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Quest Training

AI Integration with ERP & Enterprise Systems Training Course

Explores embedding AI into ERP and enterprise systems to automate processes, improve analysis and support decisions in finance, HR, procurement and operations.

AIEA · Artificial Intelligence & Enterprise ApplicationsAll Levels#2741ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Data, Analytics & Digital TransformationCategories:Artificial Intelligence & Enterprise Applications

Course Overview

This course equips participants with the practical knowledge to integrate artificial intelligence (AI) capabilities into enterprise resource planning (ERP) platforms and business systems. It explores how AI can enhance business process automation, improve data analysis, support decision-making, and optimize workflows across finance, human resources, procurement, supply chain, and operations. Participants examine AI integration architectures, application programming interfaces (APIs), intelligent automation, enterprise data connectivity, and generative AI applications. The programme also addresses security, governance, implementation planning, and performance measurement, enabling organizations to integrate AI into existing enterprise environments while maintaining data integrity, operational reliability, and business alignment.

Learning Objectives

  • By the end of the course, participants will be able to:
  • Explain the role of AI integration in ERP systems and enterprise applications.
  • Identify business processes suitable for AI-enabled automation and decision support.
  • Evaluate AI integration opportunities across finance, HR, procurement, supply chain, and operations.
  • Design integration approaches using APIs, enterprise data services, and workflow automation.
  • Understand how machine learning and generative AI can enhance ERP functionality.
  • Integrate AI capabilities with enterprise data sources while maintaining data consistency and integrity.
  • Assess technical dependencies, system compatibility, scalability, and implementation requirements.
  • Apply security, access control, privacy, and governance principles to AI-enabled enterprise systems.
  • Define performance indicators to measure operational improvements and business value.
  • Develop an implementation roadmap for integrating AI into existing ERP and enterprise platforms.

Who Should Attend

This course is designed for ERP consultants, enterprise architects, IT managers, systems integration specialists, AI professionals, business analysts, data engineers, and digital transformation leaders involved in enterprise system modernization. It is particularly relevant to professionals responsible for improving business workflows, connecting enterprise applications, automating repetitive tasks, and introducing intelligent capabilities into existing technology environments. The programme is also suitable for finance managers, HR managers, procurement specialists, supply chain professionals, operations managers, and enterprise application owners in government entities, ministries, banks, financial institutions, oil and gas companies, industrial organizations, and large corporations. Familiarity with ERP platforms, enterprise applications, business processes, or system integration concepts is recommended.

Learning Outcomes

  • Assess ERP and enterprise system environments for AI integration opportunities.
  • Map business processes to appropriate AI-enabled solutions and automation patterns.
  • Distinguish between traditional workflow automation, machine learning applications, and generative AI integration.
  • Design integration workflows connecting AI services with enterprise applications and data sources.
  • Explain the role of APIs, event-driven integration, middleware, and data pipelines in enterprise connectivity.
  • Identify requirements for data quality, synchronization, validation, and error handling.
  • Evaluate AI integration risks related to security, privacy, reliability, and system dependencies.
  • Define governance controls for AI-generated outputs and automated business actions.
  • Establish KPIs for measuring process efficiency, accuracy, adoption, and business impact.
  • Prepare a phased implementation plan covering integration, testing, deployment, monitoring, and continuous improvement.

Course Outline

Day 1

AI Integration Fundamentals and Enterprise System Architecture

  • Understanding ERP systems and their role in enterprise-wide process management.
  • Exploring enterprise application ecosystems, including CRM, HRMS, financial systems, supply chain platforms, and business intelligence solutions.
  • Understanding AI capabilities, including machine learning, predictive analytics, natural language processing, and generative AI.
  • Identifying opportunities to enhance enterprise workflows through intelligent automation and decision support.
  • Reviewing centralized, distributed, and hybrid integration architectures.
  • Understanding APIs, middleware, event-driven communication, and enterprise service interfaces.
  • Assessing system compatibility, data dependencies, process requirements, and integration constraints.
  • Mapping business objectives to AI integration opportunities and expected outcomes.
  • Practical Exercise: Assess an organization's ERP environment and identify priority AI integration opportunities across selected business functions.
Day 2

AI Use Cases Across ERP and Enterprise Business Functions

  • Exploring AI applications in finance, accounting, budgeting, and financial reporting.
  • Applying AI to invoice processing, transaction classification, reconciliation support, and anomaly detection.
  • Examining AI-enabled HR processes, including employee support, workforce analytics, and document processing.
  • Understanding AI applications in procurement, supplier evaluation, purchasing workflows, and contract analysis.
  • Applying predictive analytics to inventory management, demand forecasting, and supply chain planning.
  • Exploring AI-assisted maintenance planning, operational reporting, and industrial process optimization.
  • Integrating conversational AI with enterprise knowledge bases and employee self-service portals.
  • Evaluating the suitability of each use case based on business value, data readiness, complexity, and risk.
  • Practical Exercise: Develop a prioritized portfolio of AI use cases for an ERP environment, defining business problems, integration requirements, and expected benefits.
Day 3

