Generative AI has been a boon for businesses, helping employees discover new ways to generate content for a range of uses. The buzz has been loud enough that you\u2019d be forgiven for thinking that GenAI was the be all, end all of AI.\n\nExcept IT leaders know better than most people that before GenAI tools there were \u2014 wait for it \u2014 other AI apps.\n\n\u201cAI isn\u2019t just GenAI,\u201d John Roese, Global CTO of Dell Technologies, said on a recent Tomorrow\u2019s Tech Today webcast. \u201cIt\u2019s a whole bunch of domains and it\u2019s about moving work into machines.\u201d\n\nYou know about these more \u2014 let\u2019s call them traditional AI tools \u2014 because you\u2019ve likely implemented one or more of them. Perhaps as long ago as a decade or more, after the AI winters thawed.\n\nBut just what are some of these other AI tools?\n\nAI Vs. GenAI at a high level\n\nComputer vision and speech recognition technologies are among some of the most popular, helping organizations build anything from augmented reality software to virtual assistants. These technologies are complex, often requiring specialized talent to build and deploy them.\n\nMany enterprise apps that leverage intelligence exist in a category known as predictive AI, which make educated predictions based on historical data. Many of these tools are designed to execute specific tasks in targeted domains.\n\nFor example, apps might look for patterns in data to help avert supply chain shortages, or project expected sales relative to historical performance and current market trends. Some AI tools are designed for data protection and sniff out anomalies from vast amounts of information.\n\nSuch tools leverage highly structured approaches as they execute the equivalent of finding data needles in vast information haystacks. As a result, employees must fashion compelling stories around the data to make it actionable.\n\nWhile such tools remain critical for corporations, they\u2019re also relatively flat and robotic compared to GenAI technologies, whose sweet spot is understanding natural language prompts to generate contextually relevant information from unstructured data. \n\nGenAI large language models, or LLMs, allow workers without technical skills to create anything from marketing collateral to generating RFPs for sales. It\u2019s AI democratized for the masses.\n\n\u201cThe \u2018a-ha\u2019 moment for me was when we suddenly changed the world of AI from an ecosystem of having 100,000 experts that can use it to having all of humanity that can speak human language interact with it,\u201d said Dell\u2019s Roese. \u201cThat is the breakthrough\u2026anybody who could interact with this could start thinking about the art of the possible.\u201d\n\nEssentially, GenAI has made disruptive innovation possible by eliminating the barriers separating AI experts from business practitioners with domain expertise, Roese said.\n\nThat flashpoint is a big reason why 76% of IT decision makers estimated that GenAI will have a significant if not \u201ctransformative,\u201d impact on their organizations, according to a recent Dell survey.1\n\nUnder the hood differences\n\nThere are fundamental differences between how the various AI categories function. Let\u2019s focus on how to distinguish predictive AI from GenAI.\n\nMost predictive AI tools lean on rules-based programming or supervised learning, in which humans manually program algorithms or provide labeled training data, in a highly structured approach. They learn to identify patterns and relationships in the data and then use those patterns to make predictions or decisions. These AI tools may struggle with tasks which they were not programmed to accomplish.\n\nConversely, most GenAI systems learn from techniques such as reinforcement learning with human feedback, which combines rewards and comparisons with human guidance. Compared to the predictive tools, GenAI tools feature greater adaptability and creative potential compared to traditional AI applications.\n\nHowever, most GenAI tools draw from corpuses of internet data, which means they can produce inaccurate, biased or even harmful content.\n\nIndeed, 37% of ITDMs report some hesitancy when it comes to adopting AI, citing concerns about security risks, technical complexity and data governance, according to Dell\u2019s survey.\n\nMarket Values and Competitive Edge\n\nAs with most technologies, which type of AI you use depends on what you\u2019re trying to accomplish.\n\nYou\u2019d trust an app structured to forecast sales or supply chain performance over an LLM. But if you\u2019re crafting a sales pitch or a to-do list for your workday, GenAI is your go-to tool. There is a lot of value in both approaches.\n\nEven before GenAI tools became available, the market for vanilla AI software topped $340 billion worldwide through 2021, according to IDC. Estimating the financial impact of GenAI is tricky\u2014tool capabilities are still evolving\u2014but McKinsey expects the market will create $2.6 trillion to $4.4 trillion in global profits annually.\n\nProgressive enterprises will create a holistic AI strategy that leverages every tool at the organization\u2019s disposal. Whoever cultivates a comprehensive strategy will position their company well to compete versus rivals. Whoever doesn\u2019t face existential threats.\n\n\u201cIf you as an enterprise are not moving forward and redividing your work and incorporating AI in your business in a safe, predictable way, you\u2019re going to be behind,\u201d Dell\u2019s Roese said.\n\nHow is your AI strategy shaping up? \n\nLearn how Dell Generative AI Solutions help you bring AI to your data.