What is the right learning technology stack for enterprise AI upskilling?

Hello, and welcome back to my Blog!

With 50% + of F500 companies accelerating spending in AI, learning and upskilling become critical to close skill gaps and make employees comfortable and ready to work with Gen AI.

Companies are currently assessing the impact of Gen AI in business functions and defining the path forward.

For example, I worked with clients that have teams of data scientists and machine learning engineers working on tailor-made solutions for prioritized use cases.

Others are investing heavily in upskilling and re-skilling to develop an AI capability (BUILD) and are assessing acquisitions (BUY) to bring Gen AI-ready talent and working with third party organizations (PARTNER) to execute and deploy Gen AI projects.

When it comes to BUILDING enterprise AI talent-ready, we are going to find different type of personas that will influence your approach for your upskilling/new skilling programs.


🕵️‍♀️Creators: machine learning engineer, data scientist, NLP specialist etc.

👤Business consumers: domain knowledge experts, data analyst, business analyst etc.

👥General consumers: program managers, specialist, assistants etc.

💼Leadership: C-Suite and senior leaders

Having 4 different personas in the workplace, you will need to structure your upskilling strategy aligning on core (applicable to everybody), specialized and technical skills (depending on proficiencies and expertise). You can also include tools that are required to learn and master.

Each persona will have a learning pathway and a curriculum. In the design phase, it’s important to discuss the learning experience and the methods to deliver training and assess learning technology platforms.

💡TIP: Make sure you define clearly your strategy when it comes to roles, skills, experience and capabilities you are looking in a learning technology partner. Do not rely on vendors for recommendations. In many cases, they have a limited view in terms of strategy and connecting different moving parts when it comes to talent, skills and organization.

To get started, here are other components to consider to move from strategy to enablement:

📣AI skilling vision: align on your future state, guiding principles, capabilities and skills needed

🤝Governance and Operating Model: define clear roles, responsibilities, systems, processes and new ways of working

🛡️Technology and data: an integrated single source of skill data and clear skill data pipeline structure

📚Enterprise change enablement: prepare employees, foster transparence and trust

The one pager below provides a high-level view including some upskilling vendors.

I’d love to hear your thoughts! Share your perspective.

Connect with me to discuss further.

Note: All views expressed in this article do not represent the opinions of any entity whatsoever with which I have been, am now, or will be affiliated. My opinions are my own.

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Albert Loyola

Artificial Intelligence | Employee Experience| HR Transformation| Advisor| Speaker

Albert brings global market research experience  to help executives harness intelligent technologies, reinvent HR, re-skilling and employee experience across NAR, APAC, LATAM and EMEA regions.

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