By Dr. Robert Chen, Chief Technology Officer, Appier 

We are not playing around with our Large Language Models (LLMs) anymore. LLMs like ChatGPT and Bard have transitioned from novelty to necessity in the business world. Enterprise expenditure on GenAI solutions is projected to reach US$151.1 billion in 2027 with a CAGR of 86.1% from 2023-2027, indicating a burgeoning interest in AI applications. 

The initial spike in popularity for LLMs has also matured into a strategic enterprise focus to integrate AI more deeply into business operations. For instance, McKinsey’s annual survey on the state of AI found that one-third of businesses are using GenAI regularly in at least one business function, while 25% of enterprises using AI say GenAI is already on their boards’ agendas.

This notable shift towards increased technology usage in enterprises is set to enhance overall performance and marks 2024 as a pivotal year for LLM adoption, as the trajectory for GenAI solutions takes a more strategic and targeted approach. This evolution represents a new era, in which AI transitions from an experimental tool to a fundamental element of business strategy and operations. 

LLM applications: 2023 vs. 2024

In 2023, even when a few employees at companies were testing out ChatGPT and other LLMs, it was mainly for drafting an email or replying to a message. The gutsier individuals may have used it for proposals and other long-form documents, but for the most part, these LLM-generated products were for internal use only. Given the possibility of factual inaccuracies and other such hallucinations, it was not wise to deploy LLMs for external, public-facing content. 

Now, however, with a year of experimenting and refining, LLM developers are ready to launch–or in some cases, have already launched–business versions of their products that cater to businesses. Two areas of application that we will see great upgrades in quality and thus adoption are customer service and content creation. This is what we can look forward to this year.

Why 2024? 

Two key factors have converged to make 2024 the year for LLM applications’ mass adoption technological advancements and market sentiments. Since ChatGPT’s launch in November of 2022, LLMs have evolved from text completion and analysis models to powerful chatbots with the ability to execute code, use tools, access external knowledge, and search the web. In other words, in less than 12 months, the technology community at large has revolutionized a tool that was already revolutionary. 

Yet, these technological advancements would have no market value if executives did not buy into them—and that was what 2023 was all about. There is no doubt now that most CEOs are on board with implementing AI solutions into business operations as they have made it clear that investing in AI is a “top priority.” As more vertical-specific LLMs become available, it will only make it easier for companies to adopt and deploy these GenAI solutions. 2024 will also see LLM developers address a key area of concern: data security. By providing corporations with a solution where their data remains theirs and within their systems, technology developers can provide the market with a safer, more powerful version of their existing solutions that will make them more attractive to risk-averse CEOs. 

2023 also saw governments and other watchdog organizations struggle with regulating AI. In December of last year, the European Union came to an agreement on its AI Act, a landmark set of rules that regulate and limit the use of AI. Once enacted, this act, and the many more that will surely follow will influence how tech companies approach their AI solutions. Simply put, these acts will set the ground rules for what is allowed and what is banned. 

With more power, more buy-in, and more clarity, LLM applications are poised to reach mass adoption among the biggest corporations around the world. 

Customer Service, Content Creation & LLMs

Imagine if ChatGPT was the customer service chatbot on a website — you would literally not be able to distinguish between a real person and the bot. The integration of these LLM  chatbots into customer service presents a cost-saving opportunity for businesses to always be available to their customers. In fact, as consumers demand more from businesses and their experiences with businesses, these LLM chatbots could be the answer to provide more personalized interactions and yield better customer satisfaction. 

One of the advantages of using LLMs for content creation is speed. If you don’t like what it comes up with, rephrase your prompt and in less than a minute, you get a new result. This is especially useful in marketing and advertising where multivariate testing and iterating are key. Whether it is for an ad slogan, an informational blog post, or even a short story, LLMs can use the vast data they were trained on to generate new ideas and text. 

Beyond speed, what has gotten businesses to buy into the use of these LLMs for content creation is the variety of content — in form, style, and tone. When ChatGPT first came on the scene, a concern was that everything would end up sounding the same. Ask ChatGPT to write an SEO blog post about a topic, and all pieces about the topic will be the same. Except they won’t, because of prompt engineering. 

With a slight change in how one phrases a prompt, ChatGPT and solutions alike can generate different content on the same topic. More importantly, users can ask the LLM content creator to adopt a certain style or tone of writing. This, of course, is where iteration becomes critical, as the best users will use the LLM over and over to train it to sound more like them. The end goal, of course, is to have the LLM spew out great content that sounds like the company in the shortest time possible. 

How LLMs will redefine the future of businesses in 2024 and beyond

As 2024 unfolds, the transformation of LLMs from experimental tools to essential business assets becomes increasingly clear. These models, having progressed significantly in technological capabilities, are now crucial in areas like customer service and content creation, where they offer unparalleled efficiency and customization. This year is not just about technological advancements but also about integrating these tools into the fabric of business operations, aligning with evolving regulatory landscapes and executive strategies. 

The widespread adoption of LLMs is a testament to their potential to revolutionize how businesses operate, communicate, and innovate. As we witness this shift, it's evident that LLMs are not just part of the business future; they are actively shaping it, offering new avenues for growth and efficiency in an increasingly digital world.

About Appier

Appier is a software-as-a-service (SaaS) company that uses artificial intelligence (AI) to power business decision-making. Founded in 2012 with a vision of democratizing AI, Appier’s mission is turning AI into ROI by making software intelligent. Appier now has 17 offices across APAC, Europe, and the U.S., and is listed on the Tokyo Stock Exchange (Ticker number: 4180). Visit www.appier.com for more information.

Dr. Ming-yu “Robert” Chen
CTO

Dr. Ming-yu “Robert” Chen is the Chief Technology Officer of Appier. He possesses over 20 years of experience in driving technology strategy, leading large-scale organizations, building scalable platforms for global deployment, and conducting state-of-the-art AI technology research. 

In his role at Appier, he focuses on leading product development and technology across all of Appier’s product lines, charting the R&D directions for CrossX and ESS solutions and formulating strategies from high-value customer acquisitions, retargeting to customer retention and engagement, accelerating transaction process and insight generation. His leadership in applying advanced decision-making and generative AI technologies has been instrumental in enhancing Appier’s solutions and efficiently optimizing ROI for Appier's customers.

Dr. Chen joined Appier most recently from Compass, the real estate technology brokerage firm. At Compass, Dr. Chen built the first modern enterprise real estate end-to-end platform for agents and clients and was responsible for a global engineering team comprising over 300 engineers and scientists overseeing AI/ML, video, digital ads, marketing tech, and platform brokerage services. 

Before Compass, Dr. Chen served as a Senior Director at Zillow Group and a Principal Applied Science Manager at Microsoft. He built the industry’s first cloud-based housing valuation system in Zillow and a large-scale news recommendation system in Microsoft, improving the overall engagement of news services and users’ personalized experience through machine learning technology.

Dr. Chen received his Ph.D. in Computer Science from Carnegie Mellon University and BS degree in Computer Science and Information Engineering from National Taiwan University. He was recognized as one of IBM’s Emerging Leaders in Multimedia during his Ph.D. studies.