Kinetic Information
The key property of content that you didn’t know you needed
Content professionals involved in pushing the boundaries of content capabilities have spent years talking about intelligent content. We’ve structured it, tagged it, modelled it, and developed taxonomies for it. And yet, for many organisations, content still get locked into PDFs and stored in Digital Asset Management systems as BLObs (Binary Large Objects) or pushed out, with little or no semantic markup, to content management systems with little more than heading and body text fields.
This is exacerbated by the problem of practitioners collapsing content and data into a single entity, trying to manage content the same way as data, ignoring the complexities and nuance that separates the two concepts. That’s an argument for another time, but pertinent to this topic, I will discuss content as contextualised, human-usable data, and information as contextualised content plus data. No matter how you define and categorise the data–content–information continuum, we have to admit that information needs to perform way in more complicated ways than it used to.
Let’s face it, since the popularisation of generative AI (GenAI), the entire paradigm for content findability, retrieval, and delivery has changed.
The average user never looked for information by poring over a multi-page PDF downloaded to their phone. Now, they can get a small snippet of information served to them from whichever site has the most AI-friendly content.
The traditional skip-skim-scan method of looking at web pages has gone into high gear. As more people are relying on the AI summaries generated by search engines, they aren’t reading through long pages of text to find whatever nugget of information they need.
There is a sharp rise of organisations using AI-enabled chatbots to retrieve an accurate, concise answer from a knowledge base, whether for use by customer support agents or by end users. This means that the information needs to be written, structured, and marked up in ways that makes it more retrievable.
In other words, information needs to be kinetic: responding when called by other systems.
Kinesis: a quick physics lesson
The definition of kinesis is movement in response to a stimulus. Potential energy is stored energy; kinetic energy is energy in motion. Applied to information, kinesis is content and data that is retrievable in response to a call by users via some sort of system. The system could be anything from a web page to a chatbot to a voice assistant. You ask your voice assistant what the temperature is in whatever city you’re travelling to; the system goes out to a weather service’s data repository and retrieves the relevant data; the voice assistant injects the temperature data point into a piece of content (in this case, a sentence), turns the text into voice, and reads out the information. That’s kinesis in action.
The history behind kinetic information
Industry 4.0 laid the groundwork for the shift from static to kinetic information, with the idea of automation and data exchange in manufacturing technologies. There are four underlying principles of Industry 4.0 which allow factories to run themselves: smart manufacturing, cyber-physical systems, and the Internet of Things (IoT). Those principles are:
Interoperability — machines, devices, sensors and people that connect and communicate with one another
Information transparency — the systems create a virtual copy of the physical world through sensor data in order to contextualise information
Technical assistance — both the ability of the systems to support humans in making decisions and solving problems and the ability to assist humans with tasks that are too difficult or unsafe for humans
Decentralized decision-making — the ability of cyber-physical systems to make simple decisions on their own and become as autonomous as possible.
What is not addressed in this explanation is the role played by information. Seen through an information lens, we can bee the enabling function of what became known as Information 4.0. I’ve added, in bold, the phrases that are implicit to the principles.
Interoperability — machines, devices, sensors and people, and information that connect and communicate with one another
Information transparency — the systems create a virtual copy of the physical world through sensor data in order to contextualise information
Technical assistance — both the ability of the systems to support humans with targeted information in making decisions and solving problems and the ability to assist humans, through the use of content, with tasks that are too difficult or unsafe for humans
Decentralized decision-making — the ability of cyber-physical systems to make simple decisions, guided by information provided, on their own and become as autonomous as possible
A similar situation exists with the Internet of Services, or Service 4.0, where disruptive technology concepts are applied to value chains. A similar exercise in reading between the lines of their principles, we can see the role played by information.
Big Data and Analytics. Deeper insight using information into customer behaviour, preferences, and pathways
Bionic Computing. Natural interaction, by proving information, with virtual agents, digital devices, and services
Ubiquitous Connectivity and IoT. Ongoing connections (on-the-spot service provision, remote monitoring)
Cloud Computing. Manage large data and content volumes in open systems and provide services on demand
Cognitive Computing. Simulate human thought processes, provide intelligent virtual help
Smart Devices. Ecosystem of apps and cloud services serving up content using high-performance devices
Robotic Process Automation. Replace humans for rule-based work processes, comprehensively documented
Virtualisation. Remove reliance on specific hardware/software for operational flexibility
Augmented Reality. Provide needed information on demand (manuals, pricing, alerts)
The increasing importance of kinetic information
This is where kinetic information comes into play. Kinetic information challenges the primacy of the document as the unit of knowledge transfer. Kinetic information picks up where Information 4.0 left off. It’s no longer only about smart factories and IoT. Kinetic information is needed across the enterprise where the contexts vary but the underlying principles are very constant:
Marketing departments want to personalise their content for multiple audiences in multiple markets.
