Over the past five years, artificial intelligence (AI) adoption more than doubled from around 20% of surveyed organizations in 2017 to approximately 50% by 2022, according to the McKinsey report. The same report says that more than half of organizations surveyed are adopting AI to leverage it for predictive analytics and decision support, which are the fuel of business intelligence (BI).
With artificial intelligence, business intelligence becomes more powerful, precise, and proactive. To better understand how these two technologies fuse to enhance decision making, we discuss benefits, specifics, use cases, implementation steps, and common complexities that AI for business intelligence addresses.
The Growing Role of AI in Business Intelligence
In recent years, it has become evident that business intelligence and AI are a great match when it comes to ensuring your business is driven with proactive decisions. One of the greatest factors contributing to this is predictive capabilities powered by AI. It covers a range of tasks that were previously done manually and lacked precision. Those are the identification of trends, outcome forecasting, and uncovering hidden opportunities within vast datasets. Thanks to the execution of these tasks, businesses can create reports and dashboards that not only reflect past performance but also provide forward-looking insights to guide the company stakeholders’ decisions based on market changes, customer needs, and operational challenges.
AI-based business intelligence offers the ability to aggregate, analyze, and visualize data, making it an essential tool for achieving operational excellence across multiple sectors, including finance, eCommerce, healthcare, and manufacturing. But it can go even further with the integration with emerging technologies such as machine learning (ML), IoT, and augmented analytics. They make BI even more impactful. For example, real-time data from IoT devices can feed into BI systems to monitor equipment performance, while ML algorithms continuously refine predictions and recommendations. This seamless synergy empowers businesses to move beyond static insights.
Advantages of Business Intelligence AI
The integration of AI in BI enables businesses to make wiser decisions faster that allows companies to act ahead of competitors and, thus, future-proof their operations. This can be achieved thanks to the following capabilities.
- Automated Data Analysis: AI-powered BI significantly accelerates the execution of repetitive tasks such as data preparation, anomaly detection, and trend identification and frees up human resources.
- Enhanced Predictive Capabilities: AI allows BI tools to leverage predictive analytics to analyze historical and real-time data and anticipate demand spikes, customer churn, or operational disruptions.
- Democratization of Analytics: AI lowers the technical barriers to accessing BI insights. It empowers non-technical users to engage with data through natural language interfaces and automated visualizations.
- Personalized Insights: AI algorithms in BI platforms analyze specific user roles, preferences, or business objectives to enable personalized insights for specific business functions or targeted goals.
- Improved Decision-Making: BI tools with AI capabilities process large streams of data instantaneously, helping businesses in identifying opportunities, risks, and performance bottlenecks in real time to make a decision in no time.
- Cost and Time Efficiency: Automation brought to BI operations with AI, allows preparing data and generating reports or dashboards in minutes without the need for human intervention.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“Once a company is equipped with AI-driven BI tools and processes, it elevates decision-making, and with improved decisions, companies become more agile, resource-efficient, and more productive.”
AI BI vs Traditional Business Intelligence
In the late 1990s and early 2000s, businesses relied on traditional BI and descriptive analytics. BI specialists collected past performance data and summarized it into insights. Then, the data was presented in the form of reports and dashboards to help stakeholders make decisions. However, those decisions were based on previous outcomes and did not often guarantee that things would go according to the anticipated scenario.
At the same time, traditional BI heavily depended on predefined rules and manual efforts. This approach didn’t accommodate processing of complex, unstructured data and was prone to errors, delays, and inefficiencies. Therefore, reports and dashboards were static, often lacking the flexibility to adapt to changing business needs and the ability to uncover deeper insights.
Luckily, AI changed the rules of the game and empowered BI tools with ML, natural language processing (NLP), and predictive analytics. From now on, businesses gain real-time, actionable insights that not only explain “what happened” but also predict “what will happen” and suggest “what to do about it.” AI models continuously learn from data trends, improving accuracy and relevance over time. This allows businesses to anticipate risks, identify opportunities, and optimize operations dynamically.
AI and BI: The Power of Transformative Technologies
AI-powered business intelligence goes hand-in-hand with other technologies that will help BI perform more complex tasks and cover more functions.
Machine Learning and Deep Learning
According to GrandViewResearch, the global machine learning market size is expected to grow at a compound annual growth rate (CAGR) of 34.8% from 2023 to 2030. This is not a surprising statistic since ML serves as the foundation of enabling AI-driven insights and is essential for many businesses activities, including BI. ML expertise allows leverage complex algorithms and analyze large datasets, identify patterns, and generate predictions, continuously improving over time with more data. Deep learning goes even further and employs neural networks to uncover complex relationships in unstructured data such as images, text, and audio.
