How corporate learning and development teams are applying a Human+AI approach to create experiences that empower employees to achieve business goals.
As artificial intelligence (AI) reshapes the learning and development landscape, it is sparking a wide range of responses. Some organizations are taking full advantage of its capabilities, while others are holding back due to concerns about investment costs, uncertainty around how their data will be used, and the quality of AI output.
Despite these fears, AI is already revolutionizing how employees learn, and how learning is designed and delivered. Organizations that adopt this technology early have a competitive advantage over those that wait. But while AI offers many advantages in the learning and development space, unlocking its full potential requires a thorough understanding of how to implement AI into your organization and where to apply it to gain the most value.
What is AI in Learning and Development?
AI in learning and development is the use of artificial intelligence, including machine learning and generative AI, to design, personalize, and scale employee training. It’s applied across tasks like content creation, skills assessment, adaptive learning paths, and coaching, giving L&D teams more capacity for strategy and human connection.
Let’s take a brief look at what AI can accomplish in the world of L&D. Who better to do that than AI itself? Watch the video, co-created with AI and voiced by an AI clone of our own Matt Donovan. You can also find the script here.
AI-enabled learning means using AI to improve automation, streamline content generation and management, and deliver powerful analytics.
How to Use AI in Learning and Development: Automation, Content Generation, and Prediction
Now that you’ve given some thought to implementation strategies, your next consideration should be when, where, and how you should use AI to deliver the most value. While AI can be applied to many different applications, large learning models (LLMs) excel at doing three things: automating tasks, generating content, and analyzing large datasets to make predictions.
Using AI to Automate Tasks
Automating basic tasks will not only help save you time but also free up your employees to spend more time developing and improving your content. Here are a few areas where AI offers real value.
Streamlining Administrative Tasks
AI can automate repetitive administrative tasks such as scheduling, tracking attendance, and managing course enrollments, freeing up time for L&D professionals to focus on more strategic activities.
Creating Personalized Learning Paths
AI algorithms can automatically create personalized learning paths for employees based on their skills, job roles, and learning preferences, ensuring that each learner receives the most relevant content.
Automating Assessments and Feedback
AI can automate the creation and grading of assessments, providing instant feedback to learners and identifying areas for improvement.
Using AI to Generate Content
There are several AI tools that can help expedite the content generation process by making it faster, better, and richer. When using these resources, humans should always remain an integral part of the process, evaluating and refining AI outputs and ensuring that AI-generated content is safe, accurate, and relevant.
Developing Adaptive Learning Content
AI can generate adaptive learning content that adjusts to the learner’s progress and performance in real time, ensuring a tailored learning experience.
Curating Content
AI can curate and recommend the most relevant learning materials, helping learners access valuable content quickly.
Enhancing Interactivity
AI can create interactive simulations and other engaging content that enhances the learning experience and keeps learners motivated.
Using AI for Data Analysis and Prediction
AI empowers L&D professionals to make informed decisions, enhance learning outcomes, and proactively address learners’ needs through actionable insights.
Identifying Learners’ Behaviors
AI provides insights into training effectiveness by analyzing trends and patterns in learner data.
Anticipating Future Needs
AI can predict future learning needs and outcomes based on historical data. This enables organizations to proactively address skills gaps and plan for future training requirements.
Measuring Content Effectiveness
Organizations use AI to evaluate learner engagement and retention, enabling data-driven improvements.
Examples of AI in Learning and Development
AI provides many possibilities for learning and development teams to enhance learning, including human+AI content creation, AI avatars and digital assistants, translation, and personalized learning.
AI-Driven Content Creation
AI tools can significantly enhance the speed and quality of learning content creation. However, achieving this requires a program specifically trained to understand what constitutes effective learning content. Publicly available large language models (LLMs) might fall short, producing low-quality or inaccurate content because they primarily function as extensive databases with access to vast publicly available information.
Content creation and management tools, like our own Learning Content AIQ, helps you create higher quality learning content with a centralized environment to structure your organization’s knowledge. It is designed to excel in creating high-quality learning materials. It has been pre-trained by experts with decades of experience in the field and is engineered to assist learning creators. The program offers AI-automated processes for content authoring and instructional design, which can reduce development time by 50% or more. Users can quickly generate course outlines based on proven best practices, create assessments with built-in instructional design principles, and cut the development time for video and podcast scripts by 70%.
