What AI content tools actually do

Current AI writing tools are large language models — statistical systems trained on vast quantities of text that predict what words and sentences should follow a given prompt. They are remarkably good at producing fluent, readable prose that sounds authoritative. They are not databases of facts, and they do not reason about accuracy. When a model produces a sentence about a Canadian regulatory requirement, a product specification, or a local business detail, it is producing the text that statistically follows from the surrounding context, not retrieving a verified fact.

This distinction matters enormously for business web content. A service page for a roofing company that says "we serve the Greater Victoria area" will not be fact-checked by an AI tool — it will produce that sentence if you tell it the business is in Victoria. But a detailed technical claim about installation standards, building code requirements, or warranty terms may be confidently stated and completely wrong.

AI tools produce average text. They have been trained on what exists, so they reproduce the conventions and common framings of existing content. For topics that are widely covered in generic ways — "what is a CRM," "benefits of cloud storage" — the output will be indistinguishable from thousands of similar pages. For topics that require specific expertise, local knowledge, or genuine differentiation, the output will be generic in exactly the ways that prevent it from standing out.

Google's position and the SEO question

Google's official position is that it does not penalise content for being AI-generated — what matters is whether the content is helpful, accurate, and demonstrates expertise. The algorithmic signals they use for quality evaluation — E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) — are agnostic about production method in principle.

In practice, the situation is more complicated. Google has invested heavily in systems that evaluate content quality, originality, and user satisfaction signals. A page that gets no clicks in search results, or where users click and immediately return to the search results page (a "pogo-stick"), will not rank well regardless of how it was written. Generic AI content — the kind that rephrases what other sites say without adding original perspective, local context, or genuine expertise — tends to produce exactly these outcomes.

There is also the scale problem. One of the primary use cases people explore with AI content is bulk page generation: publishing hundreds of similar pages with minor variations (city name, service name) to capture long-tail search traffic. Google's Helpful Content system, introduced in 2022 and refined since, is specifically designed to identify and downrank sites that produce large volumes of content primarily for search engine consumption rather than to genuinely help users. Canadian businesses who went heavily into AI-generated bulk content in 2023–2024 have often seen significant traffic losses in subsequent algorithm updates.

The local SEO angle. For Canadian small businesses trying to rank in local search, AI-generated content is particularly risky as the foundation of a content strategy. Local rankings depend on signals of genuine local relevance — specific neighbourhood knowledge, local landmarks, citations, actual reviews. Generic AI content about "plumbing services in [City Name]" adds no local signal and competes poorly against pages written by people who actually work in that city.

Quality problems that are hard to spot

The most obvious AI content quality problem — factual errors — is actually easy to check if you know your subject matter. The subtler problems are harder.

Confident vagueness. AI text often states things at a level of generality that sounds informative without committing to specifics. "Our team has years of experience serving the Canadian market" is the kind of sentence that sounds fine but says nothing a potential client could evaluate. AI models gravitate toward this kind of phrasing because it is common in their training data and carries low error risk.

Hallucinated details. Models sometimes produce specific-sounding details — statistics, regulatory references, quoted studies — that are entirely fabricated. A sentence like "according to a 2023 Statistics Canada report, 68% of Canadian small businesses..." may sound authoritative and be completely invented. If you publish this and a visitor tries to verify it, the reputational damage is significant.

Homogeneity across competitors. If every business in your industry is using the same AI tools with similar prompts, the resulting content is functionally identical across competitors. When users cannot distinguish your services page from your competitor's — because both were written by the same model — you have no content differentiation, which is the opposite of what a good website achieves.

Voice and brand inconsistency. Customers who have dealt with your business develop expectations about how you communicate — your tone, your level of formality, your specific knowledge. AI content often sounds like it was written by a committee of no one in particular, which creates a disconnect for returning clients.

Canadian copyright law — the Copyright Act — requires a human author for copyright to subsist. This is consistent with how most jurisdictions approach the question: copyright exists to protect the expression of human creativity, not the output of a machine.

The practical implication is that pure AI-generated content — text produced entirely by the model with no human creative contribution — may not be protected by copyright in Canada. This is not yet definitively resolved in Canadian case law, but it is the direction the Copyright Office and legal analysis consistently point. If you publish AI-generated content and a competitor copies it, you may have limited ability to enforce copyright against them.

The training data question is separate and also unresolved. AI models are trained on vast amounts of text, including copyrighted text. Litigation in the United States and the UK is working through whether this training constitutes copyright infringement by the model developers. The legal outcomes in those cases, and any eventual legislative response from the Canadian Parliament, will shape how this question is resolved in Canada. For now, the relevant Canadian legal advice is: if your publishing involves reproducing or closely paraphrasing existing copyrighted material, AI involvement does not change your liability for that reproduction.

Accuracy risk and professional liability

For regulated industries — financial services, legal services, medical and health services, real estate, engineering and architecture — using AI-generated content without expert review introduces professional liability risk. AI models do not know when they are wrong, and in professional contexts, wrong information on a business website can cause real harm.

A law firm that publishes AI-generated content about tenant rights in BC that misrepresents current legislation has put incorrect legal information into the world under its name. A financial services company that publishes AI-generated investment guidance with errors is potentially providing unlicensed advice in addition to inaccurate content. Professional regulators across these industries have begun addressing AI-generated content specifically, and most guidance points in the same direction: professional content requires professional oversight.

Even for non-regulated industries, incorrect claims on your website can be binding. If your website says your product warranty covers five years and the AI hallucinated that detail, you may be legally held to it.

Where AI content tools genuinely help

None of this means AI tools have no place in web content production. They genuinely accelerate tasks where the underlying content is either well-established factual material or where the primary work is structural and editorial rather than creative.

Good uses include: drafting a first version of a page from an outline you have already developed, suggesting structural variations you had not considered, writing boilerplate text (privacy policy language, terms of service, meta description starting points), generating multiple headline options to choose from, and reformatting existing content into different structures.

AI tools also work well when a knowledgeable human provides the substance and uses the tool for expression. If you write down the specific things you know about your service — the actual process, the real-world details, the specific Canadian context — and ask an AI to help structure that into readable prose, the result can be efficient and good. The human knowledge is doing the real work; the tool is helping with the writing.

A workable approach for Canadian businesses

The practical recommendation is not "do not use AI" or "AI does all your content." It is to use AI tools as a production tool, not a knowledge source. The content strategy, the specific expertise, the local context, and the editorial judgment need to come from humans. The tool can help express that efficiently.

Review every piece of AI-assisted content for accuracy before publishing, particularly any specific claims, statistics, regulatory references, or pricing information. If you would not publish something a junior employee wrote without reviewing it, you should not publish something an AI produced without reviewing it — and you should be equally willing to rewrite substantially.

For service pages that are central to your business — the pages that rank for your core search terms and convert visitors into clients — the investment in human-written content that genuinely reflects your expertise is almost certainly worth more than fast AI output. The difference between a page that ranks and converts and one that does not is usually the difference between generic and specific.

The Canadian businesses that will do well with AI content tools are those that use them to produce more of the content they were already capable of producing — not those that use them as a substitute for having genuine things to say.