Generative AI or Predictive AI: Where Should Our Organization Begin Our AI Journey?
In the hit drama Person of Interest, an AI with access to government data feeds predicts terrorist activities and crimes before they happen, ultimately helping people prevent them. This is the more dramatic version of “predictive” functionality that organizations and leaders have chased in AIs for the better part of the early twenty-first century. Businesses wanted the ability to predict market trends, the evolving business landscape, competition, and so on to respond to them better. Predictive artificial intelligence (AI) systems were designed for this specific purpose. They make up most of the AI solutions across organizations worldwide. They are used for purposes as varied as early diagnosis in healthcare, loan approval by banks, and operations optimization in manufacturing. But that’s not everything that AI can do.
In 2019, Netflix dropped Zima Blue, an episode from the show Love, Death & Robots, which tells the story of an AI — a mandroid — that evolved from a pool-cleaning robot with numerous augmentations into a highly accomplished artist with galaxy-wide renown. At the time, many viewers wondered whether something like that could ever happen in reality. Ironically, this time, the real world didn’t take long to catch up with fiction. By 2024, Midjourney, Stable Diffusion, DALL-E, and numerous other AI artists were already “thriving” among us. And it’s not just image-generating AIs either. AI solutions that generate videos (Synthesia, Elai), designs (Khroma, Colormind), audio (Replica, Speechify), music (AIVA, Evoke), text (Jasper, Rytr), code (Devin), and other types of content are already a reality. However, this new AI class wasn’t met with a warm welcome, unlike the predictive AIs, for the risk they pose to jobs in their respective domains.
While the future will eventually bring more powerful AIs with complex capabilities and perhaps even artificial general intelligence, businesses today are faced with a choice between predictive AI and generative AI. How are the two different? Is one better than the other? Should we wait for newer solutions with superior capabilities to arrive? Businesses face these questions, and this post will help answer some of them.
Before we delve deeper, here’s a quick intro to the two popular paradigms of current artificial intelligence development.
Introduction to Predictive AI and Generative AI
Predictive AI solutions analyze vast quantities of historical data using statistical algorithms and machine learning (ML) techniques to identify evolving patterns against time. They then use these patterns to make reasonable predictions, projections, and forecasts about the future, including customer behavior, competition, market landscape, supply chains, etc. Predictive AI helps businesses make superior decisions by predicting the future and preparing for emerging challenges. Moreover, predictive AI has brought about a seismic shift in industries where somebody can gain deep insights from corporate data, such as fraud detection in finance, cybersecurity, demand planning in retail, risk assessment in insurance, etc.
Wherever better decisions can be made with insights gleaned from large amounts of historical data, predictive AI is almost indispensable today.
On the other hand, generative AI is a new technology that “creates” content that has never existed before. Many current generative AI solutions are trained on various data sets/inputs (images, video, audio, etc.). Generative AIs are trained on data sets much larger than predictive AI to help them understand the underlying patterns and the nature of relationships within the data. So, their output is not a low-quality replica but a high-quality original creation. For instance, an AI writer trained on many novels can “write” an entirely fresh novel that never existed before in a matter of seconds. Customer-facing generative AIs can even hold conversations with customers that feel rich, organic, and indistinguishable from human conversations.
Generative AI vs Predictive AI: Is Generative AI Better Than Predictive AI or Vice Versa?
While it’s true that generative AI uses algorithms and machine learning principles pioneered over the past decade and predictive AI’s statistical models were primarily developed several decades earlier, it’s not accurate to describe one as better than the other. The fact is that they are suited for two very different purposes, and they are becoming increasingly sophisticated, accurate, and efficient at their respective tasks. Also, they come with their own limitations. So, businesses should know how they differ and their inherent strengths and weaknesses to extract maximum value from their investments and innovation direction.
Generative AI vs Predictive AI: Differences
Although both technologies are definitively artificial intelligence, they differ in almost every way — how they work, what they do, what they can’t do, and what value they offer their users. Here are some of the crucial differences between them:
- Technology
Generative AI uses deep learning and advanced algorithms, such as GAN, VAE, LLM, etc., to create new content based on data it has already consumed.
Predictive AI uses algorithms and machine learning to provide the most likely outcome from the available choices.
- Training Data
Generative AI is trained on unstructured data. The more diverse the data, the better-quality content the generative AI creates.
Predictive AI, on the other hand, generally uses large amounts of structured historical data to learn and make predictions. The data it is fed often needs to be cleaned to remove missing values, outliers, etc.
- Output
Generative AI understands the data it is fed, learns from it, and creates completely original content that never existed before.
Predictive AI cannot create original content. It predicts likely outcomes based on the patterns that exist in the data it is working with.
- Real-world Use Cases
Generative AI can create content, such as music, videos, computer code, entire website designs, presentations, marketing materials, etc.
Predictive AI is best suited for use cases requiring a vital element of predictability, such as financial fraud detection, preventive factory maintenance, demand prediction in retail, inventory management, logistics optimization, medical diagnosis, etc.
