Businesses have always sought ways to extract more value from data. However, traditional methods are no longer sufficient, given the growing volume and complexity of the data infrastructure. That’s why organizations adopt more advanced technologies, such as artificial intelligence (AI) for business intelligence (BI).
According to the MRF report, the BI software market will grow from $34.27 billion in 2025 to around $52.76 billion by 2034. Meanwhile, about 55% of McKinsey’s 2023 State of AI report respondents said they adopted AI for at least one business function. The majority of these AI-enabled capabilities involve data analytics.
This article illustrates the practical benefits of AI-powered BI for various stakeholders and company types. It also explores real-world applications and successful deployment across sectors, specifically in financial technology and banking.
Key takeaways:
- By implementing AI, businesses can significantly improve their business intelligence capabilities, particularly data collection and processing, pattern detection, and predictive analytics.
- Generative AI and natural language processing provide business leaders with direct access to actionable insights, making complex, unstructured data understandable without extensive technical support.
- Real-world AI applications in BI demonstrated measurable outcomes, such as accelerated contract approval, optimized supply chain, proactive disruption prevention, and reduced fraud.
Benefits of using AI in business intelligence
Business intelligence involves transforming raw data from enterprise systems and databases into actionable insights for proactive managerial decision-making and automating routine tasks. AI can amplify traditional BI tools, enhancing the ability to collect, interpret, and act on complex data. This benefits both organizations as a whole and individual stakeholders within them.
Benefits for data specialists
A large portion of data scientists’ time is spent on technical overhead, such as preparing data, managing pipelines, and building queries for business intelligence systems. AI-powered BI solutions can automate some of these mundane preparation tasks. For instance, such tools can clean, categorize, and transform structured and raw unstructured data in real time, cutting away a lot of repetition.
Generative AI also allows analysts to run queries in natural language and explore more complex business questions with far less manual setup. Additionally, it can quickly translate complicated data patterns into understandable language so that executives, product managers, marketers, and operations teams can use it without any hurdles.
Neontri recommendations:
- Create feedback loops and cross-functional teams to involve data specialists in strategic decisions.
- Train data scientists to frame AI-driven insights in ways that are understandable to less tech-savvy employees.
- Avoid measuring data specialists’ performance by the number of reports; instead, shift toward their ability to produce revenue-impacting presentations.
- Encourage proactive experimentation with AI and BI tools, which often means loosening up managerial oversight.
Benefits for business leaders
Executives often have to read overly technical analytical reports that lack context for decision-making. This leads to follow-up meetings where key findings have to be explained. With generative AI and natural language processing, C-level managers can work with BI tools directly. Thus, AI tools democratize analytics, providing business executives with simple, intuitive access to actionable insights.
BI systems can also highlight anomalies, forecast trends, and recommend next steps. For example, the tool might alert a sales manager about an unusual drop in demand in a specific region. In this way, implementing AI in BI software can help executives manage risks more proactively.
Neontri recommendations:
- Ensure leaders understand how to interact with AI-powered tools (what the tool can and cannot do, how to ask questions correctly, when not to trust the generated output, etc.).
- Keep in mind the limitations of AI-powered tools, as they can make flawed assumptions, perpetuate bias, or violate privacy.
- Appoint a data specialist who can oversee the adoption of BI tools, especially among non-technical decision-makers.
Benefits for medium-sized companies
Using AI for business intelligence can help companies effectively predict and adapt to market shifts and consumer preferences, creating a solid foundation for growth. AI systems are good at identifying micro-segments within a broader customer base to craft hyper-targeted campaigns. They can also reveal the best channels for driving revenue. Similarly, predictive analytics tools within the BI suite can help determine, rather than guess, which regions are the most profitable for particular products or services.
Neontri recommendations:
- Configure AI to monitor key performance indicators (i.e., those that could tell more about what’s likely to happen and enable preemptive corrections) instead of vanity metrics.
- Balance automated insights with human validation to ensure AI outputs are constructive and logical.
- Adopt scalable cloud-based BI tools with modular architecture that are easier to integrate with existing business apps and data sources or scale depending on business needs.
Benefits for enterprises
Large-scale organizations usually rely on a wide range of systems that generate thousands of data inputs. However, traditional BI systems are often limited in handling all this information.
AI models can ingest real-time data continuously, identifying inefficiencies and anomalies before data analysts can recognize them. More importantly, they can deliver these insights granularly across thousands of business metrics.
AI integration in BI helps enterprises deal with complex legal frameworks, especially if they operate in multiple regions with highly regulated industries, such as fintech and healthcare. Among other things, these tools automate monitoring, thorough risk assessments, and audit trails.
Neontri recommendations:
- Implement tiered data access models across departments to make relevant data accessible to the right people without creating unnecessary security risks.
- Institute regular audits of AI outputs and training data to ensure the BI tools retain their accuracy (as well as prevent model drift or bias).
- Establish closed-loop feedback that allows users to flag false positives and errors in AI-generated insights or annotate findings with real-world explanations (these can be used to monitor the accuracy of the BI tool).
