AI-Driven Leadership and Product Design

Identify how leaders create the conditions for the effective development of gen AI, agentic AI, and predictive analytics capabilities.

Develop a plan to create those conditions, including anticipating likely barriers and strategies for success.

Assess organizational data readiness for AI and promote a culture of data excellence.

Understand how to configure workflows across these technologies, including reskilling needs and change management.

Design and implement workflows and assess their impact.

Assess the evolving role of managers and their responsibilities in the context of increasing reliance on AI technologies.

Understand how to design interactive experiences that embed AI at the core of product design.

Identify situations where AI is and is not the right solution for the desired user experience.

Foresee potential issues with user trust, overreliance, and error, and design to mitigate the issues.

Be able to rapidly prototype and gain user feedback on human-AI interaction.

Gain practical skills to design and evaluate AI-driven user experiences, emphasizing user control, trust, prototyping, intelligence augmentation, social AI design, and ethical data practices.

Apply structured frameworks to evaluate AI opportunities across your organization.

Develop a practical roadmap for implementing AI systems and capabilities.

Assess readiness across people, processes, data, and governance.

Lead teams through AI-driven transformation and operational change.

Design workflows that integrate generative AI and ML effectively.

Make stronger strategic decisions in rapidly evolving AI environments.

Create product experiences where AI is embedded into the workflow in ways that feel useful, clear, and actionable for users.

Recognize when generic prompt-based interfaces create friction and design more specific, task-based interactions.

Use LLM-based and Wizard of Oz prototyping methods to test AI behavior, user expectations, and interaction flows early.

Build trust in AI systems through explainability, transparency, and responsible design practices.

Balance user control and automation to create effective and reliable AI-powered products.

Identify where users may over-trust or under-trust AI, and design experiences that support appropriate reliance.

Evaluate AI-augmented design tools critically and understand where they can support, but not replace, human-centered design expertise.

Mid-to senior-level executives, including CEOs, CTOs, and leaders with decision-making authority, who want to integrate AI into their organizations

Digital transformation managers and other managers leading organizational digital initiatives who seek to adapt their leadership capabilities for the digital era

Entrepreneurs and business owners looking to implement AI strategies in their organizations

Leaders of data teams seeking to increase their impact within their organization by employing various AI tools and systems

Professionals in user interface (UI) and user experience (UX) design who want to integrate artificial intelligence into their designs and understand its impact on user experience

AI developers and engineers involved in developing AI systems who want to enhance their understanding of user experiences and improve the usability of AI applications

Product managers who wish to understand the fundamentals of designing AI-driven user experiences to create more user-friendly products

Professionals and students with experience in designing user-facing products

Frameworks to assess areas in which predictive analytics, agentic AI, and generative AI can create value

Methods to identify high-impact use cases and avoid low-value initiatives

Approaches to evaluate organizational readiness across data, teams, and workflows

Decision frameworks for integrating AI into leadership and management systems

Techniques for redesigning workflows to combine human expertise with AI capabilities

Structured approaches to manage governance, adoption barriers, and change effectively

Frameworks to evaluate the maturity and scalability of generative and agentic AI systems within organizations

Human-centered design approaches for integrating AI into product experiences

Modern large language models, such as ChatGPT, for rapid AI behavior prototyping and test AI interaction concepts

Design and interface mockup tools, such as Figma, to create and refine UI concepts

LLM prototyping methods to Wizard of Oz prototyping to simulate AI behavior before development

Methods for balancing user control and AI-driven automation

Frameworks for designing trustworthy and explainable AI experiences

Approaches for identifying and addressing ethical and user experience challenges in AI design