A whole host of issues emerged in 2021 related to AI – from leveraging AI and automation to fill in the gaps of the shifting working landscape where mass resignations are hitting every industry and algorithms falling short on promises made by industry leaders. It was a dynamic year for those in the AI industry who are trying to achieve the full potential of what AI is capable of, while avoiding the pitfalls. 

In 2022, keep a lookout for how the AI industry responds to the increased demand for AI, machine learning, and automation and lessons learned from mistakes made. We anticipate more companies will look to implement guard rails to make AI work better, smarter, and faster. 

Outlined below are three key AI trends likely to emerge in 2022.

Companies will lean more on human-powered AI to avoid “garbage in, garbage out” algorithms: As AI continues to evolve at a breakneck pace, companies often overlook the importance of keeping humans actively involved in the AI implementation process, creating a scenario where tech’s obsession with the newest, biggest thing neglects basics that make AI actually useful: plugging in useful data and teaching it how to deal with outliers. For AI to be truly useful and effective, a human has to be present to help push the work to the finish line. 

Without guidance, AI can’t be expected to succeed and achieve optimal productivity. This is a trend that will only continue to increase. Ultimately, people will have machines report to them. In this world, humans will be the managers of staff (both other humans and AIs) that will need to be taught and trained to do the tasks they’re needed to do. 

Just like people, AI needs to constantly be learning to improve performance. A common misconception is that AI can be deployed and left unsupervised to do its work, without considering the reality that our environments are always shifting and evolving. Would a manager do this with a human worker? The answer is no. 

At my company, we’re focusing on this in a very vertical-specific way within health care. When the bots get stuck, humans who are subject matter experts help them overcome the outlier tasks. That’s how the human is programming and training the bot on what to do: The bot learns from the human’s support and eventually learns to do the task automatically. This can be scaled across multiple industries – accounting, insurance, mortgages, etc. 

Machine learning and human-in-the-loop approaches to automation will displace RPA: Digital transformation efforts in a number of industries have driven massive adoption of robotic process automation (RPA) during the past decade. The hard truth is that RPA is a decades-old technology that is brittle and has real limits to its capabilities – leaving a trail of broken bots, which can be expensive and time-consuming to fix. 

RPA will always have some value in automating work that is simple, discrete, and linear. However, it’s a dead-end technology that gets you only so far. Automation efforts often fall short of aspirations because so much of life is complex and constantly evolving – too much work falls outside of the capabilities of RPA. 

Emerging machine-learning-based technology platforms, combined with human-in-the-loop approaches to automation, are already redefining what is possible to automate across a number of industries where complexity, exceptions, and outliers train the AI to work smarter, making automation stronger. 

The AI community will need to go back to basics with data labeling: Solid AI systems rely on two things: a functioning model and underlying data to train that model. To build good AI, programmers need to spend the vast majority of their time collecting, categorizing, and cleaning data. 

For many AI technologists, a gut instinct is to run towards the sexy work of creating complex AI infrastructure and neglect the basics of data labeling. What this year has taught us is that we need to go back to basics in order to make AI work at its true potential. By putting in the work and knocking out the not-so-sexy tasks, individuals can enable set automation that hits the mark more often than not, and avoid making headlines for all the wrong reasons.

The future is rarely certain. But, looking forward, it’s clear that you’ll find few companies not utilizing AI in the next year or two, and fewer using it successfully without incorporating the human element.