DEV Community: Thomas Delfing

Why We Need to Read More, Not Less, in the Age of AI

Thomas Delfing — Tue, 22 Sep 2026 03:53:11 +0000

AI can summarize a 20-page report in seconds. So why should we still spend time reading it ourselves?

Because in the age of artificial intelligence, deep reading may be becoming more valuable—not less.

AI has dramatically changed the way we access information.

It can summarize articles, analyze research papers, extract key findings, organize information, compare sources, and explain complicated subjects within seconds.

What once required 30 minutes of reading might now be reduced to a five-minute summary.

That is an extraordinary productivity advantage.

But there is a potential downside.

When AI performs more of the intellectual work for us, we need to be more deliberate about exercising the cognitive abilities that still belong to us.

And one of the simplest ways to do that is to read deeply.

What Is Deep Reading?

Deep reading is more than simply looking at words on a screen.

It means giving a text enough time and attention to:

understand complex ideas
follow an argument from beginning to end
connect information across multiple paragraphs
recognize contradictions
question assumptions
relate new information to existing knowledge
remember and apply what you have learned

That is very different from scrolling through social media, scanning headlines, or reading an AI-generated summary.

Deep reading requires sustained attention.

And sustained attention is increasingly valuable in a world where information is constantly competing for our attention.

Reading Is a Complex Cognitive Activity

Reading feels effortless when we are experienced readers.

But the brain is doing far more work than we usually realize.

There is no single "reading center" in the brain. Reading requires several systems to work together, including:

visual processing
attention
language processing
memory
meaning construction
existing knowledge

When we read a complex text, these processes have to work together continuously.

We need to recognize words, understand sentences, retain information, connect ideas, and build an overall understanding of what the author is trying to communicate.

This is why reading is not simply information consumption. It is a cognitive activity.

Your Brain Needs More Than Information

The phrase "brain training" can sometimes be misleading. The brain is not simply a muscle that becomes stronger every time we use it.

The reality is more complicated.

But one principle is important:

Cognitive abilities need to be used if we want to maintain and develop them.

Consider what happens when you read a long, complicated article.

You may need to remember an argument from three pages earlier.

You may need to compare two different ideas.

You may need to identify a contradiction.

You may need to connect something you have just learned with knowledge you already have.

All of this requires active mental processing.

Compare that with rapidly scrolling through dozens of short pieces of content.

The difference is obvious.

Scrolling moves your attention from one fragment to another. Deep reading asks you to stay with one idea.

The Attention Problem in the Digital Age

This distinction becomes particularly important in digital environments.

Our devices make it incredibly easy to interrupt ourselves.

A notification appears.

A new email arrives.

Another article catches our attention.

We open another tab.

Then another.

Before long, we have consumed dozens of pieces of information without spending significant time thinking deeply about any of them.

Research into digital reading has found that distractions can negatively affect text comprehension. The article's source material cites a meta-analysis of 32 studies that found a negative effect of attentional disruptions on reading comprehension in digital environments.

The problem is therefore not simply how much information we consume.

It is how much attention we give to each piece of information.

How AI Is Changing the Way We Read

Artificial intelligence introduces another major change.

Imagine receiving a 20-page business report.

Traditionally, you would read it.

Today, you can upload it to an AI system and ask:

"What are the five most important points?"

You can do the same with a scientific paper.

Instead of reading the methodology, results, and conclusions yourself, AI can extract them for you.

You can also provide multiple sources and ask AI to compare them.

From a productivity perspective, this is incredibly useful.

It can save time.

It can reduce information overload.

It can help you identify which documents deserve closer attention.

But there is an important distinction.

AI Assistance vs. Cognitive Delegation

Using AI to support your thinking is not the same as allowing AI to do all of your thinking.

The problem begins when assistance becomes permanent delegation.

If we always read the summary instead of the original, we receive the conclusion without necessarily experiencing the reasoning that led to it.

We get the result.

We lose some of the process.

That matters because the process itself can be part of learning.

As the source article notes, current research is examining how AI can support learning while also potentially bypassing parts of the learning process, depending on how the technology is used.

The important question is therefore not:

"Should we use AI?"

It is:

"When should we use AI—and when should we think for ourselves?"

AI Should Make Thinking Faster, Not Make Thinking Unnecessary

The answer is not to stop using artificial intelligence.

AI is an extraordinarily useful tool.

It can help us:

find relevant information
summarize large documents
identify patterns
compare sources
explain unfamiliar concepts
filter large amounts of material
accelerate research

The key is to understand when efficiency is the goal and when learning is the goal.

If you simply need to know when an event happened, there is little value in spending 30 minutes researching it.

But if you are trying to understand a new field, develop expertise, improve your judgment, or make an important professional decision, the situation changes.

In those situations, slowing down can actually be productive.

Sometimes You Should Not Summarize the Article

There is a temptation to use AI immediately.

Paste the article.

Generate the summary.

Extract the key points.

Move on.

But sometimes the struggle is the point.

Reading a paragraph twice because you did not understand it the first time is not necessarily wasted time.

Spending several minutes trying to understand a difficult argument is not necessarily inefficient.

Comparing two competing ideas in your own mind is not a failure of productivity.

That mental effort is part of learning.

For professionals, this matters even more.

People who make decisions, develop software, run businesses, conduct research, or provide consulting services cannot always rely on information retrieval alone.

They need judgment.

They need to determine whether an argument makes sense.

They need to recognize weak assumptions.

They need to understand context.

And they need to decide whether a conclusion is actually justified.

AI may make information cheaper, but it can make human judgment more valuable.

Deep Reading Could Become a Critical Skill

Perhaps we should start thinking about reading differently.

Not simply as entertainment.

Not merely as a way of collecting information.

But as training for focused thinking.

This does not mean reading every document from beginning to end.

That would be unrealistic, especially in professional environments.

AI can—and should—help us filter information.

The important part is what happens afterward.

Once AI identifies the information that matters, we should deliberately decide what deserves our full attention.

That might mean:

📖 Reading a professional book

Instead of relying on summaries, spend time with the original ideas.

🔬 Reading a study

Do not judge a research paper only by its abstract. Understand how the conclusion was reached.

📚 Working through a long analysis

Follow an argument across multiple pages rather than consuming isolated conclusions.

🧠 Spending uninterrupted time on one subject

Give yourself 30 or 60 minutes without constantly switching between sources.

These activities require something increasingly scarce:

attention.

The Most Valuable Resource Is No Longer Information

For much of modern history, accessing information was the difficult part.

Today, the situation is almost the opposite.

We have access to more information than we could ever consume.

AI makes that abundance even greater.

Information can now be summarized, translated, categorized, compared, and generated almost instantly.

But there is one resource that remains limited:

Attention.

This changes the way we should think about productivity.

Instead of asking only:

"How much did I accomplish today?"

we should sometimes ask:

"What did I actually give my attention to?"

That is a very different question.

Time Management Is Also Attention Management

This is where time management and cognitive performance become connected.

We often measure our days by hours worked.

Eight hours of work.

Two hours of meetings.

One hour of email.

Thirty minutes of administrative tasks.

But there is another measurement that can be useful:

How much time did we deliberately spend thinking?

If deep reading is important but never appears on the calendar, urgent tasks will usually take its place.

That is why making intellectual activities visible can be useful.

TimeSpin: Making Deep Work and Reading Visible

With TimeSpin, activities can be tracked and categorized so that you can see where your time is actually going.

This does not have to be limited to traditional work activities.

You can track meetings, project work, administration, research, learning, or reading.

For example, the source article describes activities such as "Genese ORGA" and "Gweb meeting", alongside a dedicated activity called "Professional Book." In the example, 1 hour and 24 minutes of time had been recorded for professional reading during the month.

That number may seem insignificant.

But it answers an important question:

Did I actually make time for reading this month?

The same principle can apply to work connected with Genese, research, professional development, or learning.

You can also consider how satisfying those activities felt.

TimeSpin does not measure whether you truly understood a book or how intensely your brain was working.

What it can do is make something visible that is otherwise easy to overlook:

Did you make time for the activities that matter to your long-term development?

What You Don't Schedule Often Gets Displaced

This is one of the biggest problems with modern knowledge work.

Urgent tasks are visible.

Deep thinking often is not.

Emails arrive.

Meetings appear.

Messages need responses.

Projects have deadlines.

Administrative tasks accumulate.

Reading can always happen "later."

And later often never comes.

That is why it can be useful to treat activities such as:

Deep Reading
Professional Reading
Research
Learning
Training
Writing
Strategic Thinking

as real activities rather than leftover time.

If something matters but is never scheduled, it will often lose to whatever feels urgent.

A Better Reading Strategy for the AI Era

The goal should not be to reject AI.

It should be to use AI more deliberately.

A simple approach could look like this:

1. Use AI to filter

When you have hundreds of documents, use AI to identify which ones are relevant.

2. Use AI to orient yourself

Ask for definitions, context, terminology, or an overview when you are entering an unfamiliar subject.

3. Choose what deserves deep attention

Not everything needs to be read in full.

Select the material where understanding matters.

4. Read the important material yourself

Do not automatically replace the original with a summary.

5. Think before asking AI for the answer

Try to form your own interpretation first.

6. Use AI to challenge your understanding

Once you have read the material, ask AI to identify alternative interpretations, counterarguments, or potential gaps.

This turns AI into a thinking partner rather than a substitute for thinking.

The Future of Productivity May Be About What We Don't Automate

AI will continue to automate more intellectual tasks.

That is likely to be one of its greatest benefits.

But automation creates an interesting paradox.

The more cognitive work machines perform, the more carefully we may need to choose which cognitive activities humans should continue practicing.

We do not need to manually perform every task simply because we can.

But we also should not automatically outsource every task simply because AI can do it faster.

The difference is important.

