Business Intelligence Archives

Modern HR Analytics in 2026: What’s Changed and Why It Matters?

Modern HR analytics is changing the role of HR. Instead of tracking what happened, predictive HR analytics help find out why it happened and what’s likely to happen next. With data becoming central to every business decision in 2026, HR leaders who understand analytics stay ahead and make smarter business decisions. “HR will not be replaced by data analytics, but HR who don’t use data and analytics will be replaced by those who do”. – Nadeem Khan Human resource management depends on experience and managerial judgment. These traditional approaches worked well in predictable business environments with stable workforce structures and uniform employee expectations. However, the modern workplace has changed. With the rise of hybrid and remote work models, the competition for top talent has increased. Organizations that focus on how talent directly shapes business performance. Therefore, they need to make decisions based on evidence and data. This is where modern HR analytics play an important role. According to Grand View Research, the global HR analytics market was valued at USD 2.95 billion in 2022 and will grow to USD 8.59 billion by 2030, growing at a compound annual growth rate (CAGR) of 14.8%. A 2024 Secondtalent report revealed that only 6% of companies have reached a stage of predictive maturity where data-driven insights influence business strategy and outcomes. As HR analytics trends 2025 continue to evolve for 2026, the focus is shifting from reactive reporting to predictive intelligence. Organizations are beginning to measure not just what happened, but why it happened and what’s likely to happen next. While organizations collect HR data, only a few convert it into meaningful action. That’s where using modern HR analytics helps to predict workforce needs, identify retention risks, improve performance, and align talent decisions with business objectives. Comparing Traditional HR Analytics and Modern HR Analytics Traditional HR practices focused on administrative tracking, such as counting employee numbers and attendance. These were record-keeping metrics designed to describe what happened. For example, HR teams monitored turnover rate or training hours per employee but didn’t connect those metrics to business outcomes like performance or profit. Modern HR analytics focuses on data-driven hiring, why things happen, and what will happen next. By using data visualization and predictive modeling, HR teams can now forecast workforce trends and measure engagement levels. They can also predict resignations or skill shortages before they occur. The table below gives a quick comparison between the two. Focus Area Traditional HR Metrics Modern HR Analytics Purpose Tracks HR activities such as hiring and payroll Connects workforce data to business strategy and performance Approach Descriptive Predictive and prescriptive Data Handling Manual input Automated data collection Tools Excel/Spreadsheets AI dashboards and analytics platforms Accessiblity Data in silos, limited access Integrated, real-time access Decision-making Reactive Proactive The 4 Pillars of Modern HR Analytics Talent acquisition analytics Talent acquisition does not mean filling positions. Rather, it is all about hiring the right people who will thrive and contribute to business goals. Modern HR analytics allows organizations to make smarter hiring decisions based on evidence, not intuition. Benefits: Performance analytics Performance analytics goes beyond traditional annual reviews by continuously measuring employee contributions and linking them to organizational outcomes. It helps HR recognize talent and optimize performance to align individual goals with business objectives. Benefits: Engagement & retention analytics Engaged employees are more productive and satisfied. HR analytics helps detect early signs of disengagement and identifies what drives retention so that proactive strategies can keep top talent motivated and committed. Benefits: Workforce planning & skills analytics Workforce planning is about anticipating the future needs of the organization and ensuring the right skills are in place. Understanding workforce analytics impact helps forecast gaps, prepare succession plans, and make learning and development investments that align with business strategy. Benefits: Why Modern HR Analytics Matters? Businesses face rapidly changing work environments, increasing costs, and growing expectations from employees and leaders. Using analytics allows HR teams to make smart and fair decisions that directly impact organizational performance. Key reasons HR analytics is critical today: The Role of AI and Automation in HR Analytics Artificial Intelligence and automation have transformed how HR operations function on a day-to-day basis. They make it easy to analyze past data and anticipate future workforce challenges. AI allows organizations to see patterns that humans might miss and deliver insights. However, without human oversight, AI can reinforce biases or make decisions that lack organizational context. Therefore, combining machine precision and human judgment is what makes modern HR analytics exceptional. Accelerate Your HR Analytics Journey PeopleBI empowers organizations to turn HR data into actionable insights, helping organizations make evidence-backed decisions. Connecting seamlessly with Power BI, it gives an interactive view of workforce trends. With PeopleBI, HR teams can spot patterns before problems arise, enabling proactive talent management. It also supports strategic workforce planning, helping organizations align learning, development, and resource allocation with business goals. Whatever our goal is, PeopleBI delivers the tools to improve HR from administrative tracking to strategic decision-making. Explore Now!! Important considerations for AI use Conclusion HR analytics helps HR departments to understand workforce trends and make decisions that align with business goals. By turning raw data into actionable intelligence, they enable HR to move from reactive problem-solving to proactive strategy. However, it is important to include human judgment. Empathy, context, and understanding remain essential. By combining data with human insight, HR leaders can make smarter decisions and retain top talent to drive business success. FAQs How can I see early signs of disengagement or turnover in my teams? Check for patterns such as frequent absenteeism and bad performance. Some other signs include: Use engagement surveys to understand employee satisfaction and seek their feedback. Track voluntary exits and internal transfers. Further, combine qualitative feedback with data for a better understanding and timely action to retain employees. Can HR analytics actually show which roles drive the most value? By analyzing performance metrics and revenue impact along with contribution to important projects, analytics show which roles add the most value. Comparing

