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It’s hard to believe that the now ubiquitous terms of AI and ML date back to the 1950s.
Since then, public interest in AI and ML has waxed and waned. But the release of OpenAI’s ChatGPT in late 2022, and its competitors that followed, brought generative AI into the mainstream.
Now it’s more powerful and easier to use than ever before, with use cases that span across both consumer and enterprise. Generative AI can plan your next vacation, write poetry in the style of William Shatner or polish the speech you need to write for your best friend’s wedding. It can also write policies for a cyber insurance application, generate code or explain the meaning of a high error in your API service.
Over the next decade, relying on AI in business will be as quotidian as flipping on a lightswitch. Gartner®’s Hype Cycle™ for Emerging Technologies 20231 claims that generative AI will reach the peak of inflated expectations in 2023. However, AI’s long-term potential is underestimated, says Hao Yang, Splunk’s VP of AI. Generative AI alone represents an annual $2.6 to $4.4 trillion in opportunity across 63 use cases, according to a McKinsey study. Spending will only continue to grow: IDC predicts that worldwide spending on AI will surpass $300 billion in 2026.
Now the ROI of those investments — a historically painful metric to quantify for AI projects — is starting to crystallize as organizations expand to more sophisticated use cases. In a PwC survey, 72% of AImature organizations surveyed (and 59% of all other respondents) are confident in their abilities to assess the ROI of their current AI initiatives, with the ability to capture both hard and soft returns and costs.
Despite the promise of AI, an overall mistrust lingers. Fifty-two percent of organizations say that risk factors are a critical consideration when evaluating new AI use cases, according to Gartner. The old adage “you can’t protect what you don’t know” rings true for AI and ML. Generative AI in particular raises deep-seated data privacy and security concerns. These AI anxieties are only natural as organizations navigate this new era of technology. The Biden Administration’s executive order on AI provides a few answers, but substantive regulatory changes are likely still a ways out.
In the meantime, the AI train has left the station, with positive outcomes that are too difficult to ignore. Usage is already widespread, with 55% of respondents in the McKinsey study reporting that their organizations have adopted AI. And organizations are realizing the value of this adoption through improvements in productivity, decision-making, customer experience, innovation and beyond.
To be sure, some of AI’s most far-reaching concepts (computers that can replicate the human brain entirely, fully autonomous robots and programs that design, code and upgrade themselves) are years away from reality; they’re still moonshots that represent the eventual apex of AI’s capabilities. But considering that AI tools can already win at Jeopardy!, are able to detect breast cancer and are logging tens of thousands of miles behind the wheel of self-driving vehicles every day, the prospect of even those moonshot concepts really doesn’t seem so far-fetched.
In other words, now is the time to learn about AI and ML. Before you can develop a thoughtful strategy that considers the risks and benefits, it’s important to clarify these common misconceptions.
It’s hard to believe that the now ubiquitous terms of AI and ML date back to the 1950s.
Since then, public interest in AI and ML has waxed and waned. But the release of OpenAI’s ChatGPT in late 2022, and its competitors that followed, brought generative AI into the mainstream.
Now it’s more powerful and easier to use than ever before, with use cases that span across both consumer and enterprise. Generative AI can plan your next vacation, write poetry in the style of William Shatner or polish the speech you need to write for your best friend’s wedding. It can also write policies for a cyber insurance application, generate code or explain the meaning of a high error in your API service.
Over the next decade, relying on AI in business will be as quotidian as flipping on a lightswitch. Gartner®’s Hype Cycle™ for Emerging Technologies 20231 claims that generative AI will reach the peak of inflated expectations in 2023. However, AI’s long-term potential is underestimated, says Hao Yang, Splunk’s VP of AI. Generative AI alone represents an annual $2.6 to $4.4 trillion in opportunity across 63 use cases, according to a McKinsey study. Spending will only continue to grow: IDC predicts that worldwide spending on AI will surpass $300 billion in 2026.
Now the ROI of those investments — a historically painful metric to quantify for AI projects — is starting to crystallize as organizations expand to more sophisticated use cases. In a PwC survey, 72% of AImature organizations surveyed (and 59% of all other respondents) are confident in their abilities to assess the ROI of their current AI initiatives, with the ability to capture both hard and soft returns and costs.
Despite the promise of AI, an overall mistrust lingers. Fifty-two percent of organizations say that risk factors are a critical consideration when evaluating new AI use cases, according to Gartner. The old adage “you can’t protect what you don’t know” rings true for AI and ML. Generative AI in particular raises deep-seated data privacy and security concerns. These AI anxieties are only natural as organizations navigate this new era of technology. The Biden Administration’s executive order on AI provides a few answers, but substantive regulatory changes are likely still a ways out.
In the meantime, the AI train has left the station, with positive outcomes that are too difficult to ignore. Usage is already widespread, with 55% of respondents in the McKinsey study reporting that their organizations have adopted AI. And organizations are realizing the value of this adoption through improvements in productivity, decision-making, customer experience, innovation and beyond.
To be sure, some of AI’s most far-reaching concepts (computers that can replicate the human brain entirely, fully autonomous robots and programs that design, code and upgrade themselves) are years away from reality; they’re still moonshots that represent the eventual apex of AI’s capabilities. But considering that AI tools can already win at Jeopardy!, are able to detect breast cancer and are logging tens of thousands of miles behind the wheel of self-driving vehicles every day, the prospect of even those moonshot concepts really doesn’t seem so far-fetched.
In other words, now is the time to learn about AI and ML. Before you can develop a thoughtful strategy that considers the risks and benefits, it’s important to clarify these common misconceptions.
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