Integration Technologies, Data Connectivity, and Intelligent Automation

  • Designing API-based integration between AI services and enterprise applications.
  • Understanding REST APIs, authentication mechanisms, data formats, and service communication.
  • Exploring middleware, integration platforms, and enterprise service buses.
  • Designing event-driven workflows for real-time processing and business notifications.
  • Connecting AI services to enterprise databases, document repositories, and approved knowledge sources.
  • Applying robotic process automation and intelligent document processing to repetitive workflows.
  • Understanding data mapping, transformation, validation, synchronization, and exception handling.
  • Managing integration failures, retries, transaction consistency, and audit trails.
  • Practical Exercise: Design an integration workflow connecting an AI service to an ERP process, including data exchange, validation rules, exception handling, and human approval where required.
Day 4

Generative AI, Security, Governance, and Operational Controls

  • Exploring generative AI applications within ERP interfaces and enterprise workflows.
  • Designing AI assistants for information retrieval, report generation, document analysis, and workflow support.
  • Connecting approved enterprise knowledge sources to AI systems using retrieval-augmented generation (RAG) concepts.
  • Establishing controls for AI-generated recommendations and automated business actions.
  • Applying identity management, role-based access, encryption, and secure API integration.
  • Protecting confidential business information and managing sensitive data exposure.
  • Addressing hallucinations, bias, prompt injection, unauthorized actions, and inaccurate outputs.
  • Establishing governance policies, approval workflows, auditability, and human oversight.
  • Practical Exercise: Review an AI-enabled ERP integration scenario and develop security, governance, validation, and human approval controls.
Day 5

Implementation Roadmap, Performance Measurement, and Value Realization

  • Planning AI integration projects from requirements assessment through production deployment.
  • Defining project scope, technical architecture, responsibilities, resources, and milestones.
  • Assessing integration dependencies, legacy system limitations, and change management requirements.
  • Developing testing strategies for functional correctness, data integrity, security, and system performance.
  • Establishing monitoring processes for integration health, failures, latency, and service availability.
  • Defining KPIs for processing time, error reduction, automation rates, operational efficiency, and return on investment.
  • Evaluating implementation costs, expected benefits, maintenance requirements, and scalability.
  • Planning pilot deployments, user training, adoption, and phased expansion across business functions.
  • Final Workshop: Develop and present an AI integration roadmap for an ERP or enterprise system, covering target architecture, priority use cases, integration methods, security controls, implementation phases, performance indicators, and expected business value.

Upcoming Dates

Selected date

Istanbul · 11 – 15 Oct 2027 · Classroom

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CityDatesPrice per ParticipantEarly Bird
LisbonScheduled
2 – 6 Nov 2026€5,400—
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LondonScheduled
9 – 13 Nov 2026€5,600—
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MadinahScheduled
16 – 20 Nov 2026€4,200—
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MadridScheduled
23 – 27 Nov 2026€5,400€4,860until 2 Nov
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ManamaScheduled
30 Nov – 4 Dec 2026€4,700€4,230until 9 Nov
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MarbellaScheduled
7 – 11 Dec 2026€5,400€4,860until 16 Nov
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MarrakeshScheduled
14 – 18 Dec 2026€4,300€3,870until 23 Nov
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MilanScheduled
14 – 18 Dec 2026€6,000€5,400until 23 Nov
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MuscatScheduled
21 – 25 Dec 2026€5,300€4,770until 30 Nov
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OnlineScheduled
27 – 31 Dec 2026€1,700€1,530until 7 Dec
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ParisScheduled
4 – 8 Jan 2027€5,700€5,130until 14 Dec
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RiyadhScheduled
11 – 15 Jan 2027€4,200€3,780until 21 Dec
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RomeScheduled
18 – 22 Jan 2027€5,800€5,220until 28 Dec
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SalalahScheduled
25 – 29 Jan 2027€5,300€4,770until 4 Jan
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Sharm El-SheikhScheduled
25 – 29 Jan 2027€4,300€3,870until 4 Jan
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ViennaScheduled
1 – 5 Feb 2027€5,700€5,130until 11 Jan
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AbujaScheduled
8 – 12 Feb 2027€5,100€4,590until 18 Jan
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AccraScheduled
15 – 19 Feb 2027€5,100€4,590until 25 Jan
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AmsterdamScheduled
22 – 26 Feb 2027€5,400€4,860until 1 Feb
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BarcelonaScheduled
1 – 5 Mar 2027€5,400€4,860until 8 Feb
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CairoScheduled
8 – 12 Mar 2027€3,300€2,970until 15 Feb
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CasablancaScheduled
15 – 19 Mar 2027€4,300€3,870until 22 Feb
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DohaScheduled
15 – 19 Mar 2027€4,400€3,960until 22 Feb
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DubaiScheduled
21 – 25 Mar 2027€4,400€3,960until 1 Mar
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GenevaScheduled
29 Mar – 2 Apr 2027€6,300€5,670until 8 Mar
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HamburgScheduled
5 – 9 Apr 2027€5,800€5,220until 15 Mar
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IstanbulScheduled
12 – 16 Apr 2027€4,700€4,230until 22 Mar
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JeddahScheduled
18 – 22 Apr 2027€4,200€3,780until 29 Mar
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KigaliScheduled
26 – 30 Apr 2027€5,100€4,590until 5 Apr
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LisbonScheduled
3 – 7 May 2027€5,400€4,860until 12 Apr
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IstanbulScheduled
11 – 15 Oct 2027€4,700€4,230until 20 Sept
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Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam

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