AI has made the process of creating texts easier than ever. But how do you make sure that all of your content is actually good?
A job description in 30 seconds. A complete email-campaign within just a few minutes. Ten different versions of a social media post with just one push of the button. Generative AI has changed the way that organisations create content forever.
Tasks that used to take hours, can now be done within just a few minutes. That offers us many new chances, but also causes some new challenges to arise.
If everyone can create content at high speed, then who will monitor the quality?
More content does not automatically mean better content
Imagine an organisation in which different departments use Artificial Intelligence.
Marketing generates campaign content. HR writes job ads. Communication Iedere afdeling werkt sneller. Maar iedereen gebruikt ook eigen prompts, instructies en werkwijzen.
Every department works faster than ever. However, they all use their own prompts, instructions and workflows.
The result? Texts with a different tone of voice. Job ads that are way too difficult to understand. A tone of voice that does not fit with the target group. Or content that differs from internal writing standards.
That does not necessarily mean that AI produces poor content. Rather, it means that good content requires more than just well written text. It must also align with the organization, be understandable to the target audience, and meet the requirements established in advance.
Why ChatGPT should never be your only quality control
Many organizations use generative AI not only to write texts, but also to check them.
That may seem efficient. You might ask, for example: “Check if this text is understandable on a B1 level.” The model then evaluates the content and suggests improvements. But how do you know whether that evaluation is accurate?
Generative AI can be highly effective at improving content, but an evaluation by the same type of technology is not always sufficient to determine whether a text actually meets specific quality standards. Moreover, evaluations can vary.
For an individual piece of content, that may not always be a problem. But for organizations that publish hundreds or thousands of texts, consistency becomes increasingly important. That is why a different approach to quality control is needed.
From AI contentcreation to smart contentcontrol
This is where Textmetrics comes in. Textmetrics helps organizations evaluate content against specific language guidelines and quality standards. Through specialized language analysis, content can be assessed based on factors such as readability and other predefined writing rules.
The goal is not to replace generative AI. Quite the opposite.
AI can help create content faster. Textmetrics helps verify whether that content meets the established requirements.
This combination is becoming increasingly valuable as AI plays a greater role in content processes.
What Does This Mean for Your Organization?
The benefits go beyond simply producing better content.
1. Less Time Spent on Quality Control
When content is automatically analyzed against predefined criteria, employees no longer need to perform every quality check entirely manually. This can help streamline review processes and reduce unnecessary rounds of revisions.
2. Greater Consistency in Communication
Whether content is written by marketing, HR, or an AI agent, the established quality criteria remain the same. This creates a more systematic approach to maintaining content quality.
3. Better Alignment with Your Target Audience
A text can be grammatically correct and still be too complex for its intended audience. By specifically analyzing readability and other linguistic characteristics, organizations gain insight into where their content can be improved.
4. Greater Control Over Internal Guidelines
Organizations often have specific requirements for their communication. Targeted quality checks make it easier to identify deviations from supported guidelines and prioritize improvements.
5. Quality Control That Scales with AI
Perhaps the most important development is that content quality control no longer needs to be limited to a separate writing process. Through integration capabilities, specialized language analysis can become part of existing systems and automated workflows. And that opens up new possibilities.
A New Step: Textmetrics for AI Agents
AI is becoming increasingly autonomous. While employees previously had to enter prompts themselves and review the results, AI agents can now perform multiple steps within a process. They gather information, write content, and make adjustments based on feedback. But these agents also need to be able to determine whether their output meets the desired quality standards.
That is why making specialized content quality control available to AI agents is an interesting opportunity. Through integrations, including MCP where supported, Textmetrics can serve as a quality control function within an AI workflow. For example, an agent can generate a job description and have it analyzed against selected language criteria. Based on the feedback, the agent can then make targeted improvements. This means organizations can use AI not only to produce content, but also to automate parts of the quality assurance process.
What Does This Mean in Practice?
Consider an HR department that regularly publishes new job postings. With generative AI, an initial draft can be created quickly. Textmetrics can then help analyze the content for readability and compliance with relevant language guidelines.
Any areas for improvement can be addressed before the job posting is published. Or consider a marketing team creating content for different campaigns. By systematically evaluating content against selected quality criteria, organizations gain greater control over the consistency of their communication.
In both examples, it is not about using more AI. It is about using AI more intelligently.
The Next Step in AI Is Not Writing Even Faster
Over the past few years, we have mainly focused on what AI can create. How quickly can content be written? How much time can we save? How much content can we produce? But as AI becomes increasingly commonplace, the question changes. It is no longer just about how quickly we can create content.
More importantly, how do we know whether that content is good enough to use?
This is where an important next step lies. Textmetrics helps organizations take that step, with specialized content analysis that supports both people and AI applications. Because the future of content is not just about speed. It is about quality, consistency, and trust.
Discover how Textmetrics can help your organization gain greater control over the quality of AI generated content.
