How to Choose the Right AI Model for Your Business Goals

Yes, AI offers numerous benefits to different aspects of your business. However, not all AI models are right for all situations. Using the wrong AI model for a particular situation can expose your organization to various risks, ranging from organizational biases to faulty outcomes that could lead to losses, lawsuits, and reputational damage. 

So, the smart thing to do is to consider the needs and goals of all AI decision-makers in your organization before choosing a model. In order to do this accurately, you need a solid evaluation framework guiding your AI selection process. This framework teaches you how to know what kind of model to use and which AI model is best for different use cases. 

In this article, we tell you what an AI model is, Debut Infotech’s custom AI selection framework, and some examples of the best AI models right now. When you’re done with this piece, you’ll be able to identify the right AI models for different use cases and actually implement AI that drives ROI. 

Let’s get straight into it!

What is an AI Model?

An artificial intelligence model is a complex program created to identify patterns and make judgments on its own using data without the need for additional human involvement. These complex programs are built to function just like humans. While humans learn from experience, AI models learn from past data and the patterns they notice in them. But they even do more than that; they use AI algorithms to examine several kinds of data in the form of texts, statistics, and images and look for trends and relationships in them. 

You’ve probably encountered a number of AI models when working with different applications. For example, a facial recognition algorithm recognizes people in new photos by learning unique traits from millions of images. The human brain does the same thing when we see someone we might have previously seen. 

There are several types of AI models, each designed for a particular job or domain. However, some of the early examples of AI models were the chess-playing AI models that were created in the 1950s. Normally, most chess-playing computer programs were built with pre-programmed or rigid moves. However, these AI models did not just repeat a bunch of programmed steps. They were making new moves on the chess board based on the moves of the human players they were up against. In fact, they were essentially “another human player.” 

Categories of AI Models

There are different categories of AI models depending on their mode of operation. The following are the most common examples:

1. Supervised Learning Models

Supervised learning models are AI models trained with labeled data points. They learn trends and patterns from previously identified input and output data points so that the model can identify similar data sets. 

For example, the model might be presented with two categories of images: cars and buses. The training data will be labeled as either cars or buses to allow the model to learn the unique features of each. After learning these trends, the AI model uses this knowledge to identify buses or cars in a new unlabeled data set. 

In real-world applications, supervised learning models are used to solve classification and regression problems. 

2. Unsupervised Learning Models

These are the direct opposites of supervised learning models because they are trained with unlabeled data points. They are programmed to simply identify the patterns and structures in the given data set on their own. When they do, they’re now expected to make the same distinction in a new data set. 

In real-world applications, unsupervised learning models are used to solve customer segmentation and dimensionality reduction problems. 

3. Semi-supervised Learning Models

You guessed it: semi-supervised learning models combine the features of both supervised and unsupervised learning models. They learn patterns and structures from their training data using a process known as pseudo-learning. 

Pseudo-learning is characterized by a process in which the algorithm is first trained partially with a small subset of labeled data. Later, the model “propagates” the insights it gained from the labeled data to label a larger set of unlabeled data. 

Semi-supervised learning models are often used for image classification, sentiment analysis, spam filtering, and anomaly detection purposes. 

4. Deep Learning Models

Deep learning models are complex AI systems capable of finding patterns and solutions in information without human intervention. Whether trained with labeled or unlabeled data, these algorithms independently create their methods of understanding a dataset without being told what to look for. 

They can be used in a wide variety of real-world applications, such as image recognition, natural language processing, speech recognition, recommendation engines, and computer vision. 

What are the Best AI Models Right Now? 

Following the advancement of Artificial intelligence technologies, different companies have launched various sophisticated AI models. The table below highlights some of the best AI models right now, belonging to the different categories discussed above and performing different functions.

In addition to the AI models listed in the table below, there are still numerous quality AI models today. So, how do you know which one to use for your organization’s unique needs? 

Discover in the next section as we explore Debut Infotech’s AI model selection framework. 

Debut Infotech’s Custom AI Model Selection Framework 

1. Pick Out a Specific Use Case

When looking for the right AI model, the AI model itself isn’t the first thing you should be bothered about. Rather, you need to worry about the exact usage scenario for which you need that AI model. There are so many possible use cases depending on your business requirements. These could be text generation, fraud detection, quality assurance, virtual assistance, and many more. 

You might need to seek the opinion of multiple stakeholders to get the actual purpose you need your AI model to serve. So, talk to your product and engineering teams, as well as business sponsors, to get their input on their expectations of the AI model. A simple and random use case could be ranking applicants in a job search based on the relevance of their resumes to the job description. 

However, while doing this, there are some subtle details about your preferred use case that might help you better describe what you need the AI model for. You need to get very granular by actually crafting a prompt and the ideal answer you would expect from the “perfect AI model.” For example, you might aspire to have an AI model that accurately lists the ten best applicants based on the resumes submitted for a job application. 

Once you have the ideal answer you want from your perfect AI model, you can then walk backward to find the right AI model for you in the subsequent points. But for now, make sure you know exactly what you need the AI model for. 

