AI Business Intelligence

Hook (Stat/Question)Artificial intelligence is rapidly reshaping business intelligence, transforming how companies gather, analyze, and interpret data to inform decision-making. AI-powered business intelligence tools are enhancing the accuracy of insights, accelerating analytics, and enabling a level of predictive capability that was once unimaginable. These advancements are not just augmenting human decision-making—they’re driving unprecedented changes in business operations, employee roles, and executive strategies. ^[1] However, with this rapid transformation comes a series of challenges and strategic choices that leaders and employees need to make to stay relevant and leverage these advancements. AI In Business Intelligence Today AI-powered BI tools are increasingly embedded in every facet of business, enabling organizations to operate more intelligently, predict trends with higher accuracy, and make data-driven decisions in real-time. Leading platforms are integrating machine learning models, natural language processing, and automation to democratize data access and provide executives and employees with actionable insights without needing deep technical expertise. ^[2] A few real-world examples: – Microsoft Power BI with Azure AI: Power BI now incorporates AI capabilities through Microsoft Azure, offering tools like anomaly detection, sentiment analysis, and even predictive modeling. This enables companies to predict customer behavior, identify potential issues in supply chains, and dynamically adjust marketing campaigns in response to customer feedback. – Tableau with Einstein Analytics: Tableau’s integration with Salesforce’s Einstein Analytics leverages AI to enhance data discovery, uncover hidden insights, and automate tasks that previously required manual data analysis. ^[3] Retailers like L’Oréal use these capabilities to personalize product recommendations and optimize supply chain decisions,

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driving both customer satisfaction and operational efficiency. – Pyramid Analytics: Pyramid Analytics is integrating LLM tools into business intelligence platforms to help non-technical users get reliable answers regarding their data, even when asking complex business questions. By using GenBI to prompt the LLM to go through the necessary analytical steps, answers and BI dashboards can be provided in as little as 30 seconds. ^[4] – IBM Watson Analytics: IBM’s AI-powered BI tool offers a range of advanced analytics features, from data visualization to AI-based forecasting. Learn more from this Extracted Content. Learn more from this Extracted Content. Learn more from this Extracted Content.

For example, Coca-Cola uses IBM Watson to identify customer preferences and optimize product distribution in real-time, ensuring that each market is stocked with the most in-demand items. – Amazon: Amazon uses AI-powered BI to analyze customer purchase history, preferences, and browsing behavior. ^[5] This enables them to optimize inventory levels, personalize recommendations, launch targeted marketing campaigns, and predict future demand. – Uber: The ridesharing giant Uber uses advanced BI fueled by AI and predictive analytics to optimize its routing, pricing, and driver dispatch instantly and continuously.

This allows Uber to maximize efficiency, customer experience, and profitability – key factors driving their meteoric rise. ^[6] Generative AI’s Growing Influence On BI The integration of generative AI is poised to make business intelligence more valuable and impactful across the entire organization. In its most basic sense, the combination of Gen AI and BI is poised to make it easier for non-technical users to ask questions and get the information they need in a way that is easy.

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for them to understand and use. Best High-Yield Savings Accounts Of 2024 Best 5% Interest Savings Accounts of 2024 The ability to describe data needs in plain language and still obtain timely, relevant results means more people will have access to insights that help them do their jobs more effectively, driving improved results for their businesses. ^[7] Implications For Employees For knowledge workers, the rise of AI-powered BI means their roles will evolve significantly.

Rather than spending countless hours manually crunching numbers in spreadsheets, employees will increasingly ^[8]## Clear DefinitionTOP COMPANIES RELY ON AI/BI The next generation of analytics is here Democratize insights from your data through AI-powered business intelligence, natively integrated into the Databricks Platform.Native Databricks tools for business intelligence AI/BI Dashboards An AI-assisted business intelligence solution, built into the Databricks Platform, to quickly create analytical datasets, interactive dashboards and data visualizations for your business teams.

AI/BI Genie A conversational experience, powered by generative AI, for business teams to go beyond BI dashboards and self-serve insights in real time from their data through natural language. Databricks SQL An intelligent, self-optimizing data warehouse built on lakehouse architecture, offering the best price/performance in the market. ^[1] Databricks One A single place for business teams to explore AI/BI Dashboards, talk with Genie and use custom-built Databricks Apps. Built for real-world analytics Get answers fast without relying on experts AI/BI Genie allows business and nontechnical users to ask questions about their data through natural language.

