By 2030, artificial intelligence (AI) is expected to eliminate 80 percent of project management tasks that are currently handled by humans. Even sooner, in 2022, spending on cognitive and AI systems is projected to be more than $77 billion, which is triple the $24 billion forecast for 2018.
AI is clearly coming of age, but it’s nowhere close to mature. Of the organizations that have answered the “if” question, many still haven’t adequately addressed the “when” and “how”—perhaps the two most important factors. Consider the following:
- Gartner’s 2019 CIO Survey reports that 37 percent of organizations have implemented AI in some form. Although that’s an increase of 270 percent from four years ago, it’s still less than half of businesses.
- According to Deloitte’s State of AI in the Enterprise, 2nd Edition, 31 percent of respondents name AI skills as a top three deployment concern, and 69 percent believe there is an AI skills gap—7 percent say it’s “extreme,” 23 percent call it “major,” and 39 percent say it’s “moderate.”
AI will not magically transform an organization. To benefit from the technology, companies need to create a solid implementation plan that focuses on people—attracting employees with advanced AI skills, reskilling existing employees, and ensuring the organization’s workforce possesses the wide range of skills that are needed to implement and manage AI projects.
In Deloitte’s survey, respondents rated each of the following a top-two needed skill to fill their company’s AI skills gap:
|AI software developers||28%|
|User experience designers||23%|
|Change management/transformation experts||22%|
|Business leaders to interpret AI results||21%|
AI/cognitive technologies hold much promise—some of which is already being fulfilled today, according to the Deloitte report. To achieve even greater success with AI, organizations must ensure their enthusiasm for the technology doesn’t overshadow their ability to implement it.
A report by MMC Ventures, a UK venture capital firm, illustrates the AI skills challenge concisely: You cannot throw AI at your data and expect to generate value.
Indeed, you can’t. You need to add people to the mix, and ensure they have the training, support and resources they need to innovate.
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