The Paperless Office: Have We Lost Something Along the Way?

 

Finding the balance between digital efficiency and the way we actually think and work

For years, businesses have been moving toward the paperless office. Documents are created electronically, distributed through email, stored in shared repositories, and reviewed on computer screens. We have eliminated filing cabinets, reduced printing costs, saved physical space, and made information accessible almost anywhere.

These are significant improvements.

But I have discovered something interesting in my own work. Just because a document can be digital doesn’t necessarily mean that working with it digitally is always the most effective approach.

Sometimes I want that piece of paper sitting beside my keyboard.

The Hidden Cost of the Paperless Office

Consider a typical business activity. I am developing a report, analyzing requirements, reviewing a proposal, or comparing information from several documents.

The reference material is stored digitally, which is wonderful until I need to use it while simultaneously working on something else.

Now I am switching between windows, scrolling through pages, trying to remember where I saw a particular statement, and occasionally losing my train of thought.

I can highlight text electronically. I can insert comments. I can open multiple windows or use a second monitor.

But these techniques don’t always reproduce the experience of spreading several pages across a desk, circling an important statement, drawing an arrow between two ideas, or writing a question in the margin.

There is something valuable about seeing the entire working landscape at once.

And sometimes the technology intended to make us more efficient introduces additional steps into a process that used to be quite simple.

Two Different Activities: Storing Information and Thinking With Information

Perhaps we have been treating two different activities as though they were the same.

Information management involves storing, organizing, retrieving, protecting, and distributing documents. Digital technology excels at these activities.

Information analysis involves reading, interpreting, comparing, questioning, connecting ideas, and making decisions.

Digital technology can support analysis extremely well, but paper sometimes provides a more natural workspace.

A document can be perfectly stored in a digital repository and still be inconvenient to work with.

The distinction matters because the purpose of a business document isn’t simply to exist. It is to help someone accomplish something.

A Better Approach: The Hybrid Document Workflow

Rather than declaring that everything must be digital or returning to stacks of paper, I propose sorting documents according to how they will be used.

Category 1: Digital Only

These documents should remain electronic because there is little benefit in printing them.

Examples include routine correspondence, reference manuals that are rarely consulted, completed reports, meeting notices, archived records, and documents primarily used for searching or sharing.

Rule: If I am storing, retrieving, transmitting, or occasionally consulting the information, digital is probably the better choice.

Category 2: Digital Working Documents

These are documents I actively edit, collaborate on, or revise frequently.

Examples include project plans, spreadsheets, Power BI requirements, presentations, shared procedures, and reports undergoing multiple revisions.

Digital tools are particularly valuable because they support collaboration, change tracking, and version control.

Rule: If the document is changing frequently or several people are working on it, keep the authoritative working version digital.

Category 3: Temporary Printed Working Copies

This is the category that deserves more attention.

These documents are stored digitally but temporarily printed because I need to study, annotate, compare, or understand them.

Examples include complex business requirements, lengthy proposals, process diagrams, meeting preparation materials, and documents containing information I must repeatedly reference while developing another deliverable.

A printed copy becomes a temporary analytical tool rather than a permanent record.

Rule: If printing the document reduces mental effort, repetitive navigation, or the risk of overlooking important information, printing may be justified.

A Simple Decision Process

Before printing, I can ask five questions:

1. Am I simply reading this document, or actively analyzing it?
2. Will I need to reference it repeatedly while working in another application?
3. Will handwritten annotations, highlighting, or spatial arrangement help me understand it?
4. Is the document relatively stable, or will a printed copy become outdated quickly?
5. Can I print only the pages I actually need?

If the document is being actively analyzed, frequently referenced, and is reasonably stable, a temporary printed copy may be the most efficient choice.

If not, I should consider whether split-screen viewing, digital annotations, or a second monitor would accomplish the same purpose.

The Most Important Step: Closing the Loop

There is one risk in a hybrid approach.

What happens to all those handwritten notes?

If I mark up a printed document and then put it into a drawer, I have created a second information repository. Worse, my most important insights may exist only on paper.

The solution is to establish a simple closing process:

• Print only the material necessary for the task.
• Use highlighting, handwritten notes, symbols, and diagrams during analysis.
• Transfer important decisions, questions, corrections, and action items back into the digital system.
• Update the authoritative document or record.
• Securely dispose of the temporary paper copy when it is no longer needed, following the organization’s retention and confidentiality policies.

The digital document remains the official record. The printed copy serves as a temporary workspace.

This distinction prevents the hybrid approach from becoming a disorganized collection of competing versions.

Efficiency Is More Than Saving Paper

Businesses often measure the success of digital transformation through reduced printing costs, fewer physical files, and faster document distribution.

