The terms artificial intelligence (AI) and machine learning are everywhere these days. Businesses are mainly invested in intelligence, with numerous tools existing to help them leverage the power of AI. Still, businesses should combine them with machine learning for the best outcomes and if they want to leverage their full potential. The reality is that many of the businesses looking to integrate AI and ML into their frameworks fail, for a variety of reasons. We will look at what these are below.
Businesses Do Not Know What to Do with Machine Learning
For a business to be successful with AI and ML, it must understand how to use the different parts of both to make them work in its favor. Machine learning depends on data, specific tools and usable insights. This could include learning to understand the behavior of different customers or analyzing sales data to find areas of optimization. If any of these parts fail, a business cannot leverage machine learning to its full potential, and those businesses end up abandoning this amazing tool.
They Do Not Hire the Right People
As with any other facet of business, you need to hire the right people for machine learning, otherwise it will fail. These are the people who understand the applied and research sides of machine learning.
People who do research for machine learning build the tools required to gather data and work with it to produce the results the business is looking for. The people on the applied side take the results produced by machine learning and apply them in a business or other area.
If you want someone with skills on both ends of machine learning, you are unlikely to find them because both are highly specialized areas. Instead, find people with an MS data science degree who have experience working on either the research or applied side and hire them to handle these respective roles.
Inadequate Understanding of the Problems Machine Learning is Supposed to Solve
Many businesses want to use the latest technologies for their problems without finding out if those technologies are a good fit. This is undoubtedly the case with machine learning and artificial intelligence.
A business has to understand the problems it is trying to solve before it builds its first machine learning model. Doing this can take time, so many businesses skip this “learning” phase.
You might be surprised to learn that doing this to save time, money, and effort can end up costing you a lot more in the long run.
Take some time to understand the business problems you want machine learning to help you with, ensure your data scientists understand your aims, and let them use the available tools to produce the solutions you seek.
Machine learning and artificial intelligence have become popular in recent years, but many businesses are disappointed with the results they produce. This is often due to the issues discussed above and many others that come with not knowing how to leverage machine learning fully.