Efficiency is not always the same as learning.

And speed is not always the same as understanding.

Read Faster When You Need Information. Read Deeper When You Need Understanding.

This may ultimately be the simplest principle for reading in the age of AI:

Let AI summarize when speed matters. Read for yourself when understanding matters.

Use AI to reduce information overload.

Use it to discover relevant material.

Use it to organize complex information.

But when something genuinely matters, give yourself the time to understand it.

Read the book.

Read the research paper.

Read the long-form analysis.

Follow the argument.

Question the assumptions.

Think about what you have read.

And give your attention enough time to do its work.

The Question We Should Ask at the End of the Week

At the end of a busy week, most of us can answer:

How many hours did I work?

We may even know how many meetings we attended or how much time we spent on specific projects.

But there is another question worth asking:

How many hours did I give my brain to read deeply, think carefully, and understand something new?

That time may not appear urgent.

It may not produce an immediate notification.

It may not create an obvious productivity metric.

But in a world where AI can increasingly provide answers in seconds, the ability to understand, evaluate, and think independently may become more important—not less.

The future of productivity may therefore not be about consuming information faster.

It may be about becoming better at deciding what deserves our attention in the first place.

And sometimes, the best way to do that is surprisingly simple:

Close the summary.

Open the book.

And read.

Why IT Specialists Have a Strong Future — If They Can Do More Than Just Write Code

Thomas Delfing — Fri, 18 Sep 2026 10:54:58 +0000

Artificial intelligence is transforming software development. But rather than making IT specialists obsolete, AI is changing what it means to be a successful developer.

Artificial intelligence is changing software development at a rapid pace. At the same time, the IT job market has become more challenging for people entering the profession. Companies are hiring more selectively and paying closer attention to the practical skills candidates can actually bring to the table.

That does not mean that IT specialists or computer scientists no longer have a future. Quite the opposite.

Companies still need people who can develop, understand, secure, and adapt digital systems to real-world requirements. However, the role itself is changing.

Those who want to succeed in the future will need to do more than simply generate code.

Good Software Starts With Understanding the Problem

In professional software development, the starting point is rarely a perfectly defined technical specification.

Users might report that a workflow takes too long, information is difficult to find, or the same data has to be entered multiple times. But they cannot always explain exactly what technical solution is needed.

That is why a developer's job is not simply to turn instructions into source code. The first step is understanding the actual problem that needs to be solved.

This means getting to know how users work, questioning existing processes, and recognizing relationships between different parts of a system.

A seemingly small technical change can affect other features, databases, interfaces, security mechanisms, or even entire business processes.

IT specialists therefore need to learn to think beyond the individual programming task. They need to understand the broader environment in which they work and recognize how a technical decision can affect other areas of a system.

“Software development is not about writing as much code as quickly as possible. It is about fully understanding a task and developing a sustainable solution from that understanding.”

— Oliver Otto, Managing Director of Genese.de

AI makes this ability even more important.

An AI system can generate code within seconds. But it does not automatically know whether that code fits the company's processes, existing systems, architecture, or the actual requirements of its users.

Communication Is Part of the Profession

Anyone who develops software for other people needs to be able to communicate with those people.

According to Oliver Otto, communication should therefore be an integral part of training for IT specialists and computer science education.

“Developers need to learn how to listen to users, ask the right questions, and explain technical relationships in a way that is easy to understand. Only then can they develop solutions that genuinely help people in their everyday work.”

— Oliver Otto, Managing Director of Genese.de

Users and developers often speak different languages.

Users think in terms of tasks, deadlines, documents, customers, and workflows. Developers tend to think in terms of data structures, functions, interfaces, and technical components.

Professional software development has to connect these two worlds.

A good IT specialist therefore needs more than programming skills. They need to be able to conduct conversations, structure requirements, and determine whether a requested feature is actually the best solution.

Sometimes communication reveals that the real problem lies somewhere completely different.

Communication is therefore not simply an additional skill for developers who enjoy working with people. It is a core professional competency.

Modern Software Is Built by Teams

Collaboration with other developers is becoming increasingly important as well.

Modern applications consist of numerous interconnected components. User interfaces, databases, web applications, mobile apps, APIs, cloud services, and security mechanisms all have to work together.

Very few developers can completely understand a complex software system on their own.

“We work in an interconnected development environment. Developers therefore need to share knowledge, discuss technical decisions together, and document their work so that others can build on it.”

— Oliver Otto, Managing Director of Genese.de

This also means being able to read and understand code written by someone else.

Developers need to accept criticism, explain their own decisions, and work with colleagues to find better solutions.

The rise of AI makes this collaboration even more important.

AI-generated code needs to be reviewed, tested, and integrated into the existing architecture. An answer generated by AI can appear technically plausible while still being incomplete, insecure, or unsuitable for a particular system.

The responsibility ultimately remains with the developers.

Personal Skills Will Shape the Future

A degree or completed vocational training remains important. However, qualifications alone do not guarantee a successful career.

Personal abilities are becoming increasingly important.

Young people who enjoy solving puzzles, think logically, and do not give up when a task becomes complicated have a strong foundation for a career in software development.

Curiosity matters too.

Successful developers want to understand how things are connected and enjoy systematically searching for errors and finding solutions.

The willingness to keep learning is equally important.

Programming languages, development environments, and technical tools continue to evolve. The ability to analyze problems and solve them systematically, however, remains valuable in the long term.

Responsibility is another critical skill.

Software now influences central business processes. Errors can disrupt workflows, compromise data, or lead to incorrect decisions.

Developers therefore need to understand that their work has real-world consequences.

IT specialists and computer scientists continue to have promising career opportunities. But that future increasingly depends on their willingness to develop their skills.

People who simply wait to receive clearly defined programming tasks may find the profession more challenging.

Those who identify problems, take responsibility, communicate with others, and evaluate technical solutions within their broader context will continue to be needed in an AI-driven working environment.

Training at Genese.de: Learning Through Real-World Projects

For more than 25 years, Genese.de has been developing software solutions for managing patents, trademarks, and other intellectual property rights.

Its applications are used by patent law firms as well as patent departments within companies.

Trainees at Genese.de therefore learn much more than individual programming techniques.

They gradually work on real software solutions and learn how complex professional requirements can be transformed into reliable applications.

The training goes far beyond writing code.

Trainees work with existing software systems, databases, user interfaces, and APIs. They learn how to understand requirements, analyze errors, and make technical decisions that can be clearly explained and justified.

Each trainee is supported by a personal mentor who helps with technical questions, explains complex relationships, and supports the transition from basic knowledge to professional skills.

The goal is not to give trainees ready-made solutions as quickly as possible.

Instead, they are encouraged to think independently, ask questions, and develop their own approaches to solving problems.

Those are precisely the skills that will become increasingly important in the future.

AI Is a Tool — Not a Replacement for Understanding

Artificial intelligence will become a standard part of software development.

It can help developers write code, create documentation, suggest test cases, and identify potential sources of errors.

But a tool is only as effective as the person using it.

Anyone who does not understand the fundamentals cannot reliably evaluate AI-generated results either.

Developers need to determine whether a proposed solution is secure, maintainable, and technically correct. They need to know which questions to ask and which AI-generated results require particularly careful review.

Trainees should therefore learn how to use AI effectively without handing over their own thinking to it.

The future does not belong exclusively to people who work without AI.

But it does not belong to people who blindly accept every answer generated by AI either.

The developers who will remain valuable are those who use modern tools while retaining professional and technical control over the results.

A Career for People Who Enjoy Solving Problems

Training as an IT specialist remains a strong career path for young people who have a technical mindset, enjoy logical thinking, and like solving problems.

It is particularly suited to people who want to understand how software works and who want to build applications themselves rather than simply use finished products.

A willingness to collaborate with colleagues and users is equally important.

Programming is not going away.

But it will continue to change.

Some routine programming tasks will increasingly be automated. At the same time, analysis, architecture, communication, quality assurance, and the ability to understand complex relationships will become more important.

This does not make the profession less interesting.

It makes it more diverse.

Training With a Future in Mind

Genese.de gives young people the opportunity to develop their technical skills in a professional environment.

Existing programming experience or school grades are not the only factors that matter.

Curiosity, reliability, logical thinking, and the willingness to engage deeply with a problem are equally important.

Applicants should enjoy asking questions, discovering new connections, and developing solutions together with others.

For those who bring these qualities, software development will continue to offer strong professional opportunities.

Companies will continue to need IT specialists and computer scientists.

But they will need more than people who can write code.

They will need people who understand problems, take responsibility, communicate effectively, and turn technological possibilities into solutions for the real world.

11 Home Office Products That Can Improve Comfort and Productivity

Thomas Delfing — Fri, 18 Sep 2026 10:20:00 +0000

Working from home is no longer a temporary arrangement for many professionals. For people who regularly work remotely, a laptop on the kitchen table is rarely enough.

A well-designed home office setup can make everyday work more comfortable, reduce physical strain, and improve productivity. But not every “smart” gadget automatically makes you more productive.

The real question is simple:

Does this product solve a specific problem?

Whether it is poor posture, limited desk space, unreliable audio during video calls, or inefficient time tracking, the right equipment can make a noticeable difference.

Here are 11 home office products and upgrades worth considering if you want to create a more comfortable, ergonomic, and productive workspace.

1. An Ergonomic Office Chair

If you spend several hours a day at your desk, your office chair is one of the most important investments you can make.

A good ergonomic office chair should support a healthy and dynamic sitting posture. Look for features such as:

Adjustable seat height
An adjustable backrest
Adequate lumbar support
Adjustable armrests

The goal is not to force your body into one rigid position. Instead, a good chair should allow you to change positions throughout the day.

This can help reduce strain and discomfort in areas such as the back, shoulders, and neck.

