An enterprise can operate without enterprise resource planning (ERP), customer relationship management (CRM), or even a dedicated project management system, but it is hard to imagine one that never uses Word, Excel, or PowerPoint. Even in the AI era, generating documents, spreadsheets, and slides remains a core capability repeatedly demonstrated by AI productivity tools.
It is time to reconsider: What problems do these three office tools solve that give them such widespread and lasting relevance?
Three basic forms of representation infrastructure
Word, Excel, and PowerPoint appear to be three different office tools. From a cognitive perspective, however, each excels at supporting a different way of organizing information.
Word primarily uses linear natural-language text, organizing information through paragraphs, chapters, and narrative sequence. It suits explanatory content and institutional policies.
Excel is built around two-dimensional tables, organizing information through rows, columns, fields, formulas, and references. It suits recording objects, comparing attributes, maintaining states, and performing calculations. It also lets people add explanations, comments, and exceptions at any time, retaining considerable flexibility between structured information and natural language.
PowerPoint offers a free spatial canvas. Text, shapes, tables, arrows, and spatial relationships can be combined freely to express processes, hierarchies, dependencies, architectures, and structures that have not yet been fully formalized.
These forms are not mutually exclusive, nor does all information have to fit into one of them. Their real value is that people can choose an appropriate way to express information and structures previously held in their minds, according to the nature of the problem.
Human thinking and collaboration have never relied on natural language alone.
What makes them infrastructure?
As soon as a problem at work becomes even slightly complex, people naturally open a document, make a table, or draw boxes and arrows. Information and structures previously held in an individual’s mind become external objects that others can view, modify, and discuss.
Shared representation is itself infrastructure for knowledge work.
From human understanding to machine operation
Yet much of the structure in traditional office tools still depends primarily on human interpretation.
When an arrow connects two boxes in PowerPoint, a person may understand it as a dependency, while a machine may be unable to determine its meaning. A row in Excel may represent a purchase order, with the same order appearing in another spreadsheet, yet neither stable object identity nor an explicit relationship necessarily connects them. A business rule written in Word may not allow a system to determine automatically whether an action is permitted.
This information is not unstructured. Rather, it lacks explicit semantics and constraints, making it difficult to use directly in business computation.
Traditional software development has always required substantial manual interpretation and conversion between requirements documents, system design, and implementation. As written, the objects, relationships, and rules in these documents cannot be used directly as executable system definitions. AI can participate in this process, but it still faces the same ambiguities and missing structures.
This is the problem Cognitive IR (cognitive intermediate representation) addresses: enabling different forms of representation—text, tables, graphs, trees, state machines, rules, and programs—to retain their expressive strengths while carrying more explicit structures that subsequent actors can continue to use.
When these structures are connected to real objects, instances, current states, and ongoing feedback, they may develop from static information carriers into digital environments that support computation and action.
The next generation of representation infrastructure
The widespread use of Word, Excel, and PowerPoint already demonstrates a fact: human work requires externalizing reality, knowledge, and thought into different forms of representation.
AI has not changed this need. It gives us an opportunity to address structure recognition, semantic correspondence, state maintenance, and collaboration across systems—tasks that have long depended on people.
The goal of the next generation of representation infrastructure is not to replace documents, spreadsheets, and slides with a single new format. It is to enable people and intelligent systems to use these different forms to jointly understand, maintain, and operate a continuously changing world.