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Business Intelligence and Artificial Intelligence: What You Need to Know/
Business intelligence (BI) and artificial intelligence (AI) are two crucial yet often misunderstood tools in an enterprise context. While BI refers to the use of various technologies and tools to collect and analyze business data, AI explores the use of computer systems to mimic various attributes of human intelligence. Both technologies have the key and in some cases overlapping enterprise applications. However, there are important differences between these technologies that businesses should understand to clarify how AI and BI complement one another.
The Goals of AI and BI Are Very Different
BI aims to streamline the process of collecting, reporting, and analyzing data to provide companies with useful information and analysis to aid decision-making. On the other hand, modeling human intelligence is one of the primary goals of artificial intelligence. By modeling human behaviors and thought processes, AI programs can learn and make rational decisions. Unlike BI, which makes analyzing data much easier but leaves decision-making in the hands of humans, AI can enable computers to make business decisions themselves.
How AI and BI Work Together
AI and BI complement each other and can help businesses save valuable resources. AI can help businesses automate tasks and processes that were previously carried out manually, while BI can provide insights and data-driven strategies to support decision-making. For instance, AI algorithms can use predictive analytics to forecast market trends, while BI tools can provide visualizations and reporting mechanisms to present the data in a user-friendly way.
Key Differences in Implementation
The implementation of AI and BI also differs significantly. BI implementation generally involves the use of dashboards, scorecards, and other visualizations, while AI implementation relies on machine learning algorithms and the development of chatbots and other AI-driven applications. AI requires large amounts of data to be trained and tested, while BI only requires a clear understanding of the data to be analyzed.
BI vs. AI Use-Cases
When it comes to business intelligence (BI), spreadsheets and data visualization tools are some of the most common applications. These tools are essential for organizing and analyzing business data and presenting it in a cohesive, easy-to-understand format. Many companies use BI to improve their understanding of customers and their operational efficiency. BI tools can track key performance indicators in real time, allowing businesses to identify and solve problems faster than they otherwise could.
In contrast, artificial intelligence (AI) offers a wide range of enterprise use cases that can improve medical diagnoses, design more efficient energy grids, and better understand retail customers. AI-powered enterprise applications can be categorized into three main buckets: process automation, cognitive insight, and cognitive engagement.
Process automation is the most common type of AI-powered enterprise application. These applications can handle back-office and administrative functions that were previously carried out by humans, such as updating customer information and records, handling customer communication, and providing basic guidance on standardized contracts and documentation. Process automation applications often come with a high return on investment.
Cognitive insight applications are more advanced than process automation applications, offering analytics that can learn and improve over time. These applications can predict customer behavior, provide improved IT security solutions, and devise personalized ads.
Finally, cognitive engagement applications interface directly with employees and customers. Chatbots are one example of cognitive engagement applications, offering a range of services such as medical advice, internal company support, and general customer service.
Does Business Intelligence Need Artificial Intelligence?
Business Intelligence (BI) and Artificial Intelligence (AI) are two distinct but complementary tools in the enterprise context. While BI refers to the use of technology and tools to collect and analyze business data, AI explores the use of computer systems to mimic various attributes of human intelligence, such as problem-solving, learning, and judgment. Although both tools have unique features, businesses can benefit from using them together.
BI aims to streamline the process of collecting, reporting, and analyzing data, providing companies with useful information and analysis to aid decision-making. But neat visualizations and dashboards may not always be sufficient. That’s where AI comes in, enabling BI tools to produce clear, useful insights from the data they analyze. With AI-powered systems, businesses can synthesize vast quantities of data into coherent plans of action.
Many tech companies, from established giants to startups, are seeking to capitalize on the confluence of AI and BI. IBM’s research division, for instance, has sought to “rethink enterprise architecture and transform business processes by combining AI algorithms, distributed systems, human-computer interaction, and software engineering.”
DataRobot is a company that develops BI solutions driven by predictive modeling and machine learning. DataRobot helped a healthcare company infuse AI into its BI systems, allowing 240 doctors and nurses to get the predictions and recommendations right in their PowerBI dashboards, which they can access through tablets and smartphones. With DataRobot’s help, the healthcare company was able to flag high-risk patients and formulate proactive treatment plans.
AI can also lead to the development of smarter, more adaptive BI tools. As these tools take in more data, interact more with users, and internalize the outcomes that their recommendations yield, they can learn what kinds of recommendations and analyses are most useful and self-adjust accordingly. AI may ultimately provide the incremental improvements that take BI tools to the next level.
In conclusion, while AI and BI have important differences, they make a powerful team. Going forward, businesses should explore and invest in ways to fully realize the potential they have in working together, helping businesses solve their greatest challenges and grow to new heights.