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’d be forgiven for thinking that GenAI was the be all, end all of AI.
Except IT leaders know better than most people that before GenAI tools there were — wait for it — other AI apps.
“AI isn’t just GenAI,” John Roese, Global CTO of Dell Technologies, said on a recent Tomorrow’s Tech Today webcast. “It’s a whole bunch of domains and it’s about moving work into machines.”
You know about these more — let’s call them traditional AI tools — because you’ve likely implemented one or more of them. Perhaps as long ago as a decade or more, after the AI winters thawed.
But just what are some of these other AI tools?
AI Vs. GenAI at a high level
Computer 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.
Many 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.
For 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.
Such 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.
While such tools remain critical for corporations, they’re 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.
GenAI large language models, or LLMs, allow workers without technical skills to create anything from marketing collateral to generating RFPs for sales. It’s AI democratized for the masses.
“The ‘a-ha’ 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,” said Dell’s Roese. “That is the breakthrough…anybody who could interact with this could start thinking about the art of the possible.”
Essentially, GenAI has made disruptive innovation possible by eliminating the barriers separating AI experts from business practitioners with domain expertise, Roese said.
That flashpoint is a big reason why 76% of IT decision makers estimated that GenAI will have a significant if not “transformative,” impact on their organizations, according to a recent Dell survey.1
Under the hood differences
There are fundamental differences between how the various AI categories function. Let’s focus on how to distinguish predictive AI from GenAI.
Most 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.
Conversely, 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.
However, most GenAI tools draw from corpuses of internet data, which means they can produce inaccurate, biased or even harmful content.
Indeed, 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’s survey.
Market Values and Competitive Edge
As with most technologies, which type of AI you use depends on what you’re trying to accomplish.
You’d trust an app structured to forecast sales or supply chain performance over an LLM. But if you’re 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.
Even 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—tool capabilities are still evolving—but McKinsey expects the market will create $2.6 trillion to $4.4 trillion in global profits annually.
Progressive enterprises will create a holistic AI strategy that leverages every tool at the organization’s disposal. Whoever cultivates a comprehensive strategy will position their company well to compete versus rivals. Whoever doesn’t face existential threats.
“If you as an enterprise are not moving forward and redividing your work and incorporating AI in your business in a safe, predictable way, you’re going to be behind,” Dell’s Roese said.
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1Generative AI Pulse Survey, Dell Technologies, Sept. 2023