Using a conversational interface, they can get answers fast and find new insights beyond conventional dashboards — without relying on expert data practitioners. ^[2] Integrate AI/BI Dashboards with your favorite websites and apps Streamline workflows and enhance productivity by embedding AI/BI Dashboards and visual analytics right inside your key business applications. Make it easy for users to view and interact with insights from data without switching context. Bring Genie to your business apps Ask Genie questions and get answers without leaving your favorite business applications. ^[3] Through Genie’s API, you can programmatically interact with Genie through user.

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interfaces you build yourself, or you can add Genie to an existing app like Microsoft Teams, Slack or Glean. Bring AI models to your business analytics Create forward-looking visual analytics by combining AI/BI Dashboards with Databricks SQL AI Functions to bring the predictive power of GenAI and ML models to your BI projects. Use SQL to query endpoints serving custom and external models, access built-in LLM functions, perform vector searches and forecast key metrics to understand likely future trends.

^[4] Take the next step Business Intelligence FAQ Ready to become a data + AI company? Take the first steps in your data transformation ^[5]## Types of AI Business IntelligenceHow AI Impacts Business Intelligence Artificial intelligence is reshaping the very fabric of business intelligence, transforming raw data into a strategic asset and enabling leaders to drive decisions based on real insights rather than intuition alone. With data volumes surging across industries, AI-driven tools are revolutionizing data analysis, predictive forecasting and strategic planning. Audio produced by Hubspot using AI narration.

^[1] As organizations increasingly rely on data to guide their operations, understanding and leveraging AI has become essential for maintaining a competitive edge. The University of San Diego’s graduate business degrees prepare leaders to harness these innovative tools, blending technical expertise with thoughtful, ethical decision-making. AI Technologies Shaping Business Intelligence Business intelligence—gathering, analyzing and presenting data to inform decision-making—transforms raw information into actionable insights, empowering leaders to understand trends, measure performance and shape strategic plans. ^[2] Today, AI is at the forefront of this transformation, with several key technologies redefining how data is processed and utilized.

• Machine learning (ML) is a branch of artificial intelligence that enables systems to learn from data and improve their performance over time without explicit programming. By analyzing large datasets, ML algorithms can identify patterns and correlations that drive informed decision-making. ^[3] For example, a retail company might use ML to predict inventory needs based on historical sales data and emerging consumer trends. – Deep learning (DL) employs multi-layered neural networks to model complex patterns. Deep learning mimics the way the human brain processes information, enabling systems to automatically extract features from data and.

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refine predictions over time. ^[4] In practice, a financial institution might deploy deep learning to detect subtle anomalies in transaction data, helping to identify potential fraud before it escalates – Natural language processing (NLP) is a specialized field within AI that focuses on the interaction between computers and human language. NLP enables machines to understand, interpret and generate human language in a meaningful way. For instance, NLP can transform customer feedback from social media or surveys into quantifiable insights, informing product development and marketing strategies.

^[5] – Cognitive computing takes AI a step further by simulating human thought processes to analyze data holistically. For example, IBM Watson’s cognitive computing capabilities have been used in healthcare to sift through patient records, medical literature and clinical trial data, providing physicians with evidence-based treatment recommendations. This approach supports complex decision-making and offers a comprehensive view of unstructured data, helping organizations navigate multifaceted challenges with greater clarity. ^[6] In addition, automation and real-time analytics are transforming how data is collected, cleaned and analyzed. AI-driven platforms now automate routine tasks, freeing teams to focus on strategic insights.

Tools such as Microsoft Power BI and Tableau—when integrated with AI—offer up-to-the-minute dashboards that empower leaders to react swiftly to market changes. ^[7] Transformative Benefits of AI in Business Intelligence Integrating AI into business intelligence is a game-changer that goes beyond faster data processing. It empowers organizations to make proactive decisions, enhance data accuracy and quality and deploy tailored, scalable analytics that adapt to evolving needs. Whether forecasting.

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trends in manufacturing, detecting risks in finance or personalizing customer experiences in retail, AI-driven insights enable leaders to shift from reactive responses to strategic, forward-thinking initiatives. ^[8] Enhanced Decision-Making AI-driven insights empower leaders to make more informed and agile decisions. For example, in manufacturing, predictive analytics powered by machine learning can forecast equipment failures before they occur. Sensors embedded in machinery generate real-time data that, when analyzed, enable preemptive maintenance, reducing downtime and cutting repair costs.

^[9] In finance, deep learning algorithms analyze transaction data to detect potential fraud, ^[10]## Benefits of AI Business IntelligenceHow forward-thinking consultants are using genAI to save time, sharpen strategy, and boost client value By now, it’s highly likely that at least some conversation around generative AI has entered your… In today’s data-driven world, the role of business intelligence (BI) has become more pertinent than ever. Organisations rely on data to inform strategy, track market trends, and maintain a competitive… ^[1] Business Intelligence (BI) is the practice of collecting and analysing data to support informed decision-making within an organisation.