Combining machine learning and business intelligence, businesses unlock:
- Predictive and Prescriptive Analytics
- Automated Anomaly Detection
- Improved Personalization
- Real-Time Insights
Natural Language Processing (NLP)
NLP complements the traditional data visualization and reporting functions of BI by making insights more digestible, uncovering hidden patterns in text data. It is done by applying AI techniques, such as ML, deep learning, and statistical modeling, to process unstructured textual or spoken data so that machines can derive meaning, context, and intent.
With NLP, businesses can support BI capabilities thanks to:
- Automated Text Analysis for Unstructured Data
- Sentiment Analysis
- Contextual Understanding and Summarization
Generative Business Intelligence
Gartner predicts that over 80% of organizations are expected to either integrate generative AI APIs or implement applications powered by generative AI technologies by 2026. Generative AI for business intelligence can solve tasks that require an intuitive, context-rich approach. Beyond static dashboards and structured queries, generative AI development for BI allows users to simply ask questions in natural language and receive explanatory narratives, visualizations, and predictions or translate raw data into conversational insights.
With generative business intelligence, companies can:
- Perform Scenario Simulation and Forecasting
- Execute Dynamic Data Storytelling
- Get Real-Time Decision Support
- Set Up Creative Data Visualizations
Computer Vision
Computer vision development allows companies to extend the capabilities of AI and enhance BI systems with abilities to interpret and understand images and videos. It is ideal for tasks like recognizing objects, detecting anomalies, and classifying scenes. By interpreting images and videos, it enables organizations to monitor processes, ensure quality control, and gain a deeper understanding of visual patterns in their operations.
BI systems powered with computer vision allows:
- Automate Quality Control
- Enable Real-Time Monitoring
- Supports Predictive Capabilities
Robotic Processes Automation (RPA)
When it comes to rules-based tasks in BI, RPA comes as an effective and highly demanded tool to automate them. In fact, RPA is the most commonly embedded tool in AI-driven products or business processes, with 39% of respondents from organizations that have adopted AI in at least one function reporting its use according to McKinsey. Although not inherently “intelligent,” RPA becomes more powerful when coupled with AI models like NLP or ML-based document recognition. It streamlines data collection and preparation steps and, hence, frees up employees to concentrate on higher-value analytics.
For instance, RPA bots help businesses to:
- Classify Incoming Data
- Automatically Input Existing Metrics into Dashboards
- Monitor Dynamic Operations
Big Data
As organizations collect vast amounts of structured, semi-structured, and unstructured information, the importance of Big Data becomes evident. It drives decisions, yet, it needs to be processed and analyzed at first. This is where AI comes into play alongside machine learning, deep learning, and advanced analytics techniques to understand complex patterns and trends hidden within these colossal datasets. In such a way, companies can harness Big Data to uncover correlations, predict future outcomes, and align resources more effectively.
Big Data enables BI to:
- Improve Accuracy and Granularity of Insights
- Support Predictive and Prescriptive Analytics
- Enhance Personalization
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“AI-driven BI inevitably works with other advanced technologies, as integration with machine learning, NLP, and big data enhances data analysis, improves accuracy, enables real-time insights, and fosters actionable business decisions.”
Key Use Cases of AI Business Intelligence Across the Industries
Large companies deal with much more data across multiple departments and stored during a long period of time. This is why, in enterprise business intelligence, AI is an integral part of processes and operations. However, each industry uses BI AI in their own way.
eCommerce
In eCommerce, BI aggregates data from customer interactions, sales, and website activity. This allows businesses to create a 360-degree view of customer behavior. Based on that data, AI-powered analytics used in eCommerce business intelligence uncover trends and preferences, which later allows crafting personalized product recommendations, optimized pricing strategies, and demand forecasting. The result of using such AI-powered BI is increased sales and revenue growth.
Finance
Financial business intelligence uses AI to aggregate data from vast transactional sources to detect anomalies, analyze patterns, and predict risks. It plays a pivotal role in fraud detection, where ML models identify suspicious activities in real-time, improving security and compliance. BI tools also enhance risk management in the financial sector. It is typically done by forecasting credit risks and market trends, helping financial institutions optimize portfolio performance.
Healthcare
With AI, business intelligence can empower the healthcare sector by aggregating patient records, medical histories, and real-time monitoring data and using all of that data for predictive analytics. In this manner, BI improves operational efficiency by suggesting diagnosis and treatment plans and, ultimately, improves patient outcomes. Plus, healthcare business intelligence can be also used for an in-depth analysis to identify disease patterns, predict patient risks, and optimize treatment plans, while visualizations assist healthcare providers in tracking performance metrics, such as treatment success rates or hospital resource utilization.