AI Avatars and Digital Assistants
AI tools such as Inworld enable developers to create chatbot avatars that possess unique personalities, knowledge, and cognition. These avatars are tied to LLMs and can use that information to answer questions and engage learners through realistic conversations.
Developers define each avatar’s knowledge base by adding specific information to the knowledge field, or by linking it to knowledge and cognition documents. They can also import personalities and backstories for their avatars, outline their moods, and customize their responses. Avatars can even role-play specific scenarios (imagine chatting with a simulated employee who has recently experienced a difficult performance conversation). This provides a valuable support system that enables learners to engage with your content on a deeper and more meaningful level.
Translation
AI makes it possible to translate learning content quickly and easily, even when it contains several different modalities. For instance, Google Translate enables you to translate entire pages of content. This includes everything on the page, such as menu options, accordions, pop-up windows, closed captioning, and video subtitles. AI will even translate user posts and questions.
Many AI tools include some form of translation. While the translations provided by these free tools may not be perfect—they tend to be roughly 80% accurate—they provide organizations with a fast, easy, and cost-effective way to translate content into dozens of different languages. This can be a game changer for organizations that normally lack the resources to reach their entire audience at scale.
This, however, is not the only option. Organizations can increase the accuracy of translations through trainable AI tools. Programs such as Smartcat use translation memories and glossaries to become more accurate over time. Users can input phrases and words into these memories to teach the program how to improve, creating a Human+AI synthesis in which AI provides the translations, humans review them, and the program learns from this feedback.
Personalized Learning
Personalized learning focuses on tailoring learning experiences to meet the individual learner’s needs, preferences, and goals. AI tools and technologies make it possible to personalize learning at scale and to drill down to the individual learner’s requirements. AI can help analyze large amounts of data, adapt content, and provide real-time support and insights to learners. Personalized learning paths and adaptive learning content are two of the use cases discussed earlier in this guide.
Tutoring is another great use case for personalized learning. Learners can engage with an online tutor powered by a tool like Mobile Coach that provides personalized instruction and feedback. Let’s say the area of focus is customer service. The online tutor will simulate the behavior of a human tutor and help the learner identify areas for improvement. If, for example, the learner has identified empathy as a key area for improvement, the tutor will then offer personalized learning that demonstrates how to apply empathy while servicing clients. This includes answering questions and providing feedback and support pulled from the content on which it has been trained.
When developing an AI tutor, you will need one or more humans to create a solid database for the AI system to pull from and to orchestrate the interactions between the learner and the tutor.
The Role of AI in Skills Development, Upskilling, and Reskilling
According to Microsoft’s 2026 Work Trend Index, employers have created at least 1.3 million AI-related job opportunities in the past two years that did not exist five years ago. To keep up with the pace of change, organizations will rely on the continuous skilling, upskilling, and reskilling of their employees to thrive in this new environment.
AI can quickly identify gaps in the current workforce and even predict emerging skill requirements based on industry trends. Once these gaps have been identified, L&D professionals can then use AI to automate the creation and delivery of new training programs tailored specifically to these needs.
The use of AI in this area offers broad advantages for the individual learner as well, through personalized learning paths, adaptive learning, and real-time feedback that provide learners with learning programs customized to their specific roles, skill levels, learning preferences, and learning goals. Even better, AI can also help employees advance in their careers by suggesting potential roles that align with their interests and abilities and by facilitating mentorship relationships with other employees based on their career aspirations and learning needs.
3 AI Technologies to Save Time in Learning and Development
ChatGPT, Copilot, Gemini, and Perplexityarepopular AI platforms thathelp people help people research topics, transcribe and summarize notes, and write and brainstorm . Let’s explore three additional AI technologies that help L&D professionals work faster.