BI helps businesses track performance, assess competition, and understand… What’s your strategy for uncovering intelligence that can give you an edge in the market? ^[2] We’ve all experienced the transformative power of data and algorithms when using Google, streaming with Netflix… When most people hear the word, “espionage,” they likely picture a spy from the big screen—James Bond for action junkies or perhaps Austin Powers for comedy lovers. But espionage isn’t confined to Hollywood… ^[3] In today’s data-driven world, the role of business intelligence (BI) has become more pertinent than ever.

Organisations rely on data to inform strategy, track market trends, and maintain a competitive edge. However, the sheer volume of data can overwhelm traditional BI processes. ^[4] Manual data collection, analysis, and reporting are not only time-consuming but also prone to human error. Artificial Intelligence (AI) offers a solution to these challenges, reshaping the BI landscape by automating key processes and streamlining workflows. AI tools, like Nexis+ AI™, enhance BI efforts by enabling users to quickly access insights from.

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vast amounts of data, improving operational efficiency and strategic decision-making. ^[5] Nexis+ AI is decision intelligence made easy. AI’s impact on business intelligence comes from several advanced technologies that improve how data is processed and understood. By integrating AI into BI workflows, businesses can move beyond manual tasks and focus on extracting actionable insights. ^[6] Among the most influential AI technologies in this space are machine learning (ML), natural language processing (NLP), and generative AI. Machine Learning allows AI systems to identify patterns within data and learn from historical trends.

For example, businesses can use ML to forecast market shifts based on past performance, helping them adapt their strategies to future conditions. ^[7] This predictive power gives companies a proactive edge, particularly in dynamic industries like finance and retail. Natural Language Processing allows AI to interpret unstructured data, such as news articles, legal filings, and internal reports. Traditionally, BI tools have struggled to make sense of such unstructured content. ^[8] However, with NLP, AI can sift through this data and extract relevant insights, saving time and reducing human error.

Generative AI is another important development, automating the summarisation and extraction of key information from large documents. With this capability, businesses can review lengthy reports or financial statements more efficiently. ^[9] Instead of manually parsing every section, generative AI can condense documents into easy-to-digest summaries. Nexis+ AI uses this technology to help professionals make faster decisions, allowing them to focus on high-priority tasks. The result is a more streamlined, efficient approach.

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to business intelligence. ^[10] By automating routine tasks like data summarisation, AI tools reduce the workload for BI professionals, freeing them to focus on strategic analysis and decision-making. The integration of AI into business intelligence enhances several core processes. By accelerating key tasks, AI improves how companies analyse data, make decisions, and optimise their workflows. ^[11] One of AI’s most important contributions to BI is its ability to process large datasets quickly. Traditional methods often require manually extracting and analysing data from various sources.

This process is not only slow but ^[12]## How AI Business Intelligence WorksWhat is Business Intelligence? What Is Business Intelligence? Business Intelligence (BI) refers to a set of software capabilities that allows businesses to access, analyze and develop actionable insights from data to make business decisions. ^[1] Typically, BI tools present information on user-friendly dashboards and data visualizations that graph and chart key metrics.

While previously a function of tech or IT teams that required specialized expertise, modern business intelligence tools bring data and predictive analytics capabilities into the hands of decision-makers, allowing them to develop reports and gain specific business insights. Traditionally, business intelligence has focused on descriptive and diagnostic reporting of historical and current business activities. ^[2] Why Is Business Intelligence important? Modern BI provides real time data driven answers to complex business questions.

Presented in easy-to-understand dashboards, visuals, or reports from multiple data sources and data warehouses, BI allows users to analyze corporate performance, discover trends and determine areas where performance is not acceptable. ^[3] Typically, it is structured to provide business insights into historical performance, including current results. Depending on the solution, users can pose questions using natural languages without the need for programmatic input. Some areas where companies use BI can include: – ROI: An intelligent business understanding derived from BI helps organizations optimize performance and return on investment through business analytics.

^[4] – Customer experience: To better understand customer preferences, buying trends and behavior to improve customer service and facilitate targeted marketing. – Monitor business performance: The use of data analysis to develop insights into company.

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performance to continually improve operations Traditional business intelligence techniques focus on historical data, providing answers to questions such as what happened and why did it happen. To achieve this, analysts structure queries that run on conventional relational databases to produce static reports. ^[5] Artificial intelligence (AI) and machine learning (ML) for business intelligence uses algorithms and deep learning techniques to analyze big data and discover patterns hidden within the data. AI allows data scientists and business analysts to automate manual processes to extract data, better understand trends, to forecast, and generate new BI reports.

