Artificial intelligence (AI) is often pitched to learning and development (L&D) professionals as a shortcut: build training faster, personalize content and iterate on demand. That promise is easier to fulfill when a project has a clear objective and audience, an identified subject-matter expert, and weeks or months to develop it. But how can L&D leaders design effective training at scale when training requirements and learner needs keep changing?

My team has had to develop an L&D workflow for this fast-moving work. At our organization, contributors known as Fellows train and evaluate AI models in their fields of expertise. Projects can ramp on short notice, requirements may change daily and large groups of Fellows may need onboarding, assessment and training at nearly the same time. The learning and training team works alongside operations teams to translate technical model-training requirements into guidance that new Fellows can apply.

This pace has required us to rethink familiar instructional design practices, including scaffolding, tools, assessment, review and knowledge management.

Although these lessons come from AI model training, they apply to any L&D team expected to build training programs faster and at greater scale than their existing systems support.

Build a Shared Foundation for Recurring Learning Needs

When a learning team supports many concurrent projects, each request can look like a one-off. A more efficient approach is to identify the learning objectives that recur across projects and build a shared foundation before learners receive project-specific training.

Rubric literacy is one example. Across AI model-training projects, learners repeatedly need to understand how to use a rubric to evaluate AI model outputs and identify evidence that meets each criterion. To address that shared learning need, we developed a foundational course that combines core concepts, worked examples and domain-specific case studies across professions. Building this capability before Fellows join a project reduces the basic instruction each project team must repeat and gives learners a stronger foundation for project-specific onboarding.

Identifying shared objectives is a valuable decision for a learning team supporting many projects. It converts repeated project-level instruction into a reusable foundation that can outlast any single project.

Choose Tools That Support Frequent Training Updates

Traditional learning management systems (LMSs) were designed for a slower content cycle: build a module, publish it, and revisit it months later. They are often difficult to adapt when multimedia-rich training must change weekly or even daily.

To meet those demands, we moved beyond a conventional LMS and began using AI-assisted web development tools that allow people without engineering expertise to build and update custom interactive training sites without waiting for a development queue. We support that workflow with tools for storyboarding, video production, image creation, interactive practice activities and task simulations.

The lesson is to evaluate whether the technology stack matches the pace of the work. A platform suited to annual compliance training may not support content that changes every week.

Review AI-Generated Training With Human Expertise

Generative AI can draft a training module, video script or assessment in minutes, but speed does not guarantee quality. Even leading models can produce generic content, introduce subtle inaccuracies, or miss nuances that a subject-matter expert would catch.

We treat AI output as a first draft that needs human expertise. The learning and training team still applies the same instructional design judgment and quality bar: clarity, accuracy, tone and learner empathy. Increasingly, however, learning designers act as editors rather than sole authors, while AI tools serve to analyze information, suggest design options and critique drafts. Human review still begins with familiar questions: Does the course meet learners’ needs? Does this activity measure the intended skill, or only whether learners can recognize the correct answer? Would a first-time learner understand the material without a live trainer to explain it?

Building human review into the workflow keeps the content accurate, relevant and credible.

Capture Design Standards in Reusable AI Workflows

AI drafts improve when they reflect the organization’s context. A generic prompt produces a generic assessment; a prompt grounded in an organization’s standards, teaching points and question formats can produce a useful first draft.

Our team built reusable AI workflows with structured prompts for drafting and reviewing assessments and for automated grading with a large language model (LLM). Instead of spending hours drafting and polishing a new assessment, a learning designer can brief the AI tool on the teaching points and preferred question formats, such as multiple-choice or open-response, and review the resulting draft for accuracy and alignment with the teaching points.

The next step is to turn several reusable skills into an agent. I also built a course-design agent that draws on historical courses, business context and learner performance data to create an initial course draft rather than starting from a blank page. The benefit goes beyond a single AI-generated document. It comes from capturing the organization’s tacit standards in a reusable form so that future requests benefit from the same context.

Reusable, fast-turnaround capabilities are becoming increasingly important: A 2026 World Economic Forum report found that more than one in three young workers globally are already in occupations with medium to high exposure to AI-driven task change, adding pressure to update workforce training. As skill requirements change, L&D teams need systems that can update training without rebuilding every course from scratch.

Apply the Approach Beyond AI Training

These strategies respond to conditions that increasingly affect corporate L&D: compressed timelines, shifting content and large, distributed audiences. Teams that treat shared objectives, flexible tools, human review and organizational knowledge as deliberate design decisions will be better prepared to respond to business needs without lowering the quality of learning.