Equipment Efficiency Drop: Real Causes and Early Warning Signs

Why equipment efficiency drops due to factors such as small process changes, missing data, or older machines that still run but don’t perform well. We’ll learn how to spot early warning signs and how to move from just fixing problems to preventing them in the first place. According to the International Society of Automation (ISA), manufacturing plants can lose 5% to 20% of productivity annually due to unplanned downtime. These numbers are even higher for large-scale plants. The “True Cost of Downtime 2024” report by Siemens revealed that unplanned downtime costs Fortune 500 companies 11% of their revenues, i.e. $1.4 trillion, equivalent to the annual GDP of a country like Spain. Such losses don’t always come from machines breaking down completely; they are due to the slow creep of inefficiency. They could be due to equipment running slower or performance drifts. These issues don’t show up in maintenance logs, but over time, they add up to massive productivity gaps. As Peter Drucker said, “Nothing is less productive than to make efficient what should not be done at all.” The same applies to equipment. You can service it regularly and still lose efficiency if you’re not tracking how it performs under real conditions. Many plants mistake maintenance for efficiency. A machine might be in good condition but still be underperforming. In this blog, we’ll explore why equipment efficiency drops even when maintenance is done on time and how to spot early signs before performance drops. What is Overall Equipment Effectiveness? Overall Equipment Effectiveness (OEE) is a key metric used in manufacturing to measure how efficiently a piece of equipment or production line is performing. It is like a fitness score of your machine. To calculate OEE, use the formula below: OEE = Availability × Performance × Quality A higher OEE score indicates greater productivity and efficiency. For example, if a piece of equipment has availability of 85%, performance of 90%, and quality of 95%, then OEE is: OEE= 85% x 90% x 95% = 72.7% Availability: Availability measures how much of the planned production time the equipment is operating. It reflects losses from unplanned and planned stops. Performance: Performance tracks whether the equipment is running at its maximum designed speed. It highlights inefficiencies from slow cycles, minor stops, or suboptimal settings. Quality: Quality measures the proportion of good units produced versus total units. Reasons for Equipment Inefficiency Equipment inefficiency doesn’t occur due to a single reason. It’s the result of small oversights that snowball into bigger performance problems. These oversights fall into four categories, discussed below. Maintenance-related issues Operational and human factors Environmental and design factors Organizational and process issues How to Catch Efficiency Loss Early Catching efficiency loss before it becomes a major breakdown helps you to sustain high OEE (Overall Equipment Effectiveness). Instead of reacting to failures, manufacturers can use data and analytics to detect performance dips early. Here are three ways to do that: Use real-time analytics Traditional maintenance systems inform you of what went wrong after it has happened. However, manufacturing analytics solutions tell you what’s about to go wrong. By monitoring live equipment data, manufacturers can detect subtle changes in behavior that indicate a decline in efficiency. Key measures to track: How it helps: Correlate maintenance data with production context Checking if maintenance was done is not enough. It is equally important to find out if it improved performance. Most manufacturers record maintenance data separately from production metrics. But the key is to connect them and extract actionable insights. What should you correlate: Why it matters: Set early-warning thresholds Machines rarely fail without warning, but most teams don’t define what “early warning” looks like. Setting clear performance thresholds helps detect deviations before they cause downtime or defects. How to define thresholds: Benefits: Improve Your OEE Performance Intelligence OEETrackBI, a ready-to-implement solution, empowers manufacturers to find hidden efficiency gaps and turn real-time data into actionable insights. Built on Power BI, it delivers a unified view of machine availability, performance, and quality, helping teams move from reactive fixes to predictive action. With OEETrackBI, production leaders can spot performance drifts early, plan maintenance intelligently, and make decisions backed by data, not assumptions. It transforms scattered equipment data into performance stories, helping you boost throughput, reduce unplanned downtime, and sustain process reliability. Whether your goal is to improve uptime, optimize cycle time, or enhance product quality, real-time manufacturing dashboards give you the tools to make efficiency measurable and continuous. Explore OEETrack BI > Conclusion Performance dips don’t happen all of a sudden. When you ignore data and make decisions abruptly, loopholes creep in. That’s where a manufacturing analytics company helps. By translating raw machine data into actionable insights, they help manufacturers identify inefficiencies long before outputs are impacted. With the help of real-time OEE dashboards, companies can visualize performance and improve production capacity while meeting quality standards. FAQs Why does my equipment’s performance dip even when maintenance is regular? Regular maintenance keeps machines running, but it doesn’t always address performance losses caused by micro-stops, suboptimal settings, operator variability, or material issues. OEE tracks these subtle inefficiencies that traditional maintenance logs miss. Even well-maintained equipment can lose performance due to unmeasured slow cycles or process bottlenecks, which can be managed using OEE analytics. How can I tell if inefficiency is from machine age or process issues? If performance gradually declines even when the cycle times stay consistent, machine wear could be the issue. However, if losses vary shift-to-shift or product-to-product, process or operational factors could be the cause. By correlating downtime, performance rates, and quality data, OEE analytics reveal patterns that pinpoint whether the issue is mechanical or procedural. Can I get real-time alerts before a machine’s performance drops? When OEE data is connected to real-time monitoring systems, you can set performance thresholds and predictive alerts. These early warnings detect anomalies like speed loss or rising defect rates before they escalate into downtime, helping you to take proactive action instead of reactive fixes. I already have SCADA data. How do I

