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The important connection between Artificial Intelligence and Business Intelligence

AI and machine learning have become increasingly embedded in enterprise technology environments and are starting to generate value for many businesses. A survey of 600 CIOs and other technology leaders found that the share of those saying their companies are not using AI today is 6% or less in any of the seven core enterprise functions. However, only a few organizations aim to become AI-driven by 2025. Addressing shortcomings in companies' data management and infrastructure, as well as the internal structure and process rigidities and talent deficits, loom large among the challenges to achieving AI goals. Key findings indicate that companies view wider AI adoption as mission-critical for their future, scaling AI successfully is priority one for the data strategy, significant spending growth is planned to bolster AI's data foundations, investment growth intentions are strongest in the financial services industry, and multi-cloud and open standards are integral to AI progress.

Artificial Intelligence (AI) and Business Intelligence (BI) are two powerful technologies that can significantly benefit businesses. AI can provide predictive insights, automate tasks, and optimize operations, while BI can provide analytics and reporting to help organizations make data-driven decisions. However, there is often a gap between AI and BI that needs to be bridged to realize the benefits of both technologies fully.

Here are some ways to bridge the gap between AI and BI:

  1. Define business goals: Define the business goals and outcomes AI and BI should help achieve. This will help identify the specific use cases where AI and BI can be most effective.

  2. Choose the correct data: AI and BI rely on data, but not all data is created equal. Choose data that is relevant, accurate, and complete. Also, ensure that the data is structured in a way that can be easily analyzed by both AI and BI tools.

  3. Use AI to enhance BI: AI can be used to improve the capabilities of BI. For example, AI can be used to automate the process of data cleaning, identify patterns in data, and make predictions based on historical data. These insights can then be used to inform BI reporting and analysis.

  4. Make BI accessible to everyone: BI tools should be accessible to everyone in the organization, not just data analysts or IT professionals. This can be achieved by creating user-friendly dashboards and reports that non-technical users can easily understand.

  5. Invest in AI and BI talent: AI and BI require specialized skills and expertise. Invest in hiring or training employees with the skills needed to work with these technologies.

  6. Foster collaboration: Foster collaboration between AI and BI teams. Encourage cross-functional teams to work together to identify new opportunities and solve business problems.

  7. Continuously monitor and optimize: Continuously monitor the performance of AI and BI solutions and optimize them as needed. This will help ensure that they are delivering the desired outcomes and providing value to the business.

By bridging the gap between AI and BI, organizations can gain a competitive advantage by making data-driven decisions, automating tasks, and optimizing operations.

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