Price alone should not determine your choice. The chair needs to match your height, weight, and preferred sitting position. If possible, try different models before buying.

2. A Height-Adjustable Standing Desk

A height-adjustable standing desk makes it possible to alternate between sitting and standing throughout the workday.

This transforms a static workstation into a more flexible workspace.

Standing does not replace exercise or regular breaks, but changing positions can help interrupt long periods of sitting and engage different muscle groups.

Electric standing desks are particularly convenient when they allow you to save multiple height settings. With the press of a button, you can return to your preferred sitting or standing position.

Desk size also matters. Your workspace should have enough room for your computer, monitors, keyboard, mouse, documents, and other essential equipment without becoming cluttered.

3. A Large Monitor or Multi-Monitor Setup

One of the most noticeable home office productivity upgrades is additional screen space.

If your work involves multiple applications, documents, or data sources, a larger display can make it easier to organize everything at once.

For example, software developers can keep source code, documentation, and testing environments visible simultaneously.

Managers and administrative professionals can arrange emails, spreadsheets, reports, and video conferences side by side.

A large ultrawide monitor and a dual-monitor setup each have advantages.

Two monitors provide a clear separation between applications, while an ultrawide display creates one continuous workspace without a bezel in the middle.

Beyond resolution, consider height adjustability and an anti-glare display.

4. A Quality Keyboard and Ergonomic Mouse

Your keyboard and mouse are among the devices you use most during a typical workday, yet they are often overlooked when building a home office.

A high-quality keyboard can make typing more comfortable and precise.

Whether you prefer low-profile laptop-style keys, mechanical switches, or a split ergonomic keyboard largely depends on personal preference and familiarity.

The same applies to your mouse.

An ergonomic or vertical mouse can help keep the wrist and forearm in a more natural position. For people experiencing discomfort, switching to a different mouse design may be worth considering.

Keep in mind that adapting to a new keyboard or mouse can take several days.

Programmable buttons can also be useful for frequently used commands, keyboard shortcuts, and applications.

5. A Docking Station for a Cleaner Workspace

A laptop docking station may not be the most exciting piece of technology, but it can dramatically simplify a home office setup.

A single connection can link your laptop to:

External monitors
Ethernet
Keyboard and mouse
Power
Other peripherals

Instead of connecting several cables every morning, you can connect your laptop with one cable.

A docking station can also help reduce cable clutter.

Before buying one, however, check which connectivity standards your laptop supports. USB-C does not automatically mean that a port supports video output or sufficient charging power.

For demanding setups with multiple high-resolution displays, Thunderbolt or USB4 docking stations may be appropriate.

6. Good, Glare-Free Desk Lighting

Poor lighting can contribute to eye strain and make it harder to maintain concentration.

A good home office desk lamp should illuminate your workspace evenly without creating reflections on your monitor or causing glare.

Adjustable brightness and color temperature can be useful.

Cooler light may feel appropriate during focused work periods, while warmer lighting can be more comfortable in the evening.

Smart lighting can automatically respond to the time of day or ambient brightness. However, smart features should not come at the expense of basic lighting quality.

A well-positioned conventional desk lamp can be more useful than an expensive connected light that does not properly illuminate your workspace.

7. A High-Quality Headset for Video Conferences

In remote meetings, professional communication depends on more than video quality.

Clear audio is just as important.

A good headset can reduce surrounding noise and make your voice easier for other participants to understand.

This becomes particularly valuable when multiple people are present in your home or when your workspace is not completely isolated.

If you do not want to wear headphones, a conference speakerphone with an integrated microphone can be an alternative, especially for longer meetings or small group discussions.

When choosing a headset, do not focus exclusively on active noise cancellation for the person wearing it.

The microphone is equally important.

A good microphone should reliably reduce background noise on the other end of the call.

8. A Dedicated Webcam for a More Professional Appearance

Modern laptop webcams have improved significantly, but their positioning can still be problematic.

When a laptop is placed directly on a desk, its camera may point upward toward your face, creating an unflattering angle.

A dedicated external webcam can be positioned above your monitor, providing a more natural perspective.

Features such as automatic exposure and good performance in changing lighting conditions can further improve image quality.

You do not necessarily need an extremely high-resolution camera for video meetings. For many professional calls, good Full HD video is sufficient.

More important factors include:

Reliable autofocus
Natural color reproduction
Good low-light performance
Consistent exposure

Privacy should also be considered. A physical webcam cover or mechanical shutter provides a simple additional layer of protection.

9. A Monitor Arm for More Desk Space and Better Ergonomics

A monitor arm can free up valuable desk space while giving you greater control over the position of your display.

You can adjust the monitor's:

Height
Distance
Tilt
Viewing angle

This becomes particularly useful when using two or more monitors.

A stable monitor arm can make it easier to position multiple displays ergonomically. Ideally, the top edge of the screen should be approximately at eye level, while the viewing distance should allow you to read comfortably without constantly leaning your head forward.

Before purchasing a monitor arm, check the monitor's weight and supported VESA mounting standard.

Large or heavy displays require appropriately rated mounts.

10. Cable Management and Reliable Power

Cable organizers, cable trays, clips, and power strips may not be glamorous technology products, but they can make a home office considerably cleaner and safer.

Loose cables do more than make a desk look untidy.

They can interfere with cleaning, become stretched when a height-adjustable desk moves, or accidentally disconnect from your devices.

A power strip mounted underneath the desk can keep electrical connections organized.

Cable sleeves, clips, and Velcro straps can help bundle power, network, and display cables together.

For expensive equipment, surge protection can also be worth considering.

And if your work depends on a reliable internet connection, a wired Ethernet connection may be preferable whenever practical.

11. The TimeSpin Cube for Easier Time Tracking

While many home office products focus on improving the physical workspace, the TimeSpin Cube addresses another important part of remote work: time management and time tracking.

The physical cube allows users to record activities through a simple turning motion.

Different sides of the cube can be assigned to different:

Projects
Clients
Tasks
Types of work

When the cube is rotated to another side, time tracking begins for the corresponding activity.

The advantage is its immediate physical interaction.

Instead of opening an application, selecting a project, and manually starting a timer, the user can change the tracked activity directly from the desk.

This can make time tracking easier to integrate into an existing workflow.

The approach may be particularly useful for freelancers, consultants, law firms, agencies, software companies, and other service businesses that regularly need to allocate working hours across different clients or projects.

TimeSpin combines a physical control device with a digital application. Tracked time can be synchronized, analyzed, and processed digitally.

That makes the system useful not only for documenting billable hours but also for understanding how working time is actually being spent.

For remote workers, where professional and personal activities can easily overlap, consistent time tracking can provide additional visibility into the workday.

It can show how much time is being spent on client projects, internal tasks, communication, or administrative work.

Final Thoughts: The Best Home Office Upgrade Solves a Real Problem

A productive home office is not created by simply adding more technology.

The most important factor is whether your equipment matches the work you actually do.

An ergonomic chair and height-adjustable desk can improve the physical conditions of your workspace.

Monitors, docking stations, keyboards, and mice can make daily computer work more efficient.

Headsets, webcams, and quality lighting can improve the experience of professional video meetings.

Tools such as the TimeSpin Cube take the concept a step further by addressing how work itself is organized and tracked.

Ultimately, the best home office upgrade is not necessarily the most expensive product.

It is the one that solves a problem you encounter every single day.

Before buying anything, identify the biggest sources of friction in your current setup. Then invest in the equipment that directly addresses them.

Working More Isn’t Enough: Why Germany Needs to Focus on Productivity, Not Just Working Hours

Thomas Delfing — Thu, 17 Sep 2026 05:46:58 +0000

Germany’s debate about working hours is often surprisingly one-dimensional. Employees are being asked to work longer, reduce part-time employment, and contribute additional hours. But a recent survey suggests that many people are willing to work more — provided there are meaningful incentives. For businesses, however, there is an even more important question: before asking employees to work additional hours, do we actually know how the hours they already work are being used?

Germany is debating work once again.

The 35-hour workweek, part-time employment, overtime, labor shortages, and the question of whether an aging society needs people to work more are all back on the agenda.

The discussion is legitimate. If fewer people are participating in the workforce while prosperity, social security systems, and public services still need to be financed, total labor input cannot simply be ignored.

But the debate becomes too narrow when it focuses primarily on the number of hours people work.

A recent survey conducted by market research institute Appinio on behalf of job platform Indeed paints a more nuanced picture. More than half of the 1,000 employees surveyed said they were generally willing to work overtime. Almost 20% could even imagine working additional hours on a permanent basis. At the same time, nearly three-quarters already work more than the hours specified in their employment contracts at least occasionally.

That challenges the idea that society is simply becoming unwilling to work.

The more important question is:

Under what conditions are people willing to contribute more?

More Effort Requires a Clear Return

The answers from employees are hardly surprising.

Higher salaries, tax incentives, flexible working hours, and bonuses were among the incentives mentioned. In other words, if employers expect additional effort, employees also expect additional value in return.

That may sound obvious, but it is frequently overlooked in the political debate.

Additional work needs to provide a tangible benefit for the individual employee. And this is not solely an employer issue. Taxation and social security contributions also influence how attractive an additional hour of work actually is.

If an additional euro of gross income results in only a small increase in disposable income after taxes, social contributions, or the loss of other benefits, the economic incentive to work more becomes weaker.

The question, therefore, is not simply whether Germany has enough workers.

It is also whether the economic system provides sufficient incentives for people to make greater use of the labor capacity they already have.

The Indeed survey offers an important indication here. Lack of financial incentives, alongside private life, health, and family responsibilities, is among the reasons employees give for not wanting to increase their working hours further.

But for business leaders, there is another question that may be even more important.

The More Interesting Question: What Happens During the Hours We Already Have?

Imagine an employee who works 35 hours per week.

Increasing that to 40 hours would theoretically increase available working capacity by more than 14%.

