Course Introduction
The Business Intelligence Automation Using AI and Low-Code Tools Training Course provides participants with the knowledge and practical capabilities required to automate data collection, processing, analysis, reporting, and decision-support activities using artificial intelligence and low-code technologies. As organizations manage increasing volumes of information across multiple systems, manual reporting processes can become time-consuming, inconsistent, and difficult to scale. AI-powered automation and low-code platforms offer opportunities to streamline these activities, reduce repetitive work, improve reporting accuracy, and make business intelligence more accessible to professionals without extensive programming experience. This course explores how organizations can combine intelligent analytics and visual workflow development to establish efficient, responsive, and scalable business intelligence environments.
The programme examines the foundations of business intelligence automation, including data integration, workflow orchestration, automated reporting, process optimization, and intelligent decision support. Participants will explore how artificial intelligence can assist with data classification, information extraction, anomaly identification, analytical summarization, and report generation. The course also introduces low-code and no-code development principles, demonstrating how visual interfaces, reusable components, prebuilt connectors, and configurable workflows can simplify the development of business intelligence solutions. Particular emphasis is placed on understanding the relationship between organizational reporting requirements, data architecture, automation logic, and AI-enabled analytical capabilities.
Participants will gain practical exposure to technologies such as Microsoft Power Automate, Power Apps, Power BI, Power Query, Microsoft Copilot, and other low-code automation and analytics platforms. The programme explores how these tools can connect spreadsheets, databases, cloud applications, enterprise systems, and collaboration platforms to create automated information workflows. Participants will examine how to configure triggers, actions, conditions, approvals, notifications, scheduled processes, and AI-assisted tasks. They will also explore how generative AI can support the creation of formulas, workflow logic, analytical summaries, documentation, and reporting narratives, while maintaining appropriate human verification and control.
A major focus is placed on developing automated business intelligence applications for financial reporting, operational monitoring, customer analytics, human resource management, procurement, inventory control, and organizational performance measurement. Participants will learn how to automate data refresh processes, generate management reports, distribute dashboards, monitor key performance indicators, and trigger notifications when defined conditions are met. The course examines how low-code applications can provide user-friendly interfaces for data submission, review, approval, and reporting, while integrated AI capabilities help identify patterns and generate useful insights. Emphasis is placed on selecting automation opportunities that produce measurable improvements in productivity, reporting speed, service quality, and management oversight.
The programme also addresses the technical and governance requirements of implementing AI-powered automation in enterprise environments. Participants will examine data quality, workflow reliability, access permissions, information security, privacy protection, audit trails, error handling, and responsible AI practices. The course explores how organizations can manage automation failures, prevent unauthorized data exposure, validate AI-generated outputs, and maintain human oversight over important business decisions. Emerging developments such as AI agents, intelligent process automation, robotic process automation, conversational workflow interfaces, and autonomous analytics are considered in the context of organizational readiness, integration complexity, and sustainable digital transformation.
By the end of the course, participants will be better equipped to identify, design, implement, and evaluate automated business intelligence solutions using AI and low-code technologies. They will gain practical capabilities in connecting data sources, building visual workflows, automating analytical processes, developing interactive reporting applications, and integrating AI-assisted insights into management information systems. The programme supports organizations seeking to reduce manual workloads, accelerate reporting cycles, improve data accessibility, and establish more efficient information management processes. It also provides a foundation for scaling intelligent automation through structured governance, continuous improvement, and measurable business value.
Duration
5 days
Who Should Attend
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Business intelligence analysts and reporting specialists.
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Data analysts and business analytics professionals.
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Business process automation specialists.
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Low-code and no-code application developers.
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IT managers and enterprise systems administrators.
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Digital transformation and innovation managers.
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Management information systems officers.
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Power BI developers and Microsoft Power Platform users.
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Financial analysts and management accountants.
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Operations managers and process improvement specialists.
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Monitoring, evaluation, and performance management professionals.
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Project managers and programme coordinators.
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Data scientists and artificial intelligence practitioners.
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Internal auditors, compliance officers, and risk management professionals.
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Senior managers responsible for organizational reporting and operational efficiency.
Course Objectives
By the end of this course, participants will be able to:
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Explain the principles of business intelligence automation and evaluate how artificial intelligence, low-code platforms, and workflow technologies improve organizational reporting and decision-support processes.
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Identify repetitive business intelligence activities suitable for automation by assessing process requirements, data dependencies, operational risks, expected benefits, and implementation complexity.
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Integrate data from spreadsheets, databases, cloud applications, enterprise systems, and external platforms using low-code connectors and automated data integration workflows.
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Design automated data preparation and transformation processes using visual development tools, Power Query, and AI-assisted capabilities to improve data consistency and reporting reliability.
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Develop automated workflows using triggers, conditions, actions, approvals, notifications, and scheduled processes to streamline business intelligence and management reporting activities.
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Apply AI and generative AI tools to support data extraction, analytical summaries, report generation, workflow configuration, and intelligent identification of business patterns.
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Build low-code business intelligence applications and interactive dashboards that enable users to submit information, monitor performance, review analytical results, and access management reports.
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Implement automated KPI monitoring, exception alerts, data refresh processes, and report distribution mechanisms to improve the timeliness of organizational performance information.
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Apply governance, access management, cybersecurity, privacy protection, error handling, and human oversight measures to maintain secure and reliable automated business intelligence systems.
