AI in Business Intelligence: Top Use Cases You Need To Know

Introduction

Business intelligence (BI) teams today grapple with massive data volumes, siloed systems, and mounting demands for real-time insights. AI technologies, especially machine learning, natural language understanding, and predictive analytics, are transforming BI by automating manual tasks, enabling dynamic dashboards, and uncovering hidden patterns. This guide explores AI’s impact on BI, illustrating tangible benefits and addressing real-world challenges. 

Qu'est-ce que l'IA et pourquoi est-ce important dans Business Intelligence?

Definition of AI and Its Core Technologies

Artificial Intelligence, or AI, refers to computer systems designed to perform tasks that normally require human intelligence, such as reasoning, learning, and problem-solving. Core technologies within AI include machine learning, natural language processing, and computer vision. These tools allow machines to process complex data, make predictions, and respond intelligently to new information. 

In business intelligence, AI is used to automate data analysis, identify trends, and deliver insights that were previously time-consuming or difficult to uncover. It enhances traditional BI by adding capabilities such as real-time forecasting, automated reporting, and conversational data access. As a result, businesses can make faster, more informed decisions with minimal manual effort. 

The Growing Role of AI in Transforming Business Intelligence

AI is changing the way businesses approach analytics by shifting from historical reporting to forward-looking intelligence. Traditional BI tools often rely on static dashboards, but AI enables systems to detect patterns and suggest insights in real time. This transformation allows companies to act quickly on emerging trends rather than reacting after the fact. 

Another major shift is the introduction of natural language processing and conversational interfaces. These technologies let business users interact with data through simple questions instead of technical queries. As a result, more employees can access and interpret insights without relying on analysts or IT teams. 

Generative AI is also making a significant impact by automating report creation and analytics workflows. It can interpret stakeholder requirements and generate dashboards or summaries based on those needs. This automation speeds up BI development cycles and ensures analytics are more aligned with business goals. 

Key Statistics and Trends Highlighting AI Adoption in Business Intelligence

AI adoption has become mainstream, with 78% of global companies using it in at least one function – up from 55% in 2022. Most now deploy AI across three or more areas, signaling its shift from pilot programs to core operations. 

Generative AI is accelerating this momentum, with 71% of organizations applying it in marketing, product development, and customer service. Adoption is highest among senior leaders, with 53% of C-suite executives using gen AI regularly compared to 44% of middle managers. 

The global AI market is projected to grow 17.3% annually, reaching $739 billion by 2030. Among businesses using AI, 54% report measurable performance improvements, and 14% see gains over 11%. These results highlight AI’s growing role in driving efficiency and decision-making in business intelligence. 

Business Benefits of AI in Business Intelligence 

AI is driving measurable value across BI by addressing core inefficiencies and expanding analytic capabilities. From smarter data prep to predictive insights, it enables faster, more strategic decisions across the organization. 

1. Enhanced Data Preparation 

AI streamlines the data preparation process by automating data cleaning, transformation, and integration across multiple sources. This minimizes manual intervention, reduces errors, and ensures more consistent and timely data for analysis. As a result, analysts can focus more on strategic tasks rather than routine data prep. 

AI tools also identify data anomalies, missing values, and outliers with high accuracy, helping teams maintain data integrity. These capabilities improve the overall reliability of insights generated from BI tools. With cleaner data as input, businesses can trust the outputs and make better-informed decisions. 

2. Semantic Search and Conversation 

Natural language processing enables users to interact with BI tools using everyday language instead of complex query syntax. This democratizes access to data, empowering non-technical stakeholders to retrieve insights independently. It significantly shortens the time between a question and an actionable answer. 

AI-driven semantic search also interprets intent behind queries, offering context-aware results rather than literal matches. For example, asking “Which region performed best last quarter?” yields a detailed, ranked analysis. This intuitive interface enhances user engagement and drives faster decision-making across teams. 

3. Predictive Insights 

Machine learning models in BI use historical and real-time data to forecast future trends such as sales, customer churn, or inventory needs. These predictive insights allow businesses to move from reactive to proactive decision-making. Teams can plan more effectively and allocate resources with greater precision. 

Predictive capabilities also support scenario planning by modeling different outcomes based on variable inputs. This helps executives assess risks and prepare contingency strategies in advance. The foresight offered by AI-driven BI contributes directly to competitive advantage and operational resilience. 