Generative AI vs Predictive AI: Strengths
Let’s take a quick look at the benefits each type of AI technology offers:
Benefits of Generative AI
- Automates content creation. All it needs is simple prompts
- Infuses innovation into content creation by offering fresh and creative suggestions
- Summarizes complex data sets, including large documents
- Manages complex query answering, such as in the case of customer support responses
- Handles unstructured data and fills in the gaps of missing data with ease
- It works seamlessly with a diverse variety of data, such as text, audio, video, UI/UX, and other data formats
Benefits of Predictive AI
- Simplifies and even automates complex decision-making, including analyses that require a significant workforce and time
- Helps make better decisions by giving forecasts and predictions based on high-quality data
- Identifies potential risks long before they manifest, helping with early diagnosis or preventive maintenance
- Optimizes and streamlines manufacturing operations, logistics, and entire supply chains
- Offers deep insights into the evolving business landscape, such as consumer trends
- Simplifies complex analysis, typically requiring a vast workforce and work hours
Generative AI vs Predictive AI: Weaknesses
Despite their vast potential, both generative AI and predictive AI have limitations, making them unsuitable for some applications. So, businesses should be aware of the following:
Limitations of Generative AI
- Generative AIs often hallucinate and present incorrect information as facts.
- It is expensive to retrain the models on new or fresh data.
- It requires massive computational power, leaving a substantial carbon footprint.
- Largely opaque in their reasoning and “thinking” process
- Removing sensitive data from their training models is difficult.
- Sourcing and copyright issues plague the data used to train the models.
- Sometimes, the results or output from these AI solutions can take time to interpret.
- Due to the colossal amounts of data and computational power required, only some organizations can muster the resources needed to experiment with them.
Limitations of Predictive AI
- Continuous updates are required to make its predictions relevant and accurate.
- Biases — systemic or otherwise — in the data get amplified.
- It works like a black box, with little transparency into its decision-making process, especially causation. A layer of explainability needs to be added.
- Ethical and privacy concerns surround the personal data fed into training the AI models.
- Lacks innovation and creative skills, giving rise to risks of overfitting the model to historical data. Makes the AI ineffective against emerging challenges, especially the unforeseen kind
Generative AI vs Predictive AI: What Works for You?
Now that we’ve understood what both types of AI technologies offer their users, perhaps you’ve already identified some areas within your business or among your clients where you can deploy them. Two general use cases are highlighted below:
- Generative AI Content for Personalized Marketing
If you’re blasting an email campaign to your audiences comprising thousands of consumers, the degree of personalization you can infuse into the emails using traditional technologies is limited. You can customize each email to the recipient’s name, suggest products or services based on purchase history, and include a discount.
With generative AI, you can send every member a personalized marketing message attuned to their needs. Furthermore, the AI can analyze the recipients’ responses to them to understand what works and what doesn’t and fine-tune all future marketing communications into their most effective versions.
- Personalized Curriculum by Generative AI
Every student learns at their own pace. A standard curriculum designed for the average student could be too slow for a few students while completely confounding others. A personalized curriculum created based on past performance, skillset, student and teacher feedback, etc., can help students learn at a pace they are most comfortable with. This can be greatly helpful to neurodiverse and disabled students who require a change of pace. Generative AI can also create realistic and immersive simulations that help learners better understand their concepts.
- Superior Healthcare Services by Predictive AI
Predictive AI is ushering in a paradigm shift in patient care. By using a patient’s medical history, lifestyle data, and self-declared information, healthcare AIs trained on vast amounts of medical research data can offer benefits like early diagnosis of life-threatening conditions, enable precision medicine, provide preventive care, and even identify risks of readmission. In conjunction with medical personnel, these benefits can drastically elevate the quality of healthcare accessible to entire populations of countries worldwide.
- Real-time Fraud Detection by Predictive AI
Curbing financial fraud and money laundering activities requires timely action. However, although quite powerful, traditional technologies need to be improved in their capabilities to analyze millions of transactions happening every second at major banks and flag them to relevant officials. However, predictive AI does just that. First, predictive AI must be trained on “normal” transactions and then used to flag transactions that deviate from that “normal.”.
Predictive AI helps fight financial fraud by understanding users’ typical spending behaviors and raising the alarm when an unusual transaction occurs.
Generative AI vs Predictive AI: What to Use?
Even as both kinds of AIs proliferate and acquire unprecedented capabilities, the world is waking up to the risks of AI and responding to them with consumer-centric AI regulation. So, whether businesses choose predictive AI or generative AI, they must do it with a forward-looking mindset and in compliance with these laws, even as they are getting passed.
For now, one thing is clear: your business stands to benefit massively from AI. It doesn’t matter what industry, geography, or size your business is. However, it would be best if you diligently explored which areas of your business would benefit from AI.
The truth is that eventually, AI will be able to improve every aspect of your business. Complex AI with both generative and predictive capabilities will emerge over time. For now, begin by identifying those areas that stand to benefit the most from AIs and start your journey there. Some will require predictive AIs, and others will require generative AIs. Use whatever is needed.
— Christina shares candid insights and ideas based on her work, network, and passion for mobile, payments, and commerce. As a frequent speaker by invitation to international events, from entrepreneurial and educational to executive audiences and settings, she has been recognized as a ‘Top B2B Influencer’, ‘Who’s Who in Fintech’, and ’40 Under 40 in Silicon Valley’. She focuses on the latest product innovations and growth for people during the day while teaching students and mentoring entrepreneurs at night. Connect with her on LinkedIn or Twitter/X. All views are my own. —