Building a Content Supply Chain in the Era of Generative AI

It’s easy to understand why executives at large enterprises want to deploy generative AI. The demand for content is growing exponentially — some predict it will increase by 5-20X in the next two years. Generative AI gives enterprises the ability to keep up with demand, while maintaining or even reducing their costs. Ultimately, what executives have in sight is the opportunity to significantly impact their bottom lines while scaling content creation exponentially.

But the sheer increase in content volume resulting from generative AI is introducing new risks. One of those risks is to make sure that their enterprise style guide standards aren’t violated.

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Why do enterprise style guide standards matter?

 A content style guide (which also may be referred to as an editorial or enterprise style guide) is typically a set of writing guidelines and terms that help enterprises maintain a consistent tone and voice across all their written content. It’s a source of truth for writers of all types of content, including blog posts, technical documentation, and content marketing assets. And the reality is that enterprises are legally, ethically, and commercially responsible for the content they publish. That’s why most enterprises document their content standards in style guides.

Style guides typically range from dozens to hundreds of pages long. And they exist for a reason. Compliance errors in content cost businesses — not just financially and legally, but also in terms of reputational damage. Other risks involve the violation of brand guidelines, accessibility guidelines, non-inclusive language, and confusing terminology that distorts and potentially impairs the customer experience.

Because enterprises are responsible for the content they produce, most want (and need) people to review it before it’s published. As production volumes increase with generative AI, humans require assistance to keep up.

For example, a company producing two billion words a year had as many as 15 million style guideline violations in their content. To mitigate those risks with a purely human solution would have cost the company more than $20 million a year, and still, it’s likely that many of the errors would be unnoticed by the human eye alone.

Achieving scale: Building a content supply chain

Enterprises can scale content production with generative AI. But, the challenge is how to achieve that scale safely.

The content production processes at most enterprises today look nonlinear, ad hoc, and often unplanned. To tell you the truth, to scale they need to look more like manufacturing supply chains with raw materials that are managed by logistics software, inventoried, measured, and stored for appropriate customer use and consumption. For enterprises that thinking is changing quickly. As generative AI technologies become embedded in authoring tools and services, and enterprises try to increase production 5-20X, a content supply chain needs to take form. To scale the content supply chain, quality assurance (QA) — of enterprise style guide standards — is needed.

The first step to establishing a content supply chain with QA is to quantify the quality of content through scoring it against enterprise style guide standards. That requires digitization of style guide standards.

Second, it requires the capability to score content when people are writing and reviewing it. When the score is low, there are issues. Technology needs to make it easy and efficient for people to make recommended changes.

Third, and most importantly, it requires the capability to automatically score large volumes of content so people know the quality of a corpus of writing. When it needs to improve, people can focus their time on correcting the problem areas.

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Content is data: Grounding AI models on quality content

 In the world of generative AI, content is data. All generative AI models are trained on large volumes of data. The quality of the output produced by the model will ultimately depend on the quality of the input used to ground it (grounding is the ability to connect and train AI model output to verifiable sources of information). But when an AI model is trained on low-quality content, whether that’s factually incorrect information, biased content, or just poorly written content, it will produce outputs that expose an enterprise to a slew of legal, financial, and reputational issues.

The capability to automatically score large volumes of content makes it possible to fine-tune LLMs only on high-quality content.

Organizations need to have policies, processes, and technology in place to confirm that content used to fine-tune AI models is high-quality. It needs to be accurate, up-to-date, inclusive, and easy to understand — and this should be reviewed on a regular cadence.

The future: Automatically aligning AI-generated content to your writing standards

 With so much potential risk associated with AI-generated content, it’s helpful to know there are tools available to automatically align your AI-generated content with your enterprise style guide.

Enterprise style guides include guidance for terminology, clarity, and compliance. By automatically aligning AI-generated content with your guidelines, you alleviate the risk of poorly crafted, non-compliant content reaching publication. What’s more, automating the alignment process reduces the risk of human errors and inconsistency, while simultaneously streamlining the content review process. All of which means that you’ll create and maintain consistency across your written content — a vital component of a strong brand identity.

Although technology exists today that takes the first steps in that direction, we can expect more robust capabilities in the future. When the technology arrives, safe and aligned content creation will be all but guaranteed.

Generative AI is here to stay. Now’s the time to harness (and guide) its potential.

In today’s digital age, content is the primary channel through which enterprises communicate with their customers. When a technology as transformational as generative AI comes into the world, despite all its current imperfections, executives are going to drive its adoption because it will impact their bottom lines.

Leading enterprises recognize that just increasing the ability to produce content isn’t enough. They need to do it safely. Doing it safely includes making sure that the content they produce adheres to their own standards — because they’re responsible for how they communicate with their customers.

Increasing content production while also maintaining standards requires new thinking. Henry Ford and Jeff Bezos style supply chain thinking. The implementation of a Content Supply Chain with quality assurance for standards is the only way to achieve associated enterprise scale.

Also Read: How the Art and Science of Data Resiliency Protects Businesses Against AI Threats

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