Manufacturing
AI works well with IoT sensors that can be attached to manufacturing equipment, and then it can collect, analyze, and visualize data from these sensors. This can be done for production lines, machinery, and supply chain equipment to enable real-time monitoring of operations, helping manufacturers identify inefficiencies and optimize processes. AI and BI in manufacturing also allows for predictive maintenance. It means that predictive analytics can check equipment performance data and signal about the necessary repair works upfront . This minimizes unplanned downtime, extends machinery lifespan, and reduces maintenance costs.
AI in BI: Step-by-Step Instruction to Strategic Implementation
The implementation of artificial intelligence in business intelligence is a multi-stages process that requires careful planning and thoughtful consideration of unique needs of a particular business. However, from our experience, some steps are essential to complete for every company.
1. Starting with a Strategy
The best way to start is always to strategize every detail. When clients turn to us for implementing AI into their business intelligence, we usually start collaboration with data strategy consulting.
During strategic sessions, we clarify overarching objectives, identify which business functions like sales forecasting, inventory optimization, or customer sentiment analysis will benefit most from AI-based business intelligence. This allows us to outline quantifiable targets and evaluate potential ROI. Based on this information, we can align AI initiatives with the overall company’s BI strategy.
2. Ensuring Data Readiness
Gartner reports that the average losses caused by poor data quality are around $12.9 million a year. To stop losing money, it is essential to prepare data for its usage by AI. We usually pay particular attention to data quality management before integrating AI for BI.
This process involves establishing data governance principles, data cleansing and standardization, consolidation of data sources, and enhancing metadata. Such an approach allows us to ensure that AI models have reliable, well-structured inputs and the insights generated by it are accurate and valuable.
3. Selecting the Right AI Tools and Technologies
By considering data security requirements, scalability needs, and budget constraints, we can see whether the AI-driven BI projects need cloud-based or on-premise solutions. Cloud infrastructure benefits rapid scalability, flexible resource allocation, and advanced analytics features, while on-premise solutions help in prioritizing data security and compliance.
Another important factor to decide in this stage is whether to leverage pre-built AI models or develop custom solutions at this stage. The former is ideal when time-to-market is critical, and resources are limited, and the latter is preferable for unique business requirements, proprietary data, or solving complex problems.
4. Integrating AI into BI Workflows
Once the tools are chosen, we achieve their seamless integration with BI systems by developing APIs or leveraging microservices architecture. With APIs, we allow systems to communicate and exchange data without disruptions. By exposing AI model outputs as API endpoints, other components of the BI system can query these insights on demand and display them in real time.
If we opt for a microservices architecture, on the other hand, we can break down applications into loosely coupled, independently deployable services, further streamline the process. In this case, each AI capability can operate as a separate service, scaling and updating as needed without affecting the rest of the analytics ecosystem.
5. Adopting Phased Implementation
When starting to integrate AI in business intelligence, it’s strategic to first focus on a single, well-defined use case rather than attempting a full-scale rollout across the entire organization. For that, we usually recommend identifying a high-impact area. This can be improving sales forecasts, optimizing inventory levels, or enhancing customer support. With a clear goal set, it is possible to pilot the project.
To understand how this project is driving value, we typically monitor its performance. At this stage, it is important to track metrics like model accuracy, user adoption rates, or time saved and collect feedback. Thus, pain points and strengths can be identified and refined according to the overarching business goals.
6. Training and Change Management
Bringing AI-driven BI processes often requires a shift in how employees work, therefore it is crucial to help them adapt to this technology. This is where training sessions become helpful in understanding how to interpret and apply the results of automated business intelligence. These training programs might include interactive workshops, hands-on exercises with real company data, and dedicated office hours where data analysts or data scientists can guide employees through specific use cases.
Another important factor is feedback. This creates a continuous dialogue that helps to refine AI models and dashboards over time as well as instill greater trust in the implemented solutions.
Challenges in Adopting AI-Powered Business Intelligence
There are an array of challenges that hinder the implementation of AI for business intelligence. If not addressed they can stall progress, diminish return on investment, and lead to frustration among stakeholders, ultimately reducing trust in the technology. Luckily, they are solvable.
The nature of data issues lies in the complexity and inconsistency of data sources. Organizations must manage disparate formats, incomplete records, and sensitive information, all while trying to maintain accuracy and comply with regulations. At the same time, the need for real-time analytics further complicates the environment, as streaming inputs can overwhelm legacy infrastructures.
