AI for Business Functions#1246
AI for Corporate Strategy Training Course
Helps executives use AI to analyze markets, shape corporate strategy and monitor its execution, strengthening competitive positioning and strategic decisions.
5 Days·Classroom
AI for Leaders & Managers: Strategy & Business Applications Training Course
Gives leaders a management view of artificial intelligence, showing how to spot high-value uses, judge its business value and guide its adoption responsibly.
AIB · AI for Business FunctionsAll Levels#2690ClassroomEnglish , Arabic
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy
Sectors:Artificial Intelligence & ApplicationsCategories:AI for Business Functions
Course Overview
Artificial Intelligence (AI) is becoming a strategic driver of organizational performance, innovation, and competitive advantage. For leaders and managers, understanding AI extends beyond technical knowledge to recognizing its business value, identifying opportunities for application, and making informed decisions about its adoption. This training course provides a management-focused perspective on AI, helping decision-makers connect emerging technologies with organizational priorities and measurable business outcomes. The programme explores how AI can support strategic planning, operational efficiency, customer experience, workforce productivity, risk management, and innovation. Participants examine practical applications across business functions and learn how to distinguish between AI initiatives that deliver genuine value and those that introduce unnecessary complexity, cost, or risk. Emphasis is placed on aligning AI investments with organizational objectives, available resources, and long-term business strategies. A central component of the course is the development of an effective AI adoption strategy. Participants explore how to assess organizational readiness, prioritize potential use cases, evaluate business cases, allocate resources, and establish appropriate governance structures. The programme also addresses the managerial implications of generative AI, predictive analytics, intelligent automation, and AI-supported decision-making, with attention to the opportunities and limitations of each approach. Responsible AI adoption requires more than technology implementation. Leaders must consider data quality, cybersecurity, privacy, regulatory obligations, transparency, accountability, and the impact of AI on employees and stakeholders. This course examines these considerations within a practical management framework, enabling participants to support innovation while maintaining appropriate controls and organizational trust. Through case studies, strategic exercises, group discussions, and an applied business workshop, participants develop an actionable perspective on integrating AI into business strategy and management practice. The programme is designed to help leaders establish priorities, guide cross-functional collaboration, evaluate performance, and create a sustainable foundation for AI-enabled organizational development.
Learning Objectives
- Explain the strategic role of Artificial Intelligence in modern organizations and its implications for leadership and management.
- Identify high-value AI applications that support organizational priorities, operational performance, and business growth.
- Differentiate between major AI capabilities, including generative AI, predictive analytics, machine learning, and intelligent automation.
- Evaluate organizational readiness for AI adoption across strategy, people, processes, data, and technology.
- Develop an AI adoption roadmap aligned with business objectives, available resources, and organizational capabilities.
- Assess AI opportunities using business value, implementation complexity, feasibility, risk, and expected return.
- Build business cases for AI initiatives using cost-benefit analysis, performance indicators, and value realization criteria.
- Apply AI-supported approaches to strategic planning, management reporting, forecasting, and decision-making.
- Establish governance principles that promote responsible, secure, transparent, and accountable AI use.
- Evaluate the implications of AI for workforce productivity, organizational structures, leadership practices, and employee development.
- Define performance measures to monitor AI implementation, business impact, adoption, and continuous improvement.
- Prepare an actionable AI implementation proposal with priorities, responsibilities, milestones, and success measures.
Who Should Attend
This course is designed for professionals responsible for organizational strategy, business performance, transformation, innovation, and management decision-making, including:
Chief Executive Officers, Managing Directors, and General Managers.
Board members and senior executives involved in strategic planning and organizational governance.
Department Directors, Business Unit Heads, and Functional Managers.
Strategy, Corporate Planning, and Business Development Managers.
Digital Transformation and Innovation Leaders.
Information Technology and Information Systems Managers responsible for business technology initiatives.
Operations, Process Improvement, and Operational Excellence Managers.
Finance Managers and business leaders involved in investment appraisal and resource allocation.
Human Resources Directors and Talent Development Managers addressing workforce transformation.
Marketing, Sales, Customer Experience, and Commercial Managers exploring AI-enabled business applications.
Risk Management, Compliance, Information Security, and Governance Professionals.
Project, Programme, and Change Managers responsible for implementing AI-related initiatives.
Public-sector leaders and managers seeking to improve service delivery, resource utilization, and institutional performance.
Professionals preparing to lead or participate in organizational AI adoption initiatives.
Learning Outcomes
- Interpret AI developments in the context of organizational strategy and management priorities.
- Recognize suitable AI applications across core business functions and operational activities.
