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What does 2022 have in store for AI in the enterprise? Will it be a robust year of world-altering developments and implementation, or will organizations struggle to gain appreciable value from an exceedingly complex technology?
In all likelihood, it will be a little of both. So as you chart a strategy for the coming year, keep an eye on what is really happening with AI right now and what remains on the drawing board.
AI – from hype to reality
If we look at Gartner’s AI Hype Cycle for 2021, it’s clear that the company has placed the majority of AI developments on the up-slope of the Innovation Trigger curve and at the Peak of Inflated Expectations. This includes everything from AI-driven automation and orchestration platforms to neural networks, deep learning, and machine learning. This isn’t to say that these applications are destined to crash and burn, just that they’re still more hype than reality at the moment – and Gartner expects it will be two to five years before they become productive assets in the enterprise.
Still, it’s hard to imagine that AI will lose steam in the enterprise in 2022, given the robust activity that is currently taking place at most organizations. In large corporate settings, it’s difficult to find anyone who isn’t experimenting with AI in one form or another, and this trend is quickly moving down-market to small and mid-sized outfits. The question is, where will AI show the most success in the coming year, and how are these successes being achieved?
According to tech advisor Bernard Marr, 2022 could see breakthroughs in the way we work and leverage data. First, he says, we could see the rise of the augmented workforce that uses AI to significantly accelerate profits and productivity. While robots already exist in the warehouse and on the factory floor, more organizations are starting to apply AI to white-collar jobs like marketing and engineering, and even legal and finance.
The goal is to speed up the pace of all these activities, in part by focusing the attention of human managers on what needs to be done right away and then ensuring that it is done as quickly, accurately, and effectively as possible. In conjunction with this, Marr expects key AI applications like language modeling and no-code or low-code development to be democratized across the entire knowledge workforce.
Keep it safe
Of course, you didn’t think you could deploy a paradigm shift in data applications and infrastructure without a change in your security posture, did you? Meg King and Melissa K. Griffith, of Washington, D.C., think tank The Wilson Center, recently highlighted the three ways AI and cybersecurity will intersect in the coming year and how organizations, including governments, should position themselves for this new reality.
First, greater attention must be paid to the security of AI platforms and systems themselves. Vulnerabilities on this level can be exploited to wreak havoc on crucial services, including transportation, healthcare, financial services, and law enforcement. The data that AI ingests and analyzes also must be protected, since it can greatly alter the outcomes and decisions that AI will be entrusted to make. At the same time, we can expect AI to play a greater role in both offense and defense in the ongoing cyberwars. Bad actors, particularly state-sponsored ones, are increasingly turning to AI to defeat security measures, so the only means of protection is to deploy AI to protect critical systems and thwart attempted attacks.
With all that’s going on with AI, it’s hard to see how developments in any single year will usher in the pivotal shift to an AI-driven enterprise. Most organizations, in fact, have only just begun the transition to fully digitized operations, meaning that in all likelihood AI won’t be making any appreciable changes to either infrastructure or processes until later in the decade, at best.
But that doesn’t mean there aren’t decisions to be made right now. Rather than wait for the breakthrough moment when AI is finally ready to go mainstream, enterprise executives should view 2022 in terms of positioning the organization for the coming year and beyond, with each move laying the groundwork for what comes next.
Ultimately, the goal should be to craft a data environment that is tailored to build up strengths and diminish the weaknesses that exist in the business model – even if it doesn’t all happen in a year.
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