7 Ways Machine Learning Can Improve Employee Productivity at Work

ways machine learning improve productivity

If it feels like your employees are unproductive at work, you’re not alone. Gallup surveyed more than 195,000 employees across the U.S. and summarized their findings is the State of the American Workplace report. According to this survey, engaged workers make only about one third of the U.S. employees. If only 33% of workers are engaged at work, then what prevents other 70% from doing their jobs efficiently?

A multitude of reasons. Focused and productive workforce is so rare, because exceptional leadership is rare. Because ineffective management and the lack of communication makes workers dissatisfied with their jobs. Because plenty of emails, meetings, and social media distracts people from work. And because too many tasks that an average employee does is drudgery. Nobody feels inspired to do mindless routine work that is both boring and hard.

Artificial intelligence can’t fix leadership problems, nor can it stop people from checking out their social feeds at work. But it can definitely help solve one of the most critical problems that deprives employers of productive workforce – it can automate routine work.

AI eliminates routine work with automation

Accenture published a study that claims artificial intelligence can increase productivity by 40% and even more.

According to a report by Ricoh Europe, 65% of workers who use AI-powered solutions say it makes them more productive.

Artificial intelligence is indispensable when it comes to collecting data, analyzing them and helping employees make faster, and more weighted decisions.

Now look around – what are those lower function tasks that your employees have to do every day? Can this routine work be done by robots? If it’s hard to immediately come up with concrete use cases, this is what we’re going to do right now.

What repetitive tasks can be automated with AI in the workplace?

Let’s looks at some concrete examples of tasks that can be automated with artificial intelligence.

1. Resume screening

A task like sifting through 250 resumes for an average job opening takes a recruitment manager about eight hours of work. An AI-powered tool can do the same job in a fraction of a second helping a recruiter significantly shorten the time-to-hire.

Resume screening isn’t the only process that artificial intelligence can automate in the human resources department.

2. Employee retention

Systems that use machine learning can also predict which employees are likely to quit their jobs soon. Here is how this works: data scientists at IBM created a fictional HR Employee Attrition and Performance dataset. It consists of historical data for each employee and factors that have the highest correlation with employee turnover. For example, distance from home, number of companies an employee has worked at, their age and marital status are important values in predicting turnover risk. A machine learning system can analyse the data on employees who left the position and had to be replaced, and identify employee candidates with a high risk of turnover.

With such insights at hand HRs can devise a plan for keeping valuable employees in the organization and improving work environment and job satisfaction.

3. Understanding customers and prospects

Understanding a customer is the most important and the most challenging task in marketing. Marketers spend hours analysing data on existing clients, looking at web analytics dashboards, browsing social media profiles, and sifting through tons of other data to come up with important insights about customers and prospects. This doesn’t only take a huge amount of time. If you manually process that much data, you’re bound to lose important details. An AI system can handle this task much better than humans can.

For example, an AI-powered application can analyze visual content from social media platforms to learn more about customer preferences and interests, their lifestyles, brands they like, and people they follow.

4. Lead generation

To close more deals, sales people are always on the lookout for new contacts. Sourcing new clients is a lot like sourcing candidates for a job. Both are very time-consuming. And not all connections result in actual sales. AI systems can help sales analysis representatives identify ideal buyers and find people and companies similar to these buyers thus increasing the likelihood of a sale being made.

Node is an AI software that helps sales and marketing teams discover new potential customers that match their ideal buyer, and recommends when to contact them and what to say to close more deals.

5. Customer service

Customers make 265 billion customer support requests every year. It costs businesses a whopping $1.3 trillion to service them. No wonder businesses have started building chatbots to reduce these costs. Chatbots have already saved $20 million globally, and according to a new study by Juniper Research they will save $8 billion per year by 2022.

A chatbot with the ability to understand user context helps customer service representatives handle traffic and focus on more specific needs of customers.

6. Accounting

To answer a question “how much money have I earned this month?” an accountant generates a revenue report. This seemingly simple procedure consists of the following steps: 1) export data from a spreadsheet, 2) sort it, 3) do calculations, 4) build a new spreadsheet, 5) create a PowerPoint slide, 6) make the data look nice, 7) send it to the boss. Wouldn’t it be easier if an AI application could just send you the answer right away?

Chata.ai does exactly that: it uses machine learning and natural language processing to understand queries, search financial data, and provide immediate answers.

If you’re wondering how AI extracts specific financial data out of documents, you can check out this article by Railsware, a company that provides AI consulting services.

7. Legal document review

Contract Intelligence software that JPMorgan uses can in seconds perform document review tasks that took legal aides 360,000 hours.

Document discovery is the most popular use case for artificial intelligence in the legal field. AI tools for document review train on millions of existing documents, case files, and legal briefs. They sort through large chunks of data and identify relevant pieces of that data using machine learning algorithms. For example, they can review Non-Disclosure Agreements (NDAs) and spot risks within the legal documentation.

When drudgery work is reduced, everyone wins

We’ve just went through seven examples of work tasks than can be automated by artificial intelligence. This is by no means a full list. Booking meeting rooms, paying bills, responding to emails, completing timesheets, aggregating data txo business intelligence reports, and other things that every worker hates can now be done by intelligent software.

Automating work with machine learning and AI in your organization can eliminate manual errors, make tasks faster to complete, and help you achieve results of better quality. But most importantly, AI makes employees more productive and happier. Because they can finally spend more time applying their mind and creativity to do the work they’re proficient in.


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