In The Death of the Document?, I explored a simple possibility: perhaps documents are no longer always necessary as the primary way in which knowledge is transferred.
Traditionally, a document has to be created before somebody knows exactly what they will want from it. An author decides what matters, how the material should be structured, how much detail should be included and in which order it should be presented. The reader then works through that predefined representation in order to extract whatever understanding is relevant to the question they actually have.
The sequence is familiar:
Knowledge → Publication → Reading → Understanding
AI makes another sequence possible:
Knowledge → Question → Synthesis
The answer no longer necessarily has to be published before the question exists. A representation can be created because somebody asks for it.
At first, this appears to be a question about documents. But once the idea is taken seriously, the same pattern starts appearing elsewhere.
A Dashboard Is Also a Predefined Answer
Consider a dashboard. A dashboard is an attempt to anticipate future questions. Someone decides that users will probably want to know how much was sold today, whether sales are rising or falling, which regions are underperforming, how margins are developing, and perhaps a dozen other things. Measures are selected, charts are designed, filters are added and drill-down paths are created. If the resulting dashboard cannot answer a particular question, somebody may eventually build a custom report. All of this happens before the user actually asks anything.
Now imagine that the user can simply say:
How much did we sell today?
The system answers.
Why is that lower than yesterday?
It investigates.
Is Sweden causing it?
It investigates again.
Then what is causing it?
The next answer is synthesized from whatever data is relevant.
The important change is not merely that the dashboard has become easier to use. The sequence of questions no longer has to be designed in advance. Each new question can emerge naturally from the answer to the previous one.
In some situations, that makes the predefined dashboard unnecessary.
Sometimes the Right Answer Is “Show Me”
Of course, not every answer is best expressed in words.
Sometimes an explanation immediately creates a different need:
Show me.
Show me the sales trend.
Show me where the customers are.
Show me what changed.
Show me how these components interact.
Show me the evidence behind that conclusion.
At that point, the system can create or retrieve a visualization appropriate to that particular question.
The visualization still matters. What changes is that it no longer has to exist permanently because somebody once imagined it might be useful. It can be created at the moment it becomes useful.
The same underlying information can also be consumed differently depending on the situation. Someone sitting at a desk may want a detailed written explanation. Someone preparing for a management meeting may want a few diagrams. Someone driving may simply want to talk. Someone investigating a difficult problem may move continuously between forms:
Tell me what is happening.
Why?
Show me.
Show me the evidence.
Explain that part.
The knowledge remains the same. The representation changes with the need.
This suggests that documents, dashboards, reports, presentations and visualizations may increasingly become temporary views over more durable knowledge and data.
The Command Prompt Returns
There is an interesting historical irony here.
The command line allowed users to tell computers what they wanted, but only if they knew how to speak the computer’s language. Commands, parameters, paths and syntax had to be learned.
Graphical interfaces made computers easier to use by reversing the relationship. Instead of requiring users to know the command, the computer exposed possible actions through menus, buttons, forms, folders, dashboards and navigation structures.
AI seems to bring us strangely close to the command prompt again. The user can once more simply express intent:
How much did we sell today?
What do we know about this customer?
Why was this architecture chosen?
What changed overnight?
Show me the trend.
The difference is that the command language is now ordinary human language.
The user does not necessarily have to know which application contains the answer, which report to open, which dashboard to inspect or which sequence of operations will produce the result. The system can determine which sources, calculations and representations are required.
The command prompt has returned. It just learned to speak human.
When the System Asks for Our Attention
There is another step beyond this. Sometimes the user should not have to ask at all. A dashboard assumes that somebody will look at it frequently enough to notice when something important changes. An intelligent agent can continuously interpret the underlying information instead.
Rather than displaying a graph and waiting for a human to notice an undesirable trend, it can say:
You may want to look at this.
Or, less formally:
Psst. You have a problem.
The human can then ask why.
The system explains.
Show me.
A visualization appears.
What changed?
The relevant history is retrieved and synthesized. The dashboard has not disappeared completely. It has become an on-demand representation inside a conversation. What used to be a permanent interface becomes something that materializes only when it helps us understand the situation.
What Should Actually Be Permanent?
This leads to a more fundamental question.
What actually needs to be durable?
We currently spend considerable effort maintaining representations. Documents have to be updated. Dashboards have to be redesigned. Reports have to remain current. Presentations are recreated. Diagrams gradually become obsolete. Interfaces are maintained because somebody may need to navigate through them in the future. Perhaps these are increasingly the wrong things to make permanent.
What needs to remain durable is the material from which trustworthy representations can be produced: knowledge, data, evidence, sources, provenance, decisions, assumptions, relationships and expectations.
Above that durable layer, representations can increasingly be generated according to the need of the moment. A document when a document is useful. A chart when a chart is useful. A spoken explanation when somebody wants to listen. A dashboard when somebody wants an overview. A detailed evidence trail when somebody wants to challenge a conclusion.
The representation can be generated, consumed and discarded. The knowledge remains.
The Death of the Predefined Representation?
This does not mean that documents will disappear. Nor will dashboards, reports, presentations, diagrams or graphical interfaces. We may actually create more of them than ever.
What may disappear is the assumption that these representations have to be designed, published and maintained before we know what somebody actually wants to know.
Instead, the sequence becomes:
Knowledge and data → Intent → Representation
The representation is created at the moment of need, for the person who needs it, for the question they are actually asking and in the form most appropriate to the situation. Read it. Listen to it. Look at it. Interact with it. Then ask the next question.
Perhaps the document was only the first thing we noticed becoming ephemeral. The more fundamental change may be the death of the predefined representation.
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