How Machine Learning Will Impact the Future of Workers

​The inclusion of Artificial Intelligence (AI) in the workplace is often viewed in a negative light due to the pretense of it being a replacement for human workers. In reality, while certain jobs will be replaced by AI, it will lead to the creation of many more. Additionally, while AI excels at reading data, and executing repeated actions, it lacks the creativity, and analytical thinking required to solve organizational problems. Hence, going forward, AI will serve more as a companion to workers rather than their replacement. Given the increase in the adoption of AI across industries, here are a few ways we at Cleanepreneur believe workers can expect their workplaces to change in the days ahead.

Faster Decision Making

​In small and big organizations alike customer relationship management (CRM) tools are widely used. While these tools provide the advantages of serving as a central database and automation, the core work of analyzing data is done manually. By incorporating machine learning with CRM software, the speed of decision-making will be increased tremendously.

No longer would you be expected to review spreadsheets for hours to find trends in the data. The machine learning software will be able to do it in a flash and provide you with a report based on which informed decisions can be made. This information can be used to improve service to customers by creating customized content on a variety of mediums such as emails, SMS, app notifications, and more.

Increased Productivity

Ever dreamed of having a personal assistant? In the near future AI assistants will be a common theme in all workplaces. According to Deloitte, similar to how AI can help improve services for customers, it can also make your work life much easier. Here are various areas AI assistants will be useful:

• Screening emails to organize them based on priority, and safeguarding you from spam content

• Scheduling appointments and creating your calendar

• Automatically taking notes during meetings

• Providing notifications based on pre-set criteria. For example, as an IT technician, you can set reminders for low server speeds, network overload, and more. Additionally, automated steps can be programmed for each scenario.

By undertaking all the repetitive and tedious tasks, the AI assistant will provide you more time to focus on projects of higher importance.

Better Interoperability

As reported by Formstack, interoperability refers to the ability of software to interact with each other. In any workplace, there are teams that use multiple software. While there may be a limited degree of interoperability between software used by a team, there is often zero connection with those used by others in the organization. This results in a lot of time and effort expended by workers to communicate project updates, team goals, and progress to others.

Machine learning can create a bridge among all software used by the organization, allowing workers to access the data in real-time. For instance, as a marketing manager, you’ll have complete access to customer data compiled by the sales team. This can be used to create better content and develop a strong social media presence.

Frequent Training

If you’re the owner of the business, consider hiring data analytics companies to implement and monitor the functioning of machine learning technology. Given that this will be a major business decision, make a shortlist of companies that fit your requirements and schedule a demo to understand if their product is the right fit.

If you’ve never used machine learning software before, it can take some time to come to grips with it. As a worker, you can expect to attend a handful of training sessions conducted by the new data analytics team. The training will cover understanding the interface, preparing data sets, programming algorithms, and more.

As this article explores, implementing AI in the workplace will serve as a breath of fresh air, making it easier for workers to handle complex tasks and experience improved productivity on a daily basis.