Generative AI Vs Predictive AI: All You Need to Know

Generative AI Vs Predictive AI: All You Need to Know

As we all know, Artificial Intelligence (AI) has two distinctive functional branches: Generative AI and Predictive AI. Both of these segments have applications that cater to various industry needs.

Starting from healthcare to fintech, logistics & edutech, the evolution and implementation of AI in various industries have been massive. This is because, the impact of these two is beyond profound, in driving efficiency, accuracy, and innovations. Now before we carry on further, you need to know that this blog is not about Generative AI vs. predictive AI.

Rather it’s a positively constructed study on both the segments of AI.

Are we clear here? Let’s move on then.

Generative AI Vs. Predictive AI: An Overview

Gen-AI applications existed for decades in various forms like Generative Adversarial Networks (GANs) & Variational Autoencoders (VAEs), but their restricted capabilities haven’t intrigued enterprises earlier.

However, the success of ChatGPT demonstrated the potential value of GenAI across enterprises by producing nuanced & articulated content. Additionally, predictive AI is also being used by the majority of AI applications in correlating data & making decisions. The most common use cases of predictive AI include — Cybersecurity, Inventory management, and Digital Twin Technology.

Thankfully, businesses have started to see the benefits of these two branches of AI. Starting from automation to creating digital replicas for stress-testing business scenarios — the implementations have come a long way. At present, it is beyond the argument of Generative AI vs. Predictive AI. Rather, the important part is to embrace and implement AI to its fullest form. Don’t you agree?

Are you implementing AI to the fullest in your enterprise? I hope you are. Regardless, having queries regarding its implementation is obvious, regardless, you can have a chat with us.

Now, let’s shift our gears to understand the mechanism of how these two AI branches function. Because before we delve into the comparison (Generative AI vs. Predictive AI), knowing the basics of Generative AI and Predictive AI is crucial.

What is Generative AI?

Starting from curating texts to codes, to music, and now, even videos — GenAI is everywhere.

Businesses across a wide range of industries are increasingly adopting it to search through large datasets & generate content instantly.

Here are some of the key areas where it is making a significant impact:

• Drug Design: The costs and time required for drug development are greatly reduced by the use of Gen-AI. As it helps in designing efficient clinical trials quickly while streamlining the regulatory & approval process. By 2025, Gen-AI will be used systematically to find approximately 30% of new medications.
• Material Science: It is also being utilized to create completely new materials with manufacturing industries like — Automotive, aerospace, defense, medical, electronics, and energy.
• Chip Design: By optimizing the assessment of microchip components, GenAI is reducing the time it takes to develop a device from weeks to hours. Additionally, It also enhances its power and performance.
• Supply Chain Optimization: The best part is here — With capabilities like route optimization, automation of low-value tasks, & optimizing supplier networks; GenAI is empowering businesses to make well-informed decisions, leading to optimizing their supply chain operations, and augmenting their profits.

Additionally, reports state that by 2026 over 80% of businesses are expected to use Gen-AI APIs & Gen-AI applications to a greater extent. This demonstrates how it is becoming more and more crucial for enterprises across various domains. However, implementation can be a challenge here. So it’s better to consult with AI experts before implementing.

How does Gen AI work?

Big data & Machine Learning(ML) are the basic source of Generative AI.

The input data can be in various forms, including text, photos, and music. Afterward, Gen-AI analyzes these examples to discover the underlying structures & once it learns them, it produces the required content.

This is an iterative process, signifying that the tool will continue to learn & improve over time. The key feature of Gen-AI is its ability to create new content without replication or prediction based on existing data.

Gen-AI Examples Across Industries

Allow me to present some of the prominent examples of enterprises utilizing Gen-AI across diverse industries:

• Healthcare Care: With the use of Gen-AI, Babylon Health has developed a digital healthcare system that enables physicians to provide more cost-effective, individualized care by understanding patients’ changing risk profiles. Also, Ada (An Healthtech App) provides multilingual medical advice using generative artificial intelligence.
• Logistics: Procter & Gamble (P&G) uses Gen-AI to improve their demand forecasting accuracy. On the other hand, Microsoft’s Supply Chain Copilot, uses Gen-AI for risk mitigation, and streamlining order fulfillment processes.
• E-Commerce: Amazon has started using Gen-AI for tailoring personalized content for their users. Another great example is Shopify Magic, which helps retailers to save their time in tailoring marketing strategies. Also, Dabur Red Paste has recently launched a special edition package using Generative AI.
• Education: Gen-AI is making a great impact in the education industry by providing seamless learning experiences & creating immersive educational content. For instance, Speechify is a Gen-AI-driven tool that is making learning more accessible for children with learning disabilities. Recently, Coursera & edX have also announced their goal of using Gen-AI in their programs.

With this, you can see the immense potential and advantages of generative AI in various industries. Even though, if you are skeptical about utilizing Gen-AI efficiently in your business, you may consider a free consultation call with us.

What is Predictive AI?

Okay, back to the introduction. What is Predictive AI when we talk about generative AI Vs. predictive AI?

Well, also known as predictive analytics, predictive AI is primarily used to predict future outcomes based on historical data, propelled with ML algorithms. It aims to help businesses make informed decisions by forecasting likely outcomes based on the data available to them.

