Good instructional design has never been about making a course look polished, filling a learning-management system, or getting everyone to click “complete.”
In 2026, good instructional design is about helping people do their work better—more confidently, consistently, safely, and independently.
That may sound obvious. But organizations still invest in training that explains a process without letting people practice it, asks learners to remember information they could access in the flow of work, or measures success by attendance and completion instead of performance.
A finished course is not necessarily a finished capability.
The best instructional designers do not begin with, “What should we put in the course?”
They begin with better questions:
Sometimes the solution is training. Sometimes it is a clearer procedure, a better job aid, a simplified workflow, stronger manager support, or all of the above.
That is why instructional design now sits inside a larger performance system. Courses matter, but they are only one possible tool.
People do not become capable because they watched a video, clicked through slides, or correctly answered three multiple-choice questions in a row. They become capable by making decisions, trying the task, receiving feedback, correcting mistakes, and applying the skill in a context that resembles real work.
Good design builds in that practice.
For a customer-service team, that may mean handling realistic customer scenarios. For a manufacturing team, it may mean identifying risks before beginning a task. For managers, it may mean practicing a difficult conversation and receiving specific feedback before the real one happens on a Tuesday morning when everyone is already busy.
The point is not to make learning harder for the sake of it. The point is to make learning useful.
Accessibility is not a last-minute compliance check. It is part of whether the learning works.
A well-designed experience considers different access needs, work environments, levels of prior knowledge, language needs, available technology, and time constraints from the start. It also represents the real range of people who will use it.
That might include captions and transcripts, screen-reader-friendly materials, readable visual hierarchy, keyboard navigation, multiple ways to engage with content, and examples that do not assume every learner has the same context or experience.
Designing for more people usually makes learning better for everyone. Clearer language helps the new employee and the experienced employee. Captions help people in a noisy workplace and people who process information differently. Well-structured content helps the person using assistive technology and the person who has 90 seconds between meetings.
That is not extra polish. That is good design.
Employees should not have to remember every policy, troubleshooting step, or decision rule they encountered in training six months ago. Nor should they have to search through a digital haystack to find the one answer they need.
Good instructional design connects learning to performance support:
This is where instructional design and knowledge management belong in the same conversation. Training helps people build capability. Good information systems help them use that capability when the work gets complicated.
AI can help instructional designers move faster. It can summarize research, organize interview notes, suggest drafts, create early prototypes, analyze learner feedback, and reduce some of the repetitive work involved in development.
But faster production is not the same thing as better learning.
The Learning Guild’s research, which included more than 500 L&D professionals, shows that AI is already widely used in workplace learning technology. The goal should not be to use that efficiency simply to produce more content—especially content learners will forget. Instead, AI should free L&D teams to focus more on the work it cannot reliably do alone: understanding learners, validating information, designing meaningful practice, identifying risks, and making thoughtful decisions about what people actually need to learn.
AI can accelerate a first draft. It cannot replace performance analysis, empathy, context, or accountability.
Completion data still has a place. It can tell you whether people had access to learning. It cannot tell you whether the learning solved the problem.
Good instructional design looks for evidence of transfer:
The World Economic Forum reports that employers expect 39% of workers’ existing skill sets to change or become outdated by 2030. That makes learning design more than a course-development function. It is part of how organizations stay ready for change.
In 2026, good instructional design is not defined by a particular authoring tool, trend, or template. It is defined by whether it helps real people succeed in real work.
And it treats learners as people with jobs to do, not as completion rates waiting to happen.
MATC helps organizations design learning that supports performance, not just participation. We connect instructional design with technical documentation, performance support, knowledge management, change management, and practical measurement so employees can build capability and use it where it matters most: in the work itself.
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“The Future of Jobs Report 2025.” World Economic Forum. 1/7/25. Accessed 9/15/26. https://www.weforum.org/publications/the-future-of-jobs-report-2025
Shaw, Christine. “The Human Touch: Why AI-Powered Learning Still Needs Us.” Learning Guil. 3/4/25. Accessed 9/15/26. https://www.learningguild.com/articles/the-human-touch-why-ai-powered-learning-still-needs-us
Torrance, Megan and Lauren Milstid. “Workplace Learning Technologies Adoption.” Learning Guild. 8/12/25. Accessed 9/15/26. https://www.learningguild.com/wp-content/uploads/GuildResearch_TL25V5.pdf