Commercial Use Cases for Large Language Models

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Large language models (LLMs) are a type of artificial intelligence (AI) that can mimic human intelligence. They are trained on massive datasets of text and code and can learn the patterns and connections between words and phrases. This allows them to perform a variety of tasks, including:

  • Generating text: LLMs can generate text, such as blog posts, articles, and even poems.
  • Translating languages: LLMs can translate between languages, such as English to Spanish or French to German.
  • Answering questions: LLMs can answer questions such as “What is the capital of France?” or “What is the meaning of life?”
  • Summarizing text: LLMs can summarize text, such as news articles or research papers.
  • Writing different kinds of creative content: LLMs can write different kinds of creative content, such as poems, code, scripts, musical pieces, email, letters, etc.

Why are LLMs Important for Business?

LLMs are important because they can help businesses improve their operations and reach customers more effectively. For example, LLMs can be used to:

  • Create chatbots that can answer customer questions and resolve issues. This can help businesses to improve customer satisfaction and reduce the cost of customer service.
  • Generate personalized marketing content that is more likely to resonate with customers. This can help businesses to reach their target audience more effectively and increase conversions.
  • Qualify leads and provide sales support. This can help businesses to close more deals and increase revenue.
  • Analyze data and generate insights. This can help businesses to make better decisions and improve their bottom line.

What are some commercial use cases for LLMs?

There are many commercial use cases for LLMs. Here are a few examples:

Customer Service: A real-world example of LLMs in customer service is the implementation of IBM Watson Assistant by businesses like Autodesk, a leader in 3D design, engineering, and entertainment software. Autodesk uses Watson to power its customer service chatbot, which handles more than 100,000 service cases monthly. The chatbot resolves simple issues directly and helps customers with complex problems by routing them to the appropriate human agent, thereby improving response times and customer satisfaction.

Marketing: In marketing, LLMs have been utilized by companies like Persado. Persado uses AI to craft marketing language that resonates more effectively with audiences. For instance, their system can analyze the language and emotions that perform best across various marketing channels and then generate multiple variations of content that are personalized and optimized for engagement, leading to higher conversion rates.

Sales: Salesforce uses LLMs to enhance its Salesforce Einstein platform, which assists sales representatives in prioritizing leads with the highest likelihood of conversion. Einstein analyzes customer interactions and predicts future behavior by understanding and processing natural language, enabling sales teams to focus their efforts where it counts and tailor their approach to individual prospects.

Content Creation: The Washington Post’s Heliograf is an example of LLMs in content creation. It’s an AI technology that has been used to automatically generate short reports and social media posts on topics like elections and high school sports games, allowing human reporters to spend more time on in-depth journalism. Heliograf has produced around 850 articles in its first year, demonstrating the potential of LLMs to scale content production efficiently.

Risk Assessment and Management: In the commercial sector, LLMs are increasingly being used for risk assessment and management, particularly in the finance and insurance industries. For instance, companies like Lemonade, a tech-driven insurance company, utilize LLMs to analyze vast amounts of data for underwriting and claims processing. These models can assess risk factors from various data sources, including textual information in insurance applications or claim reports, to make more accurate and rapid risk assessments. This capability significantly enhances the efficiency and accuracy of the underwriting process.

Potential Negatives of LLMs in Commercial Use

Spread of Misinformation in Commercial Content: In the commercial realm, LLMs could inadvertently become tools for spreading misinformation through automated content generation. For instance, if an LLM is not properly supervised, it might produce marketing material or product descriptions that are inaccurate or exaggerated claims, leading to consumer mistrust and potential brand damage. A real-world concern arose when a travel website used an LLM to create descriptions of destinations, and some descriptions were found to be embellished or inaccurate, affecting the site’s credibility.

Bias in Commercial Interactions: Commercially deployed LLMs, such as those used in customer service or recruitment chatbots, can perpetuate biases present in their training data. For example, an AI recruitment tool used by a prominent tech company was found to be biased against women, reflecting the gender bias in historical hiring data. This not only perpetuates discriminatory practices but can also lead to legal and reputational risks for companies.

Privacy Concerns in Data Handling: Businesses using LLMs to handle customer data must navigate the complex landscape of privacy concerns. LLMs that process customer inquiries may inadvertently learn and replicate sensitive information, leading to privacy breaches. A notable incident involved a fitness tracker company whose LLM-powered customer service bot leaked personal health data in its responses, highlighting the need for stringent data protection measures.

Job Displacement in Specific Sectors: The adoption of LLMs for tasks such as report generation, customer inquiry responses, and even basic legal advice could displace jobs in sectors like administration, customer support, and paralegal services. For example, a major bank introduced an LLM-driven system for generating financial reports, which reduced the need for financial analysts, affecting employment in the sector.

Mitigating Commercial Risks: To address these issues, businesses can implement measures such as thorough validation of content generated by LLMs, regular audits for bias, strict data privacy protocols, and a balanced approach to automation that includes re-skilling programs for affected employees. By acknowledging and preparing for these risks, businesses can harness the benefits of LLMs while minimizing their potential downsides.

Highlighted below are some of the positives and negatives for LLMs.


In conclusion, the commercial landscape is rife with opportunities for integrating LLMs. From revolutionizing customer service with intelligent chatbots to personalizing marketing efforts, enhancing sales strategies, and streamlining content creation, LLMs are proving to be invaluable assets for businesses aiming to stay ahead in the digital age. However, with great power comes great responsibility. The potential for spreading misinformation, inherent biases in AI, privacy concerns, and the impact on employment are challenges that demand attention.

As we continue to harness the capabilities of LLMs, it is crucial for businesses to implement these technologies with a conscientious approach. By doing so, we can mitigate risks and ensure that the deployment of LLMs contributes positively to the commercial realm, fostering innovation, growth, and trust. The future of LLMs in commerce is not just about leveraging their strengths but also about shaping a path that recognizes and addresses their limitations. In this way, businesses can deliver exceptional value to customers and stakeholders alike, setting a new standard for what is achievable with AI in the commercial sector.

Let us embrace this future, not with blind optimism, but with a vigilant and informed perspective that maximizes benefits while minimizing potential harms. The journey of integrating LLMs into commerce is just beginning, and it promises to be as transformative as it is complex.

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