AI: Beyond Hype and into Practice

As we enter the second half of 2024, the artificial intelligence landscape is shifting from excitement to practical implementation. The initial hype surrounding OpenAI’s release of ChatGPT has waned, and enterprises are now focusing on the realities of implementing AI technologies in real products. The realization has set in that while large language models (LLMs) like GPT-4 are powerful, generative AI as a whole has not lived up to the lofty expectations of Silicon Valley.

A new era of AI implementation

The debate shaping the AI landscape is the race to develop the most advanced LLM. However, the differences between leading LLMs have become imperceptible, allowing companies to select models based on price, efficiency, and specific use-case fit. OpenAI’s progress has slowed, and its rival Anthropic has caught up with its launch of Claude 3.5 Sonnet. This plateauing means that enterprises should focus on leveraging the best individual LLMs for their specific purposes, considering open LLMs that offer more control and allow for easier fine-tuning.

“The focus is on leveraging existing AI capabilities for tangible business outcomes rather than chasing the hype of AGI.”

The infrastructure reality and the potential GPU bottleneck are also key debates. While there is a demand for specialized hardware like GPUs, particularly for training large models, many enterprise use cases focus on inference rather than training. Inference can be run efficiently on non-GPU hardware, and alternative technologies are emerging to challenge Nvidia’s dominance. Most enterprise companies can rely on cloud providers like Azure, AWS, and Google’s GCP for their AI infrastructure needs.

Knowledge graph of entity nodes and relationship edges derived from a news dataset

Another critical aspect is the impact of gen AI applications on core business offerings. While AI has transformative potential, its current impact is more pronounced in enhancing existing processes rather than revolutionizing core business models. AI is being applied to customer support, knowledge base assistance, generative marketing materials, and code generation. However, it is not yet leading to massive revenue gains or business model shifts.

Companies like o9 Solutions are enhancing their Digital Brain platform with AI-powered composite agents. These agents aim to transform the execution of complex tasks, enhancing integrated business planning capabilities. The agents are built from atomic agents, AI-driven systems capable of performing tasks, retrieving information, and generating responses based on specific inputs.

AI-powered planning

In conclusion, the AI revolution is happening in offices worldwide where AI is being integrated into everyday operations. The focus is on leveraging existing AI capabilities for tangible business outcomes rather than chasing the hype of AGI. Enterprises must navigate the debates surrounding LLMs, AGI, infrastructure, legal and ethical considerations, gen AI applications, and AI agents to effectively harness AI’s potential.

The most valuable AI implementations might not make headlines but can significantly enhance productivity and operational efficiency.