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Apprenticeship for AI Allyship: A New Model Integrating Multigenerational Talent for a Human-Centric AI-Driven Enterprise

Being Human for a Better Tomorrow in the Age of AI

  • September 20, 2024
  • 16th edition
  • 5th in Vol 2
Apprenticeship for AI Allyship A New Model Integrating Multigenerational Talent for a Human Centric AI Driven Enterprise
Despite the surge in Gen AI projects and investments, the case for concrete value to businesses, employees, and customers remains unclear. According to the Deloitte State of Gen AI Report (Q3 2024), 80% of organizations have seen less than 30% production deployment. This is echoed in the State of Enterprise Tech 2024 report from Insight Partners, which states that less than 27% of projects in large enterprises are in production.

Headwinds for Enterprise AI Adoption

Enterprises face the following headwinds in AI adoption:
  • The dynamic nature of job roles and work, combined with a lack of exposure to technology, makes it difficult for employees to articulate what AI solutions they need.
  • The complexity of requirements development, including data preparation, model tuning, and refresh, makes it hard.
  • Fear and anxiety about AI, coupled with stress, exhaustion at work, and fatigue from continuous tech change, leave little inclination and no time to think.
While AI technology is evolving rapidly—with features like Chain of Thought reasoning, agentic architectures, and larger context windows—these advancements don’t fully counter the headwinds for adoption mentioned above.

Apprenticeship for AI Allyship: A New Idea

As mentioned in the previous edition, AI Allyship offers a user-centric approach for an AI-driven enterprise, focusing on high-value users like CXOs and power users across business, tech, and enabling functions.

Apprenticeship for AI Allyship is an intergenerational talent engagement model comprising:

  • The Pairing: A CXO or power user (Master) is paired with a Gen Z employee (Apprentice) with strong AI/tech skills.
  • Shadowing and Understanding: The Apprentice shadows the Master to understand their work, decision-making, and how data and intelligence can augment their role.
  • Requirement Development: The Apprentice develops AI requirements through a collaborative process with the Master.
  • AI Solution Development and Implementation: The Apprentice supports the testing and deployment of the AI solutions.
  • Outcome: At the end of the apprenticeship, the Master gains an AI Ally, and the Apprentice is well-trained for future AI-driven roles.

Benefits to the Enterprise

This model presents several advantages:
  • High impact and faster adoption of AI.
  • Addressing generational skill gaps — Masters not being tech-savvy and Apprentices not knowing business and decision-making.
  • Building a strong pipeline of future-ready talent as AI advances toward a true AI-driven enterprise.
  • Demonstrating employee-centric and human-centric approaches to encourage AI adoption.
  • Enhancing leadership talent connection and empathy.

Implementation Steps

This is an idea construct. The following steps could be taken to validate and implement the model:
  • Select Participants: Identify potential Masters and Apprentices.
  • Brainstorming Sessions: Engage in initial discussions to confirm alignment and goals.
  • Address Concerns: Resolve issues like confidentiality, stakeholder support, and measurement criteria.
  • Phased Program: Pilot and scale with feedback and improvements to cover geographies and business units.
  • Executive Buy-in: Secure approval, support, and governance from leadership.

Lessons from the 19th Century

Coincidentally, this approach echoes the Igbo apprenticeship system from Nigeria, a practice that has existed for centuries. In this system, young apprentices work under experienced mentors in a highly structured way, learning both technical and entrepreneurial skills.

The Igbo model focuses on hands-on learning and gradual empowerment, ensuring the apprentice is well-equipped to start their own ventures at the end of the training.

The same logic can be applied to the Apprenticeship for AI Allyship, where both parties grow in expertise and benefit.

We have a powerful new technology, and new ideas are also needed for adoption. Just as mounting a jet engine on a turboprop fuselage is suboptimal, new powerful AI needs radical rethinking for enterprise adoption.

Apprenticeship for AI Allyship is a business, human, and technology trifecta win. Success in this endeavor could uplift businesses, employees, and society as a whole.

Please share with like-minded HR, tech, and business leaders to evaluate, provide feedback, and engage in developing this concept.

As a beneficial AI enthusiast with a decade-long understanding of enterprise and technology adoption, I am happy to connect and support.

Cheers to Human AI Allyship for super-powered future,

Best wishes

Select Quotes

“The best co-pilot in the world is still the one who knows when to let the human fly.”
– Aviation wisdom
“AI might be smart, but it's the human imagination that gives it something to do.”
– Unknown
“The problem with AI isn’t that it’s too smart—it’s that we’re not using it smart enough.”
– Unknown
“The intern with coffee knows more than the AI about what’s going wrong.”
– Unknown
“In learning you will teach, and in teaching you will learn.”
– Phil Collins
“AI is a language. Treat it like one: practice, iterate, and mind your grammar prompts, assumptions, and verification.”

II. Questioning / Asking

Good conversations flow from well‑sequenced questions—topical, simple, coherent, cohesive.
LLM Conversation Example 1
Q: What are empirical judgments?

A: Empirical judgments are based on observation, experience, or experimentation.
Q: What are moral judgments?

A: Moral judgments are based on ethical principles and values.
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