Generative AI can help businesses quickly produce content that differentiates the brand—but it can’t do it alone.

The emergence of ChatGPT last November and Google Bard early this year created massive disruptions in the digital experience and marketing worlds. LinkedIn was suddenly flooded with speculation around whether content writers and other creators would still be needed now that large language models (LLMs) could create content that sounded credibly human.

In the ensuing months, technology companies have applied generative AI to content creation in various ways. Adobe and Sitecore both introduced gen AI content writing tools directly in their content management system authoring interfaces. The AI-powered content marketing and writing platform Writer just announced $100 million in Series B funding.

Some executives have encouraged their teams to generate as much content as possible with these tools—and replace as many of the humans involved as possible, too. Others, especially in more regulated industries, have taken a more cautious approach, sometimes outright banning any generative AI usage in their content operations.

We recommend that brands carefully consider where, when and how to integrate generative AI into their content strategies, creation processes and management. The optimal approach will differ depending on brand, industry and organizational culture and priorities.

The risks of both extremes

There are two sides of the coin when it comes to either going all-in or completely avoiding the use of generative AI for content creation and operations. The risks of going all-in include:

• Content produced with generative AI can’t be copyrighted, and it technically violates the terms of usage of ChatGPT to publish content generated by the platform as-is.
  
  
• LLMs need human-generated content to maintain their functionality; in fact, studies show LLMs degrade significantly when trained on AI-generated content. So, as brands begin flooding the internet with content that’s been partially or completely created with gen AI tools, and the off-the-shelf LLMs use this data for their outputs, it could theoretically create a vicious cycle in the erosion of content quality and usability.
  
  
• LLMs are trained on huge volumes of content, so their output is essentially the most generic version of that content. But brands need content with a unique voice and point of view. Carefully crafting the prompts entered into the LLM (sometimes known as prompt crafting or prompt hacking) can help, but only so much.

Meanwhile, there are also risks to continuing with business as usual:

• An Adobe survey of marketing leaders predicted that demands for content will increase between five and 20 times by 2025. Meeting these demands with little to no automation will prove extremely costly.
  
  
• Gartner reports that 75% of CMOs are [already] facing pressure to ‘do more with less’ to deliver profitable growth in 2023.
  
  
• With many brands already embracing generative AI, competitor brands are likely to significantly outpace non-adopters in content publication velocity.

The path forward

Clearly, neither optional is tenable. Instead, brands need to determine their best course of action based on their own characteristics and the generative AI adoption modes available to them. As we recently described, businesses have two options for creating content with generative AI:

1. Off-the-shelf tools such as ChatGPT and Google Bard, or SaaS tools, such as Writer. These tools have lower adoption and implementation costs but will require continuous and careful attention to crafting exactly the right prompts. And, while Writer will learn your brand and voice over time, ChatGPT and Bard, in their off-the-shelf formats, will always be focused on generic responses. For this reason, off-the-shelf solutions can be considered good enough for smaller companies with limited content needs.
   
   
2. Custom-trained AI content generation tools. Essentially, this means building (or working with an implementation partner to build) your own generative AI platform on the foundation of an existing LLM. While this platform would require more upfront investment and development, it would be better engineered to draft content with your unique POV, and brand woven into every output.
   
   Custom-trained tools are likely the better option for larger companies with vast content needs across business units.

Ensuring the right foundation

For companies that choose to build a custom solution, it’s essential to have a strong foundation of existing content for the model to train on. This content should be unique to the brand and offer a unique POV.

The content should also, ideally, be built in structured formats with robust metadata tagged to each atomic slice of content (essentially snippets of content that can be deployed independently into various content assets, formats and channels). The structure and metadata help the AI model better understand the content and its context.

Businesses already have this level of structure and metadata if they use a “headless,” CMS (one that acts as a content repository usable by a variety of front-end tools) or takes a modularized content approach to “create once, publish everywhere” with dynamic, defined business logic.

Content governance standards will need to be updated or created. These standards should clarify the following:

• How you’ll use generative AI to ideate, plan, create, assemble, author, publish, update and manage your content
  
  
• How much human intervention/oversight is required with gen-AI-enhanced aspects of your content workflows
  
  
• How frequently to execute processes mapped to generative AI, with humans to keep content fresh and ensure quality

How to get started

Whether you leverage generative AI or not, meeting the high demands for content across the organization requires the right content, strategies, operations, tools, technologies and processes. We recommend focusing on the following pillars when evaluating your ability to succeed with content now and in the future:

• Strategy. Do your content teams and stakeholders know who they’re targeting with content, the journeys the audiences are on and the purpose behind the content? Clear strategy results in good decisions around content and drives alignment across teams, business units and stakeholders.
  
  
• Accuracy. Is your content up-to-date and accurate? This is especially important now that content may be used to train tools like AI chatbots to answer questions about products, services and policies.
  
  
• Substance. Is your content differentiated? As more and more content proliferates across channels, the best way to stand out will be to say something different from what competitors are saying.
  
  
• Structure and metadata. Content built in a structured content model with detailed metadata is easier for machines (and humans) to understand. If you’re using generative AI or business-logic-driven automation (or, ideally, some combination of both), it’s important to help the robots understand what they’re crawling.
  
  
• Technology and tools. Headless CMS platforms (or hybrid solutions that manage the display layer and content layer simultaneously while also exporting the content layer to a structured data repository) enable your content to be stored in the consistent structures required for automation and enable a publish-everywhere approach.
  
  
• Operations, governance and process. Do you have clear roles, responsibility and ownership for your content, as well as up-to-date policies and processes? Do you have the right talent mix, distributed in the right places to remain cost-effective? Operational clarity and agility will become more and more critical the more content you publish and maintain.

To learn more, visit the Digital Experience section of our website or contact us.