Self-Service BI: AI Upgrades That Will Matter Most to CIOs & CTOs in 2026

Self-service business intelligence is a solution for non-technical employees to access data visualization and make data-driven decisions. Here, we’ll discuss the coolest AI upgrades that will matter most to C-suites as they transform Self-Service BI into a more powerful, enterprise-ready capability. Business intelligence (BI) is a collection of processes and technologies that convert the collected data into actionable insights. It includes data collection, data analytics, data transformation, and data visualization to unlock the true potential of business data for proactive decision-making. Business intelligence is a technical process handled by experts with the required domain expertise. So, how do non-technical users and other employees use BI? This is possible through self-service business intelligence, a set of processes and tools that can be used by employees from non-IT backgrounds to derive meaningful insights. There has been an increase in demand for self-service BI in recent years. According to The Business Research Company, the self-service BI market is expected to reach $11.84 billion in 2025, with an estimated CAGR (compound annual growth rate) of 17.3% to touch $22.42 billion by 2029. Self-service BI tools are user-friendly, mobile-responsive, and effective. You can select from the existing options in the market or build a customized self-service BI tool with enhanced AI capabilities for a better user experience. In fact, several organizations are transforming their self-service BI systems by partnering with a reliable AI consulting company. This gives your business a competitive edge and increases employee efficiency and performance. In this blog, let’s look at some of the coolest AI features to incorporate into your establishment’s self-service BI. Role of AI in Self-Service BI Self-service BI can be implemented in two ways: one is a simple process with pre-built reporting templates, and the other is a comprehensive platform with AI capabilities. Many businesses have been using both, starting with simple solutions and scaling to include more advanced features, but without complicating it too much. Using AI in self-service BI allows employees to work with complex databases and functionalities if they want to. Not every business can afford to have an in-house team of data analysts or BI experts. They may not want to hire third-party or offshore providers unless necessary for a big project. That doesn’t mean the business has to ignore analytics. It can still empower employees by providing tools for self-service BI. Sending queries, generating reports, and working with interactive data visualization dashboards don’t require high technical expertise. With AI-powered self-service analytics, your employees can do all these without any programming or coding knowledge. Basic training to use the interface is sufficient. This accelerates the decision-making process as valuable insights will always be available at employees’ fingertips. You can hire business intelligence consulting services to establish the initial architecture and connections for a streamlined data flow. Once it is ready, employees can access and utilize the insights for their daily activities. Coolest AI Features Transforming Self-Service BI in 2026 AI features transforming BI for self-service is not just an interesting idea or theory. It is being implemented by various enterprises to enhance the business intelligence tools and empower employees to derive insights in real-time without depending on IT teams. Automated Insights AI automation in self-service BI is one of the most popular and coolest features to incorporate into the tool. Instead of manually performing the analytics, employees can use artificial intelligence-based features to automate the process. In this, the backend steps are automated to share instant output with the users. So, the employee provides an input and gets a response in real-time or near-real-time without having to do anything much. The interactive dashboard can be customized to derive different types of analytics with a couple of clicks. Imagine not having to wait for another team to send the response to the query as the AI feature takes care of it. AI-Driven Recommendations AI agent development services allow the adoption of conversational AI in business intelligence for self-service. Non-technical users want simple systems. They want interfaces that converse with them. They want reports that can be straightforward to interpret. To ensure the self-service BI can give them that, you integrate the business intelligence tool with AI agents. That’s because the AI agent has a ‘memory’ of the previous conversations. They are trained to understand business logic and can provide recommendations that align with your organization’s vision, mission, and objectives. Predictive Analytics and Recommendations Predictive analytics for self-service BI is another cool functionality. It is a type of advanced analytics that analyzes historical data to identify hidden patterns and forecasts possible future outcomes. Predictive analytics is helpful in various ways – sales and market forecasting, risk management, identifying better opportunities, strategic planning, and so on. In fact, predictive analytics was considered the future of self-service BI due to the competitive edge it can offer to your organization. Moreover, the AI and NLP algorithms will be trained on your data to provide more in-depth and tailored insights. This increases accuracy and reliability. Smart and Proactive Intelligence Simply put, augmented analytics with AI refers to the process of integrating machine learning and NLP algorithms with data analytics and business intelligence platforms. This is done to streamline the workflows and democratize decision-making. The queries are provided as input in plain language used by humans instead of some complex code. The algorithm will understand the input and share the output by analyzing the datasets based on the query. By replacing SQL with the language we speak, AI transforms self-service BI into an everyday tool that can be used by most employees, irrespective of their technical knowledge and experience. Building a Data Model with AI Data is crucial for analytics as well as to train the AI and ML algorithms that power advanced features in business intelligence tools. A robust data model with a self-improvement cycle can strengthen the BI platform to deliver better quality insights as it learns from the input and feedback provided by the user. The AI product development company will build a data model that automatically collects

Stop Losing ROAS: How to Fix Fragmented Marketing Data Now?

Data silos can lead to many challenges, such as ineffective campaigns, a lack of personalization, lower ROI, and unhappy customers. Here, we’ll discuss how fragmented marketing data is affecting your ad performance and how it can be fixed using a robust unified solution. Marketing data is crucial for every business in today’s world. It has valuable insights about the market, customers, target audience, changing trends, and even the organization’s presence and reputation in the market. To make the most of this data, businesses use different tools and applications and derive insights. According to Mordor Intelligence, the global marketing analytics market is expected to reach $7.12 billion by 2025. It is projected to grow to $13.04 billion by 2030, at a compound annual growth rate (CAGR) of 12.87%. The report also shows that the cloud-based solutions had a larger market share at 62.12% in 2024, and this segment is expected to grow at a CAGR of 13.23% by 2030. While marketing analytics is necessary, the effectiveness of the insights depends on the data used to derive these insights. That’s where organizations face challenges. Fragmented marketing data is a real concern in many enterprises and leads to low data quality, which inevitably results in unreliable insights and incorrect decisions. Financial losses follow soon and can affect the businesses in several ways if appropriate steps are not taken to resolve the problems. In this blog, we’ll read more about the adverse impact of such siloed data, how fixing fragmented ad data is essential to achieving your objectives, and the role of data engineering services in streamlining the process. What is Fragmented Marketing Data? Data fragmentation occurs when your business data is scattered across the various systems, apps, and tools you use for different purposes. This makes it hard to manage, update, and analyze the data to derive meaningful insights. A report shows that 66% of businesses use as many as 16 or more marketing solutions. Apart from that, data silos in marketing are a common occurrence due to the following reasons: In many instances, these are not immediately visible, but their cumulative result can affect your business in several ways. However, the issues can be resolved by building a central data warehouse or a data lake and implementing unified marketing analytics with powerful AI-driven platforms. It requires expertise and industry-specific skills and knowledge to build and deploy a unified data architecture with a central data storage system and a unified dashboard tailored to align with your objectives. Impact of Marketing Data Fragmentation and How to Fix It Fragmented marketing data may not seem like a big deal until it becomes the root cause of many challenges that prevent your business from achieving its goals. Operational Inefficiency Imagine having to search for data from dozens of applications or databases. Unsynchronized data means you have to update the records manually from one department to another, such as from inventory to sales, etc., to know where things stand. This is not only time-consuming and stressful but also leads to human errors and inconsistency. In short, operational inefficiency grows to the extent of affecting all processes. It can be rectified by hiring data engineering services to automate data flow between different systems and make the latest information accessible to decision-makers. Higher Costs and Revenue Loss Not only does fragmented marketing data increase costs, but it also reduces revenue and profits, creating a lose-lose situation. Without a single source of truth, your employees cannot make decisions that align with your vision or objectives, as they have to use outdated and incorrect datasets. Additionally, the marketing strategies will not be suitable for the target audience, thus reducing the conversion rate and sales. This can be fixed by having a unified marketing hub where data from several sources is automatically collected, stored, and analyzed. Compliance Risks Businesses have to adhere to different data privacy and security regulations and industry-wide laws to prevent legal complications. However, fragmented data makes it hard to comply with regulations like CCPA, GDPR, etc., due to ineffective record-keeping. This failure can attract hefty fines and lawsuits that damage the business’s reputation. The issue can be fixed by storing data in a central repository that adheres to the global data laws and regulations. A data governance framework also makes sure that your processes are aligned with the regulations. Poor Customer Experience Customer perceptions and requirements have changed over time. Now, customers want brands that offer personalization, follow sustainable practices, and deliver quality customer service. However, fragmented marketing data can negatively impact all these since the teams will not have a clear idea of customer expectations. Marketing data integration solutions can fix the problem by automating data collection and analysis, thus providing a holistic 360-degree view of customer behavior, their purchase patterns, and their journey with the business. Wasted Marketing Budget When your data is fragmented, it gives incorrect insights, which lead to flawed or ineffective marketing campaigns and promotions. Poorly targeted ads will not reach the audience or share the right message. Ultimately, you will experience a lower ROI, fewer leads, and a lower conversion rate. The money spent on marketing will go to waste if it doesn’t give the expected results. This issue can be fixed by streamlining marketing data, storing it in a central data warehouse, and analyzing it using reliable BI and data analytics tools. The first step is to eliminate truncated data silos. Ineffective Personalization Another risk of fragmented marketing data is the lack of personalization in your offerings, be it in products and services or customer interactions. In a world where personalization is a keyword, a business cannot afford to limit its offerings or suggest the wrong products and services to customers because the marketing data is scattered across systems and hasn’t been combined and cleaned. Fixing this requires automated data pipelines to collect, clean, and store large amounts of data and use it for real-time analytics. AI-powered data engineering solutions can revamp the data architecture to accelerate the process and