But what about the time employees spend switching between applications?

What about the cognitive effort required to remember information that is no longer visible on the screen?

What about errors caused by overlooking a requirement buried in a lengthy document?

These costs are harder to measure, but they are still real.

Saving twenty pages of printing may not represent a meaningful business improvement if it adds substantial time to a complicated analysis.

That doesn’t mean printing is always better. It means we should evaluate the complete workflow rather than one isolated expense.

Technology Should Support the Way We Think

The best workplace isn’t necessarily the one that uses the least paper.

It is the one that allows employees to access information, understand it, make sound decisions, and complete their work efficiently.

Sometimes that means searching a digital repository.

Sometimes it means displaying two documents side by side.

And sometimes it means picking up a pen, highlighting a paragraph, and drawing a circle around something that doesn’t make sense.

Perhaps the future of the paperless office isn’t completely paperless after all.

Perhaps it is paper-smart.

A workplace where digital technology manages information, while people have the flexibility to choose the tools that help them think most effectively.

When Does Photoshop Become AI? Understanding the Line Between Editing and Generating

I use Photoshop extensively in creating my artwork. I also use Lightroom, Topaz, a Wacom tablet, photography, and traditional artistic techniques. Recently, however, I encountered a question that sounds simple but turns out to be surprisingly complicated:

Do I use artificial intelligence in my artwork?

My first reaction was to think about obvious generative AI, the tools where you type a description and a computer creates an image. But when I started researching Photoshop, I discovered that the line between traditional digital editing, artificial intelligence, and generative artificial intelligence is much less clear.

In fact, many Photoshop tools that photographers and digital artists have used for years incorporate some form of artificial intelligence or machine learning.

AI Does Not Necessarily Mean AI-Generated

This was the most important distinction I discovered.

There are really three categories worth considering:

Traditional digital editing

These are familiar Photoshop techniques such as layers, masks, curves, levels, cropping, color adjustments, blending modes, transformations, brushes, cloning, dodging, and burning. The computer provides the tools, but the artist makes the decisions and manipulates the image.

[EXAMPLE: Screenshot of a Photoshop Layers panel showing multiple adjustment layers, masks, and blending layers.]

AI-assisted editing

Here Photoshop analyzes an image and helps perform a task. The software may recognize a person, determine where a sky begins and ends, find an object, repair an area, or make a sophisticated selection.

The computer is helping me accomplish something, but it is not necessarily inventing the artwork.

Generative AI

This is different. Generative AI can actually synthesize new visual information, creating pixels that were not present in the original photograph or artwork.

That distinction becomes very important when someone asks, “Was AI used to create this?”

Photoshop Has Been Using AI Longer Than Many of Us Realized

Consider Select Subject.

Instead of carefully tracing around a person or object, I can ask Photoshop to identify the subject automatically. Adobe describes this as an AI-powered feature using machine learning.

[EXAMPLE: Screenshot showing an original photograph beside the result after choosing Select > Subject, with the marching-ants selection visible.]

The artistic subject has not suddenly been created by AI. It was already in my photograph. AI simply helped Photoshop recognize its boundaries.

The same general principle applies to tools such as Object Selection and Remove Background.

[EXAMPLE: Screenshot of the Object Selection Tool identifying an object in one of my photographs.]

These are excellent examples of AI assisting the artist rather than replacing the artist.

Then Things Get More Complicated

Content-Aware Fill is particularly interesting.

Photographers have used content-aware technology for years. Suppose there is a distracting telephone pole, garbage can, tourist, electrical wire, or other unwanted element in a photograph. Photoshop can analyze surrounding pixels and construct replacement information.

[EXAMPLE: Before-and-after screenshot showing a distracting object removed with Content-Aware Fill.]

Is that artificial intelligence?

Adobe includes Content-Aware Fill among its AI-powered Photoshop capabilities.

But most people would probably view removing a trash can from a photograph quite differently from asking an AI image generator to create an entire Paris street scene.

Technically, both can involve artificial intelligence. Artistically, they represent very different processes.

Generative Fill Crosses Another Line

Photoshop’s Generative Fill makes the distinction much clearer.

I can select an area of an image and ask Photoshop to add something that was not originally there, replace an existing object, or create something that fits the surrounding scene.

[EXAMPLE: Screenshot showing the Generative Fill contextual taskbar and prompt field.]

For example, imagine that I photograph a rural Pennsylvania farm. I might traditionally adjust the color, paint into the image, crop it, remove an electrical wire, or enhance the atmosphere.

With Generative Fill, however, I could potentially select an empty field and request a horse.

Photoshop would create a horse that was not in my photograph.

