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Business Intelligence (BI) is a modern solution that enables comprehensive data analytics. This system can significantly improve decision-making processes and help gain a competitive advantage in the market. In the following article, we explain when it is worth deciding to implement this tool. Business Intelligence – For Whom? A BI system (short for Business Intelligence) is an environment and set of tools used for advanced business analysis. It is a very broad field that focuses on finding savings, optimizing production, creating “what-if” analyses, or generating complete financial balance sheets. The decision-making process in many enterprises requires a parallel combination of advanced digital solutions with vast amounts of data. Many companies still use multiple systems or sources that are not integrated with each other. In the long run, this leads to significant delays and complications in key processes, over which control may eventually be lost. Business Intelligence tools are the answer to these problems, enabling effective management of a company’s most important operational areas. BI solutions allow for efficient data integration, which in the short term enables: Quick aggregation and retrieval of data. Finding relationships and correlations between individual events. Understanding these events and reaching accurate business conclusions. Current BI systems are so advanced that they can independently recognize data and then generate tables or entire spreadsheets. Naturally, all datasets can also be cleaned or processed in many ways. BI tools enable data analysis and organization using various functions, such as drag-and-drop, which significantly simplifies operation for users within a company. As it turns out, when implementing a BI system, employees do not need to have specialized programming knowledge. Examples of using these tools in daily work include: Analyzing the correlation between salary increases and staff inefficiency. Studying the relationship between demand and the price of a given product or service. Analyzing economic cycles. The ultimate goal of business analysis tools is to find dependencies between phenomena and make significant business decisions based on them. Most Popular BI Tools Knowing how analytical technology works, the next question is about specific solutions. When choosing the right BI system, it is worth paying attention to integration capabilities and market leaders. The most popular tools include: Power BI – One of the most popular systems on the market. It integrates perfectly with the Microsoft ecosystem (including Excel and Azure) and allows for the creation of highly interactive dashboards. Tableau – A powerful tool famous for its extremely advanced and aesthetic data visualizations. It is ideal for huge datasets and deep analytical explorations. Qlik – A modern BI system distinguished by its associative engine. It allows users to freely explore data in all directions instead of following predefined query paths. BI Modules in ERP systems – Many ERP systems have built-in tools that analyze company data in real-time without the need for external integrations. Does Implementing a Business Intelligence System Have to Be Expensive? Many people still believe so – however, this is not true. Currently, the market provides entrepreneurs with BI software versions that are completely free or available in flexible subscription models (SaaS). Even free demo versions, despite limited capabilities, allow for downloading sheets and basic data linking or visualization. This allows entrepreneurs and potential users to familiarize themselves with the logic of the business data analysis system before making a final investment decision. Who Is a BI System Intended For? The market for BI system users is very diverse. When focusing on an advanced implementation (which includes a pre-implementation analysis), you must ensure that your company needs such a solution. It is about economic justification—specifically, having enough data that can be turned into profit. It is difficult to set a strict limit at which a BI system becomes indispensable. However, a good evaluation method is to look at the number of employees and the company’s turnover. For smaller companies, the key criterion is the nature of the business. For example, should a company with 15 employees doing simple sales invest in a powerful analytical system? Probably not. However, there are cases where a team of only 30 people processed such vast datasets that implementing BI became a condition for their further growth. Excel and Databases Are Not Enough For Advanced Analysis Excel is well-known to employees in most companies and is very easy to implement. However, when faced with Big Data, its significant flaws appear: Static analyses instead of real-time updated models. Complicated and error-prone merging of datasets. The monotony of manual report refreshing. Limitations on the number of records that can be processed. Direct databases solve the capacity problem but create a barrier in usability – the need to know SQL. Presenting data from two tables is simple, but when there are hundreds of tables, constantly writing SQL queries becomes inefficient. This is where Business Intelligence systems come in. Data Analysis vs. ERP Systems If an enterprise already has ERP software, it is halfway there. These tools generate reports that allow for constant monitoring of company efficiency and decision-making. However, they