Applications of AI in business intelligence
The task of AI for business intelligence is to help make data-driven decisions faster with fewer blind spots and no human error. AI technologies have capabilities that transform static analytics into actionable insights based on vast amounts of real-time data sources. These advanced capabilities revolutionize how organizations leverage their data assets across various business functions.
Hyper-personalization
AI and machine learning models in BI tools help businesses decide what to personalize, for whom, and what for. Advanced solutions can analyze various aspects of customer behavior (sentiment shifts, purchase histories, content interactions) to create suggestions for development, marketing, and sales teams.
With AI, every customer click becomes new data businesses can use to dynamically adjust what’s shown for each user based on contextual cues. The systems can then refine their approach depending on how often the customers follow personalized recommendations.
Examples:
- Amazon implemented AI-enhanced BI to map purchasing behavior across billions of data points. It looks through purchases, searches, customer feedback, and reviews to provide personalized recommendations, such as similar products, accurate size charts, or preferred fabric stretch.
- Netflix uses AI and BI to optimize the recommendation layout for each user. It suggests movies that people are likely to enjoy based on past viewing patterns, skip rates, and trending data.
Fraud detection
AI-powered business intelligence tools enhance fraud detection across banking, fintech, e-commerce, and other industries. Unlike traditional solutions that rely solely on hardcoded rules (such as flagging logins from different IP addresses or unusually big money transfers), AI analyzes vast amounts of transactions and behavioral patterns to detect suspicious activity specific to each customer.
AI also helps security teams visualize larger fraud endeavors. For example, they might see a heatmap of login attempts or abnormal clusters of fund transfers in specific timeframes.
Examples:
- J.P. Morgan uses generative AI to process payment validations to lower fraud levels and reduce false positives. This helped to decrease validation rejection rates by about 20%, making the experience more pleasing for customers.
- PayPal integrates AI and machine learning to catch fraud that disperses across multiple channels. Their software also provides risk scores to help businesses manually approve or decline transactions.
- HSBC uses AI systems to check over 1.35 billion transactions for signs of financial crime. This allowed the bank to eliminate false positives by 60%.
Business forecasting
Conventional BI tools focus on historical data and single-variable projections. Predictive analytics, on the other hand, leverages real-time data from ongoing operations. Thus, it can analyze more inputs and adapt to contextual changes.
Integrating AI in the analytical loop allows BI platforms to pull information from inventory sheets, local weather forecasts, promotional calendars, economic indicators, and foot traffic data. This helps businesses predict future trends per region, store, or product line.
Examples:
- Walmart uses artificial intelligence to analyze past data related to sales performance and market trends. By combining AI with predictive analytics, the company can anticipate consumer purchasing patterns, ensuring optimal inventory placement across both retail stores and distribution centers.
- Amazon employs artificial intelligence systems to forecast daily customer demand across its catalog of over 400 million products. AI technologies also optimize delivery routes by analyzing real-time traffic conditions and weather patterns, enabling more efficient shipping operations
Anti-money laundering
Anti-money laundering (AML) is integral to financial and banking operations. It requires continuous monitoring of financial transactions, documentation, and legal frameworks. AI algorithms can automate these processes, offering deeper insights with context for human review.
Generative AI systems can cross-reference client activity with external sources, including sanctions lists and regulatory watchlists. They can recognize patterns indicative of money laundering or structuring (i.e., deliberately dividing large transactions into smaller ones to avoid detection).
Additionally, machine learning models integrated within BI tools can automatically adjust risk assessment parameters and monitoring thresholds based on new data. This eliminates the need for complete system reconfiguration, saving time and reducing the workload for both IT departments and compliance teams.
Examples:
- Ernst & Young’s artificial intelligence platforms employ natural language processing technology to streamline customer due diligence procedures and screening control mechanisms.
- Standard Chartered leveraged natural language processing and machine learning to streamline compliance checks, reducing regulatory breaches by 40% across jurisdictions.
Loan underwriting and approval
With AI-powered business intelligence, loan underwriting is transforming from a slow and tedious process to a highly automated and predictive workflow. Beyond traditional evaluation metrics such as credit scores, income-to-debt ratios, and employment verification, AI tools can analyze subtle, unconventional factors. For example, they can examine a person’s utility bill payments, e-commerce spending, social media indicators, or even device data.
These advanced systems can assess risk by examining patterns in an applicant’s utility payment history, online shopping behaviors, relevant social media indicators, and even how they use their digital devices. Furthermore, business intelligence platforms enhanced with AI can dynamically adjust the importance of various risk factors based on current economic conditions and the individual’s overall financial stability.
Example:
- United Wholesale Mortgage has implemented an artificial intelligence-powered underwriting system that significantly decreases loan processing times. This advanced technology enables the company to complete certain loan approvals in under 15 minutes.
Claims processing
BI tools help insurers handle claim intake, review, and payout processing. With AI technologies, these systems can assess a claim’s validity based on submitted evidence much faster than traditional methods. They can also help scan past the initial case, identifying repeated claims for similar damages or minor inconsistencies, especially regarding timing.
Additionally, insurers can use AI to analyze claim trends at the macro level. For example, if vehicle accident claims in one ZIP code surge after a weather event, the platform can suggest rate adjustments, identify risk clusters, or recommend underwriting strategy changes.
