Centram Centre for Applied Artificial Intelligence

AI ANALYTICS & AUTOMATION FOR PROGRAM MANAGEMENT

Course Overview

This course AI Analytics & Automation for Program Management is designed for program managers and senior project managers who need practical skills to guide teams using AI, analytics, and automation. The outline includes course goals, learning activities, assessments, tools and suggested readings.

Course title

AI Analytics & Automation for Program Management

Course description

This practical, applied course empowers Program Managers to plan, govern, and deliver AI/analytics and automation initiatives. Covers fundamentals of AI and analytics, data-driven decision-making, process automation (RPA), machine learning lifecycle (MLOps), ethical and regulatory considerations, vendor selection, change management, and hands-on project work.

Target audience

Program Coordinators, and Portfolio Managers, PMO leads, Product Managers, Senior Delivery Managers, and Technical Leads who must oversee AI/analytics and automation programs.

Learning objectives

By course end participants will be able to:

  1. Translate business strategy into AI/analytics and automation program roadmaps
  2. Scope and prioritize AI and automation initiatives with measurable KPIs
  3. Understand ML lifecycle and MLOps principles to manage deployments and model governance
  4. Evaluate vendors and tools and make build vs. buy decisions
  5. Lead cross-functional teams for data, ML, and RPA delivery
  6. Apply ethical, security and compliance frameworks to AI/automation programs
  7. Deliver a project-ready implementation plan and a working pilot/capstone

Course duration & Delivery modes

This course has 2 delivery options; a 2-week intensive boot camp plus 2 weeks of online training OR 4 weeks of intensive physical boot camp training, whichever is convenient to the participants.

Course Content

Orientation & course setup

  1. Introductions, objectives, team formation for capstone
  2. Tools setup: cloud accounts, notebooks, BI tools, RPA trial
  3.  Baseline knowledge check
  4. AI & analytics landscape for Managers
  5. What is AI, ML, analytics, automation, RPA, intelligent automation
  6. Business value drivers and common use cases by industry
  7.  AI maturity models and program lifecycle
  8. Activities: case studies, maturity assessment exercise

Data foundations & Data Strategy

  1.  Data sources, data quality, metadata, data governance basics
  2.  Data strategy, data ownership, data cataloging, privacy considerations (GDPR/CCPA)
  3. Activities: create a data inventory and gap analysis for a sample program

Analytics for Decision-making

  1.  Descriptive, diagnostic, predictive, prescriptive analytics
  2. KPI design, A/B testing basics, experimentation governance
  3.  Visualization & storytelling for stakeholders
  4. Tools demo: Tableau/Power BI
  5. Activity: build a dashboard mock-up for a program metric

 Machine learning essentials for managers

  1.  ML concepts (supervised/unsupervised/reinforcement learning), model evaluation, bias/variance
  2.  Trade-offs, feature engineering at a high level
  3.  When ML is appropriate vs. rule-based solutions
  4. Activity: interpret model results and evaluation metrics

Automation & RPA fundamentals

  1. RPA vs. intelligent automation vs. workflow automation
  2. Assessing processes for automation (process mining, value/effort matrix)
  3.  RPA governance, scaling bots, licensing, security
  4. Tools demo: UiPath/Automation Anywhere/Power Automate
  5. Activity: map a process and create automation criteria

MLOps, deployment & operations

  1. Model deployment patterns, CI/CD for models, monitoring, drift detection
  2. Versioning, reproducibility, infrastructure options (cloud, edge), cost management
  3. Tools demo: Docker, MLflow, Kubeflow, AWS SageMaker (overview)
  4. Activity: design an operational model for a pilot

Integration, APIs & system architecture

  1. Integrating AI/automation into enterprise architecture, data pipelines, event-driven designs
  2. API design, microservices basics, latency/scalability considerations
    Activity: sketch architecture for a sample solution
  3.  Ethics, compliance & risk management
  4.  AI ethics frameworks, fairness, transparency, explainability (XAI), privacy-preserving techniques
  5. Regulatory landscape, auditability, third-party risk
    Activity: develop an ethical risk mitigation plan

 Change management & operating model

  1.  Stakeholder engagement, reskilling, cross-functional team design (data engineers, ML engineers, product owners)
  2. KPIs for adoption and value realization, benefits tracking, ROI
    Activity: build a change plan and communications strategy
  3. Vendor selection, procurement & contracting
  4.  Build vs buy evaluation, vendor scoring matrices, procurement considerations for AI vendors, contract clauses (SLAs, IP, data rights)
  5. Activity: vendor evaluation exercise and mock RFP outline

Program governance, portfolio management & scaling

  1. Governance frameworks, PMO roles for AI/automation, funding models, prioritization techniques
  2. Monitoring program health, risk registers, ethical review boards
  3. Activity: draft a governance charter for a program

Capstone presentations & next steps

  1. Team capstone presentations: business case, roadmap, architecture, pilot plan, governance & KPIs, demo/prototype if applicable
  2. Course wrap-up, lessons learned, next steps for real-world rollout


Hands-on labs & tools

  1. Languages: Python (pandas, scikit-learn), SQL
  2.  Notebooks: Jupyter/Colab
  3.  BI tools: Power BI, Tableau
  4.  MLOps & infra: Docker, MLflow, Git, CI/CD tools, cloud ML services (AWS SageMaker, Azure ML, GCP AI Platform)
  5. RPA: UiPath Community Edition, Power Automate, Automation Anywhere trial
  6. Process mining: Celonis or open-source alternatives
  7. Project management: Jira/Confluence, Miro for workshops

Capstone project ideas

  1. Predictive maintenance pilot for a manufacturing line
  2. Customer churn prediction + automated retention workflow
  3.  Invoice processing automation combining RPA + NLP
  4.  Fraud detection prototype with deployment and monitoring plan
  5.  Demand forecasting + automated replenishment alerts

Suggested readings & resources

  1. “Designing Data-Intensive Applications” (Martin Kleppmann) — architecture concepts
  2.  “Machine Learning Logistics” / MLOps articles (various)
  3.  “AI Ethics” readings: Google/IBM/European Commission frameworks

 Vendor docs and tool tutorials: UiPath Academy, Kaggle micro-courses, Coursera / edX module