The case for predictive AI
AI is taking the world by storm. All forward-thinking businesses are toying with or have already invested in AI — from boutique startups to enterprise conglomerates. According to Accenture, nearly 75% of companies have already integrated AI into their business strategies, and 42% said that the return on their AI initiatives exceeded their expectations (only 1% said the return didn’t meet expectations).
The past six months have seen a particular form of AI gain popularity among business leaders: Generative AI (GenAI). According to Forrester, GenAI will have an average annual growth rate of 36% up to 2030, capturing 55% of the AI software market. However, despite this projected growth, Forrester also expects short-term adoption and productivity gains to be restricted by GenAI’s current limitations.
However, there is one form of AI that will allow businesses to see almost an immediate value: Predictive AI. Predictive AI uses advanced algorithms based on historical data patterns and existing information to forecast outcomes to predict customer preferences and market trends — providing valuable insights for decision-making. While predictive AI certainly isn’t a new concept, it’s been seen as the little brother to GenAI.
But it shouldn’t. Predictive AI can help break down the generational gaps in IT departments and address the most significant challenge for mainframe customers and users: operating hardware, software, and applications all on the mainframe.
What’s the difference?
Yes, GenAI and Predictive AI are both forms of artificial intelligence, but they have fundamental key differences that businesses must consider. It’s easy to think about these pieces of technology in two separate categories: one creates something new, the other forecasts future outcomes.
GenAI focuses on the creation of new content, generating outputs that are original and novel. It leverages techniques to learn patterns and distributions from existing data and generate new samples. GenAI models can generate realistic images, compose music, write text, and even design virtual worlds. The critical characteristic of GenAI is its ability to explicitly create something that does not exist in the training data. It captures the underlying complexity and diversity of the input and produces unique outputs that exhibit creativity and originality.
Meanwhile, Predictive AI specializes in analyzing patterns within existing data to make accurate predictions and forecasts about future outcomes. Predictive AI utilizes machine learning algorithms to learn from historical data and identify patterns and relationships. Predictive AI models can be trained to predict stock market trends, customer behavior, disease progression, and much, much more. The primary objective of Predictive AI is to extract valuable insights and make informed predictions based on available data. It aids decision-making processes, allowing businesses to optimize operations, identify potential risks, and develop data-driven strategies. Predictive AI will not only help make mainframe applications better, but it can also help predict what could potentially go wrong in an application and what to do to prevent that from happening.
Predictive AI in a hybrid cloud environment
The mainframe is a critical system of record for organizations, and its data is an invaluable source of insight for businesses. In order to operate most successfully, the mainframe must be modernized to integrate with the public and private cloud. Integrating with the cloud offers advanced analytics capabilities that can help organizations extract the most valuable insights from their data with AI in a secure environment.
Three main foundational components of technology sit on the mainframe: hardware, software, and applications. Frequently, it’s a challenge for organizations to operate all three simultaneously and securely. However, Predictive AI can help solve this operational challenge because it relies heavily on historical data, enabling users to operate the mainframe and manage enterprise applications more efficiently. Although GenAI can still be very useful to business leaders, in mission-critical environments, such as the mainframe, the technology is not yet trusted enough to be adopted.
GenAI and Predictive AI represent two distinct approaches within the broader field of artificial intelligence. Although each approach has its unique applications and use cases that empower different industries and domains, Predictive AI is best suited for work within the mainframe.
By understanding the distinctions between GenAI and Predictive AI, organizations can make better, more informed decisions for the security and optimization of the main frame. As AI continues to evolve, Predictive AI holds the potential to unlock new opportunities and shape the future of mainframe systems.
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