GenAI Exec Academy

Generative AI Executive Academy for Strategic Growth

By David H. Deans

I believe sales and marketing leaders in the B2B SaaS vendor arena must invest more time and effort to enable customers to evolve. They can learn to apply GenAI in the process of achieving a bold goal.

Online commerce is evolving at an unprecedented pace, driven by a revolutionary force. Generative AI (GenAI) technology is automating tasks, rewriting the rules of innovation, disrupting industries, and reshaping the very nature of digital business competition.

As an executive at the helm of a large enterprise, you now have a critical choice to consider: will you be a spectator of this digital transformation, or harness GenAI's power to advance your organization?

How to Join the GenAI Revolution

I can envision a “GenAI Exec Academy” that is designed for continued education. It's all about equipping leaders with strategic foresight, actionable insights, and practical tips on how to unlock GenAI's boundless potential for advancement.

Through five curriculum pillars, GenAI Exec Academy students should be able to:

The GenAI Exec Academy concept is a launchpad to achieve growth. Join us, and chart your team's transformation journey to becoming a visionary leader in the Generative AI-empowered growth era.

Empowering the Senior Decision Makers

The Academy's mission is to equip C-suite leaders and executives at large enterprises with strategic foresight, actionable insights, and practical examples of how to apply GenAI within their organizations.

The target audience for these pragmatic skills learning sessions is CEOs, CFOs, COOs, and other key C-suite senior executives, board members, or strategic business partner executive stakeholders.

The Senior Executive GenAI Playbook

GenAI Apps for Competitive Advantage

Leading with an AI-Empowered Culture

Your Senior Executive GenAI Bootcamp

Continuous Learning: The Growth Mindset

I also envision the creation of a curated GenAI Research Library offering proven industry case study reports, an online peer group community with a forum for participation in executive roundtables, plus ongoing coaching and support from experienced GenAI Exec Academy practitioner members.

Navigating the GenAI landscape alone can be daunting, but partnering with an industry ecosystem unlocks a treasure trove of potential applied technology and related business process benefits.

Imagine a network of diverse experts: technology providers offering GenAI tools, research institutions pushing the boundaries of innovation, and fellow practitioner pioneers sharing their hard-won insights.

This collaborative approach ensures that your GenAI use case is aligned with a business outcome, it's constantly evolving, fueled by the collective wisdom and shared resources of a partner ecosystem.

To help you get started, I've authored a brief GenAI Prompt Tutor example for your consideration. Based on current research, I've also created a list of Industry Applications and Use Cases for Generative AI.

Glossary of Terms

Generative AI is a type of artificial intelligence that can create new content -- such as text, code, images, and video -- using patterns it has learned by training on extensive public data with machine learning techniques.

Foundation Models are deep learning models trained on vast quantities of unstructured, unlabeled data that can be used for a wide range of tasks or adapted to specific tasks through fine-tuning.

Large Language Models make up a class of foundation models that can process massive amounts of unstructured text and learn the relationships between words or portions of words, known as tokens. This enables LLMs to generate natural-language text, performing tasks such as summarization or knowledge extraction.

Fine-Tuning is the process of adapting a pre-trained foundation model to perform better in a specific task. This entails a relatively short period of training on a labeled data set, which is much smaller than the data set the model was initially trained on. This additional training allows the model to learn and adapt to the nuances, terminology, and specific patterns found in the smaller data set.

Prompt Engineer or Prompt Design refers to the process of engineering or designing, refining, and optimizing input prompts to guide a Generative AI model toward producing accurate desired outputs.