Being able to get ahead of future trends and make operational data-backed decisions in real time are game changers for businesses. However, before you can take advantage of the opportunity, you must be familiar with productive AI and its role in conducting predictive analysis solutions. 

These solutions combine various practices and techniques to produce predictive analytics. Each technique has its own best uses and can help you manage your business more efficiently.

Tips on Successfully Implementing AI-Driven Predictive Analysis Solutions

If you’re interested in implementing AI-driven predictive analysis techniques but are feeling a little overwhelmed, you’re not alone. However, if you take a measured approach you can successfully implement AI-driven solutions that benefit your business.

Have Clear Objectives and Attainable Goals

Before you start making decisions based on AI predictive analysis, it’s a good idea to figure out what you’re trying to accomplish. 

For example, do you want to optimize business operations, boost sales, or improve customer satisfaction? Once you’ve identified your objectives and goals, it’s easy to implement solutions that produce your desired results.

Meet With Your Team

You may be able to implement the technology without any assistance but this is only the start. Unless you’re ready to handle every aspect of data analysis, you’re going to need to assemble the right team. What makes up the right team? You need people with a mix of skills.

For example, you’ll need IT specialists to help implement the software and ensure it’s working seamlessly with your systems and network. You’re also going to need data scientists and engineers, along with personnel familiar with business analytics.

If your business is lacking some key team members, consider bringing new personnel on board. You may even be able to outsource some of the necessary talent. What works best for you depends on your company’s needs and current personnel.

Get Your Data Ready

AI is a form of machine learning so you can’t just throw a bunch of random at the software and expect to get relatively accurate forecasts. You don’t need to get your data all nice, neat, and organized, but you do need to do some cleaning.

Collect all data that is relevant to your objectives and goals. If you’re trying to predict future sales you’re going to need all of your past sales data. If you notice any errors in the data, go ahead and remove the information. 

If you send error-filled data to the AI software, you’re probably not going to receive accurate forecasts. Your analytics may indicate your inventory levels are optimal, while in reality, you’re running low.

Select and Train the AI Predictive Models

Remember, AI requires machine learning to produce forecast analytics. You also have a few AI tools and platforms to choose from. What works great for a competitor may not be the best solution for your business, but it’s something you should keep in mind. 

A competitor may love their cloud-based AI analytics solutions while an on-site model is a better option for your business. Deciding on an AI model often comes down to your budget and industry data security compliance standards.

After choosing an AI predictive model, the next step is training. A good tip is to use a subset of data for training and validation purposes. Once you’re satisfied the model is producing accurate forecasts, it’s time to move on to deployment and integration.

Deploy the AI Predictive Model

Deploying and integrating the AI model is a multi-step process. After deploying the model in your production environment, this is where it can start forecasting based on new data, go ahead and automate the process. Doing so ensures the AI model is receiving data and all stakeholders have access to the insights.

Once the deployment is complete, move on to your business processes. How and where you integrate the model into your business processes depends on your objectives. If you want to forecast sales, integrate the model into the CRM system. You may use a different system if you’re tracking employee time or optimizing a schedule.

The final step is to double-check with all stakeholders to ensure they’re familiar with the AI model and can easily access and understand the insights.

Pay Attention to Industry Compliance Standards

Predictive AI models can be an effective tool but you also need to pay attention to industry compliance standards. This usually applies to privacy and transparency requirements, but every industry can have different standards. 

By making sure you’re staying in compliance, you can take advantage of the benefits you get from an AI predictive model without worrying about potentially being out of compliance.