Should Enterprises Consider Implementing Large Language Models?

While some CEOs were asking ChatGPT silly questions, their competitors were using it to improve operations. Okay, this might sound a little harsh, but the reality is, while some companies are still just dabbling in generative AI, others are actively training large language models on their data sets—and getting results.

Generative artificial intelligence is not just something casual users play around with these days; enterprises are investing millions of dollars in it because they believe in its potential. Andreessen Horowitz surveyed Fortune 500 and top enterprise leaders about GenAI, and almost all of them (a) reported positive initial outcomes from their generative AI experiments and (b) plan to boost their investment by 2 to 5 times in 2024 to expand production deployments.

Predibase surveyed over 150 executives, data scientists, machine learning engineers, and product managers from 29 countries and found that 58% of companies have started working with LLMs. And it’s not too late for your organization to jump on the bandwagon, so feel free to continue reading and make some notes as we explain how enterprises use generative AI.

Large Language Models Across Industries

The current state of large language models (LLMs) is characterized by rapid progress and increasing acceptance in various industries. This isn't surprising since generative AI has shown it can actually bring value through:

• Increased productivity. With routine tasks automated, employees can concentrate on higher-value business processes.
• Reduced costs. By automating tasks such as customer service and document review, enterprises have been able to reduce labor costs and increase efficiency.
• Improved customer experience. Instant personalized responses to user queries have led to higher customer satisfaction and retention rates.
• New opportunities. Enterprises are using LLMs to develop new products and services, such as personalized shopping experiences or advanced virtual assistants.

Indeed, companies from various sectors are looking for ways to put generative AI into action. And some have already found them.

Finance

JPMorgan Chase has introduced DocLLM, a generative language model tailored for multimodal document understanding. This lightweight extension to traditional LLMs is specifically designed to analyze complex enterprise documents, including forms, invoices, reports, and contracts.

Retail and E-Commerce

Shopify's Sidekick provides instant support and step-by-step guidance for tasks like setting up shipping and tracking inventory. It generates reports to aid business decisions and helps with brand-aligned content creation to engage customers. Sidekick helps streamline operations, enhance decision-making, and improve customer engagement for Shopify users.

Healthcare

HCA Healthcare is testing a system that automatically generates draft medical notes from conversations between doctors and patients using a hands-free app. Doctors review and finalize the notes, which are then seamlessly transferred to the electronic health record (EHR), saving time on manual entry. The company plans to expand this process to other hospitals soon.

Telecommunications

VOXI by Vodafone has introduced a generative AI chatbot developed with Accenture and powered by ChatGPT. This chatbot engages in human-like interactions, handles sophisticated customer requests, and aims to enhance customer experience by providing accurate, fast answers to natural language questions.

Such models look promising and inspiring; however, many companies are hesitant to adopt generative AI technology precisely because of the challenges involved.

LLM Advantages vs. Challenges for Enterprises

Generative artificial intelligence is not perfect—no technology is. So, using it is a matter of balancing its advantages with challenges. And acting fast. 

Advantages of Large Language Models for Enterprises

• Efficiency gains. Fine-tuned LLMs can automate customer support, document management, and other repetitive tasks.
• Improved customer experience. With their advanced natural language processing (NLP) capabilities, LLMs can provide personalized and responsive customer service through chatbots.
• Data insights. They can analyze large amounts of text data to uncover patterns and insights for strategic decision-making.
• Scalability. Large language models can simultaneously handle large volumes of requests, making them suitable for large organizations.
• Content generation. They're famous for summarizing or generating content in a specific style for marketing purposes, internal reports, technical documentation, or other forms of text.

LLM Limitations

• Development and maintenance. Training language models and deploying them requires significant computational resources and expertise, leading to high costs. This includes hardware, cloud services, and specialized personnel.
• Licensing Fees. Some advanced large language models come with substantial licensing fees, making them less accessible for smaller enterprises.
• System compatibility. Integrating LLMs into existing IT infrastructure can be complex and time-consuming, often requiring considerable process re-engineering.
• Ethical issues. LLMs can inadvertently perpetuate biases present in their training data, leading to unfair or inappropriate outputs.
• Data privacy and security. LLMs often require access to large data sets, which can include sensitive or proprietary information. Ensuring data privacy and compliance with regulations like GDPR and CCPA can be challenging. (By the way, on-premise LLM solutions like Dynamiq can address these concerns.)
• Complexity of implementation. Integrating large language models into existing workflows requires skill and time, especially when we're talking about custom models.
• Regulatory compliance. Data usage regulations may restrict the use of these models, depending on the region and industry. Plus, the regulatory landscape for AI capabilities and data usage is still evolving, leading to uncertainty about future compliance requirements and potential restrictions.
• Expertise gap. There is a shortage of professionals with the necessary skills to develop, implement, and manage LLMs, making it difficult for enterprises to build and maintain these systems.

These are some valid arguments, and it’s understandable why some enterprises are putting off implementing generative AI in their operations. However, it's important to also understand that the introduction of LLMs is not a case of slow and steady wins the race.

The Risks of Slow Adoption

The introduction of GenAI can be both scary and tempting. But we advise companies to at least try—not because we offer generative AI solutions, but because failing to act on LLMs can lead to:

• Suboptimal efficiency. Competitors using LLMs can automate and streamline processes, reduce costs, and increase productivity.
• Poor connection with customers. Large language models can improve customer service through personalized and immediate responses. 
• Inferior market position. Enterprises slow to adopt LLMs may miss out on innovative opportunities, new business models, and market trends, allowing faster competitors to capture the lion’s share of the market.
• Missed revenue opportunities. A large language model can open up new revenue streams, such as advanced analytics services, personalized marketing, and customer engagement platforms.

