Maximizing the Power of Large Language Models in Business

As artificial intelligence (AI) has rapidly evolved in recent years, the capabilities of computers and machines have expanded to more efficiently perform tasks previously managed by humans. Amid these evolutions, large language models (LLMs) have emerged as a new facet of AI, designed to understand and generate language. These models, which learn from massive databases of text to mimic written language, present unique opportunities for businesses looking to get ahead in an efficient and economical way. However, businesses must be wary of the technology’s shortcomings and develop strategies around its ethical and safe integration.

The evolution and landscape of AI

AI is an umbrella term that refers to machines that are designed to perform human-like tasks. In the 1950s, machine learning surfaced as a subset of AI in which machines were created to learn from data (recognize patterns, make predictions and solve problems) without being explicitly instructed.

AI has continued to evolve, eventually leading to additional branches of intelligence like deep learning and natural language processing (NLP). NLP teaches computers how to comprehend language, while deep learning employs a type of neural network architecture that mirrors the human brain.

Many modern deep-learning models rely on  transformer architecture which, put simply, breaks down sentences into parts and pays special attention to significant words. Given a string of text, a transformer not only comprehends each word but also remembers how each word connects to the others. This way, it can generate new sentences that make sense and sound like they were written by a person. Transformers learn from vast amounts of text, making them great at understanding and creating all kinds of language.

What exactly is an LLM?

LLMs lie at the intersection of deep learning and NLP. These two emerging subsets of AI act as the foundation for generative AI (GenAI), which includes a spectrum of AI models that can create various types of content, including language, visual, audio and mutlimodal outputs. LLMs can therefore be considered a specialized category of GenAI, as they leverage NLP and advanced deep learning techniques to perform language-related tasks to produce unique content.

LLMs present unprecedented opportunities for businesses seeking to optimize workflows and create innovative new products, services or systems. Current models exhibit an impressive ability to generate human-like text, streamlining communication and enhancing operational efficiencies across business functions.

Still, because of the novelty of LLMs, which gained mainstream attention in late 2022 with the release of OpenAI’s GPT-3.5, businesses must be wary of the technology’s shortcomings as they make decisions about its use as a strategic tool for the future. LLMs are not immune to biases present in their training data, raising concerns about the impartiality of generated content. Not to mention, hallucinations can cause models to generate incorrect information, which must be carefully navigated, especially in industries where precision is paramount.

Business applications of LLMs

Despite the uncertainty around LLMs’ current capabilities and safe integration, business leaders have felt the urgency to invest in AI for their companies. LLMs present a considerable range of opportunities for businesses, presenting both cost-savings and revenue-generating opportunities. Using LLMs to handle repetitive tasks, for example, can help businesses grow profit margins by cutting labor costs. When employees are able to focus more energy on value-added tasks, rather than repetitive ones, businesses are able to make a bigger impact and bring in more revenue.

While some businesses have turned to commercialized models to reap the benefits of AI (ChatGPT, Bard), others have built their own contextualized LLMs, which are trained on internal company data. These models can create tailored responses based on specific business needs, enabling businesses to provide more personalized and value-added solutions. Some of the key ways contextualized LLMs are currently being applied in business operations include streamlining customer experience, improving internal operational efficiencies, scaling content delivery and integrating complex systems.

Customer Experience

Major tech companies like Amazon, Walmart and Google are piloting LLMs to enhance the customer experience and drive revenue across platforms. Amazon plans to integrate an AI chatbot into its search, offering consumers personalized recommendations and allowing them to compare products. Similarly, Walmart is leaning on LLMs to enhance their existing Text-to-Shop feature, which allows shoppers to add items to their carts by sending a text message.

At IBISWorld, we use LLM technology to reformat our content from list-style insights to more thorough paragraph-style text. After our analysts research and write the list-style insights, our Paragraph Builder synthesizes the information into longer-form content that our clients can use for credit presentations, valuation reports, business plans and other deliverables. Using an LLM allows us to create a seamless customer experience for clients, who have the power to flip between either format at their leisure.

Internal Operational Efficiencies

FinTech company Klarna was one of the early adopters of ChatGPT Enterprise, which employees can use for data analysis and shopping recommendations. The business-centered version of OpenAI’s LLM offers customers enterprise-grade security, enabling them to safely use it for internal operations. Other users of ChatGPT Enterprise include Canva, PwC and Zapier, which use the technology to assist with coding and generate creative solutions.

Content Creation and Delivery

Google is testing its own LLM, PaLM2, to create YouTube video titles and descriptions. Likewise, Amazon has begun employing LLMs to help sellers create compelling product descriptions and listing details.

B2B companies are also using LLMs to generate leads. While specific B2B use cases aren’t extensively publicized because of the proprietary nature of such strategies, companies like 6Sense have emerged to identify and convert leads for B2B clients. Its AI-powered email writing feature, for example, creates personalized email campaigns.

Complex Systems Integration

Ernst & Young (EY) recently launched its own internal LLM that leverages advanced NLP capabilities for enhanced data analysis, knowledge extraction and streamlined decision-making. Within consulting and tax, EY employees can build customized business strategies or summarize lengthy documents. McKinsey has done the same with Lilli, their internal GenAI that leans on the company’s vast database to create innovative and tailored insights for clients.

The opportunities and threats of LLMs

Most companies are currently focused on using LLMs for immediate efficiency gains or are in the early stages of testing more complex applications. According to a 2023 McKinsey survey on the state of AI, less than one-third of organizations reported using AI for more than one business function, a figure that’s remained unchanged since 2021. Integrating LLMs into internal operations requires more of a structural overhaul but can offer greater benefits than using commercialized models for basic applications, like automating tasks.

Contextualizing LLMs with internal data makes sense within the Professional Services sector, in which companies have existing databases filled with years-worth of knowledge and strategy.  Internal LLMs capitalize on existing resources, protect proprietary data and reduce the risk of hallucinations or errors since the models have direct access to original data. Businesses that wish to capitalize on external LLMs, like ChatGPT, for operational efficiencies should be wary of feeding the models proprietary information as it poses a risk to data security. Regardless, most use cases come with their own set of opportunities and threats:

Benefits

• Enhanced operational efficiency: LLMs streamline operations by automating tasks, allowing employees to focus on higher-value work.
• Capitalize on existing resources: Businesses with internal databases can use LLMs to extract knowledge and create valuable insights.
• Improved customer engagement and retention: Personalized tools and targeted ads have great revenue-generating potential.

Drawbacks

• Factual inaccuracies: Content generation raises the risk of hallucinations since the models are creating rather than summarizing.
• Ethical considerations: LLMs can inherit biases from training data and produce biased outcomes.
• Evolving regulatory landscape: The regulatory landscape is still developing, raising concerns over compliance.
• Skill gaps: Many businesses lack the skilled professionals necessary to work with advanced AI models.
• Data security: Inputting sensitive information into LLMs without sufficient security measures can open up exposure to data breaches.

Businesses seeking to invest in LLMs in any way must safeguard against its potential threats. Human-in-the-Loop Integration ensures employees have an active role in the integration and development of LLMs, providing iterative feedback and mitigating biases and hallucinations.

Steps for implementing an LLM in your business

Navigating the drawbacks of LLMs takes some planning. Like any shift in your business model or workflow, sufficient due diligence is necessary before making a move.

Starting with this framework and creating a comprehensive plan of action based on these key points can help you ensure responsible and effective integration:

Careful planning and oversight throughout the implementation process can help mitigate risks and maximize the benefits that LLM solutions bring to your workplace.