Introduction
In the dynamic landscape of business, gaining a good understanding of artificial intelligence (AI) and its relationship with intelligence is crucial for leveraging its potential effectively. Let's delve into what intelligence is, how it differs from AI, the creation of AI models, the role of machine learning and deep learning, and the limitations of Large Language Models (LLM) for comprehensive business solutions.  

Intelligence vs AI
Intelligence is the ability to perceive, comprehend, and adapt to one's surroundings. It involves cognitive functions such as learning, reasoning, problem-solving, and decision-making. To illustrate, consider the Venus Flytrap—an organism that exhibits a responsive reaction to stimuli, akin to a reflex, and should not be confused with true intelligence.

Artificial Intelligence refers to the development of non-living entities capable of demonstrating human-like intelligence. It involves creating systems that can mimic cognitive functions, analyze data, and make decisions autonomously.

AI Models: Building Blocks of Intelligence
An AI model is a program designed to detect patterns, make decisions, and predict outcomes. It serves as the brain behind AI systems, processing vast amounts of data to produce intelligent responses. These models are crafted through a process known as machine learning and deep learning.

Machine Learning:
Machine learning is analogous to a child learning about fruits using flashcards. It involves the use of algorithms that enable AI models to learn from data (i.e. flashcards). This process can be supervised, where the model is trained on labeled data, or reinforced, where it learns through trial and error. Similar to a teacher guiding a child, machine learning involves layers that help the model extract relevant features before making decisions.

Deep Learning:
Deep learning takes machine learning to a another level, addressing complex considerations in real-world situations. While a child may identify fruits by appearance, the business of agriculture requires a more comprehensive approach. Deep learning allows AI models to process diverse data sources, considering factors like market trends, pricing, climate, and labor. By utilizing input, hidden, and output layers, deep learning algorithms provide intelligent responses to complex business scenarios.

Large Language Model (LLM):
A large Language Model (LLM) is one specific type of deep learning model trained to predict the next word in a sequence. However, businesses face limitations when relying solely on LLMs for broader AI applications.

Why LLM Alone Isn't Enough for Business Operations
LLM are trained using vectors and embedding and their training is specific to predicting the next word in a sequence. Business operations involve entities and  multifaceted decision-making, requiring consideration of diverse factors beyond predicting the next word.

To operate efficiently, businesses need AI models trained on specific data of entities relevant to their industry. LLMs lack the nuanced understanding of industry-specific intricacies, market dynamics, and operational challenges. The richness of business data requires tailor-made AI models crafted through a combination of machine learning and deep learning techniques.

In conclusion, businesses aiming for comprehensive AI solutions should explore custom AI models, avoiding the misconception of building AI solutions using LLM. This understanding allows businesses to harness the full potential of AI, addressing complex challenges and making informed decisions that drive success in today's competitive landscape.