are not built for predictive analytics but for handling current operations. ERP tools use OLTP (Online Transaction Processing) databases, designed for immediate, secure data entry. In contrast, BI systems are often based on OLAP (Online Analytical Processing) databases, which are most effective for analysis. It is worth remembering that ERP solutions are usually not the only ones collecting company information. Therefore, a tool (e.g., an integrated BI system) is needed to combine data from ERP, CRM, and other sources, visualizing it in one place. When To Implement a Business Intelligence System? If financial resources allow and analytical needs are growing – as soon as possible. By deciding on a BI system at an early stage of digitalization, you enforce order and a consistent data architecture. Later implementations, in an environment burdened by “technical debt” and information chaos, tend to be much more expensive. Equipping yourself with a data warehouse and appropriate BI tools will facilitate every subsequent step in the company’s technological development. Implementation Methods Every professional system should be implemented with the help of a team of specialists. There are two common methods used during implementation: Potential Analysis – The software provider, together with the client, looks for areas requiring improvement. Information problems and challenges at the intersection of different systems are identified. The competence of BI consultants is key here. Proof of Concept (PoC) – A test implementation on a limited sample of data. It checks whether a given BI system will meet the company’s requirements in practice. The PoC results in a final purchase decision. How to Avoid Failure During Implementation? Implementing BI tools is a significant challenge. The project involves both logistical and purely organizational obstacles. To avoid problems, pay attention to these key risks: Too much data – Integrating everything “as is” unnecessarily prolongs the process. You should focus on KPIs that have real business significance. Dirty data – Gaps and errors often lower the credibility of results. To avoid this, data should be cleaned before implementation. Resistance to change – Even the best system is useless if the staff avoids it. The keys are training and showing employees how the new system will facilitate their daily work. How to Maximize the Effects of Using BI? First and foremost, customize the dashboards. Different experts play different roles and need different data. A CEO wants to see margins from a “bird’s eye view,” while a production shift manager needs real-time machine failure rates. Views must be personalized. Use only the necessary tools. Depending on the technology and provider, some systems offer advanced reporting features and numerous data access points. However, there is a risk that these “gadgets” will eventually cause information noise. It is better to use only the tools you truly need – simplicity and utility win. Summary Implementing Business Intelligence is not just about mechanically replacing Excel sheets with pretty charts. A modern BI system allows you to look at your company from a completely new, often surprising perspective. A fresh perspective helps in continuous optimization and quick reactions to dynamic market changes. Before you decide to buy a specific solution, ask yourself: How will these analyses directly improve my company’s financial results?

Today, AI is no longer just a trend but a tool from which companies expect measurable benefits. As a result, managers are no longer asking whether an ERP system includes AI features, but rather what type of AI capabilities it offers. In this article, we organize the market and highlight the differences that matter for decision-makers. Just 2–3 years ago, artificial intelligence in business systems was often treated as an “add-on” to sales presentations. Today – especially from the perspective of CFOs and IT managers – it is an area that is rigorously evaluated. This is also reflected in the findings of the “Cyfrowy Menedżer” report prepared by myERP, which clearly shows a shift toward a “prove it” mindset. AI is expected to deliver results only when a company has solid foundations in the form of high-quality data and clearly defined KPIs. How to Compare AI Solutions in ERP? The biggest trap in implementing AI within ERP systems is assuming that an LLM can compensate for disorganized data and processes. From a purchasing perspective, it is better to treat AI as a productivity layer. Artificial intelligence shortens working time, supports decision-making, and automates routine tasks – but it also requires high-quality input data. IT and finance departments should pay attention to three key aspects: Scope of process interventionSome AI solutions act only as informational assistants, providing summaries or insights from reports. Others can perform actual actions within the system – such as setting credit limits or issuing documents. Sources of generated responsesSome solutions rely exclusively on internal company data, reducing the risk of AI “hallucinations.” Others – especially generative AI tools – require users to define the sources the LLM can access. Costs and technical conditionsSome AI features are included in ERP systems at no additional cost. Others offer advanced capabilities available through paid options. AI Assistants in ERP Systems The most visible form of AI for users is conversational assistants. These solutions