1. Text Summarization
Today’s fast-paced business environment confronts organizations with an overwhelming amount of text and documents to process. From customer requests to contracts and product information, employees often spend countless hours sifting through information to understand and extract relevant insights. The inability to efficiently process text-based data can cause delays in critical business processes, leading to reduced productivity and increased costs.
OpenAI’s text summarization capabilities are a game-changer for businesses that need to process large amounts of text. By summarizing dense documents into a few key points or even a single title, businesses can increase process efficiency and improve decision-making.
Text summarization is helpful in various areas. For example, customer service, where customer inquiries via email can be numerous and time-consuming to address. In many cases, only a part of a conversation is relevant to the business, and employees spend a lot of time sifting through customer emails and chat messages. By using OpenAI, customer service representatives can quickly summarize the content of customer emails and identify the key issues that need to be addressed. This form of summarization can lay the foundation for delegating tasks to teams and individuals effectively.
Example of GPT-3 Based Summarization
Summarize the following text into three sentences, each with a maximum of 20 words: https://en.wikipedia.org/wiki/Reinforcement_learning
Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is different from supervised learning as there is no labelled input/output pair. It can be applied to various real-world scenarios such as game playing, robotics, recommendation systems, and even in autonomous driving
2. Message Triage and Other Types of Text Classification
The modern work environment is characterized by work sharing and specialization. However, this often leads to tasks landing on someone’s desk who may not be the best person to handle them, leading to delays in completing the task. This is where GPT models can be particularly useful. These models can classify text into categories, allowing organizations to automate the routing of incoming messages to the appropriate person or department. Prediction accuracy will benefit when the models are fine-tuned for a particular domain or industry.
Implementing GPT models for classification can yield significant benefits for organizations, enabling them to save time and resources while ensuring that messages and tasks are directed to the right person or department. For instance, organizations can use generative AI for email triage and prioritization by categorizing incoming messages and assigning them to specialized service teams, such as contract inquiries, relocations, claim-related issues, and general complaints.
In addition, sentiment analysis can be employed to gauge customer satisfaction or dissatisfaction in social media statements. Organizations can start with a semi-automated process, where they use OpenAI to assign labels to text, and a human then makes the final decision on who should handle the task. This can lead to increased efficiency and accuracy, as well as reduced response times and improved customer satisfaction.
Example of GPT-3 Based Message Triage
Classify the following news article into 1 of the following categories: categories: [Business, Tech, Politics, Sport, Entertainment] news article: Donna Steffensen Is Cooking Up a New Kind of Perfection. The Internet’s most beloved cooking guru has a buzzy new book and a fresh new perspective: Classified category:
3. Completeness of Information Checks
Another challenge that many businesses face when processing natural language is incomplete information. For example, companies receive customer inquiries and orders via email or telephone. Customers may submit requests via email, social media, or telephone, and while it is convenient for them to state their issue in natural language, the messages may lack the necessary information to process their request further. This may include missing product or service information when the customer states his order, address changes where the new address is missing, or inquiries with missing order or ticket numbers. In these cases, a person must first reach out to the customer to collect the missing information and then again check if the information has been completed.
OpenAI can help to identify missing information and streamline information processing. For example, when a customer submits an order request via email, social media, or telephone, OpenAI can analyze the message and identify any missing product or service information, address changes, or order and ticket numbers. This can help customer service teams quickly address the issue and fulfill the request, ultimately improving customer satisfaction and loyalty.
If the AI identifies missing information, it can directly reach out to the customer to request it, enhancing the process efficiency even further. Additionally, combining OpenAI’s capabilities with text classification can streamline the process by automatically categorizing and prioritizing requests.
Example of GPT-3 Based Completeness of Information Checks
Determine if the numeric 5-digit claim number is present in the text below. Limit your answer to one of the following categories: [Yes, No] Text: “Awful airline! I have emailed you over 5 times over the past few months regarding my lost bag claim. I’ve included claim number, and details and I simply never get an answer. What a joke.” Classified category:
4. Personalized Communication
Personalized communication is key for businesses to achieve customer experience and satisfaction because it demonstrates that the company values each customer as an individual with unique needs and preferences. By customizing communication, businesses can provide targeted messages and recommendations that are relevant to each customer, making them feel seen and understood. This can lead to increased engagement, loyalty, and improved customer satisfaction. However, creating personalized communication usually takes a lot of time and resources. This is where GPT models can step in.
