Imagine a world where complex BI reporting is done with just a few clicks, mountains of unstructured data are analyzed in an instant, future predictions are as accurate as those of the best analysts. This is not science fiction, but rather what artificial intelligence (AI) is doing today in Business Intelligence (BI). In this article, we dive into this transformation, unveiling how AI is redefining BI for PMOs, automating reporting, leveraging unstructured data, and delivering cutting-edge predictive and prescriptive analytics. We will also explore the limits and areas of caution that should not be neglected. Ready to see how AI is revolutionizing BI? Let’s go!
What is BI?
Nowadays, businesses face an abundance of data from various sources such as sales, marketing, finance, and operations. To take full advantage of this data and extract useful insights, many economic players are turning to Business Intelligence (BI) tools. But what exactly is BI? Business Intelligence is much more than just a set of software tools. It is a holistic approach that encompasses methods, processes, and technologies used to collect, analyze, and visualize data in a way that supports strategic decision-making within organizations.
How is BI traditionally used?
Traditionally, Business Intelligence has been used primarily by IT teams and data analysts to generate in-depth reports and analysis from business data. These analyses provide decision-makers with crucial insights into past and current performance, as well as forecasts for the future. Dashboards are designed to display this information in a clear and concise manner, allowing end-users to easily track key metrics and make informed decisions in real-time.
However, with advances in technology and the introduction of specialized software, such as Power BI or Tableau, the use of BI is expanding beyond data scientists to include non-technical users within companies. This has led to more widespread adoption of BI and a more diverse use of its features, transforming the way organizations leverage their data to make strategic decisions.
Top three ways AI enhances Business Intelligence
1. Unstructured Data Processing and Task Automation
Before arriving at an attractive dashboard, one of the first steps in the BI process is to clean and structure the data. This step is crucial to ensure the accuracy and reliability of your analyses. Data cleansing is all about eliminating errors, duplicates, and inconsistencies, while data structuring organizes it into a consistent, usable format. Traditionally, these tasks have been done manually, a process that is not only tedious and time-consuming, but also prone to human error. On the contrary, AI can automate these tasks with remarkable accuracy and speed, allowing analysts to focus on more in-depth, strategic analysis.
It is estimated that 80% of data on the Internet is unstructured, including videos, geolocation data, scientific data, and text such as in articles, websites, product reviews, etc. This unstructured data represents immense potential for companies. They can improve the customer experience, refine product recommendations, assess the state of a market, and enrich structured databases.
Rather than manually processing this data to extract key performance indicators (KPIs), AI offers automated solutions. For example, it is possible to build machine learning algorithms that can analyze product reviews and then automatically classify them as “positive” or “negative.” This is called sentiment analysis. This allows you to quickly categorize product feedback and better understand customer feedback.
By leveraging these technologies, organizations can uncover critical insights buried in previously untapped data.
2. Report Creation and Insights
As BI develops, especially its software that requires little technical knowledge for basic use, monitoring business processes is more accessible than ever – to the point where companies today are increasingly adopting the so-called “data-driven” management system. This only increases the need for quality indicators and high-performance reports, so it is natural that data professions are developing even more and that BI tools are becoming sharper.
Today, BI tools no longer claim to simply make data analysis accessible to everyone, but they also claim to enable everyone to create effective and relevant reports. It is in this context that AI is appearing, and most BI tools have already incorporated such technology into their platforms to stay competitive:
- Microsoft has applied Copilot to Power BI,
- Sisense has launched their Simply Ask feature,
- Tableau has several AI-powered products and integrations, including Tableau AI, Pulse, and Agent,
- Google’s Vertex AI now integrates with Looker Studio, and
- Qlik has a new Augmented Analytics product that incorporates AI.
Most of these platforms use AI to automatically create reports, narrative summaries, and content suggestions based on specific prompts. Their ability to understand and respond to natural language input allows the tools to provide sophisticated report suggestions and to update queries which would have otherwise required a professional data analyst.
3. Augmented decision-making: predictive and prescriptive analytics
With predictive analytics, businesses can anticipate future trends and potential outcomes through machine learning algorithms. Unlike traditional BI, which focuses on analyzing historical data to provide insight into past performance, AI enables a forward-looking approach by identifying patterns and anomalies that often escape the human eye.
Consider the following example: a company in the construction industry can use business intelligence tools to collect and analyze historical data on the delivery times of materials from past projects. Traditionally, this provided data analysts with the means to anticipate potential delays on ongoing projects. With the introduction of AI, however, we can optimize and improve this analysis. Prescriptive analytics takes data predictions a step further by providing concrete recommendations on what actions should be taken.
Going back to the construction example, let’s say that the BI solution is enhanced with AI. If the algorithm detects a pattern that projects experience delays when specific materials are ordered from certain suppliers during periods of high demand, then the AI can suggest ordering the materials earlier or choosing a different supplier, allowing project managers to make proactive decisions to avoid delays and optimize resources.
This ability to predict and suggest specific actions allows organizations to not only react to changes but also anticipate them, paving the way for increased decision-making. With these tools, companies can reduce risk, maximize opportunities, and increase agility in an ever-changing environment.
Areas of Caution
As the integration of AI into Business Intelligence transforms decision-making, its implementation also raises several challenges. Data reliability is a major issue: how can we guarantee the accuracy of information, for example, when some external sources remain difficult to verify? In addition, companies need to choose tools capable of protecting their sensitive data while offering relevant analyses. Finally, another risk lies in the interpretation of results: AI can make erroneous correlations or introduce biases.
Indeed, AI depends entirely on the data that it feeds on: if this data is biased, incomplete, or obsolete, then it can generate erroneous analyses and amplify unsuitable patterns. Unlike humans, AI applies its models uncritically, running the risk of steering decisions in the wrong direction. Worse still, it can produce hallucinations – unfounded or misleading conclusions based on inconsistent associations of data.
In project management, these limitations are particularly apparent. AI based solely on historical data can ignore new regulations, technological advances, or project-specific risks. Its predictions of cost and schedule delays, which are only based on historical feedback, do not always consider the particularities of certain situations, unforeseen economic events, or external factors. The result could be unforeseen delays, ballooning budgets and/or skewed strategic decisions.
Conclusion
The integration of AI into business intelligence is profoundly redefining the way data is used, revealing insights and opportunities that are often imperceptible to the human eye. But far from replacing analysts, AI and human intelligence complement each other perfectly. Humans remain essential to understand abstract concepts, make complex inferences, and exercise social intelligence and common sense, while AI excels at automating heavy and repetitive tasks, such as massive data processing and detecting subtle patterns, in record time, saving teams valuable time. As an advanced statistical tool, it enriches the work of PMOs by providing them with unprecedented agility, allowing them to focus on more strategic and creative missions, and thus preparing them to meet the challenges of tomorrow.
Thank you to the MIGSO-PCUBED Digital Solutions team for contributing to this article.