2. Itemize Different Examples of Appropriate AI Models 

With a clear idea of what problem you’re trying to solve with this AI model, you can start shopping around for those whose general descriptions match your needs. You just might never know; there might be an AI model out there that already works for the specific use case you outlined in Step 1. 

Your research process needs to be thorough at this stage. Check out open-source AI models, proprietary models, and even third-party solutions. This thorough research gives you a wider sample space for understanding the strengths and limitations of each AI model so that you make a really informed decision. 

When building AI for businesses, Debut Infotech, an AI development company closely examines pre-trained foundation models that have already been fine-tuned for different reasons and specific use cases. We then run quick experiments with these highly targeted models using internal training data. As a result, businesses need less internal training data and expertise to adapt our AI models to their specific needs. More importantly, they enjoy a greater competitive advantage due to an accelerated time to value. 

3. Compare and Contrast Each Model’s Risks, Performance, and Size

Now, it’s time to narrow down the options to the best possible fits. And the way to go about this is to screen the listed AI models based on their performances, sizes, and associated risks. 

At this stage, things can get technical, so let’s divide the evaluation procedures into different criteria. 

When evaluating the listed size options, one should consider whether to choose a largeis to or a smaller AI model. The correct answer? Right size your AI model appropriately for the specific use case. 

Generally, large AI models tend to perform better than smaller models because they’re trained with larger datasets. So, they may be more accurate because they’ve noticed more complex patterns. However, the downside to using large models is that they need more processing power, are costly, and are not so accurate. 

Smaller AI models, on the other hand, are more cost-efficient and can be more accurate with more contextually relevant responses. However, you might need to prompt-tune the smaller model to more specific tasks for which you intend to use it. 

Next, you want to evaluate your listed AI models for performance. This means checking them for accuracy, speed, and reliability. In terms of these three criteria, the evaluation can be described as follows: 

• Accuracy: Check how to build an AI model generated output to the desired output. Depending on the type of AI model you’re testing, you can carry out these tests using industry benchmarks like BLEU (Bilingual Evaluation Understudy) or ROUGE (Recall-Oriented Understudy for Gisting Evaluation).
  
  
• Reliability: Measure the ability of each AI model to produce the same output when given the same prompts consistently. This is influenced by factors like explainability, consistency, and trustworthiness. It also concerns the model’s ability to avoid toxicity and bias. 
  
  
• Speed: Measure how quickly the different AI models produce a relevant output when prompted. 

Finally, regardless of the use case, you need to screen the listed options for AI models that provide full transparency into the training methodology. These kinds of AI models give you the opportunity to easily monitor and optimize the models for performance and operational risks. And that’s a good thing because it enables responsible deployment. More so, it makes your organization better equipped to handle key issues that may arise in terms of governance, risks, privacy, and bias. 

4. Test the Most Viable Options

By now, you’re already narrowing your options down to a few AI models. So, you need to close in further to find the best option for your specific use case by testing the most viable options.

This means choosing the models that have shown the most potential to produce the best results so far and testing them to see if they work. Whether you want to use the AI model for text generation, applicant ranking, virtual assistance, or chatbot purposes, you need to test it in real-life usage scenarios and see how well it performs. 

As we highlighted above, you need to assess the quality of their outputs using either your custom internal metrics or industry standards like the BLEU score.

Furthermore, if you opted for larger models, try to achieve the same performance by scaling them down to a smaller model using techniques like prompt engineering and model tuning. The key point here is knowing what your ideal outcome would be and testing the available options to identify which one produces the output closest to that. 

5. Select the AI Model Offering the Most Value

Finally, although the output’s effectiveness in terms of size, reliability, and performance is crucial, you also need to consider the bigger picture. We’re talking about the model’s value to your business in terms of return on investment (ROI) and cost-effectiveness. 

Don’t just pick the most accurate or fastest AI model if it’ll significantly cost your business more and impact your bottom line. Rather, a less accurate model that still does the job just fine might be a better fit for your business if it is more cost-effective. More so, this less accurate model may offer more to your business in terms of other factors like scalability and lower latency. Smaller models are often easier to scale across different touch points in an organization compared to larger models. Nonetheless, your business needs might be very dependent on near-perfect accuracy levels, and it would make sense to choose the larger models in such cases. 

So, really, selecting the AI model offering the most value is about finding the perfect blend of accuracy, cost-effectiveness, reliability, performance, and ROI. You might have to make some trade-offs depending on your unique situation, and the best way to make the perfect tradeoffs is when you know your business needs and the AI model’s specific use cases very well. 

Conclusion

There’s no one-size-fits-all approach to choosing the best AI model for your business. The perfect fit depends on your unique needs, the model’s specific advantages, and how you can strike a balance between the two. 

So, before making that choice, identify your specific use case and list out the AI models whose characteristics match your requirements. Then, compare and contrast in terms of risk, performance, and speed as you test the most viable options. Finally, look at the bigger picture before selecting by comparing the impact of each choice on your business’s bottom line in terms of ROI. 

This is how we select the best AI business process automation purposes here at Debut Infotech. If you would like to make the perfect pick, use our years of expertise through our AI consulting services today! 

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