4. Dynamic Visualization and Anomaly Detection

AI enhances dashboards by automatically surfacing insights like trend shifts, performance anomalies, and outlier data. Instead of sifting through large datasets, users receive automated alerts when unusual activity occurs. This real-time monitoring ensures faster responses to operational changes. 

Anomaly detection algorithms adapt over time, learning from new data to improve their sensitivity and reduce false positives. These features help businesses identify problems before they escalate, such as fraud, system failures, or demand surges. This level of automation elevates the responsiveness of BI systems. 

5. Agility in BI Development  

Generative AI accelerates BI development by automating tasks such as data modeling, dashboard design, and report generation. It reduces the time needed to go from stakeholder request to analytics delivery. Teams can iterate faster and meet business needs more efficiently. 

AI also helps align BI outputs with business objectives by interpreting natural language requirements and converting them into analytic logic. This minimizes miscommunication and ensures relevance. Ultimately, AI-driven development improves both the speed and quality of BI solutions. 

Les défis de l'adoption de l'IA dans Business Intelligence 

Despite its potential, AI adoption in BI comes with real-world hurdles that can delay or limit impact. These challenges require strategic planning, technical readiness, and organizational alignment to overcome. 

1. Fragmented Data Ecosystems

Many organizations operate with data scattered across multiple systems, from cloud platforms to spreadsheets and legacy databases. This fragmentation creates significant hurdles for AI models, which depend on unified and well-structured datasets. Without consistent access, insights are often incomplete or misleading. 

Centralizing data requires robust integration strategies and investment in modern infrastructure. Businesses must align IT, analytics, and operations to build seamless data pipelines. Without this foundation, AI initiatives in BI are prone to delays and inaccuracies. 

2. Poor Data Quality and Labeling 

AI models rely heavily on high-quality, labeled data to deliver reliable outcomes. In BI, inconsistent formats, duplicate records, and missing values can severely degrade model performance. These issues lead to flawed insights and erode user trust. 

Cleaning and labeling data demands time, domain expertise, and continuous oversight. Many companies underestimate this effort, resulting in underperforming AI systems. Sustained data governance is essential to ensure long-term success. 

3. Explainability and Trust 

AI-generated insights can be difficult to interpret, especially when based on complex models like neural networks. Users often hesitate to act on recommendations they don’t understand. This lack of transparency undermines adoption and slows decision-making. 

Explainable AI (XAI) tools are improving model visibility, but many BI teams still struggle to present insights in business-friendly terms. Bridging this gap requires both technical tools and communication strategies. Building trust remains a critical challenge for effective AI integration. 

Explore how to build trust and safeguard data in our guide to AI and data privacy. 

4. Skill Gaps in BI Teams 

AI-powered BI demands a hybrid skill set that combines domain knowledge, data engineering, and machine learning. Many organizations face shortages of talent with this blended expertise. This limits their ability to build, interpret, and maintain AI solutions. 

Upskilling existing BI professionals and recruiting specialized talent takes time and resources. Without these capabilities, teams struggle to unlock AI’s full potential. Skill development must be a parallel priority alongside technology investments. 

5. Maintenance and Scalability 

AI models need continuous monitoring, retraining, and tuning to remain accurate as business conditions evolve. This adds a layer of complexity beyond traditional BI maintenance. Left unmanaged, model drift can degrade performance over time. 

Scaling AI in BI also requires aligning infrastructure, compliance, and workflow automation. Without a long-term operational strategy, even successful pilots may fail to scale enterprise-wide. Ensuring sustainability is key to realizing lasting value from AI. 

Learn how to validate performance and reduce risk across your AI initiatives in our detailed AI model testing guide. 

Specific Applications of AI in Business Intelligence 

1. Predictive Analytics & Demand Forecasting 

Predictive analytics in Business Intelligence uses historical sales and contextual factors to forecast future demand with precision. It addresses challenges such as stockouts and excess inventory by generating actionable forecasts integrated directly into BI systems. These forecasts optimize supply chain decisions, reduce carrying costs, and improve customer satisfaction. 

Technically, this involves time-series models, including ARIMA and LSTM neural networks, trained on structured data (sales, promotions, local demographics). The models continuously update as new data comes in, and integrate with BI dashboards or ERP systems to trigger reordering or logistics actions. These systems require robust data pipelines and regular retraining to respond to shifting demand patterns. 

