A more useful question than "should we use AI?"
Most organizations are somewhere on the spectrum between "we banned all AI tools" and "we're using them uncritically for everything." Neither extreme is well-considered.
The more useful question isn't whether to use AI in your content workflow. It's which specific tasks benefit from it, and which tasks it's going to make worse.
I've worked with teams experimenting with AI-assisted content production across government communications, nonprofit publishing, and service-based web content. The patterns in where it helps and where it fails are consistent enough to map out.
Where AI genuinely helps
Generating outlines and structure
This is one of the strongest use cases. If you have a topic and a sense of what you need to cover, asking an AI tool to generate a structural outline takes 30 seconds and gives you something to either accept, modify, or react against. Even when you change the structure significantly, having a draft framework is faster than starting from zero.
The output is a starting point, not a finished product. But that's exactly what you need at the outline stage.
Writing first drafts for high-volume routine content
Regular announcements, event descriptions, job posting summaries, social media variations for a press release, FAQ answers for common questions — all of this is content with a predictable structure and relatively low originality requirements. AI can produce acceptable first drafts for routine content quickly, and an editor can refine them in less time than writing from scratch.
This genuinely reduces time in the right context. The key qualifier is "routine content with predictable structure." It doesn't transfer well to content that requires specific expertise, nuance, or organizational voice.
Metadata and SEO scaffolding
Writing page titles, meta descriptions, alt text for simple images, and brief excerpts are tasks that require clear judgment but not deep expertise. AI handles these well when given the full content and clear constraints (character limits, plain language requirements, no em dashes, etc.).
Having a tool generate five title variations for a new page is useful. You can pick the best one and refine it, rather than staring at a blank field and cycling through options manually.
Research synthesis and summarization
AI tools trained on large datasets are useful for getting a quick survey of a topic before writing. "What are the most common arguments for and against mandatory web accessibility compliance?" gives you a starting framework for research. You still verify and cite authoritative sources, but the initial orientation is faster.
This is most useful in the early stages of a content project, not as a substitute for expert knowledge in the final product.
Drafting boilerplate and structural copy
Privacy notice updates, cookie consent language, standard footer text, onboarding email sequences, 404 error pages, accessibility statements — all of this is structural copy that follows established patterns. AI generated first drafts for these categories save significant time because the organizational voice is less critical and the structure is well-established.
Where AI creates new problems
Accuracy and fact verification
AI language models don't retrieve facts from a database the way a search engine does. They generate text that sounds plausible based on patterns in training data. This means they produce confident-sounding statements that are factually wrong, out of date, or fabricated.
Every claim in AI-generated content needs fact-checking before publication. The time this adds can offset the drafting time saved, particularly for technical or evidence-dependent content.
If your content includes statistics, specific regulations, named programs, or anything with a direct real-world consequence, treat AI drafts as unverified until checked.
Organizational voice and brand specificity
AI tools don't know your organization. They produce content that sounds like many organizations, which is a different problem than sounding like yours. The more distinctive your organizational voice, the more editing AI drafts require to bring them into brand.
This isn't insurmountable. A detailed prompt with voice guidance, examples, and constraints helps significantly, and prompting specifically for EEAT-aligned web content is a learnable skill. But it requires the person prompting to have a clear sense of what your voice is, which means someone who knows your organization well enough to write a style guide editors will actually follow is the same person who needs to be supervising the AI output.
EEAT-dependent content
Google's quality guidelines include E-E-A-T: Experience, Expertise, Authoritativeness, and Trustworthiness. For content where demonstrated first-hand expertise matters — professional services, legal guidance, medical information, government policy interpretation — undifferentiated AI output is a ranking and credibility risk.
A tax planning guide that doesn't reflect the specific expertise of anyone at your organization is not demonstrating expertise. It's demonstrating that you know how to use a tool that other organizations are also using.
Content requiring original perspective
Analysis, opinion, case studies, and content that requires a genuine point of view don't benefit as much from AI drafting. The value of this content comes from the specific angle and experience behind it. An AI draft based on a brief may technically cover the topic but lack the perspective that makes the content useful and distinctive.
These are also the content formats most likely to be scrutinized for quality. Heavily AI-generated thought leadership signals its own origin pretty clearly to readers who are themselves sophisticated.
Legal and regulatory review dependencies
Anything that will go through legal review needs to be drafted with that process in mind. AI-generated regulatory language, terms and conditions, privacy notices, and accessibility statements often require significant revision by someone with legal knowledge before they're publication-ready — and that person needs to be able to account for every word. Content that originated as AI output can be harder to defend and revise in a legal review process.
A practical model for AI in a content workflow
The teams that get the most value from AI tools tend to use them at specific points in the workflow rather than trying to automate entire content types end-to-end.
The pattern that works:
That's not a limitation. That's a realistic model for where the tools are right now.
If you want to build an AI-assisted content workflow that's honest about the limits and actually saves time where it matters, get in touch and we can map out what makes sense for your team.