AI models like generative pre-trained transformers (GPT) have become imperative for businesses aiming to stay competitive. Understanding the factors that influence this decision is crucial for business leaders seeking to harness the power of AI — especially Generative AI.
By Rishi Thukral
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Maximizing Your AI Investment: Decision Factors to Select the Right GPT for Your Business
The rapid proliferation of artificial intelligence (AI) created a veritable alphabet soup – GenAI, GPT, LLMs, NLP, and much more. The jargon can be overwhelming, causing many enterprise leaders to waffle when deciding how to pilot and accelerate AI. AI models like generative pre-trained transformers (GPT) have become imperative for businesses aiming to stay competitive. Understanding the factors that influence this decision is crucial for business leaders seeking to harness the power of AI — especially Generative AI.
| Term | Definition |
|---|---|
| LLM | Large language model; a broader category of models that includes GPT and others |
| GenAI | Generative AI; based on LLMs which are a class of AI models designed to understand and generate human-like text |
| GPT | Generative pre-trained transformer; a specific type of LLM |
| NLP | Natural language processing |
| ML | Machine learning |
| Common understanding of GPTs | Specific LLMs that have been customized and/or trained on different data to address specific business challenges |
Machine learning glossary
In the artificial intelligence arms race, new AI models have rapidly emerged. As of September 2024, some of the most advanced and widely adopted models are:
| AI models |
|---|
| GPT-4 (OpenAI) |
| BERT |
| Gemini (Google) |
| CoPilot (Microsoft) |
| Claude 3 (Anthropic) |
| MetaAI (LLaMA-Meta) |
| Falcon (open source) |
Each model has pros, cons, and optimal use cases. For example, BERT is trained using a masked language model. This means certain words are masked which requires the model to gather context clues from surrounding tokens, helping to train the model and make it more contextually accurate. Alternatively, GPT-4 was trained on a large-scale corpus containing web pages from sources like Wikipedia, books, and publicly available web articles, making it more generic but with a broader set of information.
As with any business decision, leaders must weigh the pros and cons to determine the best choice for their organization. Start by identifying relevant business challenges, then evaluate GPTs based on their strengths and intended use. Simpler tasks like text generation or summarization may only require smaller models, such as GPT-2.
For more complex tasks involving deeper language comprehension or advanced natural language processing, larger models like GPT-3 or GPT-4 may be more suitable.
For general needs, choose GPTs based on their broad language capabilities. For example, specialized models like medical GPTs are tailored for healthcare, while multilingual GPTs handle multiple languages. These offer targeted solutions for specific industries.
AI vendors are rapidly developing custom models for common business needs. Over time, GPTs will become as common as desktop software. Until then, businesses should adopt a GPT composability approach, combining baseline language models with customized GPTs and retrieval-augmented generation (RAG) to address specific challenges and improve output accuracy.
Assess the computational resources and infrastructure within your organization. Large AI models demand significant computational power and memory to perform efficiently. If your organization does not have the required resources, it may be more practical to opt for a smaller, resource-efficient model. Alternatively, leveraging cloud-based solutions can significantly enhance computational efficiency while minimizing the need for extensive in-house infrastructure.
Another critical consideration is operating costs, which are often overlooked. As the user base grows, the volume of text or data generated by AI models like GPT increases exponentially, resulting in escalating costs. Incorporating these ongoing expenses into the organization’s strategic budget is essential to avoid unexpected financial challenges.
Eight Steps to Enterprise GPT Adoption
Selecting the right GPT entails a thoughtful analysis of task complexity, available resources, and specialized requirements. Each available and in-development GPT has strengths, weaknesses, complexities, and challenges. Selecting the incorrect tool can create rework, cost escalation, and suboptimal impact.
Acquis Consulting Group and Cortico-X evaluate business considerations to devise a fit-for-purpose strategy. Whether it is the comprehensive capabilities of a particular GPT, the efficiency of the GPT, or specialized variants tailored to specific industries, understanding specific business needs is key. By considering these factors, business leaders can confidently make informed decisions to select the ideal GPT for their organization’s AI endeavors, unlock new possibilities, and stay ahead in today’s dynamic and competitive digital landscape.
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