AI for Learning

AI and the Future of Work: A Leader’s Guide to Roles and Skills

December 19, 2024January 7, 2025 by Emma Klosson

As AI becomes increasingly integrated into the workplace, it is up to talent development, organization design, and L&D leaders to partner closely with the business and each other to identify the specialist roles and skills necessary for success.

As we contemplate AI and the future of work, what might these specialist AI roles and skills look like?

I asked John Cleave to share his insights with us. As SweetRush’s Senior Learning Engineer, John has been working with AI in L&D for decades, beginning with his graduate work in symbolic AI at the Northwestern’s Institute for the Learning Sciences. Here’s his take on the roles and skills leaders should consider as they partner with the business to develop an enterprise-wide strategy.

Bonus content!
As a bonus, John also shares the roles and skills that L&D teams should consider developing or adding to enable, enhance, and accelerate the implementation of the AI strategy.

What are the Specialist AI Roles and Skills Leaders Should Consider?

AI Strategy and Governance Roles

AI initiatives require careful oversight and a strong ethical framework. The following roles are crucial for any organization implementing AI, regardless of specific applications:

Governance and Regulation Specialist: Formulates and revises AI policies and practices, devises management protocols, develops and institutes controls, and advises on potential risks. This role ensures responsible AI usage across the organization.
Security and Policies Specialist: Creates security protocols (e.g., approval process), identifies potential risks, handles breaches and violations, and provides leadership with knowledge of consequences, safeguarding both data and ethical practices.
AI Generalist with HR Focus: Identifies applications of AI in HR (talent acquisition, employee development, etc.), works to integrate AI into HR practice, and stays abreast of emerging trends, ensuring alignment between AI and HR goals.

AI Data Analysis

Data analysis and model training are fundamental to any successful AI implementation. The following roles are essential for extracting meaningful insights and driving data-informed decisions:

Business (Statistical and Data Visualization) Analyst: Applies AI methodologies to evaluate data in order to gain insight into the business, visualizes data, identifies data sources, and generates data via AI. This role makes the connection between data and actionable business intelligence.
Data Analytics Expert (backend collection, reporting): Creates data handling and analysis protocols, constructs data lakes, and applies statistical analysis to operations, ensuring data integrity and efficient data management.
Machine Learning and Big Data Manager: Devises machine learning models, collects/cleanses data, evaluates results (statistically, against business norms, etc.), detects patterns, and spots new opportunities to apply AI, driving innovation through data-driven insights.

AI Construction

Building and implementing AI solutions requires specialized technical expertise. The following roles are crucial for developing and deploying AI tools and systems:

Machine Learning Engineer: Constructs machine learning models, trains and evaluates models, selects algorithms appropriate for solving business problems, and works with data analysts to refine and distill data, enabling the creation of powerful AI applications.
Natural Language Processing (NLP) Specialist: Uses NLP engines (e.g., Siri) to process NLP inputs, connects inputs to actions, and creates inputs and outputs, facilitating human-computer interaction and automating language-based tasks.
Large Language Model (LLM) Expert: Sets up an LLM for a purpose(s), creates retrieval-augmented generations (RAGs), tests outputs, manages costs, and creates application programming interfaces (APIs) and overlays, harnessing the power of LLMs for advanced language-based applications.
AI Tools Implementor: Advises an organization on tools (ChatGPT, Exemplary AI, Dall-E, Claude, Paragraph Generator, Midjourney, Writesonic, Canva, Grammarly, Podcastle, Synthesia) useful for solving business problems, conducts experiments and R&D, evaluates options, addresses challenges, and trains on tools, facilitating the effective adoption of AI tools across the organization.

AI & L&D: Specialist Skills and Roles for AI-Powered Learning Creation

This section focuses specifically on roles that leverage AI to enhance learning experiences and drive L&D innovation.

