IIBA Endorsed Certificate in AI Enabled Innovation

This course equips participants with a practical and structured approach to drive innovation in contexts where artificial intelligence can meaningfully enhance insight generation, ideation, prototyping, and validation. Participants will explore how human-centered innovation and AI capabilities can complement each other to address complex, real-world challenges. The course emphasizes disciplined problem framing, evidence-based insight generation, creative solution design, rapid experimentation, and iterative learning, while maintaining strong ethical and human considerations when using AI tools. Throughout the course, participants will work on an applied case that evolves across each module. By the end of the course, participants will have developed a coherent, AI-enabled solution concept grounded in user needs, data-informed insights, and structured design thinking practices that can be transferred to their own organizational challenges. PrerequisitesNone — open to managers at all levels

5 Days

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Course Methodology

This course is delivered through a fully applied, case-centered learning journey. Participants work in teams on a single practical case that progresses across all five modules, with each module building directly on the outputs of the previous one. Short concept briefings are used to introduce key design thinking and AI application principles, followed immediately by structured application to the case.

Course Objectives

By the end of the course, participants will be able to:

Apply design thinking principles to frame complex problems and define meaningful challenge statements for an AI-enabled case
Analyze user needs and contextual data using AI-supported research techniques to generate actionable insights
Generate innovative solution concepts by combining human creativity with AI-assisted ideation methods
Design low-fidelity prototypes that integrate AI capabilities to address defined user needs and constraints
Evaluate solution concepts through structured testing, feedback, and iteration using qualitative and data-informed inputs

Target Audience

This course is designed for professionals involved in problem-solving, service improvement, innovation, product or process design, digital transformation, and operational improvement. It is suitable for practitioners across functions who are expected to contribute to solution design and implementation, regardless of formal leadership roles. The course is appropriate for participants with varying levels of prior exposure to design thinking or AI, as it focuses on practical application rather than technical development.

Target Competencies

Human-centered problem framing
Analytical thinking
Creative ideation
Prototyping and experimentation
Evidence-based decision making
AI literacy for practitioners

Course Outline

Framing the Right Problem in an AI Context
- Understanding problem spaces, stakeholders, and constraints
- Translating broad challenges into focused questions
- Identifying assumptions and knowledge gaps
- Using AI to support synthesis and reframing
- Defining success criteria and boundaries
AI-Supported Empathy and Insight Generation
- Structuring qualitative and quantitative research inputs
- Using AI to synthesize patterns and themes
- Distinguishing symptoms from root causes
- Converting observations into opportunities
- Addressing bias and data quality risks
AI-Enhanced Ideation and Concept Development
- Divergent and convergent thinking methods
- Prompting strategies and constraints for ideation
- Screening concepts against user needs, feasibility, and impact
- Selecting priority concepts for further development
- Balancing human judgment with AI suggestions
Prototyping AI-Enabled Solutions
- Defining what to prototype and which assumptions to test
- Choosing appropriate prototype formats
- Representing AI functionality conceptually
- Identifying risks, limitations, and ethical considerations
- Designing safeguards and human-in-the-loop mechanisms
Testing, Learning, and Iteration
- Designing simple tests and feedback mechanisms
- Interpreting user feedback and data signals
- Iterating concepts and prototypes based on evidence
- Translating learning into recommendations
- Embedding continuous learning and governance for AI-enabled solutions

2026 Schedule & Fees

Course Fees Starting From

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