As we’re just scratching the surface, there is no doubt that artificial intelligence will significantly impact the Content Management System (CMS) industry. The clear use cases around content production and driving operational efficiencies are there, and most CMS vendors have integrated with ChatGPT and AWS Rekognition. There is more to explore, and the critical areas in a CMS context are re-platforming and content production. AI will significantly impact business outcomes, such as Total Cost of Ownership (TCO), Return on Investment (ROI), and revenue growth.
In the enterprise segment, re-platforming your CMS to a new vendor is time-consuming and labor-intensive. It’s not uncommon for a CMS re-platforming project to take 6-18 months to complete, from initial requirements gathering to the initial go-live.
Requirements collection & validation
CMS vendors create tons of content on their products and the solutions & use cases they support, and it’s publicly available. That makes a large number of input feeds for AI algorithms to digest, and combining that with crucial pain points to resolve, AI will collect a unique set of requirements that will fulfill your business needs and resolve your current pain points. The requirements and pain points are finite, meaning it should be a walk-in-the-digital park for an AI algorithm. Also, the AI algorithm completes your 1500-question security questionnaire in a heartbeat. Then, a short list of CMS vendors matching these requirements is automatically produced. This could shorten the buyer journey easily by a whopping ~90%.
Custom demo / Proof-of-Concept
A CMS is a powerful tool that supports many solutions and use cases, and it’s not uncommon to stick to a CMS for 5-10 years. That means you want to make a well-informed decision before you commit to a vendor. Having product demos is a quintessential part of any CMS selection project. Of course, a standard demo will provide the team with insights, but it’s better to have a custom demo / Proof-of-Concept with your content and business-critical use cases.
With the help of AI, you should be able to go to a vendor’s website, provide relevant input (existing website/application, your critical use cases, and pain points), and receive an email with URls and credentials to a dedicated sandbox of the vendor’s CMS. An AI-generated walk-through session will take you and your team through the product and demonstrate how your critical use cases are supported and pain points resolved. Gone are the days for vendors to build and maintain Ideal Customer Profile-specific standard demos and/or spend time on building custom demos. Everybody wins.
By far, the largest cost component of any CMS re-platforming project is the migration/implementation of the new CMS. It is often combined with a complete re-design and fresh styling of the website(s) and application, and it presents the end user with a large ticket that forces them to stay on a solution for 5-10 years; otherwise, the TCO and ROI would not justify the expense.
Then enters the most significant AI opportunity in the CMS industry. It should be possible to feed a set of AI algorithms with just website URLs / apps and potentially some contextual data points. In just a just minutes, the output will be your website(s) set up in a specific CMS or multiple CMSs on your shortlist. How cool is that?
This would be digital transformation 101. It’s transforming a finite set of digital objects into a different set of finite digital objects. These objects fall into two categories: Content Management Objects and Content Delivery Objects.
Content types and Widgets. Based on the content exposed on a website, an AI algorithm should be able to determine the relevant content types and their attributes. For example, suppose there is “news” on the website. In that case, it’s not that hard to train a Large Language Model to create a news content type with the appropriate attributes for the title, summary, body, and metadata (tags and categories). Widgets that pull content from external sources might be a tad harder, but providing additional contextual information to the algorithm should help the AI-driven transformation.
Themes. Styling in a Content Management System context is typically determined by a finite number of assets in the CMS and is vendor-specific. However, an algorithm can be easily trained to establish the required output based on the input (website to be transformed) and reference data (reference websites, contextual data of the styling assets, etc.).
Custom Plugins. This is where it gets a bit more challenging, but there is still an opportunity to extend the core CMS platform by creating custom plugins where AI generates the code. There are already a plethora of examples out there of AI-written code. Custom Plugins for any CMS vendor are no different. Custom Plugins can disrupt seamless upgrade paths of the core CMS. An AI-based algorithm can support rewriting the custom plugin for the new(er) version of the CMS and avoid upgrade drama.
Workflows. More sophisticated CMS vendors offer a flexible workflow management module for content approvals. In controlled industries (like financial services, pharma, medical devices, etc.). It makes sense that AI creates the required workflows and differentiates for industry-specific and sensitive content. For example, content with potential liability will have an additional approval step for legal review, whereas marketing / promotional content most likely doesn’t need legal approval.
Roles & Permissions. Applying a default content governance model or based on the contextual information (industry, company-specific governance context), the appropriate roles & permissions can be set by AI.
In the case of traditional or complete CMS, the appropriate delivery objects are needed:
Page templates. Like content types, every CMS vendor has some specific features regarding page templates, like placeholders for content, layout (rows & columns) based on a grid, and a templating language like Velocity, Thymeleaf, and Freemarker. This should be a walk in the park for an AI algorithm to transform a page into a page template for a specific language and CMS.
Navigation. It varies per CMS how navigation is supported in the product and should be easy to generate through AI in the CMS-specific logic.
Personas & Rule. If personalization and content targeting is leveraged in the legacy CMS product, an AI-based re-platforming will result in rules and persona definitions. A proper transformation will require additional context around the personas and the mapping onto specific content or their journey.
Site Search. Site search is one of the areas where purpose-built vendors are already dominating the market. It would take a lot of work for CMS vendors to catch up and compete. Integration or M&A would be the playbook.
Content migrations are infamous for any CMS re-platforming project. In an API-first world and AI-powered website scraping tools, the level of effort for this part of a CMS implementation should be minimal.
API-based integration with mainstream technologies (CRM, Marketing Automation, e-Commerce, Digital Asset Management, etc.). All modern technology vendors are API-first and have their APIs well documented, making the interoperability for AI seamless with tools like a Scripting API.
Artificial intelligence will have a prominent role in any Content Management System context, and seeing it in full fruition is probably closer than we think. For most of the use cases above, purpose-built AI tooling is already available, and for some, CMS vendors will likely create their IP. Speed of delivery and integration of relevant AI tools will determine the winner in this category.
Maintaining or achieving a global presence requires effective use of resources, time and money. Single-tenant CMS solutions were once the go-to choices for enterprises to reach out to different market...
What is cloud computing, and what benefits does the cloud bring to brands who are entering into the IoT era?
What’s the difference between a headless CMS and a hybrid CMS, and which one is best suited for an enterprise?