Instructional Design/Learning Experience Creator: Uses AI to support and enhance learning, incorporates AI into LX, provides guidance on best practices and techniques, and uses AI to generate content for training, creating more engaging and effective learning experiences.
HR/L&D Strategy and Change-Management Consultant: Provides guidance in the use of AI to bring about organizational improvement and transformation, explores use of AI to automate processes and create efficiencies, and guides the organization through the changes associated with AI adoption.
RAG Creator: Applies symbolic AI to guide and focus LLMs and deep learning (e.g., skills definition) and pairs symbolic AI and deep learning to improve AI efficacy, enabling more accurate and contextually relevant learning experiences.
Reinforcement Learning/Advanced AI Developer: Creates, evaluates, and trains AI-infused devices, constructs and shapes environments, shapes and manipulates AI models, and experiments with advanced AI techniques to create adaptive and personalized learning environments.
Expert AI Tool User: Steps in and uses AI tools (ChatGPT, Dall-E, Claude, etc.) in order to bring about organization improvement (e.g., generates content), provides guidance on best tools (evaluates alternatives), and maximizes the value of AI tools for L&D.
Asset Creators: Creates videos, audio, and/or animations using AI-based tools, streamlining the production of multimedia learning assets.

As AI continues to reshape the workplace, talent, OD, and L&D teams must work together to identify the skills and expertise needed to help organizations meet their goals for the future. Understanding the roles outlined here is a crucial first step!

Need Help Building YOUR Workplace of the Future?

As an award-winning custom learning solution provider with more than two decades of experience in digital and immersive learning technologies and over a decade of experience sourcing temporary talent for L&D, SweetRush is uniquely positioned to help you navigate this new landscape. We provide comprehensive support in the following ways:

AI Strategy and Implementation: SweetRush’s AI strategy and consulting services empower your organization to navigate the complexities of AI adoption, offering not only cutting-edge learning experiences and programs, but also holistic roadmapping, foundational assets, and ongoing support to ensure your AI initiatives are human-centered, future-proofed, and drive lasting value.

AI Training: We create custom training programs to upskill your workforce quickly and comprehensively.

AI Talent Sourcing: We can source and place the ideal candidates for the AI roles you need, whether for temporary staffing or permanent positions. We have access to a deep bench of AI experts across diverse fields who can support your needs.

AI Glossary for L&D, Part 3: Advanced Techniques and Applications

November 14, 2024November 14, 2024 by Emma Klosson

In Part 3 of our AI Glossary for L&D, we venture into the realm of the more specialized AI techniques and applications that are pushing the boundaries of what’s possible, including:

Deep learning
Supervised, unsupervised, and reinforcement learning
Transfer learning
Fuzzy logic
Explainable AI

Advanced Techniques and Applications Pushing the Boundaries of AI

What is deep learning?

Deep learning is a subset of machine learning in which artificial neural networks with multiple layers (hence “deep”) analyze data and solve complex problems. It’s like giving your AI a more intricate and powerful brain, so it can learn from vast amounts of information and discover patterns that would be impossible for humans to identify.

Imagine a child learning to identify different animals. Initially, they might just focus on basic features like size and color. But as they learn more, they start to recognize subtle differences in shapes, patterns, and behaviors. Deep learning works in a similar way—it allows AI to go beyond surface-level features and understand the nuances within data.

How does deep learning relate to other AI concepts?

Deep learning is the engine behind many advanced AI applications, including large language models (LLMs) and some types of generative AI (genAI).

What are supervised, unsupervised, and reinforcement learning?

Supervised, unsupervised, and reinforcement learning describe three fundamental types of machine learning.

Recall from Part 1 of the AI Glossary for L&D where we explained that, in machine learning, “Systems are fed vast amounts of data and use it to recognize patterns, make predictions, and take actions using the data, all without human intervention.” Supervised, unsupervised, and reinforcement learning refer to the methods in which the data is fed and how the system is trained to recognize the data.

In supervised learning, AI learns from labeled data and is given examples with known inputs and outputs.
In unsupervised learning, AI explores unlabeled data to discover hidden patterns and relationships.
In reinforcement learning, AI learns through trial and error, receiving rewards for correct actions and penalties for incorrect ones.

