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

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    • Bhaskar PathakData Whisperer
      Listening to the silent stories that data has to tell.
    03-July-2024
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    • Gen AI
    • Data Science
    • Analytics Consulting

    Editor’s Note: It is quite clear that in the coming future artificial intelligence will impact every aspect of our lives. Organizations too have realized that the use of AI will be a game changer and can help them achieve competitive success. The question in their mind is now about the capabilities of two of its branches: Generative AI and Predictive AI. How are they different? Is one superior to another?

    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?

    predictive maintenance cycle
    Be it machine learning, deep learning, generative AI, or predictive AI, all of them can have an impact on the organization’s functioning.


    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.



    Generative AI Models Use Cases Famous Tool
    Generative Adversarial Network Document generation, Product R&D StyleGAN (NVIDIA)
    Transformer-based model Software development, image creation BERT
    Diffusion model Design, marketing content -
    Variational Autoencoders Smart operational technology systems management (OTSM), Intelligent quality control -
    Large Language model Developing chatbots, social media sentiment analysis GPT 3
    Neural Radiance Fields 3D modeling, Architecture Nvidia NGP instant NeRF 3
    Recurrent neural networks (RNNs) Product and process development, Better Customer service -

    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.

    Predictive AI Models Uses
    Classification Model Email spam filtering, social media text analysis, language identification
    Clustering Model Social media analysis, market segmentation, anomaly detection
    Forecast Model Inventory optimization, demand forecast, sales forecast
    Outlier Model Machine health monitoring, Fraud detection
    Time Series Model Stock price predictions, demand forecasting for seasonal products

    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.

    Feature Generative AI (Gen AI) Predictive AI
    Primary Function Generates fresh information similar to the training data but with a unique twist. Analyzes existing data to predict future outcomes
    Output New data points (images, text, music, code, etc.) Predictions about future events or values.
    Focus Creativity, innovation, exploration of possibilities. Accuracy, reliability, and actionable insights.
    Applications Content creation (text, images, music), Product design, Drug discovery, Art generation Sales forecasting, Financial modelling, Equipment failure prediction, Customer churn prediction
    Challenges Lack of explainability (black box nature) Potential for misuse (deepfakes, misinformation) Limited to predicting based on historical data (may not capture unforeseen events)

    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.

    Value Chain Predictive AI cases Generative AI cases
    Sourcing & Procurement Supplier risk assessment- Analyzing financial condition, market, credit rating to predict the risk associated with supplier Contract Optimization- Analyze standard terms and conditions used in the past to generate new contracts
    Production & Manufacturing Predictive Maintenance- Predict potential equipment failures and schedule maintenance proactively Product design optimization- Generating multiple design iterations for a product, which can be refined digitally
    Inventory Management Demand forecasting- Future demand prediction based on historical trends Replenishment order Automation- Based on previous replenishment orders, creates new order without human intervention
    Logistics & Distribution Delivery risk assessment- Predictive AI can analyze weather patterns, historical traffic data to identify potential risky routes Dynamic Packaging Design- Generating customized packaging designs that can be created for space utilization to minimize shipping cost
    Store management Staffing optimization- Predict future staff requirements based on historical data Store Layout Optimization- Optimizing store layouts to improve customer flow and boost sales
    Marketing & sales Sales Forecasting- Predicting future sales to optimize marketing budgets Marketing Content Creation- Generate personalized marketing content
    After-sales service Customer Churn Prediction- Identifying customers at risk of churning so as to implement retention strategies Chatbot-powered Customer Support- Providing 24/7 customer support through chatbots.

    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.

    Predictive AI Generative AI
    Forecasting future outcomes
    Decision-making based on historical data
    Analysis of past trends and patterns
    Data-driven risk assessment
    Customized or personalization
    Creating new data and content
    Enhancing creativity and innovation
    Generating realistic simulations
    Filling gaps in existing data sets

    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.
    genai risks infographics
    Despite multiple benefits, some issues related to Generative AI still need to be addressed

    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.

    About Author

    Generative AI vs. Predictive AI
    Bhaskar Pathak

    Data Whisperer

    Listening to the silent stories that data has to tell.

    Generally Talks About

    • Gen AI
    • Data Science
    • Analytics Consulting

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