A 2021 study by SnapLogic found that more than 4 in 5 employees think the presence of artificial intelligence (AI) in the workplace has improved their overall performance. It’s not surprising, then, that 68% of those surveyed said they’d like to see more AI deployed by their companies: concerns about AI ‘replacing’ human jobs are not without substance, but those numbers indicate that (at least for the most part) the inherent benefits of AI are seen to outweigh its employee-ousting potential.
So, with the majority of employees embracing an AI-shaped present and future, how is AI reshaping the way we work and creating more efficient, more economical, more productive work environments?
Think about an average working day. How much of that day is spent performing tasks that actually add value? The answer will depend on your role and level of responsibility, to some extent, but it’s likely you lose precious hours every day carrying out energy-sapping, time-guzzling tasks that are necessary but monotonous. Well, using AI and machine learning technologies, more businesses are able to automate these otherwise inefficient processes, enabling employees to make more productive use of their work hours.
Take Fico, for example, a company that deals in consumer credit cards. One of its key functions is fraud detection, which requires analyzing huge amounts of daily transaction data to pick out anything that looks a little on the suspicious side. That would be a mammoth task for any human, but by deploying artificial neural networks (which essentially ‘mimic’ the functions of a human brain) to recognize the telltale signs of fraud, this can be achieved in a fraction of the time.
By now, most of us will have encountered live chat software (such as the free solution offered by Crisp), which enables businesses to connect instantly and seamlessly with their customers, but chatbots (a type of AI-based software that simulates a human-to-human interaction) take that a step further; by employing natural language processing (NLP) techniques, they can keep the conversation going even in the absence of your human customer support teams.
And while chatbots are facilitating round-the-clock support (and thus improving the customer experience for millions of users), they’re also streamlining internal customer support resources: chatbots can respond to and provide resolutions for the most straightforward queries and issues (those that don’t necessarily require a human intervention), while human teams can concentrate their efforts on more complex customer requirements that need more of a personal touch.
The most successful businesses are often the most decisive — those businesses that can make swift but well-informed decisions often leave their more hesitant competitors in the shade. And whereas a room full of butting heads can breed inconclusivity, a decision-making process that leverages AI in combination with human judgment (which might look something like the below) will invariably lead to quicker, more effective conclusions, increasing business agility and reducing the time spent on lengthy back-and-forth discussions.
Image source: hbr.org
In this model, the AI (the machine) is deployed in the processing and analyzing of ‘big data’ (which is essentially data combined from multiple sources — too much for a human team to pore over manually) and then using it to make informed recommendations and predictions about what to do next. The final judgment calls and business decisions are made by humans, but AI has already done a lot of the heavy lifting — those decisions will be easier (and less time-consuming) to come by, thanks to the invaluable insights provided by AI.
It goes without saying that a happier, more engaged employee will ultimately become a more productive employee. To that end, AI tools are increasingly enabling employers to measure engagement among their workforces, and to forge a better understanding of their individual team members by leveraging real-time, AI-led insights into their moods, their concerns, and their overall levels of happiness in their roles.
Employee engagement tools such as Humu are using AI-powered algorithms to monitor engagement and morale at an individual and team level. When the tool thinks a team member’s engagement might be waning, it can send them personalized ‘nudges’ to help them refocus on their personal goals and develop better, more productive ways of working: AI isn’t just about automation and streamlining; it can also help to foster a more inspiring company culture.
One way AI is inarguably boosting productivity and creating more efficiency is in helping to match candidates to roles during the recruitment process. While recruiters can only sift through so many CVs at a time, AI tools like Frolle’s DeepSense (which specializes in pairing managerial and executive employees with their ideal roles based on their competencies, skill sets and behavioral qualities) reduce the need for manual candidate-to-job matching.
The role of AI is becoming ever more crucial in the hiring process; not only can it improve the efficiency of recruitment teams (enabling them to match candidates to roles quicker and reduce the time wasted on unsuitable applications), but it will empower them to find candidates who are the exact right fit for their new roles, ultimately cutting down employee turnover and creating a more competent, more productive workforce.
To conclude, it’s clear that AI (and its subsets such as NLP and machine learning) are playing a pivotal role in the workplace. AI-powered tools are not only helping businesses to automate repetitive tasks and streamline processes, they’re ultimately serving to boost employee engagement, efficiency and — most importantly — productivity.