Here’s a simple analogy to illustrate this concept.

Imagine teaching a child to recognize fruits:

Applying a supervised learning approach, you show them pictures of apples, bananas, and oranges, labeling each one.

Applying an unsupervised learning approach, you give them a basket of different fruits and let them group them based on similarities.

Applying a reinforcement learning approach, you give them a fruit and ask them to name it, rewarding them when they’re right.

How do supervised, unsupervised, and reinforcement learning relate to other AI concepts?

These learning paradigms are core concepts in machine learning. They are used to train various AI systems, including deep learning models.

What is transfer learning?

Transfer learning is a technique where an AI model trained for one task is used as a starting point for a new, related task. This allows the AI to leverage its existing knowledge and learn the new task more efficiently. It’s like a chef who knows how to bake cakes using their skills to learn how to make pastries.

How does transfer learning relate to other AI concepts?

Transfer learning is a technique often used in deep learning to accelerate the training process and improve performance on new tasks.

What is fuzzy logic?

Fuzzy logic is a way of dealing with uncertainty and imprecise information. It allows AI to make decisions based on degrees of truth rather than strict true or false values. It’s like saying “somewhat likely” or “mostly true” instead of just “yes” or “no.”

Think of a thermostat. Instead of just turning on or off at a specific temperature, it can gradually adjust the heating or cooling based on a range of temperatures.

How does fuzzy logic relate to other AI concepts?

Fuzzy logic is a different approach to AI that can be used in conjunction with other techniques, such as machine learning, to handle situations where data is imprecise or ambiguous.

How and where might fuzzy logic appear in L&D applications?

Fuzzy logic can be leveraged to:

Design adaptive assessments: Tailor assessments to individual learner needs and performance levels, providing more nuanced feedback.
Model complex decision-making scenarios: Simulate real-world situations where there are no clear-cut answers, helping learners develop critical thinking and problem-solving skills.
Create personalized learning pathways: Adapt learning pathways based on learners’ preferences and interests, offering a more customized experience

What is explainable AI (XAI)?

Explainable AI aims to make AI’s decision-making processes more transparent and understandable to humans. It’s like having the AI show its work so we can understand how it arrived at a conclusion.

Imagine a doctor diagnosing a patient. They don’t just say “you’re sick”—they explain the symptoms, test results, and reasoning behind their diagnosis.

How does XAI relate to other AI concepts?

XAI is crucial for building trust and accountability in AI systems, especially in sensitive areas like education, healthcare, finance, and legal domains. It can be applied to various AI techniques, including deep learning and machine learning.

How can L&D leverage or apply XAI?

In L&D, XAI can help us understand why an AI model made a particular recommendation or assessment, fostering trust and transparency in AI-driven learning solutions.

Continue expanding your AI vocabulary

There is one more chapter in our AI Glossary for L&D to explore. Click on the link to Part 4 (below) to learn about the algorithms and architecture that form the basis of many of today’s AI platforms. Need a refresher? Visit Parts 1 or 2 to brush up on the basics.

Part 1: Foundational Concepts explores the basics of artificial intelligence including machine learning and neural networks.
Part 2: Key Technologies explores the technologies that put AI into action including natural language processing (NLP), large language models (LLMs), generative AI (GenAI), and retrieval-augmented generation (RAG).
Part 4: Algorithms and Architecture offers a peek behind the AI curtain at the algorithms and architecture that enable AI technologies and applications to perform.

AI Glossary for L&D, Part 2: Key Technologies

October 11, 2024February 26, 2025 by Emma Klosson

Welcome to Part 2 of the AI Glossary for L&D! In Part 1, we laid the foundations by defining what artificial intelligence is and exploring some of the fundamental concepts, such as machine learning and neural networks. In this second installment, we build on those foundations by examining the following key technologies that put AI into action:

Natural language processing (NLP)
Large language models (LLMs)
Generative AI (GenAI)
Retrieval-augmented generation (RAG)

If you’re enjoying this exploration of AI, stay tuned for Part 3, where we’ll explore advanced AI technologies and applications, and Part 4, where we’ll take a peek behind the AI curtain at the architecture and algorithms that make everything work

The Key Technologies Putting AI into Action

What is natural language processing (NLP)?