It is also useful for providing new insights that traditional BI techniques cannot uncover. ^[6] Another area where AI comes into play within BI is for natural language processing, where AI-powered BI can extract sentiment and information from documents, emails and transcripts from call centers. BI users can dig deeper into data without requiring analysts to create custom dashboards or reports. How is Artificial Intelligence powering Business Intelligence? ^[7] Using AI-driven business intelligence can enhance outcomes and provide deeper insights. More specifically, AI allows users to effectively analyze large amounts of data, including structured and unstructured data types.

AI-driven applications can highlight priority areas more effectively than standard BI. ^[8] Benefits include: – Enhanced BI capabilities: AI provides a greater ability to understand relationships between data, nuances, outliers, and hidden trends. – More informed decision making: The predictive capabilities of AI-driven BI allow users to more easily identify trends and make more informed decisions. – Proactive.

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decisions: AI can quickly highlight trends contained within current data, allowing analysts to identify these trends early on and make real-time proactive decisions – Smart adaptive BI: The machine learning capabilities of AI can improve BI performance thanks to AI’s ability to discover analyses and recommendations that give the best results. ^[9] – Better insights: AI-enabled BI solutions help users to better identify hidden trends and provide new insights not readily apparent with legacy BI tools. What are the benefits of Artificial Intelligence in Business Intelligence? Using AI-driven business ^[10]## Real-World ExamplesBusiness intelligence (BI) tools are undergoing massive disruption.

The powerful integration of artificial intelligence (AI) frameworks like natural language processing and automated predictive insights are transforming what BI can do for businesses. Here’s a sneak peek of what you’ll find in the report. ^[1] Industry newsletter Stay up to date on the most important—and intriguing—industry trends on AI, automation, data and beyond with the Think newsletter. See the IBM Privacy Statement. Your subscription will be delivered in English. ^[2] You will find an unsubscribe link in every newsletter. You can manage your subscriptions or unsubscribe here. Refer to our IBM Privacy Statement for more information.

^[3] A recent IBM global survey found that AI deployment amongst large enterprise is at 45 percent, while it is at 29 percent for small and medium-sized companies ( with fewer than 1,000 employees). It’s also estimated that 80-90 percent of large businesses plan to adopt the use of AI in the next two years. Companies that employ AI in BI tools are already ahead of the curve. ^[4] For example, a natural language interface allows any line of business user to query large data sets and receive powerful insights in easy-to-understand visualizations and language.

This democratization of data analysis is just one way in which AI in BI offers a huge leap forward when compared to traditional BI tools. The report also delves into another powerful component of AI in BI tools: automated data cleansing and prepping. ^[5] Instead of spending hours editing spreadsheet.

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data line-by-line, or tagging content with manual review, AI in BI does the tedious work of preparing data for analysis, freeing up people to make more productive use of their time. Rather than eliminating work, AI in BI gives you superpowers and allows you to focus on work where you can truly add value. Through the use of helpful figures and examples, The Value of AI-Powered Business Intelligence sheds light on the future of AI in BI.

^[6] Included are insights from AI subject matter experts on the features to look for in the next generation of BI tools, including predictive insights. Through this report, you will gain an understanding of how AI is fundamentally changing the discipline of business intelligence for the better. Investing in an AI-powered BI tool should be a no-brainer if you’re looking to find insights in your data to make better business decisions. ^[7] Be sure to check out The Value of AI-Powered Business Intelligence by Michael Norris to learn more. Introducing Cognos Analytics 12.0, AI-powered insights for better decision-making.

To thrive, companies must use data to build customer loyalty, automate business processes and innovate with AI-driven solutions. ^[8] Unlock the value of enterprise data with IBM Consulting, building an insight-driven organization that delivers business advantage. ^[9]## Related ResourcesArtificial intelligence is rapidly reshaping business intelligence, transforming how companies gather, analyze, and interpret data to inform decision-making. AI-powered business intelligence tools are enhancing the accuracy of insights, accelerating analytics, and enabling a level of predictive capability that was once unimaginable. These advancements are not just augmenting human decision-making—they’re driving unprecedented changes in business operations, employee roles, and executive strategies.

^[1] However, with this rapid transformation comes a series of challenges and strategic choices that leaders and employees need to make to stay relevant and leverage these advancements. AI In Business Intelligence Today AI-powered BI tools are increasingly embedded in every facet of business, enabling organizations to operate more intelligently, predict trends with higher accuracy, and make data-driven decisions in real-time. Leading platforms are integrating machine learning models, natural language processing, and automation to democratize data access and provide executives and employees with actionable insights without needing deep technical expertise.

