Understanding market and regulatory forces can help organizations shape their strategy for adoption generative AI.
The initial spike in popularity for large language models (LLMs) is growing into a strategic enterprise focus to integrate AI more deeply into business operations.
Last year, even when just a few employees were testing out ChatGPT and other LLMs, it was mainly for drafting an email or replying to a message. Given the possibility of factual inaccuracies and other such hallucinations, it was not wise to deploy LLMs for external, public-facing content.
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 in 2024.
The year of LLM mass adoption
Two key factors have converged to make 2024 the year for LLM applications’ mass adoption: technological advancements and market sentiments. Since
Technological advancements would have no market value if executives did not buy into them — and 2023 was all about that. Now, as more vertical-specific LLMs become available, adopting generative AI (GenAI) will be made even easier.
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 wary 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.
Two big leaps
One of the advantages of using LLMs for content creation is speed. If you do not 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, organizations are buying into the use of LLMs for their versatility in generating content — in form, style, and tone.
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.
Strategizing with LLMs
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 amazing 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.
As we witness this shift, it is 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.
