Published on October 23, 2023, 7:38 pm

TL;DR: Predictive AI has been used in IT departments for a while, with applications such as computer vision and speech recognition. These tools leverage historical data to make predictions and perform specific tasks. On the other hand, Generative AI (GenAI) tools like large language models (LLMs) focus on understanding natural language prompts and generating contextually relevant information from unstructured data. While predictive AI is rigid and relies on rules-based programming or supervised learning, GenAI systems use techniques like reinforcement learning. There are concerns about security risks and data governance with adopting AI, but incorporating both predictive AI and GenAI into an organization's strategy can lead to competitive advantages.

As organizations increasingly turn to Generative AI (GenAI) tools to enhance processes and boost productivity, it’s important to note that predictive AI has long been utilized by IT departments to drive business value. While GenAI has garnered significant attention in recent times, IT leaders understand that AI encompasses various domains and involves the automation of tasks.

Before the emergence of GenAI tools, there were already other AI applications at play. Some popular examples include computer vision and speech recognition technologies, which enable organizations to create augmented reality software and virtual assistants. These technologies are complex and often require specialized skills for development and deployment.

The category known as predictive AI consists of enterprise applications that leverage intelligence to make informed predictions based on historical data. These tools are designed to perform specific tasks within targeted domains. They can identify patterns in data to help prevent supply chain shortages or forecast sales based on historical performance and market trends. Additionally, some AI tools focus on data protection by detecting anomalies in vast amounts of information.

While these predictive AI tools remain critical for businesses, they have a more rigid and robotic nature compared to GenAI technologies. GenAI’s strength lies in understanding natural language prompts and generating contextually relevant information from unstructured data. Large language models (LLMs) empower non-technical employees to create marketing collateral or develop Requests for Proposals (RFPs) for sales purposes. This democratization of AI allows anyone with basic linguistic abilities to explore the possibilities it offers.

According to John Roese, Global CTO of Dell Technologies, reaching this “a-ha” moment was transformative because it meant expanding the accessibility of AI from a limited pool of experts to the larger population capable of communicating in human language. This breakthrough enabled disruptive innovation by bridging the gap between domain experts and AI practitioners.

The potential impact of GenAI is evident from a recent Dell survey where 76% of IT decision makers estimated that it would have a significant or transformative effect on their organizations. However, it is important to understand the fundamental differences between predictive AI and GenAI.

Predictive AI tools rely on rules-based programming or supervised learning, where algorithms are manually programmed or given labeled training data in a highly structured approach. These tools learn to identify patterns and relationships in the data and use them to make predictions or decisions. However, they may struggle with tasks outside their programmed capabilities.

On the other hand, GenAI systems often utilize techniques like reinforcement learning, combining rewards and comparisons with human guidance. This approach enables greater adaptability and creative potential compared to traditional AI applications. However, since many GenAI tools draw from internet data sources, there is a risk of producing inaccurate, biased, or harmful content.

Concerns about security risks, technical complexity, and data governance have led 37% of IT decision makers to express hesitancy when adopting AI, according to Dell’s survey findings. It is important to carefully consider which type of AI tool best suits your specific goals and requirements. Predictive AI is well-suited for forecasting sales or supply chain performance, while GenAI excels at tasks such as crafting sales pitches or creating daily worklists.

Even before the availability of GenAI tools, the worldwide market for traditional AI software had already surpassed $340 billion by 2021 according to IDC estimates. Estimating the financial impact of GenAI is more challenging due to evolving tool capabilities; however, McKinsey predicts that it could generate global profits ranging from $2.6 trillion to $4.4 trillion annually.

Forward-thinking enterprises will develop a holistic AI strategy that leverages all available tools within their organization. By doing so, they position themselves competitively against rivals in the market. Failure to incorporate AI into business operations in a safe and predictable manner can leave companies lagging behind their competitors.

So how does your AI strategy shape up? Explore how Dell Generative AI Solutions can help you leverage AI to unlock value from your data and drive meaningful business outcomes.


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