Generative AI was a groundbreaking development last year, with the emergence of popular chatbots such as OpenAI's ChatGPT and Google's Bard taking the technology world by storm. However, using the large language models (LLMs) powering these chatbots has several disadvantages, hindering the adoption of the technology. For this reason, LLMs will be gradually replaced by smaller, custom language models in 2024, says GlobalData, a leading data and analytics company.
Training and operating LLMs involve steep costs, with expensive computing resources required to process the vast amounts of data used in AI. Specialised models ultimately deliver better value and accuracy. Often, firms start by experimenting with huge general-purpose models to explore different use cases, only to find out later that the compute cost doesn't justify the scale of the transaction. In 2024, as Generative AI penetrates the enterprise space and business cases become more conspicuous, companies will leverage smaller models and tune them with their proprietary data to get the performance they want for a specific use case for a fraction of the cost.
Beatriz Valle, Senior Analyst at GlobalData, said that enterprises could potentially find themselves in a vulnerable legal position due to potential copyright infringements or privacy violations.
"For example, the origin of the data used to train the models is often unknown. For this reason, organisations will opt to deploy small language models (SLMs) instead. These custom models will be trained on proprietary data, rendering better results with fewer risks," said Valle.
Rena Bhattacharyya, Chief Analyst of Enterprise Technology and Services Research at GlobalData, added that 2024 will see the passage of comprehensive regulatory policies to guide AI project deployments and management.
"The world will look toward Europe, which is poised to approve groundbreaking legislation via the AI Act, which categorises the risk of AI applications and bans certain use cases, establishes requirements for high-risk applications, and requires human oversight of computer models and actions," said Bhattacharyya.
Bhattacharyya continued that the rest of the world will begin to discuss similar frameworks, and the debate around using copyrighted content to train AI models will be top of mind for executives.
GlobalData's report, "2024 Enterprise Predictions: Artificial Intelligence," also revealed that enterprises will explore using multimodal AI to generate improved outputs in various industries and applications. In addition, techniques such as RAG (retrieval-augmented generation), which is used to augment LLM prompts and responses with information from reliable internal or external sources, will become more widespread. Synthetic data will also become more common among enterprises and AI companies.
Valle said companies such as OpenAI and Cohere will study synthetic data as an alternative to real-world data to access high-quality data sets without being sued.
"For example, The New York Times recently filed a lawsuit against OpenAI, and other similar cases have occurred in the industry. We will also see more agreements and partnerships between AI and media companies. For example, OpenAI recently signed a deal with German media company Axel Springer and the Associated Press," said Valle.
GlobalData forecasts that the overall AI market will be worth $909 billion by 2030, registering a compound annual growth rate (CAGR) of 35 percent between 2022 and 2030. In the generative AI space, revenues are expected to grow from $1.8 billion in 2022 to $33 billion in 2027 at a CAGR of 80 percent.
Generative AI will impact every industry, and 2024 will see the number of live AI implementations grow exponentially in the corporate space, particularly in customer experience and marketing.