Strategically, predictive forecasting drives financial efficiency, boosts turnover, and enables dynamic repricing. Improved forecast accuracy increases ROI on inventory and marketing investments. However, success relies heavily on data quality and timely calibration during disruptions like holidays or weather events. 

Real-world example:

Lowe’s uses zip-code–level sales combined with weather forecasts to load delivery trucks with optimal product mixes for each store. This BI-driven approach has enhanced in‑stock levels and reduced logistique overhead. As a result, the company reported a 2.3% gain in comparable sales year-over-year. 

2. Anomaly Detection & Predictive Maintenance 

AI-powered anomaly detection in BI continuously scans data streams to identify deviations, such as unusual costs, fraud, or equipment failure risks, before they escalate. This replaces slow and static rule-based methods prone to blind spots. Once detected, anomalies trigger alerts and root-cause dashboards for immediate investigation. 

Technologies include clustering, statistical control charts, and autoencoders trained on operational telemetry or financial transactions. These models feed insights directly into BI tools, where dashboards visualize anomalies across KPIs and allow drill-downs. Reliable data tagging and real-time processing are critical to reducing false alerts and actionable value. 

Operationally, early anomaly detection lowers downtime, fraud losses, and incident response times. It also reinforces trust in BI outputs among stakeholders. However, careful calibration is essential to balance sensitivity with alert fatigue and maintain data integrity. 

Real-world example:

Penske Truck Leasing monitors 433,000 trucks via telematics and its Catalyst AI platform, analyzing roughly 300 million data points daily. The system identifies emerging faults before breakdowns occur, which has significantly reduced maintenance costs and unplanned downtime. 

Learn how intelligent systems detect anomalies and prevent financial risks in our guide to AI-driven fraud detection. 

3. Natural Language Query & Insight 

NLP-powered query tools in BI allow users to ask questions in everyday language without writing SQL or manual report searches. This expands access to analytics across non-technical teams and democratizes data insights. It reduces dependence on BI specialists and accelerates decision-making. 

Underlying tech includes NLP/NLU modules mapping text to metadata, intent extraction, and query generation. Inputs range from typed questions to voice commands. Outputs are dynamically generated charts, tables, and narrative summaries embedded within BI dashboards. 

This enhances user engagement, as business leaders can extract insights independently and on demand. It increases adoption of BI platforms and supports agile responses to business needs. Challenges include domain-specific language tuning, disambiguation, and maintaining query accuracy. 

Real-world example:

JPMorgan Chase implemented an NLU-powered BI chatbot, enabling executives to retrieve complex financial data via natural language. It reduced data query time by ~40% and improved decision-maker engagement. This reflects strong adoption of conversational analytics in financial BI. 

4. Integrating External Signals: Weather Analytics 

Incorporating weather data into BI systems helps businesses align inventory, pricing, and promotions with external conditions. This adds a valuable layer beyond internal sales trends when planning for events like heatwaves or storms. Weather-aware BI creates smarter, localized decisions for each region. 

Technically, BI systems pull real-time forecasts via API, preprocessing variables like temperature and precipitation. These inputs feed regression or neural models that predict demand shifts, feeding results into dashboards. The process requires accurate geo-mapping, data synchronization, and weather-feature engineering. 

Strategically, this reduces waste, boosts targeted promotions, and improves turnover of weather-sensitive items. Retailers can dynamically adjust pricing and inventory across locations. Challenges include variable forecast accuracy, integration complexity, and model sensitivity to extreme events.  

Real-world example: 

Walmart uses weather-adjusted BI to, for instance, discount sunscreen ahead of rain forecasts and shift inventory accordingly. The system anticipates buying shifts, updating pricing and stock in real time. This approach has improved product availability and reduced lost sales. 

5. Industrial Predictive Maintenance 

AI-driven predictive maintenance in BI predicts machinery failure before it occurs, based on sensor data from industrial equipment. It transforms maintenance from reactive to proactive, reducing unplanned downtime and repair costs. These capabilities are integrated into BI systems to provide visibility and scheduling support. 

It uses IoT sensors to measure vibration, temperature, voltage, and more, feeding ML models that assess degradation risk. Output risk levels sync with maintenance dashboards to trigger alerts and optimize technician schedules. This demands high-frequency data collection, edge-level processing, and ongoing model retraining. 