^[2] A few real-world examples: – Microsoft Power BI with Azure AI: Power BI now incorporates AI capabilities through Microsoft Azure, offering tools like anomaly detection, sentiment analysis, and even predictive modeling. This enables companies to predict customer behavior, identify potential issues in supply chains, and dynamically adjust marketing campaigns in response to customer feedback. – Tableau with Einstein Analytics: Tableau’s integration with Salesforce’s Einstein Analytics leverages AI to enhance data discovery, uncover hidden insights, and automate tasks that previously required manual data analysis. ^[3] Retailers like L’Oréal use these capabilities to personalize product recommendations and optimize supply chain decisions,.

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driving both customer satisfaction and operational efficiency. – Pyramid Analytics: Pyramid Analytics is integrating LLM tools into business intelligence platforms to help non-technical users get reliable answers regarding their data, even when asking complex business questions. By using GenBI to prompt the LLM to go through the necessary analytical steps, answers and BI dashboards can be provided in as little as 30 seconds. ^[4] – IBM Watson Analytics: IBM’s AI-powered BI tool offers a range of advanced analytics features, from data visualization to AI-based forecasting.

For example, Coca-Cola uses IBM Watson to identify customer preferences and optimize product distribution in real-time, ensuring that each market is stocked with the most in-demand items. – Amazon: Amazon uses AI-powered BI to analyze customer purchase history, preferences, and browsing behavior. ^[5] This enables them to optimize inventory levels, personalize recommendations, launch targeted marketing campaigns, and predict future demand. – Uber: The ridesharing giant Uber uses advanced BI fueled by AI and predictive analytics to optimize its routing, pricing, and driver dispatch instantly and continuously.

This allows Uber to maximize efficiency, customer experience, and profitability – key factors driving their meteoric rise. ^[6] Generative AI’s Growing Influence On BI The integration of generative AI is poised to make business intelligence more valuable and impactful across the entire organization. In its most basic sense, the combination of Gen AI and BI is poised to make it easier for non-technical users to ask questions and get the information they need in a way that is easy.

Key Points

for them to understand and use. Best High-Yield Savings Accounts Of 2024 Best 5% Interest Savings Accounts of 2024 The ability to describe data needs in plain language and still obtain timely, relevant results means more people will have access to insights that help them do their jobs more effectively, driving improved results for their businesses. ^[7] Implications For Employees For knowledge workers, the rise of AI-powered BI means their roles will evolve significantly.

Rather than spending countless hours manually crunching numbers in spreadsheets, employees will increasingly ^[8] TOP COMPANIES RELY ON AI/BI The next generation of analytics is here Democratize insights from your data through AI-powered business intelligence, natively integrated into the Databricks Platform.Native Databricks tools for business intelligence AI/BI Dashboards An AI-assisted business intelligence solution, built into the Databricks Platform, to quickly create analytical datasets, interactive dashboards and data visualizations for your business teams.

AI/BI Genie A conversational experience, powered by generative AI, for business teams to go beyond BI dashboards and self-serve insights in real time from their data through natural language. Databricks SQL An intelligent, self-optimizing data warehouse built on lakehouse architecture, offering the best price/performance in the market. ^[9] Databricks One A single place for business teams to explore AI/BI Dashboards, talk with Genie and use custom-built Databricks Apps. Built for real-world analytics Get answers fast without relying on experts AI/BI Genie allows business and nontechnical users to ask questions about their data through natural language.

Using a conversational interface, they can get answers fast.

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and find new insights beyond conventional dashboards — without relying on expert data practitioners. ^[10] Integrate AI/BI Dashboards with your favorite websites and apps Streamline workflows and enhance productivity by embedding AI/BI Dashboards and visual analytics right inside your key business applications. Make it easy for users to view and interact with insights from data without switching context. Bring Genie to your business apps Ask Genie questions and get answers without leaving your favorite business applications.

^[11] Through Genie’s API, you can programmatically interact with Genie through user interfaces you build yourself, or you can add Genie to an existing app like Microsoft Teams, Slack or Glean. Bring AI models to your business analytics Create forward-looking visual analytics by combining AI/BI Dashboards with Databricks SQL AI Functions to bring the predictive power of GenAI and ML models to your BI projects. Use SQL to query endpoints serving custom and external models, access built-in LLM functions, perform vector searches and forecast key metrics to understand likely future trends.