The importance of data integration is hard to overestimate here as it helps to make data homogeneous in the system. To ensure that, we specifically invest in robust data governance frameworks, standardized cleaning and validation processes, and modern integration technologies. Our team also implements ETL/ELT pipelines, data orchestration tools, and metadata management solutions to create a single source of truth. These measures allow us to comply with regulatory requirements and establish real-time data processing.
Technology and Infrastructure Challenges
Adopting AI for BI often entails integrating advanced capabilities into legacy systems and scaling up to meet rapidly growing demands. However, existing infrastructures may lack the flexibility, compute power, or automation needed to handle complex AI workloads. Simultaneously, the abundance of tools and platforms makes technology selection daunting and slows down the development.
Addressing these issues involves modernizing the underlying technology stack or migrating to cloud environments to ensure that new AI capabilities are compatible with the existing systems. Particularly, we usually choose the right technology guided by clear criteria aligned with business goals and technical capabilities. Plus, we employ MLOps practices to streamline model deployment, monitoring, and updates.
Cost and ROI Concerns
High upfront investments in infrastructure, software licenses, and specialized personnel can deter organizations from fully embracing AI. Initially, it might be challenging to quantify returns on these investments, especially before AI models reach maturity and deliver consistent value.
Here, a mentioned above phased implementation will be the most suitable way to address these concerns. We always focus on a pilot project that can provide tangible improvements early to gain understanding if the project has the desired level of accuracy, efficiency, or revenue.
Skill Gaps and Talent Shortages
Existing teams often lack the technical background needed to run complex models and interpret their results. In fact, the demand for data and AI services is surpassing the broader IT services sector, with over 90% of organizations opting to engage external service providers. And when working with vendors, it becomes difficult to stay aligned in objectives and utilization of AI capabilities.
We usually suggest investing in training, reskilling, and targeted hiring to overcome these challenges. Partnering with external service providers, as highlighted by Gartner’s research, can help fill immediate talent gaps. To further improve collaboration and AI adoption, internal training programs, certifications, and hands-on workshops are ideal for in-house teams to understand how to operate with AI-driven tools.
Business Intelligence and AI Tools and Resources
Dealing with numerous tools is inevitable. However, this is not the hardest part. The difficulty arises when choosing the right ones for specific tasks.
Data Integration and ETL (Extract, Transform, Load) Tools
To make AI models and BI platforms deliver meaningful insights, we need to focus on collecting, cleansing, and structuring data. To simplify the process, we use the following tools:
- Alteryx: Provides data blending, data preparation, and advanced analytics, needed for transforming and integrating data before analysis.
- Talend: Help with ETL, data migration, and cloud integration.
- Informatica: Automates the integration, transformation, and delivery of data across various sources.
Cloud Platforms for AI and BI Integration
To make sure that AI-driven BI systems are scalable, flexible, and secure, we opt for cloud platforms, specifically:
- Google Cloud BigQuery: Enables fast SQL analytics at scale, often used with AI/ML to analyze big datasets.
- AWS Redshift: Offers a data warehouse solution for powerful business intelligence and analytics.
- Microsoft Azure Synapse Analytics: Ensures advanced data analytics and AI-powered business intelligence.
Data Management Resources
Our team prioritizes the maintenance of high data quality and compliance. To ensure data is reliable and meets regulatory standards, we utilize data governance platforms, data lakes, and similar resources like:
- Collibra and Alation: Ensure data governance and guarantee that data used in BI and AI processes is accurate, accessible, and compliant with regulations.
- AWS Lake Formation or Azure Data Lake: Provide large-scale storage for BI and AI-driven analytics projects.
Data Science and AI Frameworks
Robust machine learning and deep learning frameworks allow us to develop predictive models, discover patterns, and derive actionable insights. We fine-tune AI models with the help of the next frameworks:
- TensorFlow: Helps build deep learning models that can be applied to predictive analytics in BI.
- PyTorch: Assists with AI research and business applications, including data-driven BI solutions.
Data Visualization Tools
After we make AI in business intelligence process and analyze data, we need to ensure that the results of these processes, specifically information obtained, is coherently visualized to offer intuitive insights. For that, we use the following platforms:
- Tableau: Enables the creation of interactive and shareable dashboards.
- Microsoft Power BI: Helps create reports and dashboards.
- Qlik: Offers associative data indexing and exploration, enabling self-service data discovery.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“Sure thing, the choice of tools depends on how well they align with your goals and the capabilities of your organizational systems. Nevertheless, they must also be user-friendly for your in-house team to utilize effectively. Therefore, selecting tools that offer seamless integration, intuitive interfaces, and robust support also have to be considered.”
