Keywords: AI

Additional Keywords not in the list above: Organizational dynamics, strategy

Learning Objective 1: Explain the basic functions of AI, Machine Learning, Deep Learning, and Generative AI

Learning Objective 2: Evaluate organizational change in different frames: adaptation, modification, and redefinition

Learning Objective 3: Explain and justify forecasts of organizational and societal changes catalyzed by Generative AI

Learning Objective 4: Plan specific actions to navigate one’s own organization through the Gen AI revolution

Abstract:

There are plenty of books, articles, and workshops about Artificial Intelligence (AI) nowadays—some of which have no doubt been created by algorithms, such as ChatGPT. We’ve tried to approach this session a bit differently.

Our tutorial is quite human-centric. It’s not a deep dive on software development or deep mathematics, nor is it another lazy walkthrough of “prompt engineering” recipes. Instead, we’ve approached Generative AI from a different lens, exploring the questions it raises about our structures and systems, ways of working, and the future of our communities. We’ve made the tutorial approachable and somewhat speculative, although grounded in science and technology as well as the authors’ unique backgrounds in defense (Sae), organizational learning (Julian), and commercial entrepreneurship (Geoff).

Our tutorial includes several parts:

We begin by reviewing the foundations, so that even those who’ve somehow avoided discussions of Generative AI can have a sense of what these algorithms can do and roughly how they work. This includes a no-nonsense overview of the relationships between Good Old-Fashioned AI, Machine Learning, Deep Learning, and Generative AI. It will also include a brief description of how Generative AI works.
Next, we consider how the mirror held up by AI helps us see ourselves (as humans) in different ways, including our biases and baser natures and our concepts of intelligence and personhood. This section explores our very human responses to AI, including how real-world unfairness infects algorithms and how bad actors are using Generative AI.
We then explore notions of change across art, culture, organizations, society, and security. How will these structures evolve as Generative AI grows more pervasive? Examples include structural changes to work, the ways we value and navigate information, new models of learning and assessment, and the increasing use of Augmented Intelligence (the pairing of humans with AI augmentation).
Finally, we end with a practical discussion designed to guide attendees through thinking about different types of change (e.g., efficiencies gained within existing structures versus disruptive paradigmatic change that changes those structures all together). This section includes a focus on how Generative AI is likely to create change within their own organizations and communities and what they should be doing to influence and navigate these changes.

This is an exciting, if volatile new world, and perhaps, the ideas explored within this tutorial will help attendees find their ways a little bit better.

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Mr. Geoff Stead