^[12] Take the next step Business Intelligence FAQ Ready to become a data + AI company? Take the first steps in your data transformation ^[13] How AI Impacts Business Intelligence Artificial intelligence is reshaping the very fabric of business intelligence, transforming raw data into a strategic asset and enabling leaders to drive decisions based on real insights rather than intuition alone. With data volumes surging across industries, AI-driven tools are revolutionizing data analysis, predictive forecasting and strategic planning. Audio produced by Hubspot using.

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AI narration. ^[14] As organizations increasingly rely on data to guide their operations, understanding and leveraging AI has become essential for maintaining a competitive edge. The University of San Diego’s graduate business degrees prepare leaders to harness these innovative tools, blending technical expertise with thoughtful, ethical decision-making. AI Technologies Shaping Business Intelligence Business intelligence—gathering, analyzing and presenting data to inform decision-making—transforms raw information into actionable insights, empowering leaders to understand trends, measure performance and shape strategic plans. ^[15] Today, AI is at the forefront of this transformation, with several key technologies redefining how data is processed and utilized.

• Machine learning (ML) is a branch of artificial intelligence that enables systems to learn from data and improve their performance over time without explicit programming. By analyzing large datasets, ML algorithms can identify patterns and correlations that drive informed decision-making. ^[16] For example, a retail company might use ML to predict inventory needs based on historical sales data and emerging consumer trends. – Deep learning (DL) employs multi-layered neural networks to model complex patterns. Deep learning mimics the way the human brain processes information, enabling systems to automatically extract features from data and refine predictions over time.

^[17] In practice, a financial institution might deploy deep learning to detect subtle anomalies in transaction data, helping to identify potential fraud before it escalates – Natural language processing (NLP) is a specialized field within AI that focuses on the interaction between computers and human language. NLP enables machines to understand, interpret and generate.

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human language in a meaningful way. For instance, NLP can transform customer feedback from social media or surveys into quantifiable insights, informing product development and marketing strategies. ^[18] – Cognitive computing takes AI a step further by simulating human thought processes to analyze data holistically. For example, IBM Watson’s cognitive computing capabilities have been used in healthcare to sift through patient records, medical literature and clinical trial data, providing physicians with evidence-based treatment recommendations. This approach supports complex decision-making and offers a comprehensive view of unstructured data, helping organizations navigate multifaceted challenges with greater clarity.

^[19] In addition, automation and real-time analytics are transforming how data is collected, cleaned and analyzed. AI-driven platforms now automate routine tasks, freeing teams to focus on strategic insights. Tools such as Microsoft Power BI and Tableau—when integrated with AI—offer up-to-the-minute dashboards that empower leaders to react swiftly to market changes. ^[20] Transformative Benefits of AI in Business Intelligence Integrating AI into business intelligence is a game-changer that goes beyond faster data processing. It empowers organizations to make proactive decisions, enhance data accuracy and quality and deploy tailored, scalable analytics that adapt to evolving needs.

Whether forecasting trends in manufacturing, detecting risks in finance or personalizing customer experiences in retail, AI-driven insights enable leaders to shift from reactive responses to strategic, forward-thinking initiatives. ^[21] Enhanced Decision-Making AI-driven insights empower leaders to make more informed and agile decisions. For example, in manufacturing, predictive analytics powered by machine learning can forecast equipment failures before they occur. Sensors.

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embedded in machinery generate real-time data that, when analyzed, enable preemptive maintenance, reducing downtime and cutting repair costs. ^[22] In finance, deep learning algorithms analyze transaction data to detect potential fraud, ^[23] How forward-thinking consultants are using genAI to save time, sharpen strategy, and boost client value By now, it’s highly likely that at least some conversation around generative AI has entered your… In today’s data-driven world, the role of business intelligence (BI) has become more pertinent than ever. Organisations rely on data to inform strategy, track market trends, and maintain a competitive…

^[24] Business Intelligence (BI) is the practice of collecting and analysing data to support informed decision-making within an organisation. BI helps businesses track performance, assess competition, and understand… What’s your strategy for uncovering intelligence that can give you an edge in the market? ^[25] We’ve all experienced the transformative power of data and algorithms when using Google, streaming with Netflix… When most people hear the word, “espionage,” they likely picture a spy from the big screen—James Bond for action junkies or perhaps Austin Powers for comedy lovers. But espionage isn’t confined to Hollywood…

^[26] In today’s data-driven world, the role of business intelligence (BI) has become more pertinent than ever. Organisations rely on data to inform strategy, track market trends, and maintain a competitive edge. However, the sheer volume of data can overwhelm traditional BI processes. ^[27] Manual data collection, analysis, and reporting are not only time-consuming but also prone to human error. Artificial Intelligence (AI) offers.

