BSPOKE Software | Digital Transformation Partners

How to propose the idea of machine learning to your team

If you’ve recognised the benefits machine learning could offer to your business, you may be wondering how to approach this subject with your team. Many individuals and businesses are very reluctant to use any form of AI within their workplace, primarily due to the fear of eventually having their job role replaced by this technology. If you are thinking about using machine learning in your workplace this year, keep reading to learn how best to discuss this idea with your team.

Identify the issue machine learning will resolve

The first step to proposing machine learning to your team is having a clear reason for using this technology within your business. By showcasing to your team the reasons machine learning will help your processes and the benefits it could offer them, they are far more likely to remain open-minded about this new addition to your business. You can research how machine learning can improve your existing offerings and services and share how it could solve problems your team currently faces on a daily basis. The more attractive you can make machine learning seem to your team, the more likely they’ll be to accept its implementation.

Listen to their concerns

Within many industries today, one of the biggest fears for the future is the replacement of humans with machines. Your team may have a whole host of reasons for not wanting machine learning to be used in their workplace. Ensure you show them the respect they deserve and listen to all of their concerns. By trying to answer their questions surrounding machine learning, you can reassure them that their positions are safe for the future. You’ll also understand how to better approach the subject with certain team members when the topic comes up again. During your initial discussions about machine learning, you’ll soon see who is most excited about the prospect of this change and who is wary about this technology. Keep your team’s feelings in mind at all times when discussing this topic, as it can be a very sensitive one for some people.

Training and education

The majority of employees don’t fully understand what machine learning is and how it’s different from other AI tools. If you are working in a sector where machine learning isn’t commonly used yet, offer your team more training and education on how it works. Provide your team with examples of top companies, such as Facebook and Netflix, who are already successfully using this technology within their workplaces. You’ll find that the more your team learns about machine learning, the more open they will be to trying it out. There is a wide selection of free or inexpensive online courses today that your employees can refer to, and if they are particularly passionate about the topic, offer them additional resources to continue their education. Get your team together from time to time to discuss machine learning and share their thoughts about this incredible technology.

Testing

Before any company starts using machine learning permanently in their business, we always recommend undertaking a pilot project. To help you out, you can bring in machine learning consultants or outside experts who can work with a select team of employees to discover how machine learning will work for your business. The project can last over two to three months, and the team involved will be able to offer the rest of your team members updates on the progress and their learnings. Keep the project to a shorter length and have very clear goals in mind. You’ll find this to be one of the most valuable tools for proposing machine learning to your team, and it will help to make everyone far more accepting of this addition to your processes.

Start with a small scale project

When you are first bringing up the idea of machine learning to your team, only consider using machine learning for a smaller project. Instead of trying to potentially replace a whole host of tasks in one go, focus on one or two areas that will help your business the most. By finding tasks that will save your team time or stop them from doing frustrating work they don’t enjoy, they’ll be far more excited about the prospect of machine learning. After your first attempt at using machine learning, you can then receive feedback from your team and decide whether you can expand the areas in which it’s used.

Constant communication

One of the biggest frustrations for employees is a lack of communication within organisations. If you do decide to go ahead with using machine learning, try to find a champion for the project within your team. They can be the main point of contact for individuals working on the project and can help to answer questions regarding the technology and how it’s being used within your workplace. Offer frequent updates to your team in team meetings and via email bulletins, and keep everyone who needs to be involved in the project updated at every stage of the process. While this is something we hope your employees are already offered, ensure they know that they can turn to you at any point in time and ask questions or share their concerns. An open chain of communication will make adopting machine learning and any other future projects much more enjoyable for everyone involved.

In conclusion

While you may be excited about the prospect of machine learning, don’t expect all of your employees to react with the same enthusiasm initially. By following these tips listed above, you’ll be able to approach the topic with tact to ensure your team understands why this change is being made and how it could positively impact their working life. While they may tell you that they are afraid of losing their job, this is very unlikely to be the case for a long time to come. Offer your team constant updates and an open line of communication, and you’ll find they will be far more likely to accept any changes you make to your business.

To find out more about custom software and how we could help to introduce new technologies to your team, contact the BSPOKE Software team.

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