What do we mean by “good quality” AI content?
As the saying goes, embracing AI has been “a journey” for many of us working in content.
In the webinar, Sarah Lewis from copywriting studio Thread said, “It hasn’t been necessarily the easiest of transitions. We’ve prided ourselves on being the people brands come to for guidance, and our role as advisers has also shifted.”
At Comtec, we have had to challenge our own perceptions of high-quality content. Initially, we resisted the idea that AI could deliver anything close to the quality our teams could produce. However, through extensive testing, feedback, and adaptation, we have learned how to harness AI effectively without sacrificing quality.
The AI era has required a mindset change. Businesses are evolving, consumer expectations are shifting and speed is often prioritised over impactful or meaningful storytelling. Agencies, in particular, must remain the guardians of quality, ensuring AI is used responsibly and effectively.
The three perspectives on AI content quality
Brands: For a global brand, good quality means decent ROI and meeting defined goals. AI allows them to scale content production, but if the content does not engage, convert or represent their brand accurately, it fails to deliver value.
Agencies: Agencies must balance commercial needs with creative integrity. Their role is to ensure AI-generated content meets the standards required for audience engagement and brand consistency. This means knowing where AI can add efficiency and where human expertise must take over.
Consumers: Consumers are not necessarily aware of whether content is AI-generated, but they recognise quality when they see it. Poorly executed AI content risks feeling generic, impersonal and untrustworthy. “When consumers know content is AI-generated, 52% feel less engaged,” Sarah pointed out in the webinar, referencing a recent study.
How to measure success beyond just “perfect copy”
For too long, content quality has been measured primarily by traditional linguistic standards—grammar, clarity, and correctness.
But in an AI-driven world, brands might need to start viewing success more broadly. Instead of asking whether AI-generated content is perfect, we should be asking, “Is it effective?”
Sarah put it best: “Right now, a lot of brands are looking at AI as a way to create more content, but not necessarily the right content. [Brands are thinking of] AI as scalability without personalisation. But if you focus on personalisation, you’re making sure that the right message reaches the right audience—that’s the real measure of success.”
This shift in thinking means evaluating content based on its technical accuracy and how well it connects with audiences. Does it engage? Does it reflect the brand’s identity? Does it drive action?
Metrics such as audience engagement, brand sentiment, conversion rates, and shareability carry more weight than flawless syntax.
The most forward-thinking brands are already evolving their content success metrics. Instead of chasing perfection, they’re measuring:
- Relevance: Does the content meet the specific needs of its audience?
- Authenticity: Does it sound like the brand or feel robotic and detached?
- Performance: Is it driving the right engagement, whether just clicks or meaningful (loyalty-based) interactions?
- Efficiency: How well does AI content support scalable production while still maintaining brand consistency?
By redefining success this way, brands and agencies can ensure AI-generated content is not just “correct,” but genuinely impactful.