Generative AI vs. Predictive AI- Choose the Best AI for your organization

While at a glance they may seem similar however they have distinct differences that suit different business requirements. This blog will explore these differences, and their use cases while also discovering how they can work together to achieve optimal results.

Introducing the AI team- Machine Learning, Deep Learning, Predictive & Generative AI

With the initial hesitation subsiding, companies are starting to explore the possibilities that AI-powered tools have to offer. According to a McKinsey survey, one-third of respondents admitted that their organization is using AI, especially Gen AI in at least one of their functions. Moreover, 40% said that their organization plans to increase investments in AI.

As we delve deeper, we are welcomed by words like machine learning, deep learning, predictive AI, and generative AI. They can create quite the confusion. Let's cut through this jargon and get to the heart of it. What do these terms mean? What is their function and how do they fit into the larger picture of artificial intelligence?

Let’s understand each of them with a bakery story. In a bakery store there are four bakers-

• Machine learning: The wise head baker- Just like a seasoned baker, machine learning knows the customer preferences by heart. It knows which cookies sell more together, at what time people buy specific cookies, and who are its loyal customers. Equipped with such knowledge, it suggests different changes in the current sales strategy to align with customer preferences.


• Deep learning: The artistic baker: DL is known as the baker with sharp attention to detail when it comes to keeping up with market trends. It has the ability to analyze photos of cookies and other desserts that are trending on social media and suggest ideas to keep up with those trends.


• Predictive AI: The fortune-telling baker: This baker possesses a unique skill - analyzing past data to predict future trends. By recognizing patterns and identifying potential customers who may not return, the baker is able to offer discounts and free cookies.


• Generative AI: The Inventive baker: GenAI is the newest addition to the team, a highly creative baker who can generate completely original cookie concepts. Depending on the data it has studied, it may even develop innovative baking techniques.

Note:  The example above is provided only for the sake of clarity and understanding. ML, DL, PdAI, and GenAI are not just limited to the functions that are mentioned above.

The "Imagine That!" AI: Unveiling Generative AI

In the past, most applications were designed to analyze existing data to generate insights. However, with Gen AI, one can produce unique and original content. Its algorithms can recognize patterns within the training data which can be from open sources or from within the organization itself. They can mimic those patterns to generate original content as needed.

At the core of Generative AI are its models, each with its advantages and limitations. Selecting a model depends on the organization’s requirements and the model's capabilities.

Unveil the Future: With the Power of Predictive AI

Imagine a situation where you can predict future demands, understand customer sentiments, optimize inventory, and troubleshoot issues before they arise. Well, these are a few of the many benefits that predictive AI has enabled for organizations. Predictive AI sees its uses when there is a need to get the most accurate data that can be used in critical decision-making processes.

However, you will be mistaken if you think of Predictive AI as a powerful trend identifier. Predictive AI goes beyond that by making informed predictions about what might happen next.

Generative vs Predictive models: How are they different?

Now that you have an understanding of the capability of Generative AI and Predictive AI, it is important to distinguish their fundamental differences. This will enable you to leverage them most effectively.

Transforming Value Chain: Generative AI and Predictive AI Uses

For an organization predictive AI and generative AI both can prove to have multiple benefits in their value chain. Generative AI is best suited for tasks that require creativity while predictive AI is better suited for tasks that demand accuracy. These fundamental differences create different use cases for each.

Now that we have seen their impact on the value chain, we should go a step further to understand how implementing them together can improve their individual potential-

• Generative AI can customize outputs (such as chatbot responses) to be more precise by utilizing predictions from predictive AI leading to improved user query solutions and greater confidence.


• Generative AI outputs can be used as additional data points for training Predictive AI models, speeding the training process. Moreover, if generative AI models have been trained on organizational data, it can generate data specifically related to the organization for Predictive AI model training. This will improve the prediction accuracy of predictive AI and better suit organizations’ needs.

So, which one should you go with?

If you were waiting for us to declare a winner between generative AI and predictive AI, then you would be disappointed. There is simply no way to rank one over the other. Each one of them serves a different purpose. What you choose will depend on your organization’s goals and the quality of available data with the organization.

Now that we are clear about Generative AI and Predictive AI uses, we should also familiarize ourselves with potential limitations that need to be considered-

• Data Dependence- The output of both Generative AI and Predictive AI depends on the quality and quantity of data they have trained on. The output carries the same inconsistencies as the training data, so, if the training data is inaccurate or biased, then the output generated will also be biased.


• Ethical Considerations- Factors like job replacements and intellectual property rights pose a serious concern to the implementation of AI.


• Control - An AI model without human checks may pass inaccurate, harmful or fake content.


• Safety and Security- You must have heard about the case when a voice Deep Fakes was used to scam the CEO for $243,000. Deep fakes and access to sensitive information are contributing to the growing mistrust towards AI.


• Limited Scope- Predictive AI can make accurate predictions based on past data. However, unexpected events can throw off these predictions.

Final Thoughts

It is quite clear that Generative AI and Predictive AI both have immense potential. Organizations must move beyond the Generative AI vs Predictive AI debate. Rather, should focus on creating a synergy between them to truly elevate their AI capabilities. This involves understanding the functionality and limitations while also pinpointing the specific areas that can be transformed with one or both together.

We at Polestar Solutions, are experts in building a robust AI and analytics infrastructure for your organization. We take a step-by-step approach, making sure that we deliver more than what you expected-

Step 1: Assess and Define Business objective- We will understand your current landscape and identify areas of improvement taking hints from many of our historically successful cases and industry examples.

Step 2: Data discovery & Preparation- We will identify and gather relevant data sources that are essential and transform data for accuracy and consistency.

Step 3: Model Development & Deployment- We will develop, train, and validate models using suitable machine learning, deep learning, or AI methods.

Step 4: Monitor, Evaluate, & Iterate- After monitoring and implementing the solution, we will establish a seamless monitoring mechanism & provide any further support when required.

Contact us if you’d like to know more about Generative AI, Predictive AI, their synergy, and implementation into the value chain.