- Compare AI technologies and approaches according to their business purpose, capabilities, and limitations.
- Assess organizational readiness and identify the capabilities required for successful AI adoption.
- Prioritize AI opportunities using structured evaluation and business value criteria.
- Formulate an AI strategy and phased implementation roadmap linked to organizational objectives.
- Prepare a business case that articulates expected benefits, costs, risks, and performance targets.
- Identify opportunities to improve forecasting, reporting, productivity, and decision-making through AI.
- Apply practical principles for responsible AI governance, data protection, transparency, and accountability.
- Anticipate workforce implications and identify appropriate approaches to skills development and change management.
- Define relevant key performance indicators (KPIs) for AI adoption and business value realization.
- Present an implementation proposal that integrates strategic priorities, governance, resources, and measurable outcomes.
Course Outline
AI Fundamentals for Business Leaders
- Understanding Artificial Intelligence and its evolving role in business and organizational management.
- Distinguishing AI, machine learning, deep learning, generative AI, predictive analytics, and intelligent automation.
- Examining the capabilities and limitations of contemporary AI tools and business applications.
- Exploring how AI creates value through productivity, cost optimization, service improvement, and innovation.
- Reviewing AI applications across finance, human resources, operations, marketing, customer service, and strategic planning.
- Understanding the relationship between AI, organizational strategy, digital transformation, and competitive positioning.
- Identifying common misconceptions, implementation challenges, and sources of unrealistic expectations.
- Assessing how leaders can support evidence-based AI adoption and informed management decisions.
- Reviewing business examples of AI adoption, including lessons from successful and unsuccessful initiatives.
- Practical Exercise: Identify and classify potential AI opportunities within a selected organization, department, or business function.
AI Strategy, Business Value & Opportunity Assessment
- Translating organizational goals into strategic priorities for AI adoption.
- Identifying business problems that are appropriate for AI-supported solutions.
- Mapping AI opportunities across customer experience, internal processes, management reporting, and business development.
- Evaluating AI use cases according to strategic alignment, business value, technical feasibility, and implementation complexity.
- Assessing organizational readiness across data availability, systems, workforce capabilities, processes, and leadership commitment.
- Establishing criteria for prioritizing AI initiatives and selecting suitable pilot projects.
- Developing AI business cases using cost-benefit analysis, investment considerations, and expected operational improvements.
- Understanding direct and indirect costs, including implementation, integration, training, maintenance, and governance.
- Defining expected benefits and distinguishing measurable outcomes from assumptions.
- Practical Exercise: Develop an AI opportunity assessment matrix and rank proposed initiatives according to value, feasibility, and risk.
Business Applications of AI & Management Decision-Making
- Applying generative AI to research, business communication, document preparation, and knowledge management.
- Using AI-supported analytics to identify trends, patterns, performance gaps, and emerging business opportunities.
- Exploring predictive analytics for demand forecasting, resource planning, budgeting, and operational decision support.
- Examining intelligent automation for repetitive administrative tasks and standardized business processes.
- Applying AI to management reporting, performance analysis, and executive decision support.
- Exploring AI applications in customer segmentation, personalization, sales effectiveness, and customer service.
- Identifying opportunities for AI-enabled workforce planning, talent development, and human resources management.
- Understanding human oversight, verification, and professional judgment when using AI-generated outputs.
- Evaluating the limitations of AI recommendations, including inaccurate outputs, bias, incomplete data, and contextual errors.
- Practical Exercise: Design an AI-supported workflow for a management challenge, identifying inputs, outputs, human review points, and expected benefits.
AI Governance, Risk Management & Organizational Readiness
- Establishing the principles of responsible AI use, transparency, accountability, and human oversight.
- Understanding data quality, data ownership, access controls, and information management requirements.
- Identifying privacy, confidentiality, cybersecurity, intellectual property, and information leakage risks.
- Reviewing the importance of applicable laws, regulatory obligations, internal policies, and sector-specific requirements.
- Developing governance arrangements for AI tools, projects, vendors, and business applications.
- Defining responsibilities for executive sponsors, business owners, IT teams, risk functions, and end users.
- Evaluating AI-related operational, reputational, financial, and strategic risks.
- Addressing employee concerns, organizational culture, communication, and resistance to change.
- Identifying the skills, training, leadership support, and cross-functional collaboration required for sustainable adoption.
- Practical Exercise: Develop an initial AI governance and risk-control framework for a proposed business application.
AI Implementation Roadmap, Performance Measurement & Leadership Action Plan
- Translating AI priorities into a phased implementation roadmap with defined stages and milestones.
