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By Vidhi Gupta & Priyanka Bansal

In this article, we are going to explore how Artificial intelligence (AI) has empowered Business intelligence (BI) in today’s world.

We can all see that the economic environment is becoming more challenging day by day and, as a company, you need to pay much more attention to client behaviour, new markets and opportunities, as well as investments in operations, sales, marketing, and other systems to support growth, all while keeping a close eye on ROI. All this means that we are being confronted with more and more data.

Without big data, you are blind and deaf and in the middle of a freeway. – Geoffrey Moore, management consultant, and author

In a world dominated by data, it’s more important than ever for businesses to understand how to extract every drop of value from the raft of digital insights available at their fingertips.

By gaining the ability to understand which datasets are relevant to particular goals, strategies, and initiatives in your organization, you’ll be able to identify trends or patterns that will help you make significant improvements in a number of key areas within the organization. This concept is known as BI.

Let’s look at one of the examples of how BI is a genuine driver of organizational success, including healthcare organisation, is Centerlight Healthcare. By developing a BI system that integrates all their data sources into a more centralized and well-governed system, the organization immediately saw an improvement in data quality. Incorrect and duplicate data were dramatically reduced, thus providing for more accurate reporting and less overhead for manual reconciliation.


Coca-Cola's BI team handles reporting for all sales and delivery operations at the company. With their BI platform, the team automated manual reporting processes, saving over 260 hours a year—more than six 40-hour work weeks.

However, BI has its own set of challenges which has been efficiently overcome by AI.

Artificial intelligence (AI) traditionally refers to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language.

How MedML boosts BI applications through AI:
  • AI expands BI functionality-There is the issue of real-time insights, given that in their current iteration, BI can process and visualise big data, but cannot predict trends or generate real-time insights. However, AI incorporates the latest technology, like machine learning to deliver real-time insights about trends that will take place in the future. Thus, expanding the functionality of BI applications and improving its value to organisations.
  • Close the gap- AI allows BI to utilise the latest technology, like predictive analytics, machine learning and natural language processing to expand insights presented. Organisations are no longer satisfied with a visual dashboard of big data trends, they need tools that can close the gap between visual representation and actionable insights. AI-powered BI can help close this gap.
  • Simplifying a complex process- Surveying big data is often a complex process even with BI applications. Professional data analysts have to survey 100s of charts and dashboards to get the insights needed. However, AI technology can simplify the process. AI technologies allow machines to better understand human language and vice versa, making it easier for data analysts to find connections and insight.
  • Solve problems related to talent storage- BI presents data findings on a visual dashboard, however, with data coming in from multiple sources, it becomes harder for dashboards to present the data in an easy to read format. However, with AI, the information can be defined at scale, making it easier to gain actionable insights.

Moreover, the emergence of AI has led to applications which are now having a profound impact on various fields. Machine Learning in the medical field will improve patient’s health with minimum costs. Use cases of ML are making near perfect diagnoses, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient

So we can conclude that AI has enabled BI tools to produce clear, useful insights from the data they analyze. An AI-powered system can clarify the importance of each datapoint on a granular level, and help human operators understand how that data can translate into real business decisions. By embracing the confluence of AI and BI, businesses can synthesize vast quantities of data into coherent plans of action.

Executive Summary

BI and AI are distinct but complementary. The “intelligence” in AI refers to computer intelligence, while in BI it refers to the more intelligent business decision-making that data analysis and visualization can yield. BI can help companies bring order to the massive amounts of data they collect. But neat visualizations and dashboards may not always be sufficient.

Basis Artificial Intelligence Business Intelligence
Philosophy AI is started with the intention of creating similar intelligence in machines that we find in humans It helps in analyzing business performance through data-driven insight i.e understand the past and predict the future
Goals To create expert systems and implement human intelligence in machines It should provide information that can enable efficient and effective business decisions at all levels of the business.
Areas that contribute AI is a combination of science and technology based on computer science, maths, Biology, Psychology It combines business analysis tools which include ad-hoc analytics, enterprise reporting, OLAP(online analytical processing)
Applications AI is used in various fields such as Gaming, Natural language processing, Expert systems, Vision systems, Speech recognition, Handwriting recognition, Intelligent Robots. It is used in Spreadsheets, querying and reporting software, Digital dashboards, Data mining, Data warehouse, Business activity monitoring.
Research Areas Research areas for AI are Expert systems, Neural networks Natural language processing, Fuzzy logic, Robotics. Research areas for BI include Data mining in social networks, process analytics, Bigdata, OLAP
Issues AI faces three issues. They are Threat to Privacy, Threat to Human dignity, Threat to safety. BI issues are classified into two types. They are Organization and People and Technology and data.

-By Priyanka Bansal & Vidhi Gupta, Actuarial Associates

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