It seems like artificial intelligence is getting a lot of attention lately. You read articles about it, see presentations that cover AI and you hear about a range of products and services that now have “smart” features.
For today’s businesses, there are a lot of options for ways AI can be used. If you are looking for the most beneficial applications, AI applied to business intelligence has a lot of potential. Using an AI-powered BI tool, businesses can streamline their analytics operations and get deeper insights.
With AI data analytics having so much to offer businesses of all sizes, more companies are looking to introduce this technology to their teams. However, many people still do now fully understand what AI can do for their data and what they might need to do to start using AI.
What can AI do for analytics?
You probably already know what AI is: it is the science of making smart machines. With powerful AI systems, machines can be taught or programmed to perform complex tasks that used to require the intelligence of a human. Data analysis happens to be one of the tasks that AI performs well at.
With enough data, an AI algorithm can find meaningful patterns and relationships in the data. In many cases, AI systems can even learn as it is exposed to more data. This means that it can get smarter and better at its job the more it works.
As it concerns business applications, you could feed all of your business data to the AI-powered analytics tool. With automated analysis, it can find insights that might benefit a business in a number of ways. It might find waste in your operation, a missed revenue opportunity, a trend in the market, a group of customers that could be valuable or any number of things.
Even beyond insights about the past and present, AI can work well for forecasting. Using predictive analytics, the AI builds models based on historical data. It then runs data about the current condition to predict what may happen in the future. Many of these systems can even run various solutions or actions a business could take to respond to the prediction through the model to help leaders find the right path forward.
In the past, all of these functions required the skills of a data scientist. Not only that, it would often take an analytics team weeks to perform these functions. With AI applied to analytics, it can make data science teams more efficient and it can also offer some of these insights to people who do not have the skills of a data scientist.
The Implementation of AI
Introducing AI to your business is not as simple as buying an AI analytics platform and feeding it some data. You need to take some time to assess the needs of your business and determine the goals you are trying to achieve.
Depending on the type of business you run and the goals you have, different analytics tools might work better for helping you reach your goals. You will need to find the right analytics tools and determine the types of resources your business will need to support those tools.
Once you have the tools, it is not as simple as turning them loose on your company and its data. You are going to need to train employees on using the tools and make sure they understand how you expect them to use the tools. Make sure employees have the training they need and brief them on the types of goals you want them to achieve when they use them.
You might also need to spend a little time promoting the use of the tools. Some employees might not take to the tools as quickly as others. Teach employees about the benefits of using analytics tools and the ways AI can help them perform better at their jobs. Employees will be more likely to embrace the use of AI when they understand the benefits and have the training they need.
AI can be a valuable tool when businesses implement it in the right way. Making data-driven decisions can be a way to put your business ahead of the competition, and with features like predictive analytics, your business can be prepared for the future. For many businesses, their ability to adopt AI and integrate it with their operations will be the difference between success and failure.