11 Microsoft Fabric Consulting Companies Helping Enterprises Move Beyond Power BI

MS Fabric is a unified platform for data engineering, analytics, and business intelligence reports, and is suitable for large enterprises. Here, we’ll discuss eleven Microsoft Fabric consulting companies that help businesses think beyond Power BI for real-time AI-powered insights. Data analytics is a vital component of data architecture, as it enables organizations to make data-driven decisions in real-time. Statistics show that the global data analytics market touched $64.75 billion in 2025 and is expected to grow at a CAGR (compound annual growth rate) of over 25% to reach $658.64 billion by 2034. While North America dominates with the largest market share, the Asia-Pacific region is the fastest-growing market. More reports indicate that 73% of businesses consider data analytics a priority in their digital transformation journey, and 70% cite it as a key factor in gaining a competitive edge. With several analytical tools in the market and complex IT infrastructure in the enterprises, it can be challenging to implement and maintain the setup in the long run. Moreover, large establishments require robust AI-powered and cloud-based solutions to handle massive data volumes daily. That’s why many businesses are adopting Microsoft Fabric Solutions to streamline and automate analytics on a unified interface that can be integrated with countless third-party tools and applications. Fabric combines Power BI, Data Factory, Synapse, and other similar tools to build a connected environment for end-to-end data management and analytics. However, Microsoft Fabric can quickly become an expensive investment if it is not optimized for performance, resource management, and scalability. This requires technical expertise and access to the various tools and technologies in the Microsoft ecosystem. Enterprises will find it convenient and cost-effective to partner with Microsoft Fabric Consulting Companies to ensure they get the expected results. In this blog, we’ll read more about the need for analyzing Fabric spend and look at the top eleven consulting companies offering tailored services for Fabric implementation. The Importance of Analyzing Your Fabric Spend Adopting Fabric and optimizing it are two different aspects. You should have a clear idea about how the platform works and what can be done to achieve the required ROI. From CFOs to department heads, team leaders, and data analysts, different people are involved in the process and can benefit from having a transparent system in the organization. By hiring MS Fabric Consulting Services, you can not only implement the solutions, but you can do it in a well-structured, informed, and cost-effective manner. For example, Fabric Spend Analyzer is a comprehensive end-to-end audit that identifies broken datasets, silent drains, idle environments, and blind spots that increase your expenses over time. It helps to highlight and fix the challenges, which results in an optimized infrastructure that consumes fewer resources but gives greater returns. It also helps in ensuring that your data, processes, and operations are healthy and efficient. Leading Microsoft Fabric Consulting Companies to Move Beyond Power BI DataToBiz DataToBiz is an award-winning business intelligence company and a certified partner of Microsoft (Gold), AWS, and Google. It is among the leading Microsoft Fabric consulting companies with a global presence. The company’s ISO and SOC 2 certifications are proof of its focus on data security, privacy, and regulatory compliance standards. It provides end-to-end Power BI consulting and Fabric implementation services aligned with each client’s needs. The Fabricspend Analyzer by DataToBiz is a powerful auditing solution designed to help enterprises identify the root causes of roadblocks and find effective ways to overcome them. In just two weeks, businesses can have a clear report on how to optimize their setup and achieve cost efficiency without compromising quality or results. The company also provides a step-by-step roadmap and has helped clients gain over 30% savings through its smarter workload design. Algoscale Algoscale is an AI-focused digital engineering company offering trusted services to varied businesses, be it Fortune 100 firms or startups. It prioritizes customer satisfaction with its product intelligence, automation, and business safeguarding solutions. The company also provides customized MS Fabric Consulting Services to build scalable data solutions and simplify complex systems. Algoscale has a proven 3R assessment framework, which it uses to understand the client’s current position and develop a strategy for the future. The company’s end-to-end Fabric consulting services include data engineering, AI automation, data governance, and many more vital solutions. It is an ISO certified company with a global client base. Capgemini Capgemini is a popular advisor and transformation partner to businesses from around the world and has vast experience in various industries. From strategy to design, implementation, operations, engineering, and more, the company provides custom services for organizations to achieve their goals and gain a competitive edge. It is also one of the Microsoft Fabric consulting companies offering cloud solutions for seamless data and analytics management. Its SaaS (software as a service) solutions are aimed at optimizing costs and ensuring on-demand, scalable offerings to clients. The company also uses AI and its proprietary platforms to provide end-to-end services aligned with each client’s requirements. Data Bear Data Bear is a data and Microsoft services provider with Gold partner certification. The company designs, trains, and supports data analytics, applications, and automations to ensure data-driven insights can be accessed by everyone in the business. It is among the well-known Microsoft Fabric Consultants that focus solely on offering Microsoft-related services. From Excel to Power BI to Fabric, Power Apps, and Copilot, the company can set up, customize, and integrate various tools, technologies, and software developed by Microsoft. Additionally, Data Bear takes care of data security, governance, and compliance requirements as well. Whether clients want the setting up of the complete analytics platform or specific services like Copilot integration, etc., the company offers solutions accordingly. VNB Consulting Services VNB Consulting is an ISO-certified, Microsoft Gold, AWS, and Snowflake certified partner helping businesses achieve digital excellence. As one of the Microsoft Fabric consulting companies, it integrates the solutions to align with the organization’s business goals and requirements. It follows industry standards and best practices to provide unique strategic frameworks and data architecture that ensure