Imagine teaching a computer to understand human language, not just as symbols or even individual words, but as meaningful sentences and stories filled with emotions and intentions. This is essentially what NLP is. It’s like giving computers the ability to read between the lines, grasp the nuances of language, and even respond in a way that makes sense to us humans.

How and where is NLP used?

NLP is the technology that enables you to have a natural conversation with your smartphone’s voice assistant. You can ask it questions, give it commands, or even just chat about your day, and it will understand what you mean, while taking into account slang, accents, or incomplete sentences.

If you’ve ever used an email spam filter, had your text automatically corrected by your phone, engaged with a chatbot, or used a language translation app, you’ve experienced NLP in action.

How can L&D leverage or apply NLP?

NLP technology could be applied to analyze learner feedback from surveys and comments to identify areas where training programs can be improved.

What are large language models (LLMs)?

Now imagine a vast library containing every book, article, and website ever written. Large language models (LLMs) are like superpowered librarians who have read and memorized everything in the library. They can understand the patterns and relationships between words, ideas, and even emotions expressed in the text. This enables them to not only answer questions accurately but also write different types of creative text formats and translate languages.

How and where are LLMs used?

With a remarkable ability to understand and generate human-like language, LLMs are incredibly versatile. LLMs are the driving force behind chatbots, language translation tools, and content-generation platforms.

If you’ve ever used a chatbot on a website to ask a question and received a helpful response, you’ve likely interacted with an LLM.

How can L&D leverage or apply LLMs?

LLMs can be used to analyze and summarize content, create personalized learning content, and develop realistic simulations for performance-based training.

What is generative AI (genAI)?

Generative AI takes things a step further. Instead of simply understanding and processing language, it can create something entirely new. Think of it like a musician who can play any instrument and compose entirely original symphonies based on their deep understanding of music theory and different musical styles, or a chef who’s sampled every ingredient and explored every cooking technique and can create new and exciting dishes. GenAI can create original text, images, audio, and more.

How and where is genAI used?

GenAI is behind tools like ChatGPT for text generation, DALL-E for image creation, and numerous music and code generation platforms.

If you’ve ever used an app to generate art from a text prompt, or seen AI-generated images in your social media feed, you’ve witnessed the power of genAI.

How can L&D leverage or apply genAI?

GenAI can be used to design engaging eLearning scenarios and simulations, creating immersive learning experiences for employees. (Download a free copy of our GenAI Playbook for more L&D use cases, tips, and inspiration!)

What is retrieval-augmented generation (RAG)?

Finally, imagine combining the vast knowledge of LLMs with the ability to instantly access and retrieve specific information. That’s the power of RAG. It’s a bit like having a detective who uses their knowledge and experience (LLM) to investigate a case, but also relies on databases and records (information retrieval) to gather evidence and verify facts.

How and where is RAG used?

RAG is used to improve the performance of LLM-powered applications, especially in domains where specific knowledge is crucial. If you’ve ever used a search engine like Google to research a topic and received a concise summary with links to relevant sources, you’ve benefited from RAG technology.

How can L&D leverage or apply RAG?

RAG technology can be used to develop AI-powered learning assistants that can answer employee questions about company policies, procedures, or training materials by pulling information from internal data sources.

Continue expanding your AI vocabulary

Click on the links to view the rest of the AI Glossary for L&D series.

Part 1: Foundational Concepts explores the basics of artificial intelligence including machine learning and neural networks.
Part 3: Advanced Techniques and Applications examines the specialized AI techniques and applications that are pushing the boundaries of what’s possible.
Part 4: Algorithms and Architecture offers a peek behind the AI curtain at the algorithms and architecture that enable AI technologies and applications to perform.