Operational gains include extended asset life, fewer disruptions, and lower maintenance spending. Centralized BI dashboards allow stakeholders to plan work weeks ahead. However, implementation complexity and sensor deployment costs must be carefully managed. 

Real-world example: 

KONUX, in partnership with Deutsche Bahn, monitors railway switch infrastructure using AI-equipped sensors. Its BI-integrated platform predicts switch failures with over 90% accuracy and optimizes maintenance timing. This has significantly reduced service delays and maintenance costs. 

6. Social Trend & Sentiment Analytics 

Social trend analytics applies NLP to online conversations to identify emerging consumer interests before they become mainstream. This enriches BI with forward-looking signals, enabling proactive product and marketing decisions. It complements internal metrics by revealing demand drivers early. 

The platform ingests large-scale public social data and applies topic modeling, sentiment analysis, and trend scoring. These signals then feed into BI dashboards, enabling cross-comparison with sales and campaign performance. Signal noise, bias, and data privacy must be carefully managed in this process. 

The strategic benefit is early detection of product or market shifts, improved campaign timing, and reduced time to market. Brands can act ahead of competitors and align innovation with consumer enthusiasm. Accuracy depends on rigorous signal filtration and alignment with business context. 

Real-world example:

Black Swan Data’s Trendscope platform mines billions of online conversations using NLP to predict trends for clients like PepsiCo and Disney. It offers BI dashboards with 89% prediction accuracy on rising behaviors. This helps brands tailor product launches and campaigns with greater confidence.

Examples of AI in Business Intelligence 

Real-world applications démontrer how AI in Business Intelligence delivers measurable impact across industries. The following case studies highlight how top organizations are turning data into strategic advantage through AI-driven BI systems. 

Études de cas réels

1. Penske Truck Leasing: AI-Powered Predictive Maintenance 

Penske Truck Leasing harnesses AI within its BI ecosystem to monitor its fleet of approximately 433,000 vehicles. The system ingests over 300 million telematics data points daily analyzed by Catalyst AI to detect anomalies before they become critical. These insights are displayed in BI dashboards, enabling preemptive maintenance and efficient resource allocation. 

This proactive strategy has reduced unplanned downtime and lowered maintenance costs across its operations. Clients such as Darigold and Honeyville report faster issue response times and improved maintenance benchmarks. Penske’s approach underscores how large-scale AI-integrated BI systems can yield tangible operational and cost efficiencies. 

2. JPMorgan Chase: AI‑Driven Conversational Analytics & Efficiency 

JPMorgan Chase has rolled out generative AI tools, like their in-house LLM Suite and conversational interface, for over 200,000 employees, integrating directly with BI platforms. These tools assist in querying dashboards, summarizing research, and retrieving client insights, enabling financial advisors to find relevant data up to 95% faster. Coach AI has transformed dashboard interaction by anticipating queries and delivering contextual insights. 

During April 2025’s market volatility, these AI tools helped the bank boost asset & wealth management sales by 20% between 2023 and 2024. The bank saved nearly $1.5 billion from fraud prevention, trading improvements, and credit decision efficiencies. This highlights AI+B I’s power to accelerate decision-making and drive revenue in financial services. 

3. Walmart: Weather-Driven Business Intelligence 

Walmart enriches its BI systems by integrating real-time weather data to drive region-specific inventory and pricing decisions . For example, the company dynamically adjusts stock and promotions to match shopper behavior. These weather-aware insights flow into BI dashboards that update store-level strategies. 

By anticipating consumer behavior shifts tied to weather patterns, Walmart avoids lost sales and inventory imbalances . The process empowers localized, demand-informed decision-making and more efficient promotions. While specific ROI figures aren’t publicly disclosed, the approach is widely regarded as a best practice in BI-driven retail analytics. 

See how AI and data chat streamlined analytics at Zara in our article on the case study of the brand’s transforming business intelligence. 

Solutions d'IA innovantes

AI is rapidly transforming Business Intelligence from static reporting tools into dynamic, decision-support systems. Innovations like generative copilots, conversational interfaces, and autonomous agents are making it easier to extract insights, automate workflows, and respond to data in real time. These advancements are helping organizations move from reactive reporting to proactive, AI-driven decision-making. 

Modern BI platforms now embed AI to automate routine tasks, generate insights, and guide users through complex data without needing deep technical skills. This shift is enhancing productivity, reducing time-to-decision, and making advanced analytics accessible across business functions. As AI continues to evolve, BI systems are becoming smarter, more intuitive, and deeply integrated into daily operations. 