CIO Strategies to Optimize Microsoft Fabric Spend Using AI

Multiple CIOs, 5 core strategies that help you cut Microsoft Fabric costs. From automating data preparation to monitoring usage and optimizing queries, these steps in AI implementation enable tech leaders to maximize the value of their Microsoft Fabric spend. As more organizations are using data and AI, managing costs and complexity in Microsoft Fabric has become a top priority. With over 25,000 organizations, including 67% of the Fortune 500 are using Fabric to make smarter decisions. These numbers highlight the importance of data-driven work for the future. Fabric is “perhaps the biggest launch of a data product from Microsoft since the launch of SQL Server.” Satya Nadella, CEO and Chairman of Microsoft Since its pricing is based on usage, each choice, from storing data to running queries, impacts costs. Here’s where using AI to optimize and control costs proves helpful. In this blog, we’ll walk you through proven strategies that leading organizations are using to optimize Microsoft Fabric spend. Understanding Microsoft Fabric Spend Microsoft Fabric uses a consumption-based pricing model. It means organizations pay based on real-time usage of compute, storage, queries, and premium features rather than fixed licenses. This means tasks such as running analytics and transferring data directly impact the bottom line, requiring ongoing cost monitoring. “In developing and launching EY Intelligence, Microsoft Fabric has been a game changer. Our unique analytics as a service offering gives the C-suite at our client organizations cross-functional transparency and on-demand insights to make better and quicker decisions.” – Swen Gehring, Director, Strategy and Transactions, Ernst and Young. How are Costs Measured in Fabric? Microsoft Fabric costs are measured in Capacity Units (CUs), which represent a shared pool of compute resources across all Fabric services and OneLake storage. To use Fabric, you purchase a capacity plan (such as F2 or F4) that provides a specified number of CUs. Billing is based on total usage, which is calculated by multiplying the number of CUs consumed by the number of hours for which they are used. You can scale resources, and they are billed according to usage, making Fabric highly flexible. However, there is a risk for overspending if workloads aren’t optimized. Below, we’ve discussed some common areas where you can overspend in Microsoft Fabric AI. Compute Storage Queries Premium Features 5 AI Strategies to Optimize Your Microsoft Fabric Spend (From CIOs Desk) Below, we discuss the best AI strategies that you can use to optimize Microsoft spend. AI-powered data preparation and profiling Microsoft Fabric makes data preparation easy by using AI to automate tasks like cleaning, checking, and transforming data. Performing these tasks manually takes a lot of time and effort. With artificial intelligence consulting insights, MS Fabric AI finds common problems such as missing values, duplicates, or errors, and suggests fixes so the data is ready for analysis. The automated data profiling detects hidden patterns and relationships in data from different sources, helping to organize and match data faster. This means teams don’t have to spend hours manually figuring out how data fits together. With tools like Power Query and Data Factory, business analysts can clean and transform data. Benefits: Using Copilot for self-service analytics Copilot in Microsoft Fabric allows users to create reports and run queries by using LLM models that understand natural language and structure queries. It interprets user inputs and identifies entities like dates, metrics, and filters, and maps them to the underlying data sources. Based on the input, Copilot generates optimized SQL or DAX queries. It automatically applies filtering, grouping, and aggregation logic to retrieve the correct data. Further, it connects with datasets from Power BI, Azure Synapse, or other integrated Fabric services to identify relevant tables, relationships, and joins, ensuring queries pull accurate information. Once the query is processed, Copilot builds charts, tables, or graphs, selecting appropriate formats based on data type and requested insights. Benefits: Predictive analytics for budget control Microsoft Fabric helps organizations control costs by using AI-powered predictive analytics to forecast when usage might increase and trigger unnecessary expenses. It collects historical data about compute usage, storage patterns, and query loads. Further, it applies forecasting models like ARIMA and advanced neural networks to predict future usage trends. This helps teams predict periods of high demand, such as month-end reporting or marketing campaigns, and plan capacity. Fabric monitors current usage and compares it against predicted patterns. If usage starts exceeding forecasts, AI-based anomaly detection algorithms (isolation forests or clustering techniques) flag them in real time. It sends automated alerts through email, dashboards, or integrations with workflow tools to inform teams. Benefits: Dynamic resource scaling with AI Microsoft Fabric helps organizations optimize costs by adjusting compute resources based on workload demand, ensuring they only pay for what they need. Fabric uses AI to monitor usage patterns and automatically scale up resources when demand increases and scale down across multiple nodes for heavy workloads. When demand decreases, it scales down to avoid idle resource costs. AI monitors scheduled workflows, batch jobs, and interactive sessions to predict an increase in demand. For example, a large dataset refresh can trigger temporary scaling. Once the workload is completed, resources are reduced, preventing a budget overrun. Working with BI consultants helps organizations establish AI-driven scaling policies, ensuring resources are allocated effectively while maintaining optimal performance. Benefits: Real-time monitoring and query optimization Microsoft Fabric helps organizations save costs and improve performance by continuously monitoring queries and optimizing workloads in real time. Fabric tracks all queries running across datasets and dashboards. AI identifies queries that consume too much compute or take longer than necessary due to poor structure, missing indexes, or inefficient joins. Instead of refreshing entire datasets repeatedly, Fabric uses incremental refresh to update only new or changed data. AI also monitors for sudden spikes in query traffic and distributes workloads or throttles non-critical jobs to prevent resource overload. AI also recommends and makes changes like rewriting queries, adding indexes, or adjusting the cache. These changes help cut down compute usage, avoid unnecessary processing, and prevent you from paying for more resources

The True Cost of Microsoft Fabric Adoption (and How to Shrink It?)

