Rather than just text and images, modern content management encompasses different types of information – such as photos, interactive graphics, videos, audio, and other digitized assets – that systems can dynamically assemble.
Modern content management serves a business purpose and streamlines the way organizations create and distribute digital experiences. And, in the future of content management, AI plays a key role. Organizations need to control the content that flows through their networks to improve digital work and create business value, which may mean adopting more advanced technologies.
The state of content management
Initially, content management sought to separate technical tasks – like website launches – from editorial tasks, like updating information. Web administrators and IT support staff also needed simple ways to hand out routine tasks, such as maintaining web pages, to non-technical users that content management made it easier to do.
Over the years, aspects of content management have evolved, including the types of content and skills required, the relevant tools and storage.
Types of content and skills required
Content management continues to support two audiences: technical professionals and business users. Yet content has evolved from information on static web pages to dynamic experiences across multiple devices, business channels, and customer touchpoints.
The evolution of content has developed new tasks, which require new business roles. And, within IT and business groups, more professionals with different technical and soft skills are performing computational tasks, producing content, and adopting innovative business applications.
With these innovative business applications, digital marketers, sales managers and other business line managers expect to combine content from disparate sources for purpose-built applications. For example, marketers can launch digital campaigns with interactive web pages, targeted emails, and personalized offers based on buyers’ intent.
Tools and storage
Content management no longer limits file storage to stand-alone repositories and content to predefined web pages, but encompasses multiple cloud-based repositories.
A modern approach to content management supports the following four key computational capabilities, known as MACHs:
- Microservices. Microservices can locate content stored in repositories and perform other discrete functions.
- Apis. Content management uses open web standards to support APIs, which can interconnect disparate repositories to develop new applications.
- Cloud environments. Organizations can store content in cloud environments.
- Headless services. Content management supports headless back-end services, so technology doesn’t make assumptions about how web pages present or display information.
The roles of artificial intelligence and machine learning
AI encompasses many computational capacities 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, compares patterns to available information, and analyzes metadata to identify what is contained in the images or videos.
- 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 does not exist in a vacuum. Instead, organizations are pairing AI with predefined tasks and activities to save workers time and effort.
Independent software vendors, ranging from industry stalwarts to startups, are integrating AI capabilities into their tools. They condense specific algorithms into microservices, making those services accessible through APIs, and rely on cloud connections to control the flow of content.
To develop content-based applications, organizations should focus on application integration. AI aims to build next-generation business applications with microservices and APIs connected through cloud environments to back-end content repositories – a MACH-based system architecture. This architecture could simplify the way AI fits into the flow of content from disparate sources, like applications, and weaves metadata.
Predictions for AI and the future of content
AI can make apps more useful, but organizations won’t produce content-driven apps overnight. Organizations should consider four trends to assess how AI will affect operations in order to prepare for the future.
AI algorithms can automate metadata management to read documents, scan images, extract meaning from text, recognize objects in digital assets, and assign relevant categories to content. AI can empower app developers to access more relevant content to build smarter apps.
AI can create micro-experiences to automate tasks, actions, and activities.
With a micro-experience of purchasable content, customers can purchase items directly in an app without a storefront or website. For example, a person can see a sweater in a photo, tap on it, and then buy it, all without visiting the brand’s website.
Likewise, copywriters can rely on writing support tools to check spelling and grammar, get tips on tone of voice, check brand terminology, verify style guidelines, and recommend revisions. AI could speed up the editorial tasks that proofreaders and editors typically do and lower production costs.
With MACH, content management could combine content from more disparate sources, and AI could make that content actionable.
Thus, compliance teams can rely on AI to monitor and decrypt large collections of documents stored in content repositories. Marketing teams can automatically verify the rights of digital images before approving them for distribution, add them to websites, and include them in advertising campaigns.
However, organizations may find it difficult to design intelligent processes that separate business problems into tasks, combine content from disparate sources, and determine how AI algorithms can improve work. Additionally, organizations need to focus on people and how they manage processes.
Even with innovative AI-based tools, human insight is important. Organizations will need new roles for employees, contractors and business partners beyond technical and business silos.
Instead, these roles should add specialist skills that combine IT and business expertise to work with innovative content management technologies. Improved work could lead to next-generation content-based applications staffed and managed with human intelligence.
Key points to remember
As content has evolved, so have the skills and tools to manage it. In the future, AI and ML will help enrich content, develop micro-experiences, activate smart processes, and create new roles for employees. But this is not a change that will happen overnight. Instead, organizations need to prepare for constant and inevitable progression.