A man wearing a robot mask waving to the camera.
Is it time to bring some robots into your content production processes? PHOTO: Guilhem Vellut

When Chris Willis, chief marketing officer at Acrolinx, asked his Boston-area CMO peers about their content review and approval process,, neverending or long were the typical responses. According to him, driving home a strong, active content governance program powered by artificial intelligence (AI) can cut down tiring, aging content processes in organizations and help deliver targeted content that resonates with intended audiences.

“Drive Content Governance with Artificial Intelligence... Like many of my fellow CMOs, I face challenges with clarity, consistency brand alignment and costs," says Willis in the recent webinar. Here are some takeaways from what an active content governance program driven by AI may bring your organization.

You’ll Have Support

Maintaining a strong content governance framework will bring all your content stakeholders support, guidance and efficiency. A good content governance framework can take the content creation from good to great. According to Willis, it can reduce the costs associated with content creation by as much as 50 percent.

You’ll Offer Guidance 

Much of the content your marketing team produces can fall on the shoulders of contractors. No matter the talent of those contracting writers and editors, they are probably unfamiliar with your brand voice and messaging; not to mention, your target audiences. 

In an active content governance model, organizations can offer guidance to writers who can then better keep the brand’s voice in mind when producing content. People with different points of view, style and even language can collaborate over content more effectively, resulting in better content. “It's adding structural components to the process and allowing for automation,” says Willis.

AI Helps Eliminate Problems Earlier

We all know how manual content approvals go. Someone hands in rough copy. It gets edited for tone, brand voice and clarity. That gets passed on to another editor, who makes more changes. AI engines are designed to eliminate quality issues early in the process, allowing writers to deliver content that can be considered ready for review. And that review, Willis says, is going to focus on what the words say and mean rather than what errors the content contains. “What if you could go beyond spelling and simple grammar to provide guidance and clarity on consistency, style, tone of voice and brand compliance?” Willis asks. It would result in a process where every one of your writers can better deliver content that’s aligned with your company voice without the need to go through countless review cycles, he says.

Leveraging Infinite Virtual Editors

Powering AI through content governance models means organizations can build a multi-step automated process into their content machine. The AI filters can confirm or correct alignment with your organization’s guidelines and therefore eliminate human intervention, “leaving our valuable editors to actually create better content automating the process... Moving from a highly manual editing process to a model it's powered by AI automation means that you're leveraging infinite virtual editors that all know your business inside and out, your language and your rules. They can scale infinitely now. I think you see where this is the big step from passive to active, the ability to grow it and scale,” he says

Predict Content Success

Screenshot from the CMSWire-Acrolinx webinar, “Drive Content Governance with Artificial Intelligence.”
Click on this image to access the on-demand version of the CMSWire-Acrolinx webinar, “Drive Content Governance with Artificial Intelligence.”

Being able to leverage a KPI model that helps you predict the value of content is critical to the success of the content program. And AI engines help greatly here, according to Willis. Rather than analyze content performance and conversion metrics after the fact, an active content governance program powered by AI can predict the performance of future content. Willis said it takes measurement of quality, style, consistency, tone and terminology to create a model of predictive analytics. Constant measurement by AI engines will build models that show future success with content and what will provide value to your readers.