MS Fabric is a powerful unified platform for end-to-end data management and analytics without third-party integration and scalability. Here, we’ll discuss the true Microsoft Fabric adoption cost and ways to reduce them effectively to increase ROI for your business. Microsoft Fabric is a comprehensive SaaS (software as a service) data analytics platform with end-to-end solutions to manage the entire data flow from collection to visualization. It is a robust platform developed for large and multinational organizations to streamline and automate various components in their data architecture. It can be integrated with business intelligence suites like Power BI to provide real-time analytical insights for smart decision-making. In today’s fast-paced world, data analytics and business intelligence are not optional; they are mandatory for a business to survive competition and gain an edge over others. Statistics show that the global data analytics market is valued at $64.75 billion in 2025, with a CAGR (compound annual growth rate) of 29.40%. Similarly, the business intelligence market is projected to grow at a CAGR of 16.2% to reach $26.5 billion by 2033. However, MS Fabric adoption comes with its share of concerns, which have to be strategically addressed by enterprises. A major issue with Fabric is the costs that can pile up if you haven’t optimized the platform and its integrations. Despite the flexible pricing models, businesses find it complex to manage the various add-ons and other features, resulting in paying more for the services and generating less ROI. That’s why most of them hire Microsoft Fabric Consultants or partner with certified service providers to optimize the platform and continuously monitor its performance to ensure you get the desired outcome. In this blog, we’ll read about various Microsoft Fabric expenses and how to reduce them through different optimization strategies Ways to Shrink Microsoft Fabric Adoption Costs MS Fabric adoption can easily become complicated without proper optimization and continuous monitoring. Moreover, implementing the solution is not as straightforward as it appears to be due to the various connections, features, tools, technologies, and functionalities involved in the process. It requires expertise, skills, knowledge, and access to the vast Microsoft ecosystem. That’s why many enterprises collaborate with certified Microsoft partners to implement and optimize Fabric. Here, we are categorizing the various optimization methods into three broad categories: Streaming Optimization Streaming or ETL (extract, transform, load) optimization can help reduce the costs incurred from constantly moving data and running lengthy processes. The focus here is to improve batch efficiency and leverage scaling seamlessly to handle spikes. The data engineering team can run hundreds of separate data factory pipelines, each with several records in it. This alone could increase the Microsoft Fabric adoption cost by a huge percentage. By switching this to batch processing, the individual pipelines are combined into a few with more records. This allows more data to be processed in less time. Imagine ingesting thousands of transaction records per day and then running micro-batches for MERGE operations. Instead, you can batch up the merges and opt for periodic compaction. In this, the micro-batches are collected into a large batch and then merged in a single process, thus saving great costs for your business. Instead of having dedicated Spark clusters that run around the clock, using Spark compute allows autoscaling. This reduces the cost of MS Fabric adoption as the capacity and processes are scaled based on the workload. Moreover, cluster pools can be created based on workloads to optimize usage. Data files can be massive and occupy a lot of storage space. Transferring and moving them around also requires more resources. Additionally, the large files can cause delays in processing queries. Using built-in and third-party compression tools, the data files can be compressed to a smaller size without compromising quality or damaging the data. Analytics Optimization Optimizing ad-hoc analytics workloads reduces the cost of Microsoft Fabric implementation by eliminating idle time and minimizing the amount of data scanned, thus making it less expensive to run queries and derive insights. This allows end users to access more actionable insights without piling up the expenses for your business. Imagine an unstructured data table with millions of rows and columns. Each time a query is sent, the entire data is processed, irrespective of whether it is required or not. By partitioning and clustering the physical layout of the Delta Lake, you can process a smaller amount of data when you run queries. This is done based on common filtering words. Similarly, storing data as individual, smaller files can lead to duplication and more processing time. Also, the streaming tool will have to scan a lot of metadata, thus slowing down the query processing time and consuming more resources. MS Fabric Consulting companies implement a format optimization and file consolidation step where scattered, smaller files are neatly structured and stored in appropriate partitions. Additionally, the data lake can be tiered to implement hot and cold storage tiering. You don’t always run the same amount of data or analytics. The workload varies based on various factors and requirements. Sometimes, you may have to generate more insights, while other times, the workload could be less. When your needs are variable, the resource allocation process should also be dynamic and adaptable to ensure the MS Fabric adoption cost doesn’t exceed the budget. Dynamic cluster scaling allows your teams to run analytics seamlessly without increasing expenses. That’s because the resources are automatically scaled up or down based on the workload. Additionally, you can set up predictive scaling or go for serverless analytics (SQL or Spark) to automate resource management. Another way to reduce expenses and optimize Microsoft Fabric cost governance is through data caching. Business intelligence and data analytics teams tend to use the same datasets for various reports. For example, for quarterly reports, the focus is on data belonging to the current quarter. Instead of repeating the process and increasing workloads, you can add an intelligent caching layer on Spark. This automates caching (so the manual process is also eliminated) and allows your teams to