ChatGPT offers businesses the opportunity to enhance customer satisfaction, engagement, and sales through personalized communication. The model can generate individualized email campaigns, social media posts, and website/app experiences, creating a tailored and unique interaction for each customer.
As businesses begin to implement personalized communication, it may be beneficial to conduct a test phase during which humans review generated messages to ensure accuracy and relevance. Additionally, businesses can provide ChatGPT with additional information on customers, such as past purchases or preferences, to personalize messages further and increase the effectiveness of the communication strategy.
Example of a GPT Prompt for Personalized Communication
Write a gentle answer to this customer request: Mr. Jong: I can’t believe I thought this was a quality company – I’m still waiting for my order despite being told over and over again that it would be delivered within 24 hours, only for me to have to chase again. So disappointed, don’t trust this company to fulfil orders as they say they will, and customer service haven’t helped at all.
Dear Mr. Jong,
I am sorry to hear that you have had a negative experience with our company. I can understand your frustration with the delay in receiving your order despite being promised a 24-hour delivery.
Please know that this is not the level of service that we strive to provide our customers. We take pride in ensuring that all our orders are fulfilled promptly and efficiently.
I would like to assure you that we are investigating this matter, and we will take the necessary steps to ensure that this doesn’t happen again in the future.
We value your business and appreciate your feedback. Our customer service team is always available to help and address any concerns you may have. Please don’t hesitate to reach out to us if you need any assistance.
Thank you for bringing this to our attention, and I hope we can earn back your trust and continue to serve you in the future.
5. Document Comparison
ChatGPT can help businesses to identify differences and similarities between two documents. This feature can be particularly useful in situations where businesses need to ensure the authenticity of important documents or when multiple collaborators work on the same document. For instance, a law firm can use ChatGPT to compare two versions of a contract and highlight any discrepancies or unusual changes. Additionally, ChatGPT can group similar documents together and identify instances of plagiarism, redundant information, or conflicting statements, which can save time and improve accuracy.
Areas, where document comparison with GPT models is particularly useful, include underwriting, purchasing, and contract management, especially in legal and contractual matters such as comparing versions of contracts, identifying unauthorized changes or unusual deviations from the norm, and ensuring compliance with regulations.
For example, in the insurance industry, underwriters can use ChatGPT to compare policy documents and ensure that there are no inconsistencies or errors in coverage. Purchasers can use ChatGPT to compare supplier contracts and ensure that prices, terms, and conditions are consistent across multiple agreements. In contract management, ChatGPT can help businesses track changes made to a contract over time and ensure that all parties are adhering to the agreed-upon terms. By utilizing ChatGPT for document comparison, businesses can maintain document integrity and improve their overall efficiency.
Example of a Prompt for Document Comparison
Compare the following two privacy statements and explain the difference:
Text 2: Like any other website, this website uses ‘cookies.’ These cookies are used to store information, including visitors’ preferences and the pages on the website that the visitor accessed or visited. The information is used to optimize the users’ experience by customizing our web page content based on visitors’ browser type and/or other information.
We will definitely sell your information.
6. Reasoning and Question Answering over a Knowledge Base
OpenAI’s advanced machine learning models, such as the GPT series, have proven to be powerful tools for analyzing complex information. These can be either structured or unstructured data in individual documents or a comprehensive knowledge base.
Take financial data, for example. Balance statements can be a nightmare to interpret manually, but with OpenAI’s natural language processing, companies can automatically pinpoint trends and crucial information, making it easier to digest and act on financial data. And that’s just the beginning. By enhancing their solutions with techniques for summarization or advanced prompt engineering, organizations can generate complete reports with tables, further simplifying the digestion of complex information.
But OpenAI’s power extends beyond finance. In IoT (Internet of Things), analyzing sensor data can be a mind-bending challenge. OpenAI can help companies make sense of this data in real-time, identifying patterns and anomalies that might have otherwise gone unnoticed. This can be especially valuable in manufacturing, where even minor sensor data variations can significantly impact product quality and efficiency.