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a solution to these challenges, reshaping the BI landscape by automating key processes and streamlining workflows. AI tools, like Nexis+ AI™, enhance BI efforts by enabling users to quickly access insights from vast amounts of data, improving operational efficiency and strategic decision-making. ^[28] Nexis+ AI is decision intelligence made easy. AI’s impact on business intelligence comes from several advanced technologies that improve how data is processed and understood. By integrating AI into BI workflows, businesses can move beyond manual tasks and focus on extracting actionable insights.

^[29] Among the most influential AI technologies in this space are machine learning (ML), natural language processing (NLP), and generative AI. Machine Learning allows AI systems to identify patterns within data and learn from historical trends. For example, businesses can use ML to forecast market shifts based on past performance, helping them adapt their strategies to future conditions. ^[30] This predictive power gives companies a proactive edge, particularly in dynamic industries like finance and retail. Natural Language Processing allows AI to interpret unstructured data, such as news articles, legal filings, and internal reports.

Traditionally, BI tools have struggled to make sense of such unstructured content. ^[31] However, with NLP, AI can sift through this data and extract relevant insights, saving time and reducing human error. Generative AI is another important development, automating the summarisation and extraction of key information from large documents. With this capability, businesses can review lengthy reports or financial statements more efficiently. ^[32] Instead of manually parsing every section, generative AI.

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can condense documents into easy-to-digest summaries. Nexis+ AI uses this technology to help professionals make faster decisions, allowing them to focus on high-priority tasks. The result is a more streamlined, efficient approach to business intelligence. ^[33] By automating routine tasks like data summarisation, AI tools reduce the workload for BI professionals, freeing them to focus on strategic analysis and decision-making. The integration of AI into business intelligence enhances several core processes. By accelerating key tasks, AI improves how companies analyse data, make decisions, and optimise their workflows.

^[34] One of AI’s most important contributions to BI is its ability to process large datasets quickly. Traditional methods often require manually extracting and analysing data from various sources. This process is not only slow but ^[35] What is Business Intelligence? What Is Business Intelligence? Business Intelligence (BI) refers to a set of software capabilities that allows businesses to access, analyze and develop actionable insights from data to make business decisions. ^[36] Typically, BI tools present information on user-friendly dashboards and data visualizations that graph and chart key metrics.

While previously a function of tech or IT teams that required specialized expertise, modern business intelligence tools bring data and predictive analytics capabilities into the hands of decision-makers, allowing them to develop reports and gain specific business insights. Traditionally, business intelligence has focused on descriptive and diagnostic reporting of historical and current business activities. ^[37] Why Is Business Intelligence important? Modern BI provides real time data driven answers to complex business questions. Presented in.

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easy-to-understand dashboards, visuals, or reports from multiple data sources and data warehouses, BI allows users to analyze corporate performance, discover trends and determine areas where performance is not acceptable. ^[38] Typically, it is structured to provide business insights into historical performance, including current results. Depending on the solution, users can pose questions using natural languages without the need for programmatic input. Some areas where companies use BI can include: – ROI: An intelligent business understanding derived from BI helps organizations optimize performance and return on investment through business analytics.

^[39] – Customer experience: To better understand customer preferences, buying trends and behavior to improve customer service and facilitate targeted marketing. – Monitor business performance: The use of data analysis to develop insights into company performance to continually improve operations Traditional business intelligence techniques focus on historical data, providing answers to questions such as what happened and why did it happen. To achieve this, analysts structure queries that run on conventional relational databases to produce static reports.

^[40] Artificial intelligence (AI) and machine learning (ML) for business intelligence uses algorithms and deep learning techniques to analyze big data and discover patterns hidden within the data. AI allows data scientists and business analysts to automate manual processes to extract data, better understand trends, to forecast, and generate new BI reports. It is also useful for providing new insights that traditional BI techniques cannot uncover. ^[41] Another area where AI comes into play within BI is for natural language processing, where AI-powered BI.

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can extract sentiment and information from documents, emails and transcripts from call centers. BI users can dig deeper into data without requiring analysts to create custom dashboards or reports. How is Artificial Intelligence powering Business Intelligence? ^[42] Using AI-driven business intelligence can enhance outcomes and provide deeper insights. More specifically, AI allows users to effectively analyze large amounts of data, including structured and unstructured data types. AI-driven applications can highlight priority areas more effectively than standard BI. ^[43] Benefits include: – Enhanced BI capabilities: AI provides a greater ability to understand relationships between data, nuances, outliers, and hidden trends.

