From creating websites, writing drafts of lawsuits, doing tax, and composing songs to wearing multiple hats and personas, it seems GPT-4 has no shortage of tasks that it can accomplish. In the enterprise, GPT-4 could change the way businesses interact with customers, open new ways of working, and enable greater flexibility and fluency. However, organizations need to carefully weigh if jumping into the GPT-4 bandwagon will be worth its salt.
We previously explored GPT-4’s remarkable features as well as limitations. Here, we’ll dig a bit deeper into how businesses can take advantage of GPT-4 — with some caveats.
How can businesses avail GPT-4’s features?
Businesses can pay US$20 per month to subscribe to ChatGPT Plus, the premium version of ChatGPT, which runs on GPT-4. On top of this, they must pay for prompt tokens and completion tokens. Prompt tokens comprise inputs submitted to ChatGPT, and completion tokens are the outputs subsequently generated by ChatGPT.
A token can be described as an item that the model understands. In human terms, the closest thing that a token can be compared to is a word, though note that generative AI models processes things differently. To illustrate, humans may understand acronyms like GPT as one complete word, but the AI may read the acronym as “generative pretrained model,” which is three words. This difference is why 1,000 tokens is equivalent to approximately 750 English words.
The price of the tokens increases as the size of the context window grows, as can be seen in the table below:
| 8,000 context window (approx. 13 pages of text) | 32,000 context window (approx. 52 pages of text) | |
| Cost of 1,000 prompt tokens | US$0.03 | US$0.06 |
| Cost of 1,000 completion tokens | US$0.06 | US$0.12 |
Microsoft Azure’s token billing rates according to context window size
How can GPT-4 benefit enterprises?
Paying AI costs might sound exorbitant, though its benefits can outweigh the costs. In advanced analytics, for instance, GPT-4’s larger context windows enable users to query business insights more seamlessly and derive more value from their data. Moreover, GPT-4's ability to interpret complex information, including graphs and images, allows organizations to skip some of the computation involved in descriptive and diagnostic analyses. By feeding time series data directly into the model, businesses can efficiently generate insights without the need for extensive feature engineering and time series analysis.
GPT-4 can significantly enhance business intelligence through:
- Importance sorting. GPT-4 can prioritize data points based on their relevance to a particular business context. This makes it easier for decision-makers to focus on the most critical information.
- Predictive modeling. GPT-4’s ability to process vast amounts of data allows it to create more accurate and reliable predictive models, improving forecasting and strategic planning.
- Automated anomaly detection. GPT-4 can analyze time series data and graphs to quickly identify unusual patterns or outliers, alerting businesses to potential issues or opportunities.
- Advanced trend analysis. By interpreting complex data visualizations, GPT-4 can help organizations recognize emerging trends and patterns, enabling proactive and timely decision-making.
- Business context integration. By understanding the unique needs and characteristics of a specific business unit at a specific moment in time, GPT-4 can tailor insights to be more relevant and actionable.
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Sample use cases of GPT models based on OpenAI’s examples
Source: OpenAI
What are examples of GPT-4’s use cases?
Let’s illustrate GPT-4’s use cases with how it aced the bar exam. This accomplishment indicates the capability for intelligent contract management as well as legal procurement and finance. Here are some examples of its applications:
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Healthcare contract management: Healthcare organizations may utilize GPT-4's capabilities to manage contracts with suppliers, service providers, and insurance companies. This could allow them to optimize contract terms, reduce costs, and improve patient outcomes.
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Intellectual property (IP) management: Law firms and corporations may use GPT-4 to manage complex IP portfolios, including patent and trademark applications. The AI’s natural language understanding could help identify potential infringements and streamline the IP management process.
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Real estate contracting: GPT-4 may be employed to automate contract drafting, negotiation, and execution, which can speed up the process for both buyers and sellers.
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Supply chain management. Companies may use GPT-4 to optimize contracts with suppliers to ensure cost-effective procurement and adherence to delivery timelines.
What does the future hold with GPT-4?
As of this writing, only GPT-4’s text input mode is available to the public via ChatGPT Plus. That is, its image input mode is still being worked on.
Enterprises may join a waitlist to use the OpenAI’s API to integrate GPT-4 with company apps on a pay-per-use basis. Companies that are reportedly on that waitlist include Stripe, Morgan Stanley, and Duolingo. Additionally, Microsoft’s Azure clients may apply for access to GPT-4 via their Azure OpenAI Service.
Glimpses of GPT-4’s current capabilities allow us to imagine generative AI’s potential. Data scientists, engineers, and analysts can use it to mine the virtual mountains of data in their data centers to achieve greater efficiencies. Even regular staff will be better equipped to handle countless emails, chat threads, and stored files to generate summaries, reports, projections, and whatever else they may need.
Developers can use GPT-4 to improve their enterprise’s existing internal and consumer-facing apps and create new ones. For example, they could create virtual assistants that can solve problems and exhibit domain expertise.
Taking full advantage of GPT-4 as soon as it becomes viable requires preparing for it now — including the technical expertise to handle it. Lingaro Group adopts an end-to-end approach to using generative AI — from developing a strategy that addresses the business's unique needs, preparing data, developing models and tools, and operationalizing it across the enterprise to maintaining and scaling them for their future use.
Read more about GPT-4:
