The release of large language models (LLMs) on the public was pivotal, and the tools’ popularity suggest that they’re here to stay —  ChatGPT gets approximately 5.2B visits per month, according to SEMRush web traffic data (at time of writing). Naturally, there’s some apprehensions about what this means for workers. A Pew Research Survey found more than half of workers reported being worried about future impacts of artificial intelligence (AI) in the workplace, while 32% reported worried about its impact on job opportunities.

From academics to content development, generative AI has been strategically adopted within workflow management. In this article, learn practical use cases across two of these sectors.

The five-second elevator pitch: AI isn’t replacing trainers or content developers, but it is changing how workflows are managed.

How Instructional Designers Actually Use AI

AI is helping instructional designers think faster, align more clearly and reduce friction in the everyday work of course production. Here are a few examples:

Resource Alignment

One of the most practical ways to use ChatGPT or Claude is to check learning resource alignment. Before sending anything to a subject matter expert, an instructional designer can ask the model to check whether readings, videos and activities support the course outcomes and assessments. It’s a logic check. It helps catch disconnects early, before someone with deep content expertise spends time fixing structural issues.

Assessment Development

Another use of AI is to draft formative assessments and knowledge checks with distractors and feedback aligned to current best practices. An instructional designer can prompt an AI to generate 10-12 questions at varying levels of complexity, mapped to the objectives. Depending on the platform these questions are going into, it can generate them directly in HTML. No questions go into a course untouched, but it’s a lot easier to revise decent first drafts than to invent questions from scratch under a deadline.

Framing Texts

Another area where AI adds value is in framing texts — those small, overlooked connectors that set up a resource or introduce a concept. Instructional designers can use it to generate options that can quickly be adapted. A few well-phrased paragraphs can clarify the learning arc of an entire module.

Connecting Learning to Real-World Work

For more authentic assessments, course designers can ask AI to brainstorm real-world project ideas tied to roles. Not just generic assignments, but specific tasks someone in that field might actually be asked to do. That process helps push course design past the quiz and discussion cycle toward real-world application of skills.

Training Consistency

To maintain consistency across programs, there’s a technique called stylometry, which analyzes writing style and tone across samples. Instructional designers can feed in language from existing courses and prompt the AI to draft content that sounds like it came from the same voice, even if it’s being built by different teams.

Subject-Matter Expertise

When dropped into a new subject area, instructional designers can also use AI to teach themselves — definitions, context, tension and common misconceptions. It helps designers build a course with the kind of clarity you only get when you understand why the material matters.

In these ways, AI helps instructional designers get to the meaningful work faster.

How Content Developers Actually Use AI

Generative AI is helping content developers across learning and development add value in the following ways:

On-Brand Sandboxes

Style guides should be living, breathing documents that evolve as brands grow. Maintaining them, and efficiently publicizing changes across the team, can be trickier.  Feeding internal style documentation into a closed AI chatbot can be a two bird, one stone scenario:

• Admins can centralize style guide maintenance; furthermore, closed systems are IT-managed and can help protect IP.
• Team members can learn brand voice in an isolated sandbox where they can iterate.

Equally helpful: tools like text snippets can centralize preapproved product boilerplates, including common trademark treatments (e.g., sub-brands, product IP, etc.).

Evolving Template Library

• Design language: Making reusable templates for trainings can help democratize design (and assure dueling deadlines from stakeholders across multiple business units are met). This frees content developers to work on larger priorities. It also helps shepherd in a more universal design language where strategists can easily share their creative vision with creative professionals, with fewer revisions needed from communication breakdown.
• Quick-and-dirty videos: Generative AI video tools like Synthesia enable resource-strapped teams to add motion and template-based animation to courses, microlearning and other resources — built in a similar sandbox to ensure deliverables are consistent with visual branding guidelines. These tools similarly democratize some of the repeatable, production design-level tasks, which are then proofed and finalized by visual designers.

Fighting Misconceptions — Finding Workflow Balance

A reminder: AI, particularly generative AI, is perhaps the most disruptive and noisy tech introduction since the industrial revolution. Surely its originators want the noise, because even the controversial press about black boxes and ethical dilemmas drives clicks and indirect adoption. But as history reminds, the printing press didn’t displace the Luddites; it empowered information dissemination, making way for new industries and more widespread literacies. The loom didn’t destroy craftsmanship; it allowed for more advanced artisanship on a larger scale, empowering seamstresses to become visionary clothing designers.

Generative AI tools, likewise, offer a mostly positive outlook. Using them within workflows is no different than how writing and design work has always evolved — from cave drawings to computer-aided design to AI-assisted workflows. The next evolution of digital tools will assist the creative professional as they look to grow more strategic and effective in their roles.

Generative AI won’t “revolutionize” your work, give you “superpowers” or “unlock new frontiers of productivity.” Superfluous language and overzealous verbs are, respectively, hallmarks of reliance on AI tools to generate mass AI slop or simply marketing gimmicks from the AI developers themselves.

Creative professionals, though, don’t need to be afraid of generative AI if they use it wisely. View it as a new tool in your box, ready to use and break through the parts of the creative process you most scorn. Use it to become a better creative, not one incapacitated by misplaced fear.