• More informed decision making: The predictive capabilities of AI-driven BI allow users to more easily identify trends and make more informed decisions. – Proactive decisions: AI can quickly highlight trends contained within current data, allowing analysts to identify these trends early on and make real-time proactive decisions – Smart adaptive BI: The machine learning capabilities of AI can improve BI performance thanks to AI’s ability to discover analyses and recommendations that give the best results.

^[44] – Better insights: AI-enabled BI solutions help users to better identify hidden trends and provide new insights not readily apparent with legacy BI tools. What are the benefits of Artificial Intelligence in Business Intelligence? Using AI-driven business ^[45] Business intelligence (BI) tools are undergoing massive disruption. The powerful integration of artificial intelligence (AI) frameworks like natural language processing and automated predictive insights are transforming what BI can do for businesses. Here’s a sneak peek.

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of what you’ll find in the report. ^[46] Industry newsletter Stay up to date on the most important—and intriguing—industry trends on AI, automation, data and beyond with the Think newsletter. See the IBM Privacy Statement. Your subscription will be delivered in English. ^[47] You will find an unsubscribe link in every newsletter. You can manage your subscriptions or unsubscribe here. Refer to our IBM Privacy Statement for more information.

^[48] A recent IBM global survey found that AI deployment amongst large enterprise is at 45 percent, while it is at 29 percent for small and medium-sized companies ( with fewer than 1,000 employees). It’s also estimated that 80-90 percent of large businesses plan to adopt the use of AI in the next two years. Companies that employ AI in BI tools are already ahead of the curve. ^[49] For example, a natural language interface allows any line of business user to query large data sets and receive powerful insights in easy-to-understand visualizations and language.

This democratization of data analysis is just one way in which AI in BI offers a huge leap forward when compared to traditional BI tools. The report also delves into another powerful component of AI in BI tools: automated data cleansing and prepping. ^[50] Instead of spending hours editing spreadsheet data line-by-line, or tagging content with manual review, AI in BI does the tedious work of preparing data for analysis, freeing up people to make more productive use of their time. Rather than eliminating work, AI.

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in BI gives you superpowers and allows you to focus on work where you can truly add value. Through the use of helpful figures and examples, The Value of AI-Powered Business Intelligence sheds light on the future of AI in BI. ^[51] Included are insights from AI subject matter experts on the features to look for in the next generation of BI tools, including predictive insights. Through this report, you will gain an understanding of how AI is fundamentally changing the discipline of business intelligence for the better.

Investing in an AI-powered BI tool should be a no-brainer if you’re looking to find insights in your data to make better business decisions. ^[52] Be sure to check out The Value of AI-Powered Business Intelligence by Michael Norris to learn more. Introducing Cognos Analytics 12.0, AI-powered insights for better decision-making. To thrive, companies must use data to build customer loyalty, automate business processes and innovate with AI-driven solutions. ^[53] Unlock the value of enterprise data with IBM Consulting, building an insight-driven organization that delivers business advantage.

^[54] —### References^[1]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[2]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[3]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[4]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[5]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[6]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[7]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[8]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[1]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[2]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[3]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[4]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[5]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[1]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[2]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[3]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[4]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[5]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[6]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[7]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[8]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[9]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[10]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[1]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[2]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[3]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[4]: Extracted Content – 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https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[1]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[2]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[3]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[4]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[5]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[6]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[7]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[8]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[9]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[10]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[1]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[2]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[3]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[4]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[5]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics.

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^[6]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[7]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[8]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[9]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[1]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[2]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[3]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[4]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[5]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[6]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[7]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[8]: Extracted Content – https://www.forbes.com/sites/davidhenkin/2024/12/17/ai-powered-business-intelligence–a-new-era-of-insights/ ^[9]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[10]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[11]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[12]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[13]: Extracted Content – https://www.databricks.com/product/business-intelligence ^[14]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[15]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[16]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[17]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[18]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[19]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[20]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[21]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[22]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[23]: Extracted Content – https://businessstories.sandiego.edu/how-ai-impacts-business-intelligence ^[24]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[25]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[26]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[27]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[28]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[29]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[30]: Extracted Content – https://www.lexisnexis.com/blogs/ae/b/research/posts/how-ai-is-transforming-business-intelligence ^[31]: Extracted Content – 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https://aws.amazon.com/what-is/business-intelligence/ ^[41]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[42]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[43]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[44]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[45]: Extracted Content – https://aws.amazon.com/what-is/business-intelligence/ ^[46]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[47]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[48]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[49]: Extracted Content – https://www.ibm.com/think/insights/ai-powered-business-intelligence-the-future-of-analytics ^[50]: Extracted Content – 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