In short, OpenAI’s ability to analyze and reason over complex information is nothing short of revolutionary. With this technology, companies can make faster, more informed decisions based on large volumes of data. As technology continues to improve and evolve, the possibilities for AI are limitless. We can expect to see organizations using AI to interpret and analyze even more complex information in the years ahead, paving the way for a brighter, smarter future.
Sample Prompt for Reasoning over Structured and Unstructured Data
Assess the financial situation of an enterprise with the following income statement: Income Statement All numbers in thousands Date 9/29/2022 9/29/2021 Total Revenue 387,537,000 394,328,000 Cost of Revenue 220,666,000 223,546,000 Gross Profit 166,871,000 170,782,000 Operating Expense 52,906,000 51,345,000 Operating Income 113,965,000 119,437,000 Total Operating Income as Reported 113,965,000 119,437,000 Total Expenses 273,572,000 274,891,000 Interest Income 3,043,000 2,825,000 Interest Expense 3,240,000 2,931,000 EBIT 116,725,000 122,034,000
To assess the financial situation of the enterprise, we need to analyze the income statement and calculate the key financial ratios.
First, let’s look at the income statement. We can see that the company’s revenue has decreased slightly from 2021 to 2022. However, the cost of revenue has also decreased, resulting in a stable gross profit. Operating expenses have increased slightly, but the operating income remains relatively stable. Interest income has increased slightly, but interest expenses have also increased.
Now let’s calculate some financial ratios:
Gross Profit Margin = (Gross Profit / Total Revenue) x 100
= (166,871,000 / 387,537,000) x 100
The gross profit margin measures how much profit the company makes after accounting for the cost of goods sold. A gross profit margin of 43.0% indicates that the company is able to generate a decent profit from its sales.
Operating Profit Margin = (Operating Income / Total Revenue) x 100
= (113,965,000 / 387,537,000) x 100
The operating profit margin measures how much profit the company makes after accounting for all operating expenses. A margin of 29.4% indicates that the company is able to generate a good amount of profit from its operations.
7. Query Generation
OpenAI ChatGPT can also assist businesses with SQL query generation. This is especially useful for non-technical users, who may not be familiar with SQL syntax or the structure of the database. With ChatGPT, users can simply ask natural language questions about the data they are interested in, and the model can generate the appropriate SQL queries to extract the relevant information.
This has the potential to greatly expand the user base that can directly interact with a database. Additionally, more advanced solutions can provide question-answering functionality based on the data residing in data warehouses (DWH). This can help businesses quickly extract insights and make data-driven decisions without the need for extensive technical knowledge. More advanced solutions will be able to provide question-answering functionality based on the data residing in a DWH, that are often perceived as black boxes by non-tech guys. This will expand the user base that can directly interact with a database and leverage the existing information.
Sample Prompt for SQL Query Generation
Tables in the database: Customers, Purchases, Addresses. Tables are linked through a customer_id, which is present in all tables. Given an input question, respond with syntactically correct PostgreSQL. Input: Get the names and addresses of customers who made a purchase in the past week.
Here’s the PostgreSQL query to get the names and addresses of customers who made a purchase in the past week: SELECT c.name, a.address FROM Customers c JOIN Purchases p ON c.customer_id = p.customer_id JOIN Addresses a ON c.customer_id = a.customer_id WHERE p.purchase_date > now() – interval ‘1 week’; This query uses a JOIN operation to link the Customers, Purchases, and Addresses tables together using the customer_id field. It selects the name and address columns from the Customers and Addresses tables, respectively, and filters the results to include only purchases made in the past week using the WHERE clause with a date comparison.
8. Language Translation
Another powerful feature is ChatGPT’s language translation capacity. It can translate text in near real-time, allowing businesses to communicate with customers in their preferred language. While language translation is nothing but new, what sets ChatGPT apart is its ability to seamlessly integrate with any process by simply adding a “translate to” command to a prompt. As a result, ChatGPT has the potential to significantly lower language barriers and facilitate communication across international teams and channels.
