Selection and management of artificial intelligence applications for strategic projects #254022

Upon completion of this course, participants will be able to:

• Align AI with Business Strategy: Identify and prioritize AI opportunities that directly support key strategic goals and provide a competitive advantage.

• Evaluate and Select AI Solutions: Apply a structured framework to critically assess AI vendors, technologies, and build-vs.-buy decisions for strategic projects.

• Manage the AI Project Lifecycle: Lead AI projects from ideation through to deployment and scaling, navigating unique challenges like data dependency, model drift, and ethical considerations.

• Calculate and Demonstrate ROI: Build a robust business case for AI investments, defining and measuring key performance indicators (KPIs) that matter to executives and stakeholders.

• Govern and Mitigate Risk: Establish effective governance structures to manage project risks, ensure ethical AI use, maintain model performance, and achieve regulatory compliance.

• Lead Organizational Change: Develop strategies to overcome resistance, foster cross-functional collaboration, and build AI literacy across teams involved in strategic projects

This course is designed for professionals who lead, sponsor, or manage high-impact projects and initiatives:

• Strategic Project & Program Managers

• Digital Transformation Leaders & Chiefs of Staff

• IT Directors & Heads of Innovation

• Business Unit Heads & Senior Line Managers

• Product Portfolio Managers

• Enterprise Architects

• Management Consultants

• VPs of Operations, Marketing, or Strategy

• C-Suite Executives (e.g., CIO, CTO, CDO, COO) involved in major project oversight

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: Foundations of Strategic AI Investment

• AM: AI as a Strategic Imperative

  • Beyond the Hype: Understanding AI capabilities and limitations in a business context.

  • Linking AI to Corporate Strategy: Using frameworks to identify high-impact AI opportunities across functions (e.g., growth, efficiency, customer experience).

  • The Strategic Project Portfolio: Balancing quick wins with long-term transformational AI investments.

• PM: The AI Project Lifecycle: A Leader’s Perspective

  • Stages of an AI project: From problem definition and data readiness to deployment and scaling.

  • Key differences between AI projects and traditional IT projects.

  • Workshop: Scoping a strategic AI initiative for a real-world business problem.

Day 2: The Selection Process: Choosing the Right AI Tool for the Job

• AM: The Vendor Landscape & Build vs. Buy Analysis

  • Mapping the AI solution ecosystem: off-the-shelf SaaS, custom platforms, and open-source tools.

  • A structured framework for evaluating AI vendors: technology, viability, security, and support.

  • Conducting a build-vs.-buy analysis: weighing control, cost, time-to-market, and internal capability.

• PM: Technical Diligence and Proof of Concept (PoC)

  • Key questions to ask vendors about data needs, model accuracy, integration, and scalability.

  • Designing and running a effective PoC to validate value before full commitment.

  • Case Study: Evaluating two AI solutions for a predictive maintenance strategic project.

Day 3: Managing Implementation and Integration

• AM: Execution and Agile Management for AI

  • Adapting Agile and Scrum methodologies for the unpredictability of AI development.

  • Managing cross-functional teams: data scientists, engineers, domain experts, and business analysts.

  • Critical milestones and metrics during the development and testing phases.

• PM: Integration and Change Management

  • Strategies for integrating AI outputs into existing business processes and systems (ERP, CRM).

  • Overcoming user resistance and fostering adoption of AI-driven workflows.

  • Workshop: Creating a change management and communication plan for an AI rollout.

Day 4: Governance, Risk, and Measuring Success

• AM: AI Governance and Ethics for Strategic Projects

  • Establishing model governance: monitoring for drift, bias, and performance decay.

  • Ensuring ethical AI: addressing fairness, transparency, and accountability in high-stakes projects.

  • Navigating regulatory compliance (e.g., EU AI Act) for strategic initiatives.

• PM: Measuring ROI and Value Realization

  • Defining success: Moving beyond technical metrics to business KPIs (e.g., revenue impact, cost savings, customer satisfaction).

  • Building the business case: Calculating Total Cost of Ownership (TCO) and Return on Investment (ROI).

  • Creating a value realization framework to track benefits throughout the project lifecycle.

Day 5: Leadership and Capstone

• AM: Leading AI-Driven Transformation

  • The role of leadership in fostering an AI-ready culture.

  • Talent strategy: hiring, upskilling, and partnering for strategic AI capabilities.

  • Communicating AI strategy and project progress to the C-suite and the board.

• PM: Capstone Project: The AI Project Charter

  • Final Exercise: Participants will develop a comprehensive project charter for a strategic AI application of their choice. This will include:

    • Business Case & ROI Model

    • Vendor Selection Scorecard

    • High-Level Implementation Plan

    • Risk & Governance Register

    • Change Management Strategy

  • Capstone presentations and peer feedback.

  • Course Wrap-Up: Developing a personal action plan for applying course concepts to current strategic projects.