That sounds significant.

But what if 10% or 15% of the employee’s existing working time is already being lost to avoidable coordination, poorly organized processes, duplicate data entry, unnecessary meetings, missing information, outdated systems, or manual routine tasks?

In that case, a company could achieve a comparable increase in productive capacity without extending contractual working hours by a single minute.

This is where the Indeed survey becomes particularly relevant.

Employees identify staff shortages, unnecessary bureaucracy, slow processes, unclear objectives, management problems, excessive meetings, and outdated technology as factors preventing them from accomplishing more during their existing working hours.

For managers, this may be one of the most important findings of the entire survey.

Because it changes the question.

Instead of asking:

How can we get five additional working hours?

companies should also ask:

How can we generate more productive output from the hours we already have?

Working Time Is a Resource — Not a Performance Metric

Many organizations still implicitly equate working time with performance.

But 40 hours of presence do not equal 40 hours of value creation.

A software developer can spend an entire day dealing with problems that could have been avoided with a better development environment.

A sales employee can spend hours manually maintaining data instead of speaking with customers.

A project manager can move from one meeting to another without a single meaningful decision being made.

On a timesheet, all of these days may look identical.

For the business, they are not.

That is why managers should distinguish between three dimensions:

Time. Activity. Outcome.

Only when these three dimensions are connected can productivity be evaluated meaningfully.

The relevant management question is not:

How many hours did someone work?

It is:

What did our organization actually use those hours for?

This perspective is becoming even more important as artificial intelligence changes the workplace.

If AI takes over routine tasks, analyzes documents, prepares content, structures information, or automates administrative processes, productivity gains should not simply mean that employees perform the same processes faster.

Organizations need to deliberately redirect the time they save toward higher-value activities.

Companies Know Their Costs Better Than They Know Their Activities

There is an interesting contradiction in many organizations.

Management teams often analyze financial metrics in extraordinary detail. They know personnel costs, revenue, contribution margins, budgets, and investments down to the smallest figures.

But ask:

How many hours does our software development team actually spend creating new features?

How much time is consumed by internal coordination?

Which projects consistently require more capacity than planned?

How much time do managers spend dealing with operational problems?

Which recurring activities could be automated?

The answers are often surprisingly imprecise.

That represents a significant productivity opportunity.

Modern activity and time tracking should therefore not exist solely for billing or payroll purposes. It can also become a management instrument.

At TimeSpin, this principle is central to the approach. The recorded hour is not the ultimate objective. The valuable information is understanding which activity consumed that hour.

When employees can record activities with minimal effort and the resulting data can be analyzed systematically, organizations can build a much clearer picture of how work is actually being performed over weeks and months.

Patterns begin to emerge.

Which projects consistently consume more capacity than expected?

Which customers require particularly intensive support?

How much time is spent in meetings?

Which supposedly minor administrative tasks accumulate into significant costs across an entire organization?

That visibility can change management decisions.

Productivity Starts With Transparency

Anyone who wants to improve productivity first needs transparency.

This does not mean monitoring individual employees.

Such an approach can damage trust and, more importantly, miss the real purpose of the data.

The more useful perspective is to identify aggregated patterns.

Suppose a developer spends five hours every week dealing with a particular administrative process.

The management question should not be:

Why does this employee take so long?

A better question is:

Why does this workload exist in the first place?

Perhaps an integration is missing.

Perhaps information is being entered twice.

Perhaps an approval process is unnecessarily complicated.

Perhaps software could automate the task.

This is where activity analysis connects directly with digital transformation and artificial intelligence.

Before companies can automate something, they need to know what should be automated.

More Work and More Productivity Are Not Opposites

Germany’s working-hours debate often presents two ideas as if they were mutually exclusive.

One side calls for more working hours.

The other calls for greater productivity.

A competitive economy may need both.

If people voluntarily want to work additional hours and those additional hours are economically attractive, the system should not unnecessarily discourage them.

At the same time, it would make little economic sense for companies to demand additional working hours while existing capacity is being wasted through inefficient processes.

Businesses have considerable control over this.

They can simplify processes, accelerate decision-making, clarify responsibilities, reduce unnecessary meetings, adopt better software, and use AI to automate routine activities.

Most importantly, they can start treating working time not merely as something to record, but as something to understand.

The Better Productivity Formula

The debate about the future of work should therefore not begin with the length of the working week.

The more important equation is:

Working time × productive use of working time = economic output

Increasing only the first variable leaves a significant part of the potential untapped.

The Indeed survey provides an interesting indication of this. Many employees appear willing to contribute additional effort if there is a meaningful benefit for them. At the same time, those employees identify bureaucracy, slow processes, inadequate technology, and unclear structures as obstacles to productivity.

Businesses and policymakers should take both observations seriously.

Germany needs an environment in which additional work and performance can provide meaningful economic benefits.

But companies also need to ensure that additional effort does not disappear into inefficient processes and organizational structures.

The defining productivity question of the coming years may therefore not be whether we work 35, 38, or 40 hours.

It may be:

What do we actually get from every hour we work?

Why AI Won’t Replace the User Interface in IP Management — It Will Redefine It

Thomas Delfing — Wed, 16 Sep 2026 15:46:56 +0000

Artificial intelligence is fundamentally changing how users interact with software. Instead of navigating through menus, forms, and search dialogs, users can increasingly describe in natural language what they want to know or accomplish.

For providers of specialized software, this raises a central question:

Will users even need a traditional user interface in the future — or will an AI agent take over the entire interaction with the system?

In IP management, however, it would be too simplistic to view the future as nothing more than a chat window.

Patent portfolios, trademarks, deadlines, procedures, documents, costs, tasks, and IP families form complex professional structures. An AI agent can make access to this information significantly easier. But that does not necessarily mean it will replace the user interface.

The more likely future is a hybrid interface, in which AI agents, domain-specific logic, and the traditional user interface each perform different tasks.

From Menus to Intent

Traditional enterprise software often requires users to understand the structure of the system.

For example, someone who wants to analyze specific IP rights may need to know which module contains the relevant information, which screen to open, which filters to apply, and how individual pieces of information are connected.

The traditional workflow can be simplified as:

Module → Menu → Screen → Filter → Record → Function

AI agents can fundamentally change this process.

Instead, the user can simply describe their professional objective:

“Show me all European patent families in our portfolio where a cost-related decision is due within the next twelve months.”

Or:

“What open tasks exist for the IP rights of competitor X?”

Or:

“Give me a summary of the current situation of this patent family.”

The agent interprets the request, accesses the data and functionality of the IP management system, and provides an initial answer.

The entry point into the software therefore increasingly becomes the user’s intent, rather than navigation through the application itself.

The Agent Becomes the New Entry Point

AI agents are particularly well suited to research, search, navigation, and simple actions.

Within an IP management system, an agent could, for example:

Search IP rights based on professional criteria
Summarize patent families
Identify relevant deadlines
Display tasks and responsibilities
Find documents
Structure portfolio information
Combine data from different sources
Direct users to relevant information

The key advantage is that users no longer need to know exactly where a piece of information is stored.

They only need to describe what they want to know or accomplish.

This fundamentally changes the interaction model of specialized software.

Why the Traditional User Interface Will Still Matter

This development does not mean that a specialized user interface will become obsolete.

Quite the opposite.

For complex professional tasks, a structured interface continues to offer significant advantages.

A patent manager, patent attorney, or IP specialist often does not want just a single answer. They need to understand relationships, compare records, examine family structures, review documents, monitor deadlines, or evaluate multiple issues simultaneously.

A visually structured professional interface remains better suited to these tasks than an exclusively conversational interface.

The user interface remains particularly important for:

Control and Validation

AI can summarize information and generate suggestions. Users must nevertheless be able to understand which data those suggestions are based on.

Comparing Complex Data

Tables, file views, family structures, deadline overviews, and portfolio dashboards can present an information density that is difficult to achieve through a chat interface alone.

Detailed Work

When working with individual IP rights, users often need to see numerous interconnected pieces of information simultaneously.

Approvals and Decisions

Cost decisions, procedural actions, or changes to master data should not necessarily be executed solely on the basis of a potentially misunderstood natural-language instruction.

Exceptions

Specialized software is often defined by situations that do not completely follow a standard process. Users will continue to need flexible ways of handling such cases.

The user interface therefore does not lose its purpose.

Its role changes.

The Agent Should Not Replace the Professional Interface — It Should Open It

A particularly powerful combination emerges when the AI agent does not attempt to display every piece of information itself.

Instead, it can open the appropriate professional view within the IP management system.

For example, a user might ask:

“Which German and European IP rights belonging to product group X require a renewal decision this year?”

The agent first provides a concise summary.

It then automatically opens a corresponding filtered view within the professional software.

There, the user can:

Compare individual IP rights
Examine patent families
Open documents
Review costs
See responsible individuals
Follow comments and notes
Prepare decisions

This creates a new interaction model:

Intent → Agent → Result → Professional View → Decision

The agent handles the journey to the information.

The user interface handles the deeper professional work.

Critical Actions Still Require Controlled Processes

The importance of the hybrid model becomes particularly clear for transactional processes.

An agent might receive the instruction:

“Approve the abandonment of all IP rights in this family.”

Technically, it could be possible to execute such an action immediately.

In a professional IP management system, however, this would often be inappropriate.

Such a decision can have significant legal and financial consequences.

The agent should therefore first:

Identify the affected IP rights
Explain the potential implications
Display the relevant data for review
Ask the user to confirm the transaction

The actual action can then be executed within the controlled environment of the system.

The process can also record:

Which user initiated the action
Which data was affected
Which decision was made
Which information the decision was based on
When the change was made

The hybrid model therefore combines the speed of AI with the requirements of governance, accountability, and traceability.

