Implementing AI for Business Innovation #254006

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

• Identify and prioritize high-impact AI use cases that align with core business objectives and drive innovation.

• Develop a comprehensive AI implementation strategy, including a phased roadmap for piloting, scaling, and managing AI projects.

• Evaluate the building blocks required for AI success: data readiness, technology stack options (build vs. buy), and team structure.

• Calculate the ROI of AI initiatives and construct a compelling business case to secure executive buy-in and funding.

• Navigate the ethical, legal, and organizational challenges of AI implementation, including bias, change management, and governance.

• Lead cross-functional teams to deploy AI solutions successfully and foster a culture of continuous AI-driven innovation.

This course is designed for professionals responsible for driving innovation, digital transformation, and strategic initiatives within their organizations.

• Product Managers & Innovation Managers

• Digital Transformation Leads & Strategy Directors

• Business Unit Heads seeking to innovate their function

• IT Directors & CTOs involved in AI project execution

• Data/AI Project Managers & Program Managers

• Senior Executives (VPs, CXOs) sponsoring AI initiatives

Prerequisites: A strong understanding of business operations and strategic thinking is required. No deep technical expertise is necessary, but a comfort with data-driven concepts is beneficial.

• 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: Laying the Foundation: AI as an Engine for Innovation

• Module 1: The AI Landscape for Innovators

  • Demystifying AI, ML, and Generative AI: Business capabilities, not just technology.

  • How AI drives innovation: From process automation to new business models (e.g., product-as-a-service, hyper-personalization).

• Module 2: Identifying AI-Powered Innovation Opportunities

  • Frameworks for mapping AI capabilities to business problems and opportunities.

  • Techniques for ideation and use case discovery across the value chain.

• Workshop: Brainstorming and prioritizing AI use cases for your own business challenges.

Day 2: Building Your AI Strategy and Roadmap

• Module 3: Crafting an AI Implementation Strategy

  • Defining strategic goals: Competitive parity, differentiation, or market creation?

  • Building a phased implementation roadmap: Pilot, scale, and integrate.

• Module 4: The Business Case for AI Innovation

  • Defining and measuring success: Key Performance Indicators (KPIs) for AI projects.

  • Building a financial model: Calculating ROI, TCO, and articulating value.

• Workshop: Drafting a one-page strategy and business case for a selected use case.

Day 3: The Execution Engine: Data, Technology, and Teams

• Module 5: The Fuel: Data Strategy for AI

  • Assessing data assets and readiness. Introduction to data pipelines and MLOps.

  • “Good enough” data: How to start without a perfect data lake.

• Module 6: The Toolbox: AI Technology Stack

  • Build vs. Buy vs. Partner: Evaluating custom models, cloud AI APIs, and SaaS solutions.

• Module 7: The Talent: Building Cross-Functional AI Teams

  • Assemblying the right team: data scientists, engineers, domain experts, and product owners.

  • Managing AI projects: Agile methodologies for AI development.

• Case Study: Deconstructing a successful AI product launch from team formation to go-live.

Day 4: Navigating Risks and Leading Change

• Module 8: Responsible AI: Ethics, Governance, and Risk

  • Identifying and mitigating risks: bias, privacy, security, and model drift.

  • Implementing AI governance frameworks for trust and compliance.

• Module 9: The Human Element: Change Management

  • Overcoming cultural resistance to AI-driven decisions.

  • Upskilling the workforce and redesigning processes for human-AI collaboration.

• Panel Discussion: A legal expert and an HR leader discuss the practical challenges of deploying AI responsibly.

Day 5: From Project to Scale: Creating a Culture of Innovation

• Module 10: Scaling AI Across the Organization

  • Moving from pilot to production: operationalizing models and integrating with business systems.

  • Creating a center of excellence (CoE) to democratize AI.

• Module 11: The Future of AI Innovation

  • Trends on the horizon: Generative AI, autonomous agents, and their business implications.

  • Fostering a culture of continuous experimentation and learning.

• Capstone Project: Participants present their complete AI implementation plan—from strategy and use case to roadmap and risk assessment—to a panel of instructors and peers for feedback.

• Conclusion: Committing to Action. Developing a personal 90-day implementation plan.