Example:
- State Farm uses intelligent document processing software to extract key information from contracts, score customers based on thresholds, and identify risky clauses.
Data visualization
AI can generate visuals highlighting key BI processes and findings, which is a great alternative to manually customizable dashboards showing raw metrics. Businesses can use AI-powered BI tools to create data-driven storytelling, presenting executives with information about what happened, what caused it, and what to do next.
Beyond automated reports, AI makes information in datasets more accessible to stakeholders without technical expertise. These BI platforms can also translate visualizations into plain English or answer questions conversationally. The dashboard can adapt visuals based on the employee’s role so they see what matters most to them.
Examples:
- Salesforce Einstein enables users to build visual charts from large sets of business data through simple natural language queries. Furthermore, the platform provides explanatory insights about the factors influencing the displayed results.
- Pyramid Analytics combines artificial intelligence and large language models to allow non-technical users to create dynamic, real-time dashboards by asking questions in everyday language. The system automatically selects the most appropriate visualization format for the requested data, which users can then refine through additional queries.
Data pattern and trend identification
AI ingests structured and unstructured data streams to define non-obvious relationships that human analysts can miss. When an AI for business intelligence is connected to other enterprise platforms (CRMs, ERPs, social media signals, etc.), companies can identify what happens and correlate it with other factors.
For example, if a business sees a sudden sales spike in one geography, the AI might correlate that with recent regional marketing efforts, weather conditions, or economic incentives. Unlike traditional BI tools, AI-enhanced platforms can connect all these seemingly disparate variables and evolving patterns into a cohesive image. They also simply trend comparisons over time across cohorts, channels, and regions.
Examples:
- H&M Group adopted AI-based BI to understand purchasing trends and customer preferences across stores by analyzing receipts, returns, and loyalty card data. Its algorithm also scours social networks and search engines for insight into fashion trends.
- Uber implemented AI to track supply and demand between drivers, food delivery services, and customers. It also helps anticipate changes in traffic patterns to identify the most time-efficient routes in cities.
Interactive scenario modeling
AI enables businesses to simulate thousands of realistic outcomes across real-world scenarios. For instance, they can simulate the impact of price changes, marketing campaigns, supply chain delays, or geopolitical events.
Moreover, scenarios can be adjusted in real time based on fresh data inputs, allowing businesses to rerun projections as conditions change. Decision makers can model several scenarios of a particular action affecting the company and act accordingly based on the results. These BI capabilities are often used in retail and fintech (for example, to predict profit margins or simulate interest rate changes).
Examples:
- Acme Solar Technologies uses AI simulations to evaluate risks introduced by raw material fluctuations, government subsidy reductions, and consumer demand shifts.
- BMW Group uses AI to create a 3D mapping of future manufacturing structures and simulate employee workflows on assembly lines.
Supply chain optimization
Thanks to predictive analytics and forecasting, BI software can adjust inventory to avoid overstocking or missed sales. With the help of advanced AI, it can evaluate real-time traffic, vehicle capacities, weather prognosis, or fuel costs to optimize transportation routes.
Artificial intelligence also plays a key role in avoiding costly disruptions. It can scan regional economic indicators, suppliers’ or manufacturers’ networks, news, and other geopolitical factors affecting the supply chain. If this happens, the BI tool sends automated alerts and recommends contingency plans to ensure continuity.
Real-life examples:
- Coca-Cola European Partners leverages IBM’s AI to extract strategic insights into its procurement. As a result, the company cut about $5 million in annual costs and improved its catalog coverage.
- United Parcel Service (UPS) uses an AI-powered BI platform to predict the best logistical routes and optimal delivery options, significantly reducing delivery miles and vehicle maintenance costs.
Custom intelligence solutions with Neontri
At Neontri, we offer over a decade of proven expertise in fintech, banking, and e-commerce software solutions. We design and implement bespoke business intelligence platforms tailored precisely to your organization’s unique challenges and objectives. Our solutions seamlessly integrate with your existing infrastructure while enhancing analytical capabilities through advanced AI technologies.
Our seasoned team of developers, data scientists, and industry consultants has successfully delivered cutting-edge technological solutions for organizations ranging from emerging startups to established financial institutions. Whether you want to enhance existing BI capabilities or build entirely new intelligent systems, Neontri offers the technical expertise and industry knowledge to deliver transformative results.
The future of business intelligence
Leveraging AI unlocks advanced analytics capabilities, automation features, and predictive insights that transform traditional business intelligence software. As these technologies continue to mature, we anticipate:
- Conversational analytics emerging as the standard interface for intuitive data exploration;
- Automated insight generation identifying critical patterns and anomalies without human intervention;
- Predictive modeling delivering increasingly accurate forecasts through accessible platforms;
- Decision intelligence frameworks that analyze complex data and recommend specific, actionable business strategies;
- Cognitive automation that continuously refines its functionality by learning from each user interaction.
Discover how these innovative capabilities can drive measurable growth for your organization. Contact us today for a personalized consultation and propel your company to the forefront of AI-powered business intelligence.
Resources
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