Now, let me share some of the significant areas where predictive AI is making great progress:

• Banking: Banks like JPMorgan use Predictive AI to analyze credit risk by evaluating customer data to anticipate loan default possibilities.
• Cloud Optimization: Airbnb & Dropbox use predictive AI to optimize their AWS pricing, manage capacity, & build custom cost plans. This proactive, data-driven approach allows for precise resource allocation, preventing both overprovisioning and underutilization.
• Uptime/Reliability Optimization: For companies like Netflix, Uber & Salesforce, who rely on web services, maintaining uptime & reliability is a top priority. Here, predictive AI helps by scanning systems, networks, and processes for inefficiencies or any looming threats, based on the data fed to it.

Subsequently, all of these use cases signify how predictive AI is becoming an integral tool for businesses. Such insights into customer data & the ability to predict future behaviors will allow you to tailor your business strategies accordingly.

The predictive AI model focuses & trains on machine learning algorithms to identify various kinds of patterns & trends. It uses the insights gained from the training to make predictions about future trends for businesses to tap into.

Likewise, by analyzing various large datasets & the amplification of ML algorithms, it provides accurate & improved decision-making capabilities. As a result, businesses that leverage predictive AI will be better positioned and will thrive in the ongoing business landscape.

Lastly, predictive AI implementation often involves combining other analytics tools to mitigate underlying limitations. So, for that, you can seek out our guidance & navigate yourself through any complexities while implementing it.

Predictive AI Examples across industries

Here’s a list of use cases and examples of companies using Predictive AI across various sectors:

• Finance: It has revolutionized the finance industry by providing smarter, safer, and more convenient ways to access and invest money. For instance, fintech firms like Enova & BlackRock use predictive AI in their platform to provide advanced financial analytics & credit assessment.
• HealthCare: Philips & GE Healthcare are using predictive AI to improve precision in patient positioning & CT image reconstruction. Due to this, the speed-up of image acquisition in MR has increased rapidly.
• Heavy Industry: BAE systems employ predictive AI to increase operational efficiency and safety in their manufacturing plants. Also, The New York City Subway uses it for predictive maintenance, minimizing downtime by scheduling maintenance before issues disrupt service
• Consumer Goods: It is streamlining operations, by predicting market trends, & demands in the consumer goods sector. For instance, BloomsyBox uses predictive AI to generate a more profound consumer connection with their brand. Also, Presidion (formerly SPSS Ireland) employs Predictive AI to optimize inventory and enhance customer satisfaction.

Hence, it has become evident that the versatility & potential of Predictive AI is quite revolutionary for businesses to future-proof themselves. What do you think? Let us know!

Generative AI Vs. Predictive AI: Key Differences

So, it’s actually not a ‘versus’ at all.

Both Generative AI & Predictive AI represent a very distinct model in the field of Artificial Intelligence. While Gen-AI focuses on creating new content by learning from existing data, predictive AI uses historical data to forecast future outcomes. Nevertheless, these two approaches are not isolated at all, they are very beneficial & symbiotic in developing overarching business strategies.

All in all, you can say that predictive AI is best suited for tasks requiring prediction & forecasting. Whereas, Gen-AI excels in tasks requiring creativity and originality. However, both models require extensive knowledge of data governance & cyber-security to function ethically.

Thus, we advise you to seek consultation to ethically use AI models in your business operations.

The Future of AI: Generative vs Predictive

McKinsey research says, generative & predictive AI features stand to add up to $4.4 trillion to the global economy annually. So, it’s time to have a look at these pointers signifying the expansion of these AI models:

Generative AI:

• Task Personalization: Generative AI is set to become increasingly personalized, and adaptable to specific tasks.
• Understanding Human Psychology: Future models of Gen-AI will grasp human psychology and creative processes more efficiently. After that, it will lead to more improved user experience.
• Adapting Content to Individual Preferences: Gen-AI will tailor content to individual preferences more accurately, enhancing user interactions.

Predictive AI:

• Advanced Natural Language Processing (NLPs): It will cater to better comprehension and generation of human-like language.
• Autonomous Systems: We will see significant growth in self-driving automobiles, drones, and robotics.
• More Realistic Expectations: The corporate community will have a more nuanced grasp of AI-powered solutions for a wide range of tasks.

Both Generative & Predictive AI have distinct strengths, and uses, and their ongoing growth will influence the future of AI. As these technologies progress further, businesses must start gearing up and consider implementing these AI tools to thrive.

Bottom Line

Summing up, both Generative & Predictive AI have proven to be powerful in driving efficiency, & accuracy across a wide range of sectors. Their distinct capabilities cater to a wide range of applications, from healthcare to fintech, and their impact is profound. While they might have some limitations now, the benefits vastly exceed them, making them necessary in today’s technology landscape.

However, navigating through this AI landscape can seem a bit complex. So, whenever you encounter any doubts or need guidance, do not hesitate to consult TheCodeWork.

Remember, the future of business is intertwined with AI, & taking the right steps today can lead to significant rewards tomorrow.

Originally published at https://thecodework.com on April 10, 2024.