Your decision on whether or not to use generative AI should include a clear assessment of specific business requirements, data availability, and resources. But remember, your competitors are also thinking about generative AI and how best to use it.

Enterprise Use Cases for Generative AI

Generative AI and LLMs are improving enterprise operations across departments and functions. While most of the companies Andreessen Horowitz surveyed are still wary of delegating external functions to generative AI, they’re compensating for this by focusing on internal use cases. 

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Here’s how enterprises can effectively use GenAI and LLMs:

Customer Support

Generative AI powers intelligent chatbots that can handle various client requests with high accuracy and natural language understanding of human feedback. These AI-driven systems can provide instant responses around the clock, handle multiple customers simultaneously, and forward more complex issues to human representatives. This improves customer experience through faster and more accurate responses and allows human agents to focus on more nuanced customer interactions.

Co-pilots for Internal Teams

Thanks to generative AI tools that require little or no code, departments can create AI-driven assistants suited to their specific tasks.

HR

Fine-tuned AI co-pilots can support HR teams by automating routine tasks, such as generating responses to frequently asked employee questions, scheduling interviews, or screening CVs to identify appropriate candidates. They can also help onboard new employees by providing them with tailored information and resources.

Legal

In legal departments, AI can help by drafting standard contracts based on the context provided, reviewing compliance documents, and even suggesting revisions based on current laws and regulations. This ability to generate text can significantly speed up legal workflows, reduce error rates, and free up lawyers’ time for other tasks.

Software Engineering

For software developers, AI helpers like GitHub Copilot use LLMs to suggest code completions and documentation. Some use it for easier, routine tasks like writing boilerplate code snippets, however, others have learned to cooperate with co-pilots when they write code for complicated features.

Back-Office Automation

Generative AI tools can automate various back-office functions such as data entry, invoice processing, and report generation. For example, AI can extract relevant data from unstructured formats and enter it into databases or spreadsheets. It can also create financial reports by retrieving data from various sources and even provide forecasts.

Knowledge Management

AI-driven knowledge management tools can change the way information is stored, accessed, and used in a company. Large language models can summarize long documents, generate answers to queries by searching through extensive digital archives, and even suggest relevant documents for specific projects or problems.

Overall, the use of generative AI and LLMs streamlines workflows, improves accuracy, and provides insights—all things necessary for a high ROI.

How Can Enterprises Maximize ROI from GenAI?

Currently, business leaders evaluate the return on investment of AI primarily in terms of productivity gains, with customer satisfaction and NPS (Net Promoter Score) serving as secondary metrics. To make more accurate assessments, they look at specific metrics such as revenue increase and efficiency improvement.

According to Andreessen Horowitz, the focus on a clear definition of ROI will increase over the next few years, even though many currently rely on anecdotal evidence from employees about efficiency gains.

Implementing a large language model in a way that maximizes ROI requires strategic planning, careful resource allocation, and consideration of business objectives. Here’s a step-by-step approach for enterprises:

1. Identify clear use cases. Pinpoint where LLMs can address your business challenges—for example, customer support, marketing, or data analysis. Then, estimate the value, such as cost savings, improved customer satisfaction, or enhanced productivity.
2. Assess data infrastructure. Ensure that your enterprise has access to sufficient, high-quality data and develop protocols for secure and compliant handling of sensitive information.
3. Choose the suitable model. Pre-trained models can reduce development time but may require fine-tuning for specific applications; on-premises models provide better security control, while cloud solutions offer scalability.
4. Start with a prototype. A small-scale pilot program will help you test viability, gain insights, and refine the model.
5. Set KPIs. Establish key performance indicators to measure the pilot's effectiveness.
6. Let it learn. Allow the generative AI model to learn from new and the company's own data, refining its outputs over time.
7. Teach your staff. Teach your employees prompt engineering. Train them to interact with the large language model or interpret its outputs. This may also require fostering a positive attitude toward LLM adoption to minimize resistance.
8. Manage ethical and compliance issues. Use diverse training data and synthetic data to ensure outputs align with industry regulations and your company values.
9. Keep an eye on the performance. Monitor model performance regularly, making adjustments as needed.
10. Scale up. Expand implementation once the pilot demonstrates value and aligns with your strategic goals.

Following these steps doesn’t guarantee a high ROI, but it does allow enterprises to better align LLMs with their business strategies and optimize their investment, leading to more effective adoption and use of generative AI.

How Dynamiq Can Help

Dynamiq helps businesses use generative AI models like LLMs more effectively, thanks to:

• Better customer support. Dynamiq helps create smart chatbots that can handle basic customer questions, allowing staff to focus on more complex problems.
• Smarter decisions. Dynamiq makes it easier for businesses to combine their data with a large language model to analyze information like customer feedback and market trends, helping leaders make informed choices.
• Improved efficiency. Dynamiq streamlines tasks such as summarizing documents and managing emails while fitting easily into existing systems.
• Encouraging innovation. Dynamiq enables businesses to use large language models to come up with new ideas and solutions, helping them stay competitive.
• Staying ahead of the pack. By using LLMs through Dynamiq, businesses can maintain a lead over competitors, especially as the AI learns and adapts based on their data.

Overall, Dynamiq offers a straightforward way for companies to build, test, and improve AI tools without needing a lot of resources, making it easier to adopt and benefit from AI technologies.

Conclusion

As various industries recognize the capabilities of LLMs, their use is expanding across sectors. So don’t let your enterprise miss out. By carefully implementing GenAI models to meet your organization’s goals and effectively integrating it into current processes.

As these models improve and adapt and new applications are discovered, LLMs have the potential to change entire industries. And if you need someone to guide you through this transformation, turn to Dynamiq. We can help you develop and implement AI solutions that add value to your business.