Technical writers need to deliver product knowledge in multiple deliverables for multiple products in multiple product lines for multiple verticals in multiple markets.
Customer support agents, under time pressure, need to retrieve answers to a wide range of questions from a range of customers in a range of locations while assured it’s reliable information.
Employees need to retrieve information from their intranets, from Standard Operations Procedures (SOP) to backgrounders for business analysis to how to file an expense report, and be confident that they have the right information for their particular circumstances.
The move to ubiquitous computing, where content is made to appear any time and anywhere, using any device, in any location, and in any format, absolutely needs kinetic information to succeed.
Microcontent content as the new unit of information
One of the more consequential shifts in Information 4.0 thinking is the move away from the document and toward microcontent as the smallest unit of content that can still retain its validity in context. Microcontent carries one fact or idea. It has a unique purpose. Microcontent is, by design, ready to be assembled, delivered, and consumed independently of the document that once contained it.
Microcontent goes by several names. (Yes, our industry has a naming convention problem.) Data, content components, microfacts, bite-sized content, snackable content, atomic content, snippets, nuggets. If I were to define microcontent, it would be small, modular units of information that have self-contained meaning and can be used independently. Microcontent is also volatile and state-dependent, which means that it contains enough semantics to be context-ready for real-time, continuous delivery.
This matters enormously for the delivery channels that have come to define how users now seek information. AI-enabled chatbots and query interfaces have changed user expectations around information retrieval. Users increasingly expect real-time, precise answers to specific questions rather than a link to a page where the answer might be. A well-formed piece of microcontent functions as a direct response to a user intent in a way that an editorially structured document doesn’t.
The traditional method of creating new content specifically for each new channel: chatbot responses, dedicated help widget copy, channel-specific FAQs, UI strings locked into software interfaces is not sustainable. A more durable strategy is to restructure a single source of content as microcontent, marked up in machine-readable formats for query-response delivery channels and capable of being called by downstream systems as needed.
What makes information kinetic
To be called kinetic, information objects or units need to have the following properties, arranged into a convenient-to-remember acronym, PODIUMS:
Profiled: personalised and automatically directed to the right audience according to context.
Offered: rather than simply stored, content objects are assembled and delivered when called by downstream systems, not just when a user happens to search.
Dynamic: updated automatically in response to new data, usage metrics, and content needs.
Independent: usable across contexts without editorial rework or reformatting.
Ubiquitous: always ready for use, online, searchable, and findable wherever a user might look.
Molecular: stored as independently-usable modular units of microcontent ready to be delivered as Content as a Service (CaaS).
Spontaneous: information assembly triggered by context in real time, delivered without human intervention.
The layering of AI across every part of the content ecosystem, from authoring to delivery to conversational interfaces, has changed what systems can do and what users expect. AI doesn’t retrieve documents, though it may cite sources. AI generates responses, and those responses are only as good as the source content. It’s become increasingly clear that unstructured, document-bound content is poorly suited to AI-mediated delivery. Content that is modular, metadata-rich, context-aware, and independently valid is.
Organisations can no longer avoid investing in structured content, information architecture, and semantic metadata. The need to have a robust infrastructure that determines how information performs in an increasingly AI-mediated world is here, now. For organisations navigating digital transformation, content operations maturity, or AI deployment, kinetic information is a useful lens for evaluating readiness. The narrative goes from which complement of content is available to whether content has the attributes required to behave intelligently at the point of use.
In other words, the work of making content kinetic is the work required to make it AI-ready. This ties back to the Content Integrity Model, that balances the strategic, editorial, operational, and infrastructure aspects of content production in order to produce AI-ready, kinetic information.
As an aside, if you recognise your own need to understand more about how kinetic information works, how to formulate a strategy for it, or how to implement it, a relatively new professional association has been formed for exactly that purpose. An umbrella home for the various professions that come together to solve real-world business problems related to content. the Kinetic Council serves to fill that gap for professionals looking for unbiased answers from industry experts.