Enterprises across industries are experiencing a seismic shift as they gear up to embrace Large Language Models (LLMs). Their extensive adoption has opened a new set of opportunities for professionals and enterprises enabling them to enhance decision-making, undertake a digital transformation, and drive innovation like never before.
As per market statistics, nearly 67% of organizations have incorporated this advanced tech into their workflows and are utilizing GenAI capabilities to unlock unprecedented insights, automate tasks, and optimize processes.
By the time you finish this blog post, you will have a better understanding about:
- Different enterprise use cases of LLMs
- How do Walmart, Stellantis, and Commvault leverage LLMs to elevate customer experience, streamline processes, and democratize content creation
- What roadblocks hinder the adoption of LLMs, and what strategic steps do enterprises take to mitigate them?
and much more.
So, without further ado, let’s begin:
How Are Businesses Using LLMs? – Key Enterprise Use Cases You Must Know
From conversational interfaces to personalized recommendations and predictive analytics, LLMs are transforming and driving innovation across Finance, Healthcare, Retail, and other industries. They also assist enterprises in managing content, engaging proactively with customers, and offering innovative solutions to complex challenges across different business functions.
To streamline processes, enterprises can leverage the modern tech stack and harness the LLM potential to deliver exceptional outcomes and customer experience.
With that understanding, let’s explore the significant enterprise use cases of LLMs:
Walmart
They implemented an automated voice ordering system that utilizes a combination of cutting-edge AI models to accelerate customer orders on the go and improve the ordering experience.
Stellantis
The organization focuses on enhancing customer personalization by understanding the specific needs of its customers based on the context of their car-buying journey and tweaking its strategy to provide a seamless and customized customer experience.
Commvault
They took a unique approach to content creation by democratizing the process. Their tool allows users, regardless of their technical writing skills, to co-write blogs and prospective emails for Salesforce, further empowering individuals to produce content that adheres to the organization’s style guidelines.
The tool synthesizes information from various sources, such as interviews and case studies, enabling users to succinctly summarize customer pain points, market perspectives, product details, and competitor analysis.
Overall, we can say that these companies embraced innovative strategies to enhance their respective industries and provide exceptional customer experiences.
Generative AI First Solutions: A Quick Overview
The massive upsurge in GenAI and LLMs is reshaping the corporate dynamics, and even organizations are extensively using tools like ChatGPT/Bard to automate a fair portion of internal processes and increase productivity.
The following examples reconfirm how industries beyond Technology are adding AI capabilities and potentially driving AI innovation way above the usual industry standards:
GPT for Numbers
The tool simplifies the data analysis process using a user-friendly interface (in a Text Box form), allowing the users to connect various data sources and ask questions related to the data. The tool generates an HTML response encompassing charts and explanations and prompts the user for further questions based on the provided response.
Kognitos
It leverages Natural Language Processing (NLP) to automate business processes. While the automation scripts may not be entirely in Natural Language, the interface allows users to incorporate natural language and syntax that closely resembles natural language to create automation. For instance, users can utilize natural language to read invoices and extract contents while defining rules for when the process requires human intervention.
DeepMind
This GenAI solution stands at the forefront of language modeling and operates on the principles of unsupervised learning., i.e., it learns from unannotated text and develops a rich understanding of language structures and relationships.
It proactively captures intricate semantic nuances, understands context-aware applications, and enables enterprises with content summarization, document understanding, and more. This amalgamation marks a transformative shift across the enterprise and clarifies how they process and derive value from text-based information.
What are the Key Stumbling Blocks of LLM Adoption?
Reliability
Reliability is a significant concern for practitioners and companies utilizing Language Model Models (LLMs) in their production processes. LLMs tend to hallucinate, as acknowledged by Nick Frosst, co-founder of Cohere, who quotes, “All it does is hallucinate, it is just amazing how it gets anything right.”
The non-deterministic nature of all ML models, coupled with the hallucination potential of LLMs, makes it challenging to ensure their accuracy. While companies like Arize.ai offer tools to detect LLM hallucinations, this remains an area of ongoing research.
As a result, incorporating human-in-the-loop solutions and co-pilots that assist in verifying LLM outputs is a promising approach for deploying LLM-powered use cases in production environments.
Privacy Attacks
Privacy attacks have become an ever-present concern in this digital era. In their recent survey, Conversica states that 40% of organizations adopted AI-powered services, but only 6% have clear guidelines on ethical and responsible use.
With LLM models generating content in masses, organizations must take robust measures to avoid privacy attacks. With extreme personalization becoming a part of our lives, asking a query and gaining access to sensitive information becomes effortless, breaching your privacy boundaries.
Bias and Discrimination
This issue concerns all major enterprises developing Large Language Models (LLMs) since most LLMs get trained on extensive datasets derived from web content created by humans, which inherently introduces biases into the models. Hence, it becomes crucial to train LLMs on diverse datasets, closely monitor and identify biases, and proactively adjust to address this issue. Failure to handle biases appropriately can potentially damage the reputation of your brand.
Misinformation
LLMs have the potential to spread misinformation, so to avoid miscommunication, the enterprises must develop robust foundational models by providing accurate data insights and adjusting the internal settings to respond only to the given context. Implementing these measures can help mitigate the potential for LLMs to disseminate false or misleading information.
Pricing
The cost of inference and training on the models remains considerably high and might decrease over the years. However, companies must still substantiate the return on investment (ROI) that the LLMs provide.
These are just a few blockers enterprises experience while discussing LLMs or GenAI adoption within their processes. But the future of LLMs is bright, and our blog on the future of LLMs and their influence on reshaping enterprises discusses the same aspect.
Are you considering incorporating LLMs and GenAI capabilities within your organization? If yes, utilize our latest iOPEX.AI framework to scale up your AI capabilities across industries, build connected ecosystems, accelerate business growth, drive efficiency and profitability, and do much more.
The Bottom Line
The swift emergence of LLMOps (Large Language Model Operations) indicates the growing significance of LLMs. As ML teams within the enterprise pivot towards LLMs, it’s time to brainstorm innovative strategies and ensure LLM applications are ready to detect potential pitfalls and issues post-deployment.
LLMs have vast potential, and with AI constantly evolving, it will accelerate more in the coming years. Hence, it will be interesting to watch how enterprises keep themselves abreast of the latest trends and leverage the benefits these groundbreaking models offer.
Let us know what steps you’re taking in 2024 and beyond to bring these Next-Gen solutions into your workplace, thus making your enterprise future-ready.
