How to choose an AI model for your business
Selecting the right artificial intelligence model can seem like a daunting task, especially when you have limited time and need to manage multiple projects simultaneously. This guide will help you make a quick and informed decision by showing you the key steps, practical examples, and a short code snippet you can copy and paste into your initial tests.
1. Define your business needs
Before evaluating any model, clarify what you want to achieve. Ask yourself these questions:
- What is my primary goal: text generation, analysis, chatbot, forecasting, or something else?
- What specific capabilities do I need (e.g., multimodality, reasoning, integration with company data)?
- What are my budget limitations and available computing resources?
Creating a summary sheet with this information will help you stay focused during evaluation.
2. Evaluate model capabilities
Many foundational models today offer similar functionalities, but there are important differences that can impact your results:
- Size and specialization:Larger models like GPT-4 Turbo or Claude 3 excel at complex reasoning, while smaller models like LLaMA-2-7B are more cost-effective for simple workloads.
- Multimodal support:If you need to process images or code, choose a model that natively supports it (e.g., GPT-4 Vision or Gemini-1.5).
- API integration and latency:Check response speed and per-token costs. Many companies today prefer models with latency under 200 ms per interaction.
Example comparison:Suppose you want a chatbot for customer service. A quick benchmarking test might show:
- GPT-4 Turbo: 98% accuracy, $0.06 per 1k tokens, latency 150 ms
- Claude 3: 96% accuracy, $0.04 per 1k tokens, latency 180 ms
- LLaMA-2-13B: 92% accuracy, $0.01 per 1k tokens, latency 250 ms
This helps you decide based on the quality-price ratio.
3. Test with an MVP (Minimum Viable Product)
Don’t blindly trust specifications. Build a small pilot project that uses the selected model and test your actual tasks.
Below is a Python snippet you can copy and paste to start a test with the OpenAI API:
import openai
import os
openai.api_key = os.getenv('OPENAI_API_KEY')
response = openai.chat.completions.create(
model='gpt-4-turbo',
messages=[
{'role': 'system', 'content': 'You are a concise technical support assistant.'},
{'role': 'user', 'content': 'How do I reset my password?'}
],
temperature=0.2,
max_tokens=150
)
print(response.choices[0].message.content)Modify the model, system instructions, and parameters to see how the model fits your use case. Run the same test with other models (e.g., Claude 3 or LLaMA-2) and compare speed, cost, and quality.
4. Consider security and compliance (important in 2026)
With increased enterprise adoption, security and compliance are absolute priorities:
- GDPR compliance:Ensure the model does not store personally identifiable data or offers guarantees for data deletion.
- Authentication and access control:Use protected API keys and role-based permissions.
- Bias monitoring:Many vendors now provide bias-assessment dashboards. Include them in your evaluation process.
For example, M&T Bank implemented AI copilots for over 15,000 employees, integrating security controls from day