enable interaction with ERP systems using natural language, inspired by tools like ChatGPT or Gemini. They also help accelerate onboarding for new employees. ChatERP from Comarch ChatERP is a built-in chat assistant that allows users to interact with ERP in natural language. Ultimately, it is intended to cover both on-premise and cloud versions of all Comarch ERP systems. Currently, it is available in BETA. Its functionality includes: Access to company data available in the system Data analysis and reasoning Suggesting system features Executing tasks on user request A key aspect is the ability to perform business operations such as setting credit limits or issuing invoices. In practice, this requires strict permission and audit mechanisms. Without them, the risk of incorrect commands increases. Comarch ensures the protection of personal and sensitive data in ChatERP. Queries and responses may be processed by technology subcontractors, but the AI should not disclose business secrets. Still, companies with high security requirements should formally define data-sharing rules before implementation. Genius by Asseco Business Solutions In terms of declared functionality, Genius is closer to the concept of a digital coworker that monitors tasks, supports decisions, and suggests actions. According to Asseco BS, it notifies users about pending decisions and tasks, answers ERP-related questions, and supports processes such as orders, invoices, and warehouse documents. Additionally, based on user-provided context, the assistant can deliver actionable recommendations. This approach is enhanced by two important elements: Adaptive interface – AI analyzes user behavior and suggests changes to layout, menus, or screen elements, implemented only after user approval. Analytical layer – Genius provides intelligent insights based on real-time ERP data. MAiA in Monitor ERP System Monitor ERP includes its own AI assistant that “structures, compiles, and analyzes data.” Its main goal is to handle time-consuming tasks. MAiA is not just a chatbot – conversational mode is only one interface. It also works through automated summaries and analyses embedded directly in business processes, similar to how Gemini Pro summarizes documents in Google Drive. Importantly, Monitor’s AI relies exclusively on internal business data, ensuring data integrity and control. MAiA also supports text-related tasks – summarizing notes, translating emails, and refining communication tone. MAiA is available in two versions: Basic – included for all customers Pro – available with a monthly per-user fee The Pro version is initially offered as a free trial. Monitor ERP continues to develop AI features and actively collects user feedback via its Ideas Forum. AI Application Ecosystem Instead of a Single Feature An interesting approach comes from Proalpha, which in 2025 introduced its Industrial AI platform. This is a catalog of over 30 AI applications covering core processes – from procurement and production to service. The platform integrates AI solutions from Empolis and Nemo and is built in a SaaS architecture, enabling smooth integration with both Proalpha’s ecosystem and third-party systems. Nemo’s AI capabilities include: Identifying correlations and anomalies in processes Defining recommended actions Evaluating optimization potential in financial terms In this platform-based approach, AI becomes the “engine” of data integration and analytics. For decision-makers, two key implications stand out: Data processing approach – Industrial AI handles both structured (tables) and unstructured data (documents, notes), turning hidden knowledge into actionable insights Automated recommendations – which can be implemented based on diagnosis and trend forecasting The Microsoft Ecosystem and AI in ERP A unique position in the market is held by Microsoft Dynamics 365 – a scalable ERP/CRM platform deeply integrated with other Microsoft services. Implementations are delivered by multiple myERP partners, including companies such as Companial, Integris, MS POS Poland, xalution Group, IT.integro, and Solemis. Copilot Microsoft has embedded Microsoft Copilot in ERP systems in two ways: as a conversational assistant and as embedded functionality within system features. Key capabilities in Dynamics 365 Business Central include: Conversational guidance on system functionality Data analysis using filters and sorting Creation of sales documents (quotes, orders, invoices) Marketing content generation E-document mapping Bank reconciliation Document numbering automation Product substitution suggestions Order processing automation Power BI Many organizations want ERP data to be consumed in a self-service analytics model. In this context, Copilot in Microsoft Power BI provides significant value: Fast creation and modification of reports and visualizations Automatic report summaries Conversational interaction with data However, Copilot in Power BI is a paid feature (Fabric or Premium). Additionally, organizations must ensure high data quality for AI to function effectively. AI in ERP – What Should You Choose? There is no single “best AI” solution for all organizations. The right choice depends on the dominant challenge within the company – whether it is low user productivity, the need for stronger financial control, or real-time production optimization. Key takeaway:AI in ERP should not be treated as a standalone feature, but as a strategic layer that enhances how people work with data, processes, and decisions.