[EXAMPLE: Before-and-after example showing an original photograph and a clearly labeled demonstration in which Generative Fill adds an object.]

That is no longer simply identifying or adjusting pixels that already exist. New visual content has been synthesized.

For purposes of describing an artistic process, I consider that generative AI.

Generative Expand Is Another Good Example

Suppose my photograph is horizontal but I need a taller composition.

Traditionally, I might crop differently, clone existing areas, paint additional background, or decide that the photograph simply will not work in that format.

Generative Expand gives me another possibility. Photoshop can extend the canvas and generate plausible visual information beyond the boundaries of my original photograph.

[EXAMPLE: Three screenshots showing the original image, expanded canvas, and final Generative Expand result.]

The original photograph may still constitute most of the finished artwork, but some portion of the scene was generated by AI.

That is something I think is reasonable to disclose.

Even the Remove Tool Has a Surprise

One of the most interesting things I discovered involves Photoshop’s Remove Tool.

Removing an unwanted object sounds like ordinary photo retouching. However, current versions of Photoshop can provide options controlling whether generative AI is used during removal.

Depending upon the setting, generative AI may be on, off, or Photoshop may automatically decide whether to use it.

[EXAMPLE: Screenshot of Photoshop’s Remove Tool options showing the Generative AI setting.]

That means an artist could potentially use generative AI without thinking of the operation as “generating an AI image.”

This illustrates why a simple yes-or-no question about AI does not always adequately describe a modern digital workflow.

Neural Filters Are AI Too

Photoshop’s Neural Filters provide another category.

These filters use machine learning for sophisticated image processing. Depending upon the filter, Photoshop can perform operations that would once have required considerable manual editing.

[EXAMPLE: Screenshot of the Neural Filters workspace showing available filters.]

Again, I think context matters.

Using machine learning to assist with an editing operation is not necessarily equivalent to asking an AI model to invent the subject, composition, lighting, and content of an artwork.

Both involve AI technology, but the artist’s role can be dramatically different.

My Working Definition

After researching this, I have developed a much more useful way of thinking about AI in my own artwork.

I divide the process into three categories:

Traditional digital tools: I manipulate the image.

AI-assisted tools: Photoshop analyzes the image and helps me manipulate it.

Generative AI tools: Photoshop synthesizes visual content that was not originally present.

[EXAMPLE: Graphic showing these three categories side by side: Traditional Digital Editing → AI-Assisted Editing → Generative AI.]

That distinction is much more meaningful to me than simply asking whether a computer program containing AI was involved.

What About the Artist?

There is another issue that gets lost in discussions about artificial intelligence: authorship involves much more than pixels.

I decide what interests me.

I take the photograph or develop the concept.

I choose the composition.

I determine what stays and what disappears.

I decide on color, texture, mood, and presentation.

I may paint digitally using my Wacom tablet. I may combine photographs. I may use Photoshop, Lightroom, or Topaz. I may work from photographs, sketches, imagined places, or combinations of them.

And, yes, I may sometimes incorporate AI-generated elements into a larger work.

[EXAMPLE: A complex Photoshop project with the Layers panel open, illustrating the number of individual components and artistic decisions involved.]

The finished image may therefore be the result of photography, digital painting, compositing, conventional image processing, AI-assisted tools, and sometimes generative technology.

Calling the entire result simply an “AI image” does not adequately describe that process.

Neither, in some circumstances, would saying that “no AI was used.”

Why Disclosure Matters

This became particularly relevant to me because galleries, exhibitions, and organizations increasingly ask artists about AI.

Those questions are legitimate. A gallery has every right to establish rules concerning what it will exhibit.

But artists also need terminology that accurately describes contemporary creative tools.

If I use Photoshop’s AI-powered Select Subject to isolate a person whom I photographed, that is fundamentally different from generating a fictional person.

If I remove a telephone wire using an intelligent editing tool, that is different from generating an entire landscape.

And if I use Generative Fill to create an object that was not present in my photograph, that should be distinguished from both.

[EXAMPLE: A three-panel visual using one photograph: original; AI-assisted selection/edit; generative addition.]

The Question Has Changed

Perhaps the question should not simply be:

“Did you use AI?”

Today, a better question may be:

“How was AI used?”

Was it used to make a selection?

Was it used to remove a distraction?

Was it used to enhance image quality?

Was it used to extend an existing photograph?

Was it used to generate one element?

Or was the entire underlying image generated from a prompt?

Those are very different creative processes.

For me, the goal is not to hide the technology I use. It is to describe it accurately.

Photography itself has always evolved with technology, from film emulsions and darkroom manipulation to digital sensors, Photoshop, computational photography, and now artificial intelligence.

The tools continue to change.

The more interesting question is what the artist chooses to do with them.