#1 Colossyan and Synthesia for Generating Video
AI video generation has advanced dramatically, evolving from clunky, basic outputs to lifelike, seamless videos—like those on platforms such as Runway and Google Veo 3 . Tools like Colossyan and Synthesia empower teams to create engaging, realistic videos for applications like sales simulations, complete with customizable avatars, interactive features, and predesigned templates.
#2 ElevenLabs for Creating Audio and Voice-Over
AI is transforming voice-over narration with tools like ElevenLabs, which offer text-to-speech capabilities using a global library of digital voices, voice cloning, and translation features that maintain speaker delivery across 29 languages. This is ideal for creating dynamic, accessible L&D materials for diverse audiences.
Listen to this quick example we created for our podcast.
#3 Quizgecko for Creating Assessments
AI tools like Quizgecko streamline learner assessments by generating customizable quizzes from text, web pages, or videos, offering various question formats, LMS integration, analytics, and the option to enhance quizzes with AI-generated imagery.
How to Implement AI in Learning and Development
Now that you understand what AI can bring to learning and development, it is time to apply these ideas to your organization. To get started with AI:
Acquiring AI Tools: The Buy, Build, Borrow, Bend Strategy
When considering how to incorporate AI into your organization, focus on the Build, Buy, Borrow, Bend Strategy. These approaches help organizations adopt AI solutions that align with their unique needs.
Buy
In this approach, you’re investing in proven AI tools from external providers. These systems are ready to deploy and can instantly streamline automation, accelerate content creation, and generate robust analytical insights. This is a great option for organizations looking for quick implementation with minimal development.
Build
For more tailored solutions, consider building your AI tools in-house. While more resource-intensive, this approach gives you full control over how AI automates tasks, generates content, and provides real-time analysis.
Borrow
For more tailored solutions, consider building your AI tools in-house. While more resource-intensive, this approach gives you full control over how AI automates tasks, generates content, and provides real-time analysis.
Bend
This approach means adapting the AI tools and platforms you already have, rather than acquiring something new. By adjusting prompts, workflows, and configurations, you can extend existing tools to handle new use cases in automation, content creation, or analysis. Bending is the fastest, lowest-cost path forward, and it’s a strong option for organizations that already have AI licenses or platforms in place but haven’t yet tailored them to their L&D needs.
Gauging Maturity: GP Strategies’ AI Maturity Model
AI maturity is about more than which tools you use. It’s about how deeply AI is embedded in your processes, governance, and culture. GP Strategies developed this five-phase model to help learning leaders identify where their organization stands today and what it takes to move forward.
Most organizations still fall in Phase 2 or early Phase 3, still building the foundational practices that come before real transformation. Use the phases below to find your starting point.
Phase 1 | Ad Hoc
Individuals use AI sporadically and independently, with no formal process, governance, or shared knowledge across the business. Adoption is low, and experimentation is informal.
Phase 2 | Exploratory
Small groups explore AI together and begin sharing best practices and templates. Role clarity and workflow are starting to form, and AI use is typically role specific.
Phase 3 | Structured
Teams adopt standardized processes for AI use, with governance, version control, and ethical guidelines in place. Tools are selected to fit specific needs, and decisions start to be AI-informed.
Phase 4 | Integrated
AI is intentionally embedded into core workflows across multiple teams, with cross-functional integration spanning L&D, HR, compliance, and IT to manage and optimize processes.
Phase 5 | Transformational
AI drives strategic transformation enterprise-wide, with continuous improvement and measurable impact as the norm. Human-AI collaboration is standard, and responsible AI use is a core value.
Ensuring AI Adoption: The BRAVE Framework
Even well-designed AI tools stall when employees quietly go back to their old workflows. One in six workers admit to faking AI use, and the reason is rarely a skills gap. It’s fear. GP Strategies’ BRAVE framework addresses the psychological and motivational barriers that training alone can’t fix.
- Belonging: Build psychological safety by positioning AI as augmentation, not replacement.
- Relevance: Tie AI use cases to the daily workflows employees already care about.
- Access: Give employees hands-on practice in safe environments where mistakes are part of learning.
- Visibility: Normalize AI use through visible peer and leadership modeling.
- Empowerment: Let people experiment without fear of punishment, and celebrate intelligent failures.
Resources for AI in Learning and Development
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