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AI Use Cases in Finance, HR, Marketing & Operations — Applying AI Across Core Business Functions

AI Use Cases in Finance, HR, Marketing & Operations provides a cross-functional view of how artificial intelligence can improve decisions, productivity, customer outcomes, and operational performance across core business functions. Participants examine realistic applications such as forecasting and financial analysis, workforce and talent support, personalization and campaign optimization, demand and process intelligence, and AI-assisted knowledge work. The program emphasizes selecting valuable and feasible use cases, redesigning workflows around human-AI collaboration, defining appropriate controls, and translating functional opportunities into measurable implementation priorities.

Kuala Lumpur

5 days

December 7, 2026 – December 11, 2026

Price

EUR 5,900

Initial Enrollment Request

Learning Objectives

Identify high-value AI applications across finance, HR, marketing, and operations.
Match functional business problems with appropriate predictive, generative, analytical, or automation approaches.
Evaluate use cases using business value, data readiness, feasibility, risk, and adoption criteria.
Redesign functional workflows to combine AI capabilities with human expertise and accountability.
Define suitable KPIs and controls for measuring the impact and reliability of AI-enabled processes.
Build a prioritized cross-functional AI use-case portfolio and implementation plan.

Detailed Daily Schedule

Day 1 — Building a Cross-Functional AI Use-Case Framework

Distinguish predictive, generative, optimization, and automation use cases.
Translate functional pain points into clear AI opportunity statements.
Evaluate opportunities using value, feasibility, data, risk, and adoption criteria.
Define human accountability and success measures before selecting technology.
Build a common use-case assessment canvas for all four functions.

Day 2 — AI in Finance

Explore forecasting, scenario analysis, anomaly detection, and decision support.
Examine AI-assisted management reporting, research, and financial narrative generation.
Identify opportunities in transaction processing, controls, and finance operations.
Address accuracy, auditability, confidentiality, and human-review requirements.
Design and evaluate a finance AI use case with measurable outcomes.

Day 3 — AI in HR and Marketing

Examine talent sourcing, workforce analytics, learning, and employee-service applications.
Address fairness, privacy, explainability, and sensitive-data considerations in HR.
Explore segmentation, personalization, content, lead, campaign, and customer-insight applications.
Design review controls for brand, factual, privacy, and reputational risk in marketing.
Compare functional value drivers and adoption challenges across HR and marketing.

Day 4 — AI in Operations

Apply AI to demand forecasting, scheduling, quality, maintenance, and process intelligence.
Explore AI-assisted knowledge work, service operations, and exception management.
Identify opportunities to combine AI with automation and operational analytics.
Define human intervention points for safety, quality, and complex decisions.
Build an operations use case from problem definition through workflow design.

Day 5 — Portfolio Prioritization and Implementation

Compare use cases across functions using a consistent decision framework.
Identify shared data, platform, governance, and capability dependencies.
Define pilots, owners, KPIs, review controls, and adoption requirements.
Balance quick wins with strategically important cross-functional capabilities.
Produce a prioritized enterprise AI use-case portfolio and implementation roadmap.