Context Is Critical in IP Management

The biggest challenge for AI agents is not access to individual data fields.

It is understanding the meaning and context of that data.

An IP management system, for example, understands that multiple national rights may belong to the same patent family.

It understands relationships between:

Applications
Priorities
Publications
Grants
National phases
Deadlines
Procedures
Documents
Costs
Inventors
Products
Responsible individuals

A generic AI agent can only use these relationships reliably when the professional software provides the underlying information in a structured way.

This is precisely why the interface between specialized software and AI becomes strategically important.

GeneseConnect as a Foundation for Agent-Based Workflows

Genese is structurally positioned for this development.

With GeneseConnect, external applications, services, and potentially different AI agents can be connected to the IP management system in a controlled manner.

This means Genese does not necessarily have to develop one universal AI assistant for every conceivable use case.

Instead, an open integration architecture can support different scenarios.

Possible applications include:

AI assistants within Genese
Specialized agents for individual IP processes
Enterprise-wide AI systems that can also access Genese
Microsoft-based assistants
Custom enterprise AI solutions
External analytics platforms
Future agent-based applications

The key point is that these systems should not have uncontrolled access to data.

Instead, they should communicate with the professional logic of Genese through defined interfaces and controlled integration mechanisms.

Not One AI Interface — but Multiple Ways to Access the Same IP Data

This could represent a significant shift from traditional software architecture.

Historically, it was generally assumed that the software provider also determined how users interacted with the application.

In the future, there may be several parallel access routes.

One user may continue to work primarily through the traditional Genese interface.

Another may use an AI assistant.

A third may access selected information through Microsoft environments.

A management user may want to ask portfolio questions entirely in natural language.

An IP specialist, by contrast, may require a detailed and highly customized professional workspace.

A modern IP management platform therefore does not need to choose between AI and the user interface.

It needs to support both.

A Configurable UI Remains a Major Advantage

Genese already follows an approach that allows its user interface to be extensively adapted to different requirements.

That remains an important advantage in the age of AI.

Not every user needs the same information.

A patent manager works differently from an IP administrator.

Management requires different dashboards from a patent attorney.

A trademark department has different processes from a patent department.

The traditional user interface can therefore continue to be customized according to roles, processes, and information requirements.

AI adds another way of accessing this structure.

The result is not the elimination of the UI.

It is an expansion of the available interaction models.

From IP Management System to IP Platform

In the long term, this could lead to an even broader development.

An IP management system may no longer be simply an application through which people work using screens and menus.

Instead, it could become a professional IP platform connecting different users and systems to the same underlying IP data.

People access it through the traditional user interface.

AI agents use defined interfaces.

Other applications integrate selected data and functions.

External services connect through APIs.

The focus is no longer exclusively on the interface.

The real foundation becomes:

Data, domain logic, processes, semantics, and controlled access.

For specialized professional software such as Genese, this development creates significant opportunities.

The more AI changes how people interact with software, the more important the reliable professional infrastructure behind that interaction becomes.

Conclusion: The Future Is Not UI or AI — It Is UI and AI

AI agents will increasingly take over parts of navigation and information retrieval in enterprise software.

For IP management, however, this does not mean the end of the traditional user interface.

Instead, the roles are being redistributed.

The agent understands the user's intent.

The IP management system provides the data, relationships, and domain logic.

The user interface visualizes complex relationships and enables controlled, detailed work.

Interfaces connect Genese with different AI systems and agents.

The result is a hybrid model that can be considerably more powerful than either approach on its own.

With GeneseConnect, multiple integration possibilities, and a highly configurable user interface, Genese provides a technological foundation for this evolution.

The key question is therefore no longer:

“Will AI replace the user interface?”

Instead:

“How can we combine AI agents, domain logic, and the user interface so that users can reach better, faster, and more traceable decisions?”

That could be the next stage in the evolution of modern IP management systems.

IP Management for SMEs: Turning Ideas into Business Value

Thomas Delfing — Wed, 16 Sep 2026 15:28:53 +0000

Intellectual property does more than protect innovation. When managed strategically, it can strengthen market position, profitability, and long-term business value.

Small and medium-sized enterprises (SMEs) are often highly innovative. New technologies, specialized processes, distinctive brands, product designs, software, and valuable know-how frequently emerge directly from their day-to-day operations.

Yet in many companies, intellectual property only becomes a priority when a competitor adopts an idea, a deadline approaches, or investors and financing partners request reliable information about the company's IP portfolio.

The European Patent Office and national IP authorities have repeatedly highlighted the economic relevance of intellectual property for SMEs. At the same time, companies holding patents, trademarks, or designs tend to demonstrate stronger economic performance than companies without such rights.

This correlation does not mean that intellectual property rights alone cause business success. It does, however, illustrate an important point: professionally managed intellectual property is often closely associated with innovative, productive, and growth-oriented companies.

IP Is More Than a Patent

Intellectual property encompasses a broad range of protection mechanisms.

Technical inventions can be protected through patents or utility models. Trademarks protect names and distinctive signs, while registered designs protect the visual appearance of products.

IP can also include:

Copyright
Trade secrets
Software
Data assets
Business know-how
Contracts and contractual rights
Operational expertise and "show-how" — the knowledge of how a product or service can be successfully delivered and commercially utilized

For SMEs, the first task therefore should not be to register as many rights as possible.

The first question is much simpler:

What intellectual property does the company actually have?

A structured IP audit can help identify inventions, trademarks, designs, confidential information, contractual rights, and potential protection gaps.

It should also examine:

Who is responsible for individual IP assets?
Are there gaps in protection?
Are third-party rights involved?
What contractual arrangements are in place?
Which funding or support programs may be available?

This is particularly important because not every good idea automatically belongs to the company, and not every innovation can still be protected after publication.

Clear agreements with employees, development partners, and cooperation partners — together with controlled handling of confidential information — are therefore just as important to an IP strategy as filing an application with the relevant authority.

Protection Should Follow Business Objectives

For SMEs operating with limited budgets, prioritization is essential.

The key question is not:

"What can we register?"

It is:

"Which innovation protects our current or future business success?"

A commercially focused SME should consider questions such as:

Which technology, brand, or design contributes significantly to revenue and margins?
Which innovation clearly differentiates the company's offering from competitors?
In which countries are customers, production facilities, licensees, or relevant competitors located?
Is a registered IP right appropriate, or would controlled confidentiality provide better protection?
Can — and does — the company intend to enforce the right if it is infringed?
Which IP rights support future products and business areas, even if they do not generate revenue today?

The answers form the foundation of an effective IP protection strategy.

Such a strategy connects legal opportunities with market planning, product strategy, and available resources.

Thorough research is an essential part of this process. It can reduce the risk of infringing third-party rights and prevent companies from spending money on applications whose validity or commercial value may be questionable from the outset.

Filing an Application Is Only the Beginning

When evaluating intellectual property economically, looking only at the initial filing fee is not enough.

Over the entire lifecycle of an IP right, companies may incur costs for:

Drafting and preparation
Legal advice
Official procedures
Translations
International applications
Validations
Renewal and annual fees
Searches and monitoring
Enforcement

A single invention can develop into a large international IP family involving multiple jurisdictions, deadlines, administrative processes, and recurring costs.

But these costs need to be considered alongside the different forms of economic value an IP right can create.

Some rights generate direct licensing or sales revenue.

More often, however, their value is indirect. They may:

Protect product margins
Keep imitators out of the market
Make market entry more difficult for competitors
Strengthen a company's negotiating position
Support partnerships and licensing agreements
Improve the company's position in financing discussions

As a result, an IP right does not need to generate licensing revenue directly to be highly valuable. A patent protecting a company's core product, for example, can have considerable strategic value even if it is never licensed to a third party.

Four Factors for Evaluating an IP Portfolio

A robust portfolio decision should bring together at least four dimensions:

Current and expected future costs of maintaining the IP right
Directly attributable revenue, such as licensing income
Contribution to products, markets, margins, and competitive position
Strategic importance for future innovations and business opportunities

This is not simply an accounting exercise.

The value of a patent, for example, cannot always be attributed precisely to a specific percentage of company revenue.

Nevertheless, paying annual fees year after year without understanding which product, market, or strategic objective the right protects is difficult to justify.

The opposite approach can be equally short-sighted: abandoning an IP right simply because it does not currently generate revenue, even though it protects a core technology for the company's next generation of products.

Every important IP right should therefore have a clear strategic rationale.

The company should be able to answer whether it intends to:

Maintain it
Expand it geographically
License it
Sell it
Enforce it
Or deliberately abandon it

These decisions should be reviewed regularly and at defined milestones — for example, before an international filing, entry into national phases, or payment of significant renewal fees.

Funding Can Lower the Barrier — But It Does Not Replace Strategy

For 2026, the German Patent and Trademark Office (DPMA) points SMEs toward the SME Fund, financed by the European Commission and administered by the European Union Intellectual Property Office (EUIPO).

Depending on the voucher, certain IP-related activities and applications may be eligible for financial support.

However, the required sequence is important:

Apply → receive the funding decision → begin the eligible activity.

Funding can reduce costs, but it does not answer the strategic question of which IP rights a company actually needs and wants to maintain over the long term.

Financial support is therefore an enabler — not a substitute for IP strategy.

From IP Administration to Professional IP Management

As an IP portfolio grows, spreadsheets, calendars, and scattered document folders eventually become insufficient.

They may show that a deadline is approaching, but they rarely answer the management questions that matter most:

Which patent family protects which innovation?
Which products and markets depend on it?
What costs will arise over the next several years?
Which licensing revenues or other economic benefits can be attributed to it?
Where are the protection gaps?
Are there overlapping rights?
Which rights no longer have a clear strategic purpose?

A professional IP Management System (IPMS) provides a centralized and reliable data foundation.

