Every technology introduces paradoxes. But AI seems to have more than its share — it giveth, it taketh away — all at once. Of course, this makes it harder and more confusing for business and IT leaders to make decisions on AI in their organizations, especially since it involves considerable budgets, convincing everyone, and shifting of resources.
Here are some prominent AI paradoxes:
1. AI reduces labor requirements. AI increases skill requirements. Putting together AI-driven capabilities requires skills to do so — and this is one of its greatest challenges. For example, AI replaces tasks formerly performed by humans, with 62% of respondents to a Rackspace Technology survey saying that it has led to reduced headcounts within their organization. At the same time, the issue or obstacle most often faced is a shortage of skilled talent to make AI happen, cited by 67%.
2. AI is complicated to develop and deploy. AI makes it easier to develop and deploy applications. The people seeing the most benefits from AI technologies to date are technologists themselves — automating their operations and quality assurance, enabling faster application development, greater network optimization, and eliminating manual task work. artificial intelligence, a survey from IBM’s Watson Group finds.
3. AI is expensive to implement. AI helps manage and reduce IT costs. For example, the rising practice of FinOps — which encourages intelligently controlling technology spending — may benefit from AI and machine learning, an analysis out of the FinOps Foundation predicts. At the same time, FinOps and other cost mitigation efforts may be needed to manage and build AI capabilities.
4. AI automates and mechanizes work. AI demands greater creativity in work. Authors of the future jobs report out of the World Economic Forum estimate that 44% of workers’ skills will be disrupted in the next five years, and cognitive skills are reported to be growing in importance most quickly, “reflecting the increasing importance of complex problem-solving in the workplace.”
5. AI won’t help companies that really, really need it the most. There’s a tendency to assume by taking the latest and greatest technology, dropping tons of money on solutions and associated consulting, and, presto! Miraculous growth and happy customers overnight. The slow and inefficient organizations that would benefit the most from AI are less like to embrace it in a productive way. The organizations with forward-looking cultures that would succeed without AI are its biggest proponents.
6. AI requires huge data sets. AI can alleviate data management requirements. AI requires the highest-quality data. AI can assure greater data quality. While AI is a data resource hog, it can be instrumental in identifying and preparing the data needed for analytics-driven systems.
7. AI brings incredible intelligence, but is really dumb. AI may be able to crack quantum physics, but cannot be taught the simplest of tasks. This is Moravec’s paradox, coined by Hans Peter Moravec of Carnegie-Mellon University, who observed that “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.”
AI is extremely promising technology for many business problems and opportunities. But the trade-offs are interesting — and will certainly perplex us for some time to come.