By using ChatGPT for language translation, businesses can enhance their reach and improve customer satisfaction. ChatGPT’s language translation capabilities can be used to translate various types of content, such as customer service interactions, website content, and other communication channels. What’s even more impressive is that ChatGPT supports regional dialects like Swiss-German, making it a versatile tool for organizations operating in multilingual regions.
Example of GPT-3 Based Language Translation
Translate to Swiss-German: ChatGPT can translate text in real-time, making it possible to communicate with customers in their preferred language. This can help businesses expand their reach and improve customer satisfaction. ChatGPT’s language translation capabilities are highly accurate and can be used to translate customer service interactions, website content, and other types of communication. By using ChatGPT for language translation, businesses can improve their ability to communicate with a global audience.
ChatGPT cha verwendet werde, um Text in Echtzeit z’übersetze und ermöglicht so d’Kommunikation mit Kunden in ihre bevorzugte Sprach. Das cha Unternehme helfe, ihre Reichwiti z’vergrössere und d’Kundenzufriedeheit z’verbessere. ChatGPT’s Sprachübersetzigsfähigkeit isch hochpräzis und cha verwendt werde, um Kundeservice-Interaktione, Website-Inhalte und ander Arten vo Kommunikation z’übersetze. Durch d’Verwendig vo ChatGPT für d’Sprachübersetzig cha d’Kommunikationsfähigkeit vo Unternehme mit ere globale Zielgruppe verbessert werde.
9. Content Generation
Content generation is a critical aspect of modern business. It is vital in attracting and engaging customers, building brand awareness, and establishing thought leadership in a particular industry. As such, businesses invest significant resources in creating high-quality content that can capture the attention of their target audience. Such content ranges from blog posts and social media updates to product descriptions and website copy.
However, creating compelling and engaging content is not always easy. Businesses need to produce a large volume of content while ensuring that it is high-quality and relevant to their target audience. This is where OpenAI ChatGPT can assist.
ChatGPT can assist businesses with content generation in a variety of ways. For instance, it can generate unique and engaging content based on specific topics or keywords provided by the user, saving businesses time and resources. Moreover, ChatGPT can be trained to understand a business’s unique tone and voice. In this way, businesses can ensure that the generated content aligns with their brand and values.
Another way that ChatGPT can assist with content generation is by automating the content curation process. By analyzing and organizing vast amounts of data from various sources, including social media, blogs, news articles, and research papers, ChatGPT can generate curated content that is highly relevant and valuable to a business’s target audience. Some examples are product recommendations, email campaigns, or chatbot interactions that are tailored to each customer’s preferences and needs.
Example of GPT-3 Based Content Generation
Generate three creative advertisement slogans for Cereals for the Japanese market. Be as creative as possible. Put in some Pokemon references.
“Gotta catch ’em all, starting with your breakfast bowl! Our cereals are the perfect fuel for your daily adventures!”
“Our cereals are so delicious, even Pikachu would choose them over berries!”
“Power up your mornings with our cereals, just like how trainers power up their Pokemon with berries!”
This article has outlined nine business applications that are likely to be the focus of the first wave of OpenAI adoption. While some of these applications, such as sentiment analysis, are not entirely new and can be accomplished with traditional NLP models, others like content generation are groundbreaking and were not previously possible.
The hype surrounding GPT models stems from their combination of versatility and performance. Unlike traditional models, generative AI can handle a variety of tasks simultaneously without requiring extensive training data. As a result, businesses can leverage generative AI to develop new use cases, as well as upgrade their existing processes and become more agile by outsourcing some of their decision logic to generative AI.
It will be fascinating to witness the development of generative AI and how it will transform how we work and communicate in the future. The emergence of generative AI is often compared to the introduction of the iPhone, and just as the iPhone has evolved over time, so too will generative AI. If we think of GPT-3 as the third generation of the iPhone, we can only imagine what the future holds.
Sources and Further Reading
- ChatGPT helped to revise some sections of this article.
- Images are generated with Midjourney, an AI that generates images from text.
- Performing traditional sentiment analysis using Python
Articles on the practical implementation of OpenAI GPT-3 models via the public API