Explore how AI streamlines workflows and drives performance improvements in our guide to unlocking operational efficiency with AI. 

AI-Driven Innovations Transforming Business Intelligence 

Emerging Technologies in AI for Business Intelligence 

AI is transforming business intelligence by enabling faster, smarter data interpretation. Machine learning finds patterns, detects anomalies, and predicts outcomes in real time. This shift moves teams from reactive reporting to proactive, data-driven decisions. 

Generative AI makes insights more accessible by turning questions into visualizations and summaries. Non-technical users can explore data without expert support. Computer vision also extends BI capabilities by analyser visual information for deeper insights. 

Le rôle de l'IA dans les efforts de développement durable 

AI is helping businesses drive sustainability by identifying inefficiencies and reducing waste across operations. Predictive analytics forecasts demand and optimizes supply chains, cutting excess production and lowering carbon footprints. These insights help companies balance environmental goals with profitability. 

Smart systems powered by AI monitor energy use and adjust consumption in real time. This not only reduces costs but also supports compliance with green regulations. Over time, these tools build more resilient and environmentally responsible operations. 

How to Implement AI in Business Intelligence

Implementing AI in business intelligence isn’t just about adding new tools, c'est about reshaping how your organization makes decisions. To get real value, you need a thoughtful, step-by-step approach that starts with readiness and ends with long-term adoption. 

Étape 1 : Évaluer l’état de préparation à l’adoption de l’IA

Before introducing AI into your business intelligence strategy, evaluate where your organization stands digitally. Start by identifying processes that consume time through repetitive reporting or manual analysis – these are often ideal for early automation. Pinpointing such areas helps you demonstrate quick wins with minimal operational risk. 

You should also consider organizational mindset and leadership support. AI adoption requires cross-functional collaboration and a willingness to adapt established workflows. Without alignment from key stakeholders, even well-designed initiatives can struggle to take root. 

Étape 2 : Construire une base de données solide

A successful AI strategy starts with high-quality, well-structured data. Business intelligence tools depend on accurate inputs from systems like CRMs, ERPs, and marketing platforms to deliver reliable insights. If data is incomplete or inconsistent, your AI models won’t perform effectively. 

Create a unified data architecture where systems can easily exchange information. Implementing strong data governance practices ensures your data stays clean, compliant, and secure. With this foundation in place, AI tools can generate insights you can trust. 

Découvrez comment our data analytics services can strengthen your AI models and accelerate business intelligence outcomes. 

Étape 3 : Choisir les bons outils et les bons fournisseurs

Choosing AI tools for business intelligence is about more than advanced features, it’s about strategic fit. Look for platforms that integrate smoothly with your existing data ecosystem and align with your analytics goals. The best tools offer intuitive interfaces and can scale as your business grows. 

Vendor transparency is equally important. Understand how each tool manages your data, including privacy, model training, and ownership rights. A good partner should offer not only robust functionality but also clear documentation, responsive support, and a strong roadmap. 

Étape 4 : Tests pilotes et mise à l’échelle

Start with small, focused pilots to validate the effectiveness of AI in your analytics workflows. For example, test AI to automate quarterly reporting or predict product demand in a single region. These targeted experiments help fine-tune your approach and generate valuable internal case studies. 

Use what you learn from the pilot to guide broader implementation. Measure performance improvements, gather feedback from stakeholders, and iterate where needed. Once you show tangible benefits, expanding to other departments or processes becomes a natural next step. 

Étape 5 : Former les équipes pour une mise en œuvre réussie

Your teams need more than just access to AI, they need to understand how to use it effectively. Offer role-specific training that covers not only the tools themselves but also the new decision-making processes that AI enables. This builds confidence and increases user adoption. 

Encourage ongoing collaboration between business analysts, data scientists, and operational leads. When technical and domain experts work together, AI becomes a tool that enhances – not replaces – human judgment. Investing in people is just as important as investing in the technology itself. 

Measuring the ROI of AI in Business Intelligence 

Indicateurs clés pour suivre le succès

Understanding the return on investment from AI in business intelligence begins with tracking performance over time. Start by measuring improvements in forecasting accuracy, decision speed, and the amount of manual work eliminated through automation. These metrics help quantify productivity gains that directly impact revenue and efficiency. 