Decision Intelligence Platforms: The Ultimate MS Excel Alternative

While MS Excel has its advantages, it is no longer enough for businesses to effectively manage their data and insights. Here, we’ll discuss the benefits of adopting decision intelligence platforms to make informed and smart decisions and gain a competitive edge. For years, Microsoft Excel has been the go-to choice for creating databases and generating reports, graphs, tables, etc. The introduction of business intelligence platforms like Power BI has upped the game to allow organizations to convert Excel sheets into powerful databases. Then, decision intelligence has furthered the development to unlock the full potential of business data to derive meaningful insights and make data-driven decisions. According to Fortune Business Insights, the global decision intelligence market was valued at $16.79 billion in 2024 and expected to reach $19.38 billion in 2025, with a projected growth rate (CAGR) of 16.9% to reach $57.75 billion by 2032. The statistics clearly indicate that businesses are shifting from traditional Excel sheets to powerful decision intelligence platforms, providing valuable insights and analytical reports to inform proactive business decisions. In this blog, we’ll read more about decision intelligence and the importance of revamping the infrastructure to support business intelligence system adoption, and advanced analytics. What is Decision Intelligence? Decision intelligence uses machine learning and automation to provide faster and more reliable insights and enable data-driven decision-making in enterprises. Typically, it combines data analytics, artificial intelligence, and data science to provide a holistic view of the situation and give contextual information required to make the necessary decisions. Decision intelligence platforms are not limited to historical data, but also support advanced analytics like predictive and prescriptive analytical insights to prepare for the future and make proactive decisions. Additionally, it is a part of modern data engineering and can optimize the outcomes to improve all quality, efficiency, and performance. This brings greater consistency to the process and allows you to benchmark higher standards and adhere to the compliance regulations. MS Excel vs Decision Intelligence Microsoft Excel is a spreadsheet tool for basic data analysis and reporting. It works best for small and medium-sized datasets and reports that don’t require complicated analytics. Though Excel can be integrated with AI tools, on its own, the functionalities can be limited, especially for large enterprises that require powerful insights. Excel’s limitations in analysis make it less effective for day-to-day decision-making, also because it requires manual effort. On the other hand, decision intelligence platforms are known for their analytical and automation capabilities. Furthermore, they support descriptive, predictive, diagnostic, and prescriptive analytics in real-time. Transparency, data-centric systems, explainability, flexibility, scalability, and continuous improvement are the key principles of decision intelligence. It is a must-have solution to implement modern data analysis in your organization and benefit from data-driven models to gain a competitive edge in global markets. Decision Intelligence Platforms Benefits Working with massive amounts of business data needs powerful systems that can seamlessly handle complex requirements and share actionable insights in real-time. Decision intelligence platforms are a perfect solution as they offer the following benefits: Seamless Data Integration There’s no need to struggle with collecting and storing data from multiple sources. The DI platforms can be integrated with several data sources to automate data collection and streamline data flow within the systems. This eliminates the need for human intervention and saves time and resources spent on managing the datasets. A centralized data repository is created to store, clean, and convert raw data into actionable insights. Democratized Data Access Data collection and storage are only a part of the process. This data and insights have to be accessible to all decision-makers across the enterprise. That requires providing authorized and restricted access to employees based on their job descriptions and roles. It also reduces the load on the technical team since employees can directly access the required data and reports through personalized dashboards. Additionally, your employees will have more control over the situation. Faster and More Accurate Insights Traditional analytics are prone to biases and blind spots, which are inherent to legacy systems. Additionally, decision-makers may also make biased interpretations and decisions, which can impact the business in various ways. Such risks can be minimized by implementing modern data analytics solutions and decision intelligence platforms that provide a unified and holistic view of the situation. DI eliminates the risk of inaccurate analysis made from low-quality data. Thus, your decisions will be more aligned with your objectives. Uncovering Hidden Patterns When you work with large amounts of data, it is not easy to identify hidden patterns, trends, connections, and correlations between data points. Decision intelligence uses advanced technologies like AI, ML, etc., which can see what humans cannot immediately detect when processing massive datasets. This allows you to get deeper and richer insights about the market, customers, competitors, products/ services, and much more. You can identify the root cause of problems and come up with comprehensive solutions to resolve them permanently. Maximizing ROI Return on investment is necessary for every business. How soon you can start to generate ROI indicates the efficiency of the solutions. In today’s fast-paced world, businesses have to get quick results and returns to generate profit and mitigate risk. Decision intelligence can help with this by accelerating ROI and maximizing it. Instead of making decisions based on outdated and incomplete data, you use reliable and meaningful insights to power your decisions and actions, thus enhancing revenue and profits. Scalability and Adaptability Decision intelligence platforms empower you to future-proof your infrastructure by offering scalability, flexibility, and adaptability. There’s no need to replace the systems with new ones. Instead, they are periodically upgraded to handle your growing needs and support the increased volume of transactions. Furthermore, this is achieved while increasing the quality and efficiency of the systems through automation. NLP (natural language processing) ensures that the DI platforms provide contextual insights with greater accuracy. Demand Forecasting Predictive analytics helps with sales and demand forecasting, which allows you to be prepared for future market changes and opportunities. Decision intelligence empowers different teams to collaborate and come up with

Data Governance in Self-Service BI: Managing Risks Without Data Gatekeepers

Self-service BI is more efficient and reliable when you have a robust data governance framework to streamline and standardize the process. Here, we’ll discuss how data governance in self-service BI helps with risk management. Business intelligence is a collection of processes that convert raw data into actionable insights. A traditional BI setup is highly technical and requires data analysts, data scientists, statistical analysts, and BI experts with relevant skills and knowledge. This team manages the processes and shares the insights with other employees to help them make data-driven decisions. However, there’s a branch of business intelligence that has simplified the process for non-technical employees and end users. This is known as self-service BI. According to The Business Research Company, the self-service BI market was $10.02 billion in 2024 and is expected to grow at a CAGR (Compound Annual Growth Rate) of 17.3% to reach $22.42 billion by 2029. Self-service BI tools enable users to sort, analyze, derive insights, and generate data visualizations without requiring extensive technical expertise. Be it frontline employees or executives, they don’t have to contact the tech team with queries and wait for the insights/ reports to be sent. With self-service BI, they can perform the activity on their own and make data-driven decisions. While this made self-service BI popular across industries, it also led to certain challenges and issues, especially with data management and governance. That’s because self-service BI also requires BI consultants to work on the backend and ensure that the data quality is as it should be to derive accurate insights. In this blog, we explore the challenges of self-service BI and how data governance plays a crucial role in managing risks when data gatekeepers step back. Challenges without Data Governance in Self-Service BI The major challenges of using self-service BI deal with data. While most businesses know the importance of data in deriving insights, not many have a clear picture of how to handle data or ways to ensure its quality, compliance, etc. This results in a mismatch of expectations and outcomes. It turns self-service BI into a frustrating tool, resulting in employees sending emails to the BI with their queries and requests. Data Inconsistency and Trust Issues It’s no surprise that a business has vast amounts of data to deal with. Transactional data, data from social media and websites, data brought by stakeholders, customer data, etc., are all important and should be used for analytics. However, this raw data has duplicates, incomplete information, and other errors. Ensuring data consistency is a big challenge as low-quality data can result in incorrect insights. Complexity Instead of Simplification The market has several BI tools with extensive features and capabilities. Vendors promise flexibility, interactive features, and access to numerous data visualizations. While these sound great in theory, the practical application can be confusing and overwhelming. Which visualization should an employee use for which report? What happens if the wrong type of graph or chart is created? BI risk management is also about ensuring that the customized dashboards don’t complicate things when they should be simplifying the process. Report Sprawl Interactive dashboards are easy to use. Hence, employees can generate reports with a couple of clicks. Over time, this results in too many reports created by employees from across the organization. Quality, relevance, and accuracy can take a backseat without a proper understanding of why these reports are generated and how they are used. Repot sprawl leads to confusion and miscommunication, which can result in wrong decisions. Lack of Standardization Consistency in how your employees use self-service BI tools is vital for a business to be efficient and achieve its goals. This requires standardization of processes – the data used for insights, the types of reports generated, the validation process, when to use data-driven analytics, etc. This is more of a strategic plan than a series of operations or actions. A business cannot afford for each employee to follow a different standard or process when making data-driven decisions. Absence of Governance Data governance has to be a priority, but some businesses ignore it. When you don’t manage data and the analytics process with a proper framework, it can complicate the operations, lead to unverified reports, and may even attract lawsuits from outsiders or stakeholders due to various reasons. Data governance is not optional. It is mandatory even for self-service BI. That’s why many enterprises hire business intelligence consulting services to add a robust governance layer to their data-driven models. What is Data Governance? We mentioned data governance a few times. What does it actually mean? Data governance is a collection of principles, practices, and tools that help manage the data assets of a business throughout the lifecycle. Aligning data requirements with business vision, mission, objectives, and strategy is important for seamless data management. It also includes data security and data compliance, where the data used for analytics is safe from unauthorized access and adheres to the global data privacy regulations, like GDPR, CCPA, etc. The data governance framework empowers you to leverage your data assets to unlock their true potential and derive meaningful and accurate insights for proactive decision-making. From optimizing resources to reducing costs, increasing efficiency, and standardizing processes, data governance plays a crucial role in protecting your organization’s data and reputation. How Data Governance Helps Manage Risks in Self-Service BI Data governance is the solution to managing risks and challenges of using self-service BI tools in your business. Third-party and offshore BI consultants can help implement data governance practices. Clear and Measurable Goals The easiest way to complicate things is to be vague and directionless. You need clear and measurable goals when implementing business intelligence in your organization. The same applies to building the data governance framework. In fact, your goals and strategies should be aligned at all times to get the expected results. Be specific about the outcomes you expect, such as reducing the request rate by a certain percentage, increasing meaningful dashboard activity by X times, and so on. Make data compliance