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Evaluate the effectiveness of automated BI solutions using measurable indicators such as processing time, reporting accuracy, workflow completion rates, cost reduction, user adoption, and operational efficiency.
Comprehensive Course Outline
Module 1: Foundations of Business Intelligence Automation and Low-Code Technologies
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Understanding the evolution of business intelligence from manual reporting and traditional data processing to AI-powered automation and intelligent analytics.
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Examining the relationship between business intelligence, artificial intelligence, low-code development, no-code platforms, and intelligent process automation.
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Identifying repetitive reporting, data management, analytical, and administrative activities that offer opportunities for automation.
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Evaluating the organizational benefits, limitations, costs, and adoption considerations associated with low-code business intelligence automation.
Module 2: Low-Code Platforms and the Microsoft Power Platform Ecosystem
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Exploring the capabilities and architecture of Microsoft Power BI, Power Automate, Power Apps, Power Query, and related Microsoft Power Platform services.
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Understanding visual application development, reusable components, prebuilt connectors, workflow templates, and integration capabilities across low-code environments.
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Comparing low-code and no-code platforms with traditional software development approaches in terms of flexibility, scalability, maintenance, and governance.
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Assessing platform selection criteria, licensing considerations, cloud integration requirements, and organizational readiness for low-code automation.
Module 3: Automated Data Collection, Integration and Transformation
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Connecting spreadsheets, relational databases, cloud storage, enterprise resource planning systems, and external data sources through automated connectors.
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Designing automated data extraction, transformation, cleaning, and loading processes using Power Query and other visual data preparation technologies.
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Configuring scheduled data imports, automated refresh operations, and synchronization workflows to maintain current and consistent reporting datasets.
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Applying AI-assisted data profiling, classification, extraction, and validation techniques to improve data quality and reduce manual preparation.
Module 4: Workflow Development Using Low-Code Automation Tools
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Designing automated business workflows using triggers, actions, conditions, variables, expressions, and reusable workflow components.
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Developing scheduled, event-driven, and approval-based workflows for management reporting, information processing, and organizational communication.
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Integrating email, collaboration applications, databases, forms, and enterprise systems to automate information movement between departments.
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Applying workflow testing, exception handling, error notifications, version management, and performance monitoring to improve automation reliability.
Module 5: Artificial Intelligence and Generative AI for Business Intelligence Automation
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Exploring how machine learning, natural-language processing, and generative AI support intelligent data processing and automated business intelligence activities.
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Using AI assistants and Microsoft Copilot capabilities to support workflow development, formula generation, report preparation, and analytical interpretation.
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Applying AI-assisted document processing, information extraction, text classification, and summarization to convert unstructured information into useful business data.
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Evaluating AI-generated insights, analytical narratives, and automated recommendations through validation procedures and appropriate human oversight.
Module 6: Automated Reporting, Dashboards and Performance Monitoring
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Developing interactive Power BI dashboards that integrate automated data sources, performance indicators, filters, and management reporting components.
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Configuring automated report refresh, scheduled distribution, subscriptions, and notifications to improve access to current organizational information.
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Establishing automated KPI monitoring and exception alerts that identify performance deviations, missed targets, and emerging operational concerns.
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Integrating AI-generated summaries, trend explanations, and anomaly indicators into reporting workflows to improve management interpretation.
Module 7: Low-Code Business Applications and Intelligent Information Management
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Designing Power Apps and other low-code applications for data entry, information collection, reporting requests, approvals, and performance monitoring.
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Connecting low-code applications to organizational databases, SharePoint, Dataverse, and other supported enterprise information repositories.
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Developing automated approval, review, and notification processes that improve accountability and reduce administrative delays.
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Exploring AI-enabled application features, conversational interfaces, and intelligent data assistants that simplify user interaction with business information.
Module 8: Functional Applications of AI-Powered BI Automation
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Automating financial reporting, budget monitoring, expenditure tracking, invoice processing, and management accounting information workflows.
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Applying intelligent automation to procurement, inventory management, supply chain reporting, vendor monitoring, and operational performance analysis.
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Developing automated customer analytics, sales reporting, marketing performance monitoring, and customer service information workflows.
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Automating human resource reporting, project monitoring, monitoring and evaluation processes, and organizational performance management activities.
Module 9: Automation Governance, Security and Enterprise Integration
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Establishing automation governance frameworks covering workflow ownership, approval responsibilities, documentation, monitoring, and change management.
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Applying role-based access controls, data loss prevention policies, secure connectors, and privacy protection measures in automated reporting environments.
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Managing workflow failures, duplicate processing, unauthorized access, AI-generated errors, audit trails, and exception escalation procedures.
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Exploring robotic process automation, APIs, enterprise integration, AI agents, and intelligent process orchestration for more advanced automation requirements.
Module 10: Implementation Strategy, Optimization and Emerging Developments
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Conducting business process assessments to prioritize automation opportunities based on organizational value, feasibility, complexity, and implementation risks.
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Developing pilot projects that demonstrate automated data integration, workflow orchestration, AI-assisted analysis, and intelligent dashboard reporting.
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Measuring automation benefits through reporting cycle time, processing accuracy, resource utilization, workflow reliability, cost savings, and user adoption.
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Examining emerging developments in autonomous AI agents, intelligent process automation, low-code AI development, and the future of automated business intelligence.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
