Thus, it means that in the context of the fast-growing tendencies of the business activity, the key source of competitive advantage is the ability to drive more efficient decisions more quickly. Enter Artificial Intelligence (AI) in Business Intelligence (BI): a potentiated relationship that is revolutionising how entities leverage information to gather intelligence.
The Evolution of Business Intelligence
It is important to understand the journey that Business Intelligence has gone through in a bid to deliver the solutions that are used in organization today.
Business Intelligence has always been about turning data into meaningful information. Historically, BI used only methods of reporting and application of the dashboard to present data. Although helpful in many cases, this approach lacked in giving forecasts or trend analysis or instant information. With BI coming up, the application of AI has shifted BI from a descriptive form to a predictive and prescriptive form.
How AI Improves Business Analytics
There are ways at which AI improves business analytics:
- Automated Data Processing
AI fast tracks the process of integrating and cleaning up of data to remove human input and errors while enhancing the speed at which data is prepared for analysis. Big data can be structured and unstructured and structured data can be channeled through AI tools to process it giving organisations up to date information.
- Advanced Analytics
AI brings in concepts like the machine learning algorithms into BI and aid in analytics such as predictions and outliers. They enable businesses to foresee trends, assess threats, and rediscover extra values that may not have been visible to other unaided naked eyes.
- NLP involves using a set of NLP pre-processing techniques
Using NLP powered by AI approach, BI systems can automatically understand query posed in natural language. This implies that decision makers can pose questions, which in this case will be in plain English like, “Which products sold most in the last quarter?” and get immediate results they can understand.
- Personalized Dashboards and Insights
It enables organizations to develop Directdashboards and insights for every person that incorporates vitals of the organization.
AI also adapts its recommendations regarding content with the user’s roles and permission levels. For example, sales teams may use forecasts and pipeline reports while human resources will be interested in workforce reports. This way, specific customers get what they need and meet their needs without having to go through data clutter.
- Real-Time Decision-Making
BI in the traditional sense can take times between when data is collected up to the point when it is analyzed. Seamless integration is achieved by subsequent AI in BI that delivers immediate analytics and efficient decision-making.
Key Use Cases of AI in BI
Several key uses of AI in Business Intelligence (BI) are:
- Retail
BI led by Artificial Intelligence enables retailers to manage stocks efficiently and predict customers’ needs as well as customize offers.
- Healthcare
The very nature of patient care, from analytical perspectives to organizational processes, benefits from the ability of machines to foresee outcomes in patient care as well as sides of resource management.
- Finance
Filters fraud activities for financial institutions, analyses credit risks, and supports accurate financial forecasting.
- Manufacturing
Machine learning, another subset of artificial intelligence, can support the most important aspect of equipment maintenance involving less down time while supply chain management enhances the regular flow of operations.
Challenges and Considerations
Despite its transformative potential, implementing AI in BI isn’t without challenges:
- Data Privacy: Companies need to manage strict policies and controls, while guarding data security.
- Integration: Integrating the new AI tools with a BI system can be challenging at times.
- Cost: The introduction of AI technology and implementation of expertization may require the creation of major sources of finance in the first place.
- Skill Gaps: AI implementation calls for knowledgeable employees to handle as well as understand these technical systems.
The Future of AI in BI
It is also projected that as the AI technologies increase it is liquified that the technologies will be merged deeper into the BI platforms. Some of the emerging trends of Analytics 4.0 include augmented analytics, where besides explaining what it sees, analytics also suggests how it interprets it. Also, corporate ethical artificial intelligence and explainable artificial intelligence (XAI) will also feature, because companies cannot afford to have their AI making some decisions without an understanding of why the decision was reached.
Conclusion
Business Intelligence is changing with the help of AI and giving organizations a tool to make the best value of data they collect. Thus, AI helps organizations bring efficiency to their operations, underpin data analyses, and provide a timely response for succeeding in a modern economy.