How Large Language Models Aid Your Business Intelligence Investments?

It is time to automate your BI systems and make them more accessible to non-technical users across the organization. Here, we’ll discuss how large language models (LLMs) can aid your business intelligence (BI) investments and increase ROI. Business intelligence helps enterprises transform their raw data into actionable insights through a series of tools, technologies, strategies, and processes. It helps employees and executives make data-driven decisions and gain a competitive edge. According to Business Research Insights, the global BI market is $30.72 billion in 2025 and expected to reach $50.66 billion by 2034 at a CAGR (compound annual growth rate) of 5.72%. More statistics show that 67% of organizations will have adopted business intelligence by 2025, while 91% of enterprises have plans to increase their BI investments. These reports clearly indicate that business intelligence is becoming a part of most organizations. Now, traditional BI includes various manual processes that are time-consuming and stressful. Data analysts and BI experts must provide insights requested by employees from across the organization, handling all the necessary work. However, thanks to artificial intelligence, you can automate and streamline various BI processes to save time and increase efficiency. For example, using LLMs for Power BI can empower the platform and your employees to deliver better outcomes. In this blog, we’ll read about how large language models (LLMs) can be integrated with your BI systems and what benefits you can achieve from this integration. What are Large Language Models (LLMs)? Large language models (LLMs) are a type of AI and ML program that are trained on large datasets to recognize and generate text based on user input. The LLMs use a type of machine learning called deep learning to understand unstructured data (words, text, characters, sentences, etc.) and recognize patterns, distinctions, etc., without human intervention. The models are further trained and fine-tuned to suit the specific requirements of different businesses. This is done by training the LLMs on proprietary data to ensure the results are more relevant, accurate, and aligned with the outcomes you want. Typically, large language model development is done for various reasons or tasks. You can hire an LLM development company to build, deploy, customize, and integrate the model as per your requirements. Some use cases of LLMs are as follows: GPT-3, GPT-4, BERT, LLaMA, etc., are some examples of large language models. GenerativeAI vs. LLMs Though the terms generative AI and LLMs are used in similar contexts and even interchangeably at times, they are not the same. As the name suggests, generative AI generates content (text, audio, video, images, etc.). LLMs are limited to the text generation process of GenAI model development, though here ‘text’ can also include programming code, biological sequences, etc. Simply put, LLMs are a type of generative AI, but not every genAI model is an LLM. From BI to AI: Using LLMs in Business Intelligence LLMs have diverse use cases already, depending on how you want to integrate them into your existing systems. Empowering business intelligence is one of the important applications of large language models in today’s world, where data and insights play a crucial role in business growth and expansion. LLM consulting companies offer tailored solutions to fine-tune the models with business data and integrate them with your existing BI platforms like Power BI. This makes the BI platform more powerful, scalable, and flexible. It provides insights quickly and reduces technical complexity for the end users. For example, using an AI chatbot in BI (Power BI has an AI chatbot capability through its Copilot integration) changes how employees interact with the platform. Instead of sending queries in SQL and using highly technical processes, employees can input their queries in English or other human languages. LLM integration enables the BI platform to read and understand the input, providing relevant insights. Even the insights can be simplified and summarized for employees to easily grasp the key points and make a decision quickly. Similarly, there are many applications of large language models (LLMs) in business intelligence development. Application of LLM in BI Development LLMs nowadays have a vital role in an organization’s Power BI journey. They can offer robust data analytics, business intelligence, and reporting capabilities, which can be refined for greater accuracy and relevance as you continue to use the systems for data-driven decision-making. LLMs can be used in BI development in the following ways: Sentiment and Customer Behavior Analysis Customer behavior and feedback are critical for any business to ensure that its products and services are aligned with the market demands. Traditionally, enterprises used manual interpretation methods, which had a greater risk of error or misunderstanding. However, with large language model consulting for business intelligence, you can not only automate processes to save time but also minimize human error. This allows you to make swift decisions and keep your customers happy and satisfied with your offerings. Data Preparation and Modeling Large language models are good at data preparation and modelling, especially when dealing with massive datasets. Since both these steps are crucial in the business intelligence process, they can be streamlined and automated to optimize resources, save time, and reduce workload on employees. Moreover, LLMs are trained to study datasets to identify patterns, trends, and correlations that humans may not see immediately due to the extensive range of data that has to be analyzed. This makes it easier to identify variables, trace relationships, etc. Interactive and Conversational BI Business intelligence tools like Power BI have interactive dashboards that create graphical data visualizations in real-time. However, understanding complex reports requires technical knowledge. Enabling Power BI dashboard integration with LLM-powered chatbots bridges the gap. You can convert the dashboard into a conversational interface where the chatbot summarizes or simplifies the reports for non-technical users. This reduces the risk of misinterpretation and makes BI reports accessible to more employees across the organization. The chatbots are trained to understand the context and semantics when providing information to the users. Scaling Insights Seamlessly Businesses don’t have the same needs throughout. As your transactions