Why AI and Business Intelligence Implementation Requires a Professional Approach
In-house development teams, focused on company-specific processes, do not always have enough resources to navigate AI-driven technologies. They may lack the required expertise and experience to implement AI in BI processes. On the other hand, a professional team that is specialized in AI, ML, data science, and analytics knows how to address the common complexities, namely:
- Complexity of Integration and Customization: The integration of ML, NLP, and predictive analytics requires customizing algorithms to suit specific business needs, connecting disparate data sources, and ensuring compatibility with legacy systems.
- Ensuring Data Quality and Governance: Poor data quality, stemming from inaccuracies, inconsistencies, or missing information, can hinder an accurate analysis and lead to misguided decisions.
- Dealing with Data Security and Risks: Dealing with vast amounts of sensitive data, especially in FinTech and healthcare sectors, requires implementing advanced encryption protocols, access controls, and monitoring systems to protect data from breaches or unauthorized access.
- Handling Ethical Challenges: AI systems can create biases, misinterpret data, or generate insights that might lead to discriminatory decisions, which create the need for explainable AI development.
Consider SPD Technology as Your Business Intelligence & Artificial Intelligence Partner
With the rise of artificial intelligence, our team swiftly adopted this top-notch technology alongside ML, data science, and advanced analytics to develop sophisticated solutions for our clients. Our professionals combine this expertise with:
- Expertise in Data Integration and Real-Time Processing: Our team excels in integrating data from diverse sources and handling data streams, ensuring that the AI-driven solutions we deliver provide timely, actionable insights.
- Focus on Data Governance and Security: We understand the importance of robust data governance frameworks and implement advanced security protocols, 2FA, and role-based access to safeguard sensitive information.
- Cross-Industry Experience: With experience in FinTech, eCommerce, healthcare, and manufacturing, our specialists know how to navigate the typical challenges and compliance regulations like GDPR, HIPPA, and PCI DSS.
- Proven Methodology for Successful Implementation: We follow a structured methodology like Scrum and Agile that ensures seamless AI implementation from initial strategy development to deployment and optimization.
Business Intelligence and Artificial Intelligence Transformation for HaulHub: SPD Technology’s Success Story
To enhance the capabilities of business intelligence, artificial intelligence was incorporated into HaulHub’s platform, which is a transportation construction ecosystem.
Business Challenge
HaulHub faced the complex challenge of coordinating logistics across numerous suppliers, vendors, contractors, and agencies. They needed real-time, reliable insights drawn from diverse data sources to streamline workflows, reduce costs, and optimize resource allocation. The existing processes struggled to provide accurate workload forecasting, material tracking, and responsiveness to road traffic.
SPD Technology’s Approach
Our team implemented an AI-driven data strategy that integrated disparate information into a unified BI platform. We designed and deployed advanced analytics tools that enabled ML models to process and interpret large volumes of data at scale. This involved setting up robust data pipelines, refining data governance, and embedding predictive analytics capabilities directly into decision-making workflows. As a result, complex logistics scenarios became transparent, with actionable insights readily accessible to managers and field personnel.
Value Delivered
With our help, HaulHub gained real-time visibility into their operations. More precise material tracking and optimized workloads allowed the client to improve productivity and agility while reducing operational costs.
Conclusion
Artificial intelligence completely changes the traditional approach to business intelligence. While previously BI was based on descriptive methods, now the approach is predictive and forward-looking, enhanced with ML, NLP, computer vision, generative AI, Big Data, and RPA. AI-driven BI suits many industries, including finance, eCommerce, healthcare, and manufacturing.
To start improving BI with AI, it is important to complete several steps. At first, it is important to develop a strategy to know how to approach AI BI project. After that, companies need to ensure data readiness and select the suitable tools. Only then, they can integrate AI thanks to phased implementation. Last but not least, training is essential to make all company staff work seamlessly with AI-based tools for BI.
During AI implementation, several challenges (e.g. data, technology, cost, expertise issues) may arise. They are best solved with professional help. AI specialists can navigate complexities and use specific tools for integrations, cloud computing, data management, and data visualization to ensure smooth work of advanced BI tools with existing business processes.
We also help companies to make a shift towards AI-driven business intelligence. If you need a hand, you can contact us, and our experts will come up with the most suitable strategy to approach your BI project.
FAQ
- How AI Is Transforming Business Intelligence?
AI is improving business intelligence by automating data analysis, enhancing predictions, and delivering actionable insights through advanced ML models and integrated workflows that streamline decision-making processes across the organization.
- How can AI Be Used in Business Intelligence?
AI can be used in by leveraging NLP, predictive analytics, and generative models to deliver personalized dashboards, improve operational efficiency, and empower users with data-driven recommendations.