Cost savings also play a major role in ROI. Reductions in resource usage, operational errors, and data processing time can all translate into measurable financial benefits. By aligning these results with initial implementation and training costs, companies can clearly assess whether AI is driving sustainable business value. 

Études de cas démontrant le retour sur investissement

One real-world example comes from a major U.S. retailer that partnered with EXL to enhance supply chain forecasting. Within one year, the company boosted revenue by 21% and cut costs by 20% by aligning inventory more accurately to demand. The result was fewer stock-outs and smarter ordering across their network. 

Penske Truck Leasing used AI to predict maintenance issues across its large fleet. This reduced vehicle downtime, extended equipment life, and lowered repair costs. The financial return was clear, with operational savings that far exceeded the cost of the AI system. 

Pièges courants et comment les éviter

Many organizations struggle to see ROI from AI because they rush in without a clear strategy. Jumping straight into flashy dashboards without clean data or measurable goals often leads to low adoption and failed projects. To avoid this, businesses should invest upfront in strong data practices and align AI initiatives with specific business challenges. 

Another pitfall is underestimating the importance of change management. Teams need to understand how AI supports their roles, not threatens them, so clear communication and training are critical for long-term ROI. 

Future Trends of AI in Business Intelligence 

Prévisions pour la prochaine décennie

AI is rapidly shifting business intelligence from static reporting to dynamic, real-time decision-making. Generative AI will streamline analysis, automate reports, and offer predictive insights with minimal manual input. This evolution will make strategic planning faster, smarter, and more adaptive. 

Autonomous AI agents will take this further by running experiments, optimizing operations, and adjusting to change automatically. These systems will integrate across teams, driving more connected and responsive organizations. Staying ahead will require strong data governance, ongoing training, and a commitment to responsible AI use. 

Comment les entreprises peuvent garder une longueur d'avance

To stay competitive, organizations must treat AI as a strategic pillar of business intelligence. This includes building scalable data systems, developing internal expertise, and fostering collaboration across teams. AI should be integrated into core decision-making processes, not added as a secondary tool. 

Equally important is maintaining a culture of adaptability and continuous learning. As AI evolves, teams must evolve with it. Leading companies will be those that use AI to enhance agility and insight across the enterprise. 

Learn how AI-driven strategies can propel your business forward in our guide to harnessing the power of AI. 

Conclusion

Principaux points à retenir

AI is reshaping business intelligence by delivering faster insights, predictive accuracy, and greater decision-making power. It moves business intelligence from static reports to dynamic, real-time analysis that drives measurable business impact. 

Achieving success requires clean data, strategic implementation, and a commitment to ongoing learning. When executed well, AI in business intelligence not only boosts efficiency but becomes a key source of long-term competitive advantage. 

Moving Forward: A Strategic Approach to AI-Driven Transformation 

As AI continues to redefine business intelligence, organizations have an opportunity to unlock deeper insights, faster decision-making, and significant operational gains. From enhancing forecasting accuracy to automating analysis and scaling strategic planning, AI is rapidly becoming essential for maintenir a data-driven competitive edge. 

À Développement intelligent, we provide tailored AI solutions that integrate seamlessly into business intelligence ecosystems. Whether the goal is to streamline reporting, implement predictive analytics, or effet de levier generative tools for faster insight delivery, our team collaborates closely to align technology with strategic outcomes. 

Explore our AI-powered software development services to see how we build intelligent solutions tailored to the unique data and decision-making needs of B2B organizations, from insight generation to strategic execution. 

Contactez-nous aujourd'hui to discover how AI can transform business intelligence capabilities and drive smarter, faster growth across your organization. 

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Références:

1. The state of AI: How organizations are rewiring to capture value | McKinsey & Company
2. 105 AI Statistics (2025) – Adoption & Growth Data | DemandSage
3. Lowe’s: AI Use Cases 2024 | Pitchgrade
4. NLP in Business Intelligence: 7 Success Stories, Benefits, and Future Trends | Coherent Solutions
5. Retailers Turn to Weather Strategies as Climate Changes | Inc.com
6. Penske is using AI to get ahead of costly truck maintenance issues | Business Insider
7. JPMorgan says AI helped boost sales, add clients in market turmoil | Reuters
8. Utilities are tiptoeing into AI as climate change and data center growth add stress to the energy grid | Business Insider
9. Automating Business Intelligence Requirements with Generative AI and Semantic Search | arXiv