Machine learning is an often-discussed topic these days. The technology is now substantially more accessible due to many service providers specializing in it. That increased access encourages more leaders to think about using machine learning for business purposes.
Successfully running an enterprise means relying on all the information available to you and making the best decisions based on it. However, reaching those all-important conclusions isn’t easy, even for professionals with decades of experience. Machine learning (ML) could help with the task.
It’s a subset of artificial intelligence (AI) that can analyze data to look for trends and make predictions while getting more accurate with experience. It can also help you automate some tasks normally performed by humans or efficiently draw conclusions from data. Relying on machine learning for your enterprise could certainly pay off, but you must strategize to get optimal results while avoiding pitfalls.
Map Your Business Processes to Discover Opportunities to Use Machine Learning
Machine learning is a powerful technology, but not every workplace task is well-suited for it. Duties that are repetitive, labor-intensive and require lots of manual data processing are some that you might have ML handle. However, you don’t want to miss a chance to deploy the tech on an overlooked task.
One smart way to avoid that is to take part in business process mapping. It answers questions like:
- Who performs the process?
- How do they do it?
- What standard does the completed process require?
- How does the company or responsible party gauge their success with the task?
Consider using flow charts or other graphical representations to make it easier to see how some processes depend on multiple parties or departments. Your company can engage in process mapping to look for ways to improve. When making progress is the main goal, some people say there are two possible destinations from the company’s current state — where it could be and where it should be.
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The first of those options happens when you successfully integrate a new technology — machine learning in this case — to improve a workflow. Then, getting a process to where a business should be optimizes a process so that there is no wasted effort or need to go back to earlier points to revisit a step. Machine learning could help you get to that second phase, but not immediately.
Any business process with all or mostly manual steps could be ideal for machine learning, particularly because of the time-consuming aspect and the likelihood of human error. Before using machine learning applications to assist with any processes, gather metrics of outcomes without machine learning. Then, see what changes when you start using it. That way, you’ll know whether deploying ML for the task was a worthwhile move.
Define the Problems You Want to Solve
Besides getting familiar with which company processes best align with what machine learning can do, you should spend time thinking about which issues hinder your enterprise’s growth and success. ML could help some of those problems become less prevalent and even eliminate them.
One way of using machine learning for business is to build predictive maintenance algorithms for industrial equipment. A concept called feature engineering is an essential part of building a machine learning model. It involves identifying the most effective and efficient features and variables. If you’re making a model to prevent an industrial pump failure, start by identifying all of the possible fault states — such as blockages, leaks and worn bearings.
Perhaps the issue is that people typically don’t interact with your app or website for more than a few minutes. A related problem may be that they rarely make large purchases during those visits. If so, think about using ML to offer tailored recommendations to customers based on their past actions. Brands like Spotify, Netflix and Amazon display recommended content and products according to what people heard, watched and purchased before.
ML could also help you enhance customer service efforts. If a high percentage of people complain about long wait times when contacting your representatives by phone, a machine-learning-enabled chatbot could answer some of the most common questions and direct the others to customer service teams in the right departments.
Do you want to streamline warehouse operations? There are plenty of related machine learning applications to explore in that realm, too. Algorithms could shorten the order fulfillment time and make predictions about the most in-demand items so that companies don’t run out when customers want them most.
Avoid Difficulties During Your Machine Learning Journey
Now that you know how to successfully determine which processes suit machine learning and how to use it to solve problems, let’s go over a few ways to steer clear of preventable challenges:
- Research vendors thoroughly before choosing: Since machine learning is such a buzzworthy topic, some companies try to capitalize on the trend by offering solutions that are not true representations of the technology. Request proof of the product’s performance and be wary of companies that give vague answers to your questions with few or no specifics.
- Remain empathetic and receptive to employees’ concerns: When workers hear the company has purchased a machine learning product and will unveil it soon, their first fear may be that technology will take over their jobs. Help them have a balanced perspective by seeing how technology could bring benefits like reduced mental fatigue and more time to work on rewarding tasks. Let workers know you’re open to their feedback.
- Hire people who can maximize ML outcomes: When your company starts from scratch with machine learning, the best way forward is often to bring some new team members on board who have a wealth of relevant knowledge to offer. Talking to vendors and committing yourself to growing your understanding helps but doesn’t replace the expertise of someone with years of experience.
- Show flexibility and patience: You won’t find the most appropriate machine learning applications for your enterprises overnight. Some algorithms don’t give the expected returns on investment right away and require tweaks. Keep an open mind about machine learning and how to get the best results from it. Realize that harnessing this tech in the most appropriate ways takes time.
Making the Most of Machine Learning for Business
Bringing machine learning into your company is not something to do hastily. Instead, going about it methodically can help you apply the tech in ways that make your company resilient and competitive.