It can bring together:

IP rights
Patent families
Deadlines
Tasks
Documents
Responsibilities
Contracts
Planned and actual costs

When combined with product, market, licensing, and valuation information, this transforms basic document administration into a manageable and strategically valuable IP portfolio.

Solutions such as Genese and Gweb Workspace are designed to support this professionalization.

Genese combines, among other capabilities, file, document, and deadline management with reporting, planned-versus-actual cost tracking, and cost forecasting.

Gweb Workspace provides web-based access to files, IP families, deadlines, and tasks while supporting structured collaboration through roles and access permissions.

An IPMS does not replace legal advice or entrepreneurial decision-making.

What it does provide is something equally important: a reliable information base for making better-informed decisions.

The Value of Transparency for SMEs

For SMEs, transparency is one of the most important benefits of professional IP management.

Management, R&D teams, IP professionals, and external advisors can maintain a shared view of the company's most important innovations.

Costs can be evaluated not simply as a total figure, but in relation to the individual:

IP right
Patent family
Product
Market

This makes it possible to identify where IP rights are making a measurable contribution to the business, where they are securing strategic opportunities for the future, and where ongoing expenditure may no longer have a sufficiently clear business rationale.

In other words, an IPMS can help management move from asking:

"What IP rights do we have?"

to asking:

"What value are our IP rights creating, and what role do they play in our business strategy?"

Conclusion

Intellectual property does not become a business asset simply because it has been registered.

It becomes strategically valuable through the combination of identification, protection, economic prioritization, consistent monitoring, and regular portfolio decisions.

This approach is particularly important for SMEs.

Their resources are limited, while their competitive advantages often depend on a relatively small number of critical innovations.

A professional IPMS such as Genese or Gweb Workspace can help make those innovations visible, secure deadlines and responsibilities, and compare ongoing IP costs with both current and expected economic value.

The result is more than a collection of files.

It is an actively managed IP portfolio — one that does not merely administer rights, but supports innovation, strategic decision-making, and long-term business growth.

Sources & Further Information

How to Patent an Invention: Costs, Process and International Protection

Thomas Delfing — Mon, 14 Sep 2026 05:54:04 +0000

From the first application to an international patent portfolio — what companies need to know about the process, costs and strategy in 2026.

A patent can be a valuable business asset. It can protect technical innovation, create competitive advantages, support licensing opportunities and increase the value of a company.

But filing a patent is more than an administrative formality. Companies need to consider prior-art research, examination, international filings, renewal fees, deadlines and the possibility of opposition after a patent has been granted.

This guide explains the key stages of the patent lifecycle and highlights the most important official fees as of August 2026.

Important: The figures below are official patent-office fees. Patent attorneys, translations, foreign counsel, validation services and other professional costs are additional.

What Does a Patent Protect?

A patent protects a technical invention. To be patentable, an invention generally needs to be new, involve an inventive step and be industrially applicable. The invention must also be described sufficiently for a skilled person to reproduce it.

One of the most important practical rules is simple: do not publicly disclose the invention before filing the patent application. An earlier disclosure can jeopardize novelty.

A patent also protects more than an abstract idea. The scope of protection is largely determined by the patent claims. Narrow claims may be easier to obtain but can provide limited commercial protection. Broad claims can offer greater protection but face more demanding examination.

That makes the quality of the original application critically important.

Before Filing: Research and Patent Strategy

Before filing, companies should research existing patents and technical literature. This can help determine whether the invention appears novel, identify competitors and shape the patent claims.

It is equally important to decide where protection will be needed.

Patent rights are territorial. A German patent generally protects an invention only in Germany. Protection in countries such as the United States, China, Japan or France requires additional filings.

Fortunately, companies do not have to make every international decision immediately.

The first application normally establishes a priority date, giving the applicant 12 months to file corresponding applications abroad while claiming that original priority.

This 12-month period is one of the most important deadlines in international patent strategy.

German Patent Applications: What Does It Cost?

For companies initially seeking protection in Germany, filing with the German Patent and Trademark Office (DPMA) can be a cost-effective starting point.

As of 2026, the key official fees include:

Fee	Official fee
Electronic application, up to 10 claims	€40
Each additional claim (electronic)	€20
Search request	€300
Examination after prior search	€150
Examination without prior search	€350
Opposition	€200

For an electronic application with up to ten claims, a search request and subsequent examination, the initial official fees are:

€40 + €300 + €150 = €490

If examination is requested without a separate search request:

€40 + €350 = €390

These figures do not represent the total cost of obtaining a patent. Drafting the application, legal advice and managing the examination process can add substantial professional costs.

What Happens After Filing?

Filing establishes the application date, but it does not automatically create a patent.

The DPMA first checks formal requirements and then examines whether the invention meets the legal requirements for patentability.

The examiner may raise objections. The applicant can respond and, where appropriate, amend the claims.

If the objections can be overcome and the legal requirements are satisfied, the patent can be granted. Otherwise, the application may be rejected.

Patent applications are also generally published around 18 months after the filing or priority date, making the technical disclosure publicly accessible.

How Long Does a Patent Last?

A German patent can generally last for up to 20 years from the filing date, provided the required annual fees are paid.

The annual fees increase considerably over time:

Patent year	Annual fee
3	€70
5	€100
10	€430
15	€1,130
20	€2,030

If all annual fees are paid individually through the 20th year, they total approximately €14,160.

Combined with the initial electronic application, search and examination fees, this produces roughly €14,650 in official fees over the full potential lifetime under this example.

The economic question, however, is more important than the administrative one: Is the patent still worth maintaining?

A patent protecting a successful product may be worth far more than its annual fee. A patent covering obsolete technology may no longer justify the expense.

Patent management therefore requires regular commercial evaluation, not just deadline management.

Protecting an Invention Across Europe

Companies seeking protection across multiple European markets can file a European patent application with the European Patent Office (EPO).

As of April 2026, selected EPO fees include:

Fee	2026 amount
Online filing	€135
European search	€1,595
Designation	€720
Examination	€2,010
Grant and publication	€1,135
Opposition	€880

These basic fees total approximately €5,595.

Additional costs can arise from lengthy proceedings, applications exceeding certain page or claim limits and annual fees payable while the application remains pending.

After grant, companies must also decide how the European patent should be given effect in individual countries.

That decision can involve translations, national fees and other formalities, so selecting countries based purely on geographic coverage is rarely the best strategy.

The right question is:

Where are our important markets, production facilities, competitors and licensing opportunities?

The Unitary Patent

The Unitary Patent provides another option for participating EU countries.

It still requires a European patent to be granted first. After grant, the applicant can request unitary effect for the participating states.

As of 2026, the system covers 18 EU member states.

One advantage is centralized administration: instead of managing separate renewal fees in every participating country, the patent holder pays a single annual fee to the EPO.

Whether a Unitary Patent is more economical than traditional validation depends on the countries in which protection is actually needed.

The PCT: Buying Time for International Decisions

For companies with global ambitions, the Patent Cooperation Treaty (PCT) is an important strategic tool.

There is no such thing as a single worldwide patent. Instead, the PCT allows applicants to file one international application that can preserve options across a large number of contracting states.

The PCT currently covers 158 states.

The major advantage is time. National or regional phases typically begin around 30 months from the priority date, although the exact deadline depends on the relevant jurisdiction.

This gives companies additional time to evaluate:

Market potential
Technical development
Financing
Competitor activity
Licensing opportunities
The cost of entering individual countries

For PCT applications filed through the DPMA from April 1, 2026, with the EPO conducting the international search, official fees start at approximately €3,081 for a fully electronic XML filing.

Later national or regional phase fees, translations, local attorneys, examinations and renewal fees are additional.

The PCT should therefore be viewed less as a cheap international patent and more as a strategic tool for delaying major country-by-country decisions.

One Invention Can Become an Entire Patent Family

A single invention can quickly develop into a complex international patent family.

For example:

German filing → 12-month priority period → European/PCT filing → national or regional phases → grants → validations/unitary effect → annual fees

Each stage creates additional files, deadlines, documents, fees and responsibilities.

This is where professional patent and IP management becomes increasingly important.

Instead of treating every application as an isolated file, companies need visibility across the entire patent family.

What Happens If Someone Opposes Your Patent?

Granting a patent does not make it permanently unchallengeable.

In Germany, third parties can oppose a granted patent within nine months after publication of the grant. The current official DPMA opposition fee is €200.

An opposition can result in the patent being:

Maintained as granted
Maintained in an amended form
Revoked

European patents can also be opposed within nine months of publication of the grant. The EPO opposition fee is €880 as of April 2026.

These proceedings can have significant commercial consequences. A patent may protect an important product line, while a competitor may have a strong incentive to limit or eliminate that protection.

The Real Cost of a Patent

Looking only at the filing fee gives a misleading impression of patent costs.

The €40 German electronic filing fee is relatively small. The larger expenses accumulate throughout the patent's lifecycle:

Patent drafting and attorney fees
Examination and prosecution
International filings
Translations
Foreign counsel
Validation
Annual renewal fees
Opposition and appeal proceedings
Potential litigation

For an international portfolio, these costs can become substantial over many years.

Patent protection should therefore be viewed as a long-term investment in an intangible business asset, rather than a one-time administrative expense.

Why Patent Portfolio Management Matters

As a portfolio becomes more international, managing deadlines and relationships between applications becomes increasingly complex.

A single German filing can lead to a European application, a PCT application and multiple national proceedings. Each may have different deadlines, fees and procedural requirements.

A professional IP management platform such as Gweb Workspace by Genese can help companies and law firms centralize patent files, deadlines, tasks and related documents.

The platform is designed to connect national and international matters and provide structured deadline and task management. It can also help users view complete patent families and manage portfolios in context.

This is particularly valuable when companies need to answer strategic questions such as:

Which countries should we protect? Which patents should we maintain? What will this portfolio cost over the next five, ten or twenty years?

