The state of content management

Initially, content management sought to separate technical tasks -- like website launches -- from editorial tasks, like information updates. Web administrators and IT support staff also needed easy ways to hand off routine chores, like webpage maintenance, to nontechnical users, which content management made easier.

Over the years, aspects of content management have evolved, including types of content and required skills, relevant tools and storage.

Types of content and required skills

Content management continues to support two audiences: technical professionals and line-of-business users. Yet, content has evolved from information on static webpages into dynamic experiences across multiple devices, business channels and customer touchpoints.

The content evolution developed new tasks, which require new business roles. And, within IT and line-of-business groups, more professionals with different technical and nontechnical skills perform computational tasks, produce content and embrace innovative business apps.

With those innovative business apps, digital marketers, sales executives and other line-of-business managers expect to combine content from disparate sources for purpose-built apps. For example, marketers can launch digital campaigns with interactive webpages, targeted emails and personalized offers based on buyers' intent.

Tools and storage

Content management no longer limits file storage to self-contained repositories and content to predefined webpages, but encompasses multiple cloud-based repositories.

A modern approach to content management supports the following four key computational capabilities known as MACH:

• Microservices. Microservices can locate content stored within repositories and perform other discrete functions.
• APIs. Content management uses open web standards to support APIs, which can interconnect disparate repositories to develop new apps.
• Cloud environments. Organizations can store content in cloud environments.
• Headless services. Content management supports headless back-end services, so the technology doesn't assume how webpages present or display information.

The roles of artificial intelligence and machine learning

AI encompasses many computational capabilities to create and analyze information. Organizations can use AI and machine learning (ML) to recognize patterns in data and metadata in the following ways:

• Natural language processing combines computational linguistics and other techniques to extract meaning from text.
• Predictive and prescriptive ML algorithms calculate the best actions to respond to queries, tasks or activities.
• Image recognition analyzes patterns in images or videos to distinguish objects, matches patterns against available information and analyzes metadata to identify what the images or videos contain.
• Audio and speech recognition algorithms analyze wave patterns in audio streams to determine words, tone-of-voice, music and other acoustic characteristics.

While AI algorithms require employees with the expertise to implement and maintain them, this technology doesn't exist in a vacuum. Instead, organizations tie AI to predefined tasks and activities to save workers time and effort.

Independent software vendors, ranging from industry stalwarts to startups, embed AI capabilities in their tools. They condense specific algorithms into microservices, making these services accessible through APIs, and rely on cloud connections to control content flows.

To develop content-powered apps, organizations must focus on app integration. AI aims to create next-generation business apps with microservices and APIs connected through cloud environments to back-end content repositories -- a MACH-based architecture. This architecture could simplify how AI integrates into content flows from disparate sources, like apps, and weaves together metadata.

Predictions for AI and the future of content

AI can make apps more useful, but organizations won't produce content-powered apps overnight. Organizations should consider four trends to assess how AI will affect operations to prepare for the future.

Content enrichment

Content management depends on explicit and implicit metadata. As organizations develop business apps, they define metadata categories through information architectures.

AI algorithms can automate metadata management to read through documents, scan images, extract meaning from text, recognize objects within digital assets and assign relevant categories to content. AI can enable app developers to access more relevant content to build smarter apps.

Micro-experiences

AI can create micro-experiences to automate tasks, actions and activities.

With a shoppable content micro-experience, customers could buy items directly in an app without a storefront or website. For example, a person might see a sweater in a photo, tap it and then buy it -- all without visiting the brand's website.

Similarly, writers may rely on writing support tools to check spelling and grammar, get advice on tone-of-voice, verify brand terminology, check style guidelines and recommend revisions. AI could expedite editorial tasks that proofreaders and editors typically perform and reduce production costs.

Smart processes

With MACH, content management could combine content from more disparate sources, and AI could make that content actionable.

Thus, compliance teams may rely on AI to monitor and decrypt large document collections stored within content repositories. Marketing teams can automatically verify digital image rights before they approve them for distribution, add them to websites and include them in advertising campaigns.

However, organizations may struggle to design smart processes that separate business problems into tasks, combine content from disparate sources and determine how AI algorithms can enhance work. Additionally, organizations must focus on people and how they handle processes.

New roles

Even with innovative AI-powered tools, human insight matters. Organizations will require new roles for employees, contractors and business partners beyond the technical and line-of-business silos.

Instead, these roles should add specialized skills that combine computational and business expertise to work with innovative content management technologies. Improving work could lead to next-generation content-powered apps, staffed and managed with human intelligence.

Key takeaways

As content has evolved, so have the skills and tools required to manage it. In the future, AI and ML will help enrich content, develop micro-experiences, enable smart processes and create new roles for employees. But this isn't a change that will happen overnight. Instead, organizations must prepare for a steady and inevitable progression.

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