AI Glossary for L&D, Part 1: Foundational Concepts

September 27, 2024February 26, 2025 by Emma Klosson

As L&D professionals, we’re no strangers to jargon. Remember when “LIFOW” was just a bunch of letters? Now, “learning in the flow of work” is a cornerstone of our learning experience design (LXD) strategies. But AI has thrown us a curveball. Suddenly, we need to be fluent in a whole new language, complete with its own set of jargon and acronyms.

So, if you’re unsure what “NLP” and “LLMs” are—and how they differ—or are curious about “Deep Learning” and “Backpropagation,” help is at hand! Our AI Glossary for L&D is your go-to guide for simple, straightforward explanations of the concepts, tools, technologies, and methodologies that fall under the umbrella of “AI.”

To make things even easier, we’ve broken the glossary into four parts. In this first part, we introduce the foundational concepts of AI. Over the coming weeks, we’ll continue adding items to the glossary as we expand on the concepts and dive deeper into the “AI-verse.”

The Building Blocks of AI

Let’s begin by laying a solid foundation with the core concepts that underpin the field of AI. In this foundational section, we’ll look at the building blocks of AI, specifically:

AI
Machine Learning
Neural Networks

For each glossary term, you can find a high-level description as well as examples of its common uses and how and where it can be leveraged by L&D teams.

What is AI?

AI is the umbrella term for a wide range of technologies and approaches that are designed to mimic human reasoning and problem-solving abilities.

How and where is AI used?

AI is all around us, powering applications like social media, facial recognition, recommendation systems, and chatbots.

How can L&D leverage and apply AI?

Here are two ways L&D can leverage AI:

To enhance the learner experience via AI-powered learning solutions that provide opportunities for personalized and adaptive learning.
To enhance learning design practices and processes. Learning experience designers can leverage AI throughout their learning solution design workflow. From needs analysis to design, development to implementation and evaluation, there are dozens of tools and technologies to support this work.

Where applicable, we’ll share specific examples of these applications.

What is machine learning?

Machine learning is a subset of AI where systems learn from data and improve their performance on a task without being explicitly programmed. Systems are fed vast amounts of data and use it to recognize patterns, make predictions, and take actions using the data, all without human intervention. There are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We’ll explain these advanced concepts in a later addition to the AI Glossary for L&D.

How and where is machine learning used?

Machine learning is the engine behind dozens of everyday applications such as fraud detection, image recognition, and recommendation systems. If you’ve ever used streaming platforms like Netflix or Spotify, you’ve experienced machine learning in action. These platforms analyze your viewing and listening habits to identify patterns and make recommendations tailored to your preferences.

How can L&D leverage and apply machine learning?

Machine learning can be used to analyze learners’ behavior and create personalized learning pathways. Machine learning might also be used to analyze and predict learner performance, recommend relevant content, and even automate the analysis of learner data.

What are neural networks?

Neural networks are computational models inspired by the human brain. Neural networks used in machine learning recognize patterns in data. Think of them as interconnected networks of artificial neurons that process information and learn from it, much like our own brains do.

How and where are neural networks used?

Neural networks are the backbone of technologies like image recognition, natural language processing, and speech recognition.

How can L&D leverage and apply neural networks?

Neural networks can be configured to power personalized learning experiences, generate adaptive assessments, and provide intelligent feedback.

Ready to dive deeper? Continue exploring the AI Glossary for L&D

Now that you have some of the basic concepts of AI down, why not take a deeper dive into the technologies and applications that power AI.

Click on the links to view the rest of the AI Glossary for L&D series.

Part 2: Key Technologies explores the technologies that put AI into action including natural language processing (NLP), large language models (LLMs), generative AI (GenAI), and retrieval-augmented generation (RAG)
Part 3: Advanced Techniques and Applications examines the specialized AI techniques and applications that are pushing the boundaries of what’s possible.
Part 4: Algorithms and Architecture offers a peek behind the AI curtain at the algorithms and architecture that enable AI technologies and applications to perform.