In this ever-evolving AI world, selecting the right model is a game-changer. As more businesses join the Generative AI wave to tackle various challenges (such as these), and an array of models become accessible, the importance of choosing the right tool is crucial. This article can help you simplify this decision-making process and guide you through it.
Recently, Heath Brownsworth and I attended a fireside chat at the TrailBlazerDX 2024 conference in San Francisco. The chat featured AI insights from AWS’ VP of AI, Matt Wood, and Salesforce AI’s CEO, Clara Shih. Both speakers stressed the significance of choosing the most suitable tool for AI projects. Matt humorously compared using the same AI model across a company to a builder wielding only a Swiss army knife, drawing a chuckle from the audience.
At AAXIS, we follow a straightforward step-by-step process for selecting the right model, providing businesses with a solid starting point for their AI journey. Here are the five key parameters we consider in our model selection process:
1. Functional Fit
Functional fit gauges how well the model can perform the task you need, and ensures the type of data your model works with matches your requirements (text, image, voice). Think of it like using a screwdriver for a screw, not a hammer! Whether training your own model or using standard ones, it’s important to align your data with the model’s complexity. Then, evaluate performance using standardized tests and create your own relevant tests for a comprehensive score.
2. Non-functional Fit
While functional fit focuses on skills, non-functional fit considers performance, latency, availability, and scalability. Performance assesses how fast the model responds, latency looks at the time lag, availability ensures the model is operational, and scalability checks its ability to handle increased workloads. Combine these factors into a score between 1-10 for a comprehensive evaluation.
3. Pricing
Pricing matters. Consider the range – from free open-source options to expensive proprietary models. It’s important to factor in additional costs like infrastructure, data acquisition, training, maintenance, and support. Then, calculate the total cost of ownership (TCO) for each model under consideration and assign a score based on your budget and cost expectations.
4. Trust, Data Privacy, and Control
Beyond functionality, trust with users and responsible data practices are crucial. It’s critical to research the provider’s track record on responsible AI development and data security to ensure the provider follows ethical AI practices and implements robust security measures. Then, you must review the data usage policy and consult with legal experts to review agreements with AI providers.
5. Company Policies and Other Considerations
Consider compliance with licensing models, intellectual property rights, and environmental impact. Also evaluate your internal capability to deploy and maintain the chosen AI model. Align the model with your company’s values and commitment to responsible AI use.
After computing the individual scores, you can combine them using weights that are relevant to you. AAXIS recommends staying within the top three choices or not deviating more than 15% from the best option available. Going beyond this point is like justifying using the screwdriver attachment of a Swiss Army knife – it might get the job done, but it won’t make anyone happy, including you.
Now, more than ever, it’s crucial to integrate generative AI into your enterprise operations, covering aspects such as merchandizing, customer services, marketing, user experience, call center operations, and more. The groundbreaking transformative power unleashed by these integrations will undoubtedly confer a distinct advantage in today’s fiercely competitive environment.
So, take charge, stay ahead, and leverage the full potential of artificial intelligence.
Interested in an assessment to determine the right AI model for your business success? Let’s chat!