That moves patent management beyond simple deadline tracking and toward strategic IP management.

The Patent Lifecycle at a Glance

A patent strategy does not end when an application is submitted.

The lifecycle typically looks like this:

Invention → Prior-art research → First filing → 12-month priority period → International filings → Examination → Grant → Validation or national phases → Annual fees → Portfolio review → Opposition or invalidity proceedings, if necessary

Each stage requires different decisions.

Instead of asking only:

How much does a patent application cost?

companies should also ask:

Where do we actually need protection? How will the patent family develop? What deadlines and costs are coming? And what economic value does the patent provide?

Conclusion

A German patent can be filed electronically for an official fee of just €40, with €490 in initial official fees when combining filing, a separate search and subsequent examination for an application with up to ten claims.

But the filing fee is only the beginning.

Annual fees increase over time. European and international protection introduces additional costs and strategic decisions. The PCT can provide valuable time to assess international markets, while European and Unitary Patent strategies can help structure protection across Europe.

Most importantly, a patent portfolio can quickly become a complex network of applications, countries, deadlines, fees and legal proceedings.

Effective patent protection therefore depends not only on filing a strong application, but on managing the entire lifecycle strategically.

For companies building valuable intellectual property, professional portfolio management can turn a collection of patent files into a structured long-term business asset.

Fee status: August 2026. Official fees and legal requirements may change. For specific applications and international patent strategies, always verify the current requirements with the relevant patent office or qualified patent professional.

If Everything Is Urgent, Nothing Is Important: Why Leaders Need to Make Time and Activities Visible

Thomas Delfing — Sun, 13 Sep 2026 15:01:03 +0000

Productivity does not come from permanent time pressure. It comes from consciously deciding where available time should be spent.

“ASAP,” “as soon as possible,” “highest priority,” or “we actually needed this yesterday.”

Phrases like these have become part of everyday business language. They communicate determination and responsiveness. From a management perspective, they can even feel reassuring. A task is assigned, a deadline is attached, and everything initially appears organized.

The problem begins when genuine exceptions become the permanent way of working.

A recent discussion in Der Spiegel addresses precisely this phenomenon, asking how employees can challenge artificial urgency and push back against deadlines that lack a clear rationale.

From a leadership perspective, however, simply telling employees to resist unnecessary deadlines is not enough.

The deeper issue is this:

Leaders need to understand which activities actually consume time, how changing priorities affect ongoing projects, and whether the perceived workload matches the actual distribution of work.

This turns activity tracking into a management tool.

Not a tool for controlling people, but a foundation for objectively understanding how work is actually being done.

The Dangerous Confusion Between Urgency and Importance

A deadline is not inherently negative.

Projects need schedules. Customers expect results. Contracts contain deadlines. Proposals need to be submitted, systems delivered, and regulatory requirements fulfilled.

The problem begins when the proximity of a deadline is automatically interpreted as the business importance of a task.

Researchers led by Meng Zhu have described this phenomenon as the “mere urgency effect.” Their experiments showed that people tend to favor urgent tasks over more important tasks, even when the supposedly urgent tasks provide less actual value.

Simply putting a time limit on something can influence our attention so strongly that we lose sight of the task's actual importance.

For companies, this effect is highly relevant.

Imagine a strategically important concept for a major client that has two weeks before its deadline.

At the same time, an employee receives an email asking for a relatively unimportant analysis “by this afternoon.”

Psychologically, the second task suddenly has greater presence.

Without conscious prioritization, the deadline wins over importance.

That is where leadership responsibility begins.

Leadership Also Means Filtering Urgency

A leader should not simply pass organizational pressure down the hierarchy.

They need to evaluate it.

Imagine management forwards a request to a department head. The department head passes it to a team leader as “urgent.” The team leader then turns it into an immediate task for an employee.

Within minutes, an ordinary request can become a perceived crisis.

And nobody has stopped to ask why the result is actually needed today.

For me, professional leadership therefore always includes the question:

What actually happens if this task is completed tomorrow, next week, or at the originally planned date?

The answer is often surprising.

For a legal deadline, the consequences may be significant.

The same can be true for a production outage. If an important customer is waiting for a proposal, speed may provide a genuine competitive advantage.

But for many internal requests, the honest answer is:

Practically nothing.

The deadline exists because someone named a date. That date is passed along, gains an appearance of legitimacy, and eventually displaces other work.

A good leader needs to interrupt this mechanism.

The Invisible Problem: Every New Priority Displaces an Old One

In project-based work, time has one particularly important characteristic:

It cannot be allocated to multiple things at the same time.

If a developer spends four hours preparing an urgent special analysis, those four hours are unavailable somewhere else.

If a lawyer suddenly prepares an internal report, they are not working on a client matter during that period.

If an engineer stops a customer project to prepare an unexpected presentation, the development work does not continue during those hours.

Yet new tasks are often treated as if they simply appear on top of existing capacity.

The right management question is therefore not:

“Can you get this done today?”

It is:

“Which other task should we postpone so that this task can be completed today?”

That small difference fundamentally changes leadership.

A new priority now has a price.

And leaders can only evaluate that price if they understand what the organization's available time is actually being spent on.

A Full Workday Says Little About Productivity

Many companies now have excellent project management systems.

They know their tickets, tasks, milestones, budgets, responsibilities, and deadlines.

What is often missing is the connection between these structures and the actual time spent doing the work.

A project can be 80% complete according to the project plan and still be economically off track.

Conversely, a project may be behind its original schedule even though the team is working highly productively because unexpected customer requests or internal special assignments have consumed a significant amount of capacity.

Without an activity dimension, leaders primarily see outcomes.

They do not necessarily see how available capacity was used to produce those outcomes.

This is particularly important in knowledge work.

Microsoft has described a working environment characterized by an exceptionally high number of digital interruptions. Employees in highly communication-intensive environments are frequently interrupted by meetings, emails, and messages.

An organization can therefore appear extremely active while producing surprisingly little focused value.

Context Switching Is Not a Free Resource

Permanent urgency becomes particularly problematic when it creates frequent task switching.

An employee is working with concentration on a project.

A message arrives.

An allegedly urgent issue needs to be resolved.

Then comes a phone call.

Afterward, the employee returns to the original project.

A traditional time-tracking system might record 30 minutes for the interruption.

But the actual organizational impact can be considerably larger.

Research into the phenomenon known as “attention residue” suggests that our attention does not necessarily move completely from one task to the next. Parts of our thoughts remain with the previous task, potentially reducing performance on the new task.

That is why looking at activities can be much more informative than simply looking at attendance time.

As a manager, it is not particularly useful for me to know that someone worked for eight hours.

I want to know whether those eight hours consisted of, for example:

Six hours of focused project work
One hour of communication
One hour of administration

Or whether those eight hours consisted of twenty small activity blocks spread across five different projects, internal requests, emails, and meetings.

Formally, both are eight-hour workdays.

Organizationally, they are completely different.

Our Perception of Time Is Surprisingly Unreliable

There is another problem:

People are not always good at estimating how much time they actually spend on something.

This is not a question of competence. It is a well-documented psychological phenomenon.

The American Psychological Association describes the planning fallacy as the tendency to underestimate the time required for future tasks, partly because we base our estimates on overly optimistic assumptions about how things will unfold.

From a leadership perspective, this is highly relevant.

An employee might genuinely believe:

“These meetings barely take any time.”

An activity analysis may show that several hours per week are being spent on them.

A project manager might believe:

“The customer only asks occasional follow-up questions.”

Over four weeks, those questions can turn into a substantial amount of additional work.

A manager might believe:

“My team spends almost all of its time on our strategic projects.”

An activity analysis may show that administration, internal coordination, support, and short-notice special assignments are consuming a significant proportion of available capacity.

None of these people necessarily have to be deliberately wrong.

Their perception may simply be inaccurate.

That is precisely why structured activity tracking is so valuable.

Activity Tracking Is First a Mirror for the Employee

Time tracking is often viewed exclusively from the company's perspective.

The company wants to calculate project costs, bill services, or document working hours.

But an important benefit is often underestimated:

Recorded activities give employees a more objective view of their own working lives.

That can be extremely valuable.

If someone looks back on Friday and simply feels that they have been busy all week, there is little to learn from that feeling alone.

But if they can see that, for example:

35% of their time went to customer projects
20% went to meetings
15% went to internal coordination
15% went to support
15% went to administrative activities

they have a completely different basis for reflection.

They can then ask concrete questions:

Does this distribution match my actual responsibilities?

Did I spend my time on the areas I am accountable for?

Which activities took significantly longer than expected?

Which tasks repeatedly interrupted focused work?

Which supposedly urgent tasks were actually important in hindsight?

Where might I have created urgency myself even though it was not necessary?

Activity tracking therefore becomes a tool for personal time management and self-reflection.

For Leaders, a Second Perspective Emerges

The same information serves a different purpose at management level.

Here, the focus is less on an individual workday and more on patterns.

If a project consistently requires significantly more time than planned, the reason could be an inaccurate estimate.

But it could also be changing requirements, excessive coordination, technical problems, or a large number of unplanned additional services.

Without activity data, many of these discussions remain subjective.

The project manager says the customer is generating an unusually high workload.

Sales says the project was correctly estimated.

The employee says they are constantly dealing with follow-up requests.

Management simply sees that the project budget has been exceeded.

Structured analysis of actual activities creates a shared factual basis.

In Project-Based Businesses, Time Becomes an Economic Variable

This point becomes even more important for project-oriented companies.

Project controlling should not begin when an invoice is issued or a project has already been completed.

Leaders need to identify as early as possible whether actual effort and estimated effort are beginning to diverge.

Knowing the total amount of time is not enough.

The structure of that time matters.