- Selecting pilot projects and establishing criteria for testing, evaluation, and scaling.
- Defining roles, responsibilities, resource requirements, dependencies, and decision-making arrangements.
- Establishing key performance indicators (KPIs) to measure adoption, efficiency, quality, cost, and business impact.
- Monitoring implementation progress and evaluating results against the approved business case.
- Establishing feedback mechanisms to improve AI solutions, workflows, and user adoption.
- Integrating AI initiatives with existing transformation programmes, project portfolios, and operating models.
- Developing communication and change management approaches that support employee engagement and organizational alignment.
- Identifying common implementation barriers and preparing mitigation measures and contingency actions.
- Final Workshop: Prepare and present an AI Strategy & Business Application Plan covering business priorities, selected use cases, value assessment, governance, risk controls, implementation milestones, ownership, and performance indicators.
- Group Discussion: Review proposed plans, challenge assumptions, exchange management perspectives, and refine recommendations for practical implementation.
- Individual Action Planning: Define immediate next steps and priority actions for advancing AI adoption within the participant’s organization.
Upcoming Dates
Selected date
Abuja · 4 – 8 Jan 2027 · Classroom
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| City | Dates | Price per Participant | Early Bird | |
|---|---|---|---|---|
MilanScheduled | 2 – 6 Nov 2026 | €7,000 | — | |
ParisScheduled | 9 – 13 Nov 2026 | €6,700 | — | |
RiyadhScheduled | 16 – 20 Nov 2026 | €4,800 | — | |
MuscatScheduled | 23 – 27 Nov 2026 | €6,300 | €5,670until 2 Nov | |
RomeScheduled | 23 – 27 Nov 2026 | €7,000 | €6,300until 2 Nov | |
SingaporeScheduled | 30 Nov – 4 Dec 2026 | €9,100 | €8,190until 9 Nov | |
Sharm El-SheikhScheduled | 30 Nov – 4 Dec 2026 | €5,300 | €4,770until 9 Nov | |
ViennaScheduled | 7 – 11 Dec 2026 | €6,400 | €5,760until 16 Nov | |
AmmanScheduled | 13 – 17 Dec 2026 | €4,000 | €3,600until 23 Nov | |
SalalahScheduled | 14 – 18 Dec 2026 | €6,300 | €5,670until 23 Nov | |
AmsterdamScheduled | 21 – 25 Dec 2026 | €6,000 | €5,400until 30 Nov | |
BarcelonaScheduled | 28 Dec 2026 – 1 Jan 2027 | €6,000 | €5,400until 7 Dec | |
AbujaScheduled | 4 – 8 Jan 2027 | €7,700 | €6,930until 14 Dec | |
CairoScheduled | 4 – 8 Jan 2027 | €3,800 | €3,420until 14 Dec | |
DammamScheduled | 11 – 15 Jan 2027 | €4,800 | €4,320until 21 Dec | |
CasablancaScheduled | 11 – 15 Jan 2027 | €4,700 | €4,230until 21 Dec | |
AccraScheduled | 18 – 22 Jan 2027 | €7,700 | €6,930until 28 Dec | |
DohaScheduled | 18 – 22 Jan 2027 | €4,700 | €4,230until 28 Dec | |
DubaiScheduled | 24 – 28 Jan 2027 | €4,700 | €4,230until 4 Jan | |
FrankfurtScheduled | 1 – 5 Feb 2027 | €6,200 | €5,580until 11 Jan | |
GenevaScheduled | 8 – 12 Feb 2027 | €7,300 | €6,570until 18 Jan | |
BakuScheduled | 8 – 12 Feb 2027 | €5,300 | €4,770until 18 Jan | |
IstanbulScheduled | 15 – 19 Feb 2027 | €5,300 | €4,770until 25 Jan | |
JeddahScheduled | 21 – 25 Feb 2027 | €4,800 | €4,320until 1 Feb | |
LisbonScheduled | 1 – 5 Mar 2027 | €6,000 | €5,400until 8 Feb | |
BangkokScheduled | 1 – 5 Mar 2027 | €6,700 | €6,030until 8 Feb | |
Kuala LumpurScheduled | 1 – 5 Mar 2027 | €4,700 | €4,230until 8 Feb | |
LondonScheduled | 8 – 12 Mar 2027 | €5,800 | €5,220until 15 Feb | |
MadinahScheduled | 15 – 19 Mar 2027 | €4,800 | €4,320until 22 Feb | |
BeirutScheduled | 21 – 25 Mar 2027 | €4,500 | €4,050until 1 Mar |
Available in: Istanbul · Dubai · London · Riyadh · Doha · Amsterdam
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