Developing Business Intelligence through Artificial Intelligence

AI-enabled technologies have a remarkable position in triggering the capabilities of businesses. Be it about smart robots, autonomous vehicles, Machine Learning, or Deep learning technologies, Artificial Intelligence has made its roots powerful across the world. The rising complexities of business have given birth to the commercial adoption of AI in business analytics systems across industries. Besides, AI education has evolved to incorporate business intelligence into the curriculum since these AI skills can contribute to improved productivity in the workplace.

The major use of Artificial Intelligence in Business Intelligence tools is to drive actionable insights from complex databases and augment enterprises to deliver user-friendly business recommendations.

Business Intelligence– What does it mean?

The technology that helps businesses to organize, evaluate, and contextualize the business data from the company is called Business Intelligence (BI). It incorporates multiple techniques and tools that aid the transformation of raw data into actionable information.

Marketing, sales, Operations, and finance departments leverage business intelligence tools to conduct quantitative analysis, measure the performance against business targets, gather customer insights, and share the data to explore new opportunities.

Let’s make it more meaningful through an example. Consider a hotel owner who uses BI analytics applications to collect statistical information that includes room rate and average occupancy. This helps to get aggregate revenue generated for every room. It can also collect data on customer surveys, market share, etc. to identify the competitive position compared to various markets. Continuous analysis every day can help the management to provide discounts on the rentals and keep an eye on the trends.

Great brands have made success with a well-structured Business Intelligence system and made decisions that rely on actionable insights. Starbucks stores would be the best example, with the store location they have chosen based on the factors like population density, consumer demographics, average income levels, public transport hubs, traffic patterns, and the types of businesses in the location.

How Business Intelligence Supports Businesses

Ever thought about how Netflix, the giant entertainment platform, generates 1 billion USD annually only through automation tools, like custom recommendations? The powerful aspects of AI merged into the tools have given rise to business success.

As per the statistics provided by Snaplogic, 61% of workers express that AI in the workplace has boosted their productivity. Rather than spending too much time on editing spreadsheets or using a manual review, Artificial Intelligence can perform the tedious work for you seamlessly. This frees up the workforce to make time for more productive tasks. Instead of eliminating the work, Artificial Intelligence can help you to focus on the work that adds more value to your business.

As per the recent IBM global survey, 45% of the large enterprises engage in AI deployment, while this is 29% in SME companies, i.e. organizations with less than 1000 employees. Also, about 80% of large companies plan to leverage AI in the coming years. Nevertheless, at least 17 out of every 20 CEOs have claimed that AI would be their mainstream technology. Also, 86% of CEOs agree that business and AI go together since Artificial Intelligence is the core technology in the offices, says PwC.

It is evident that organizations that use AI can cut down their business costs. The McKinsey report states that about 44% of firms that use AI have witnessed a reduction in their business costs after the implementation. Hence, developing Business Intelligence through AI tools and technologies makes businesses smarter, more profitable, and more productive.

Steps to deploy AI-powered intelligence in Businesses

If you can empower your Business Intelligence team, you can bring versatile data science methods to your business tactically. This can bridge the gap between AI and BI, and automate the data analytics processes. Here are the steps to implement AI-fueled intelligence in businesses.

1. Have a question in mind and a clear focus on the business point you need to move. Get a clear understanding of how you use the predictions to make things happen.  For example, data analysts can use predictive models to build a scoring system that identifies customers based on discounts and retention. Thus, you can predict and understand the customer outcomes with a clear understanding of how to get customers to come back.
2. Don’t leap over the so-called perfect data. Every new data project needs some weeks to complete the validation and preprocessing. Business analysts might have surplus data to analyze and you don’t have to ensure every data point is counted. Utilize the BI-ready data, i.e., the data in the state where you can process analytics, and choose a predictive analytics solution to automate data, making it AI-ready.
3. Build A/B tests to evaluate the accuracy of predictions. This way can give major results even while you use small sample sizes. After you develop a model, test the effects of using the model with a control group managed by your business. If you are not aware of testing how the model integrates with your business process, you can’t figure out if the model can generate the business outcomes you desire.
4. Enhance the data you own– the customer data and the internal transaction is the best starting point you can go for while using predictive analytics. Businesses can also benefit from data enrichment with external data sources, like public health data, holiday and weather data, etc. With automation tools, you make sure that the accuracy and use of models continue to add value.
5. Plan for model monitoring– It is believed that Machine learning models work better over time and on their own. However, this is not true. The models have the least shelf life, and they work amazingly for a while, with performances dropping after various strategies and customer behavioral patterns shift over time. To save your resources and time, automated solutions can track and retrain the models to let them deliver high performance and great business impact for a long time.

Wrap Up

The future of BI goes hand-in-hand with AI since they make a powerful team together. In the way forward, businesses should consider AI and BI as fused technologies, since they together help businesses resolve complicated problems and make crucial decisions. AI education can let you know how to mitigate business concerns and make the business process act intelligent for timely resolution and data management. With AI certifications you can gain all the necessary business intelligence tools and AI tools necessary to make transformations in business processes.