Suppose 120 hours were budgeted for a project and 80 hours have already been consumed halfway through the project timeline.

That is an obvious warning signal.

The information becomes even more valuable when you can see why those 80 hours were used.

Were more development hours required?

Were there unexpectedly many customer meetings?

Were services delivered that were not originally included in the scope?

Are internal quality processes more complex than expected?

Or is the project repeatedly being interrupted by urgent tasks from other projects?

Only then can leadership intervene effectively.

Time Tracking Must Not Be Confused With Employee Surveillance

At this point, an important boundary needs to be established.

Good activity tracking must not become a system for evaluating every minute of an employee's day.

The wrong question would be:

“Why did Employee A need 43 minutes for this task while Employee B only needed 31?”

That kind of analysis can quickly create a culture of surveillance and destroy the actual value of the data.

Complexity, experience, quality of work, customer requirements, and different approaches cannot be reduced to individual minute values.

Much more useful questions are:

Why does this type of activity now require 25% more time than it did six months ago?

Or:

Why is the proportion of unplanned work in this team continuously increasing?

Or:

Why has the capacity allocated to a strategic project not been achievable for four consecutive weeks?

Activity data should therefore primarily be used to explain processes, not judge people.

In my view, that is one of the most important conditions for employee acceptance.

Tactile Time Tracking Can Significantly Reduce the Barrier

There is still a practical challenge.

The more complicated time tracking becomes, the less effectively it works in everyday life.

If an employee has to open a program, search for a project, select an activity, and start a timer every time they change tasks, time tracking becomes an additional administrative process.

With frequent task switching, that process quickly becomes disruptive.

This is precisely where TimeSpin takes a tactile approach.

The TimeSpin dodecahedron can be individually configured with activity or project labels. Its different sides can represent customer projects, internal projects, administration, meetings, support, or other relevant activities.

When the activity changes, the user simply turns the cube to the corresponding side.

Time tracking follows the actual action.

The data is automatically synchronized and can then be analyzed.

In my view, the tactile interaction also creates an additional benefit: it makes the activity change conscious.

When someone turns the cube from “Project Development” to “Email / Administration,” they immediately register that a context switch has taken place.

The technology therefore does not merely support later reporting and controlling.

It can also support self-awareness during the working day.

The Weekly Review Matters More Than the Individual Minute

Such tracking becomes particularly valuable when viewed over a week or a month.

Not every individual deviation matters.

Patterns matter.

An employee may discover after four weeks that they spend an average of 12 hours per week in internal meetings.

A team may discover that almost one-quarter of its available project capacity is being consumed by short-notice support requests.

A manager may see that a project is progressing according to plan but requires significantly more coordination than originally estimated.

A CEO may discover that strategic topics are repeatedly being displaced by supposedly urgent operational tasks.

The conversation then changes.

Instead of:

“We don't have enough time.”

the question becomes:

“What are we actually using our time for?”

From a leadership perspective, that is a much better question.

Time Data Can Lead to Better Project Planning

Tracking actual activities also offers a long-term advantage:

It improves future project planning.

If historical data shows that a particular project phase consistently requires 30 hours, even though proposals are repeatedly calculated with 20 hours, the next project should not simply be planned with 20 hours again.

Activity tracking can help counteract the psychologically established planning fallacy.

We have to rely less on assumptions and can instead use historical experience.

Project management research has long highlighted the role of optimism, overconfidence, and the planning fallacy in systematic errors in project forecasting.

Historical activity data provides an external perspective:

How long did comparable activities actually take?

Time tracking can therefore turn historical data into organizational knowledge.

Leaders Also Need to Question Their Own Urgency

Responsibility should not be placed exclusively on employees.

Leaders themselves are often the biggest producers of artificial urgency.

A short message from a CEO may be nothing more than a question from their perspective.

To the recipient, however, it may immediately become the highest priority.

“Could you take a look at this?” can be interpreted by a team as:

“The boss expects this today.”

“We should clarify this soon” can become:

“Do this immediately.”

That is why leaders need to learn to communicate priorities more precisely.

If something is needed by Friday, say Friday.

If something should only be done after other priorities are completed, say that as well.

And if immediate action really is necessary, leadership should simultaneously decide which existing task will be postponed.

Prioritization without deprioritization is not prioritization.

Transparency Also Changes the Conversation About Overload

This insight is particularly important when managing teams.

When an employee says:

“I can't keep up with my workload anymore.”

there are two bad reactions.

The first is:

“Then you need to organize yourself better.”

The second is:

“Then we immediately need more employees.”

Either response could be correct.

But either could also be wrong.

The first step should be understanding what is actually causing the workload.

Perhaps the employee genuinely has too many tasks.

Perhaps they are spending an unusually large amount of time on activities that could be automated or delegated.

Perhaps they are constantly being interrupted by other departments.

Perhaps project budgets were unrealistically calculated.

Or perhaps certain processes have become so bureaucratic that they consume a disproportionate amount of working time.

A transparent activity structure makes these causes visible.

This also changes the quality of the conversation between employees and managers.

The discussion becomes less dependent on subjective impressions and more grounded in information that both sides can understand and evaluate.

Time Tracking Should Not Become an Isolated System

For professional business environments, it is important that activity data does not remain trapped inside a technical silo.

Time tracking creates the greatest value when the data can flow into existing project management, ERP, accounting, practice management, or billing systems.

TimeSpin provides integration capabilities for this purpose. Its API enables external systems to exchange and process time data as part of existing workflows.

For patent and trademark professionals, Genese provides specialized IP management software, and TimeSpin can connect recorded time with relevant Genese case files.

This can turn a simple tactile interaction at the workplace into a continuous information chain:

Activity → Time spent → Project → Controlling → Billing → Management decision

The real value is therefore not the timer itself.

The value lies in the insights generated from the data.

Conclusion: Leadership Needs Less Urgency and More Transparency

Companies do not become more productive by labeling as many tasks as possible as urgent.

They become more productive when employees and leaders can distinguish which activities actually matter and where the limited resource of time is truly being spent.

That requires more than project plans and calendars.

It requires a realistic view of everyday work.

Activity tracking can help on three levels.

For the employee, it creates a more objective basis for self-reflection. Their perception of how they spent the week can be compared with the actual distribution of activities.

For the manager, it creates transparency around capacity, interruptions, project effort, and organizational patterns.

For the company, it creates a data foundation for better project estimates, improved processes, and a clearer distinction between necessary urgency and artificially created urgency.

A simple tactile solution such as TimeSpin can integrate this transparency into everyday work without turning time tracking itself into another administrative burden. Activities can be represented on the sides of the dodecahedron, changes are recorded by simply turning the device, and the data is automatically synchronized.

But ultimately, what matters is how leaders use that data.

The central question should not be:

“Did you document every minute?”

The much more important question is:

“Did we use our shared time for the right things?”

That is where productive leadership begins.

Daylight Saving Time 2026: Why Modern Time Tracking Shouldn’t Fear the Repeated Hour

Thomas Delfing — Sun, 13 Sep 2026 14:39:46 +0000

On October 25, 2026, Germany will switch from daylight saving time back to standard time. For people, that means one extra hour of sleep. For professional time-tracking systems, however, the change should be little more than a change in how time is displayed.

In fact, the annual clock change is an excellent test of how well software actually understands time.

During the night of October 24–25, 2026, Central European Summer Time will end in Germany. At 3:00 a.m., the clocks will be set back to 2:00 a.m. As a result, the hour between 2:00 and 3:00 a.m. occurs twice.

Germany's Physikalisch-Technische Bundesanstalt (PTB) even distinguishes the two periods as 2A and 2B to make them unambiguous.

For most people, this simply means an extra hour in bed. From an IT perspective, however, it raises a surprisingly fundamental question:

What does it actually mean to say that something happened at 2:30 a.m.?

When 2:30 A.M. Happens Twice

Imagine an employee starts working at 2:15 a.m. on October 25 and finishes at 2:45 a.m.

At first glance, the calculation seems obvious: 30 minutes of work.

But which 2:15 a.m. was it?

Was it 2:15 before the clock was turned back, or 2:15 after the transition to standard time?

The problem becomes even more obvious when an activity starts at 2:50 a.m. during daylight saving time and ends at 2:20 a.m. during standard time.

If software simply subtracts the displayed local times, it could conclude that the employee somehow worked a negative amount of time.

In reality, 30 minutes have passed.

This is precisely why professional time-tracking systems cannot treat the displayed clock time as the complete representation of time.

The underlying data needs to be based on an unambiguous timeline. Events should be associated with a unique timestamp and only converted into the user's local time zone for display.

That way, a point in time remains identifiable regardless of whether the local clock is moving forward, moving backward, or being displayed according to another time-zone rule.

Real Time Doesn't Stop When the Clock Changes

Germany's legal time system distinguishes between Central European Time (CET) and Central European Summer Time (CEST). Under the current rules, daylight saving time ends on the last Sunday in October, when the clock moves from 3:00 a.m. CEST back to 2:00 a.m. CET.

From the perspective of modern software, there is therefore no reason to suddenly "delete" an hour or artificially create one.

The underlying timeline continues.

What changes is the relationship between that timeline and the local clock displayed to the user.

This may sound like a small technical distinction. For employee time tracking, project tracking, billing, and documentation, however, it can be extremely important.

Time Tracking Is More Than a Clock on a Screen

The distinction becomes particularly important for companies that track working hours, project time, billable activities, or tasks down to the minute or second.

A modern time-tracking system needs to distinguish between three different concepts:

A point in time
A duration
A displayed local time

When calculating how long an activity took, the system needs to know the amount of actual elapsed time between the beginning and the end of the activity.

The local clock is primarily a representation of that information for the user.

The same principle applies when employees travel between time zones.

Imagine someone starts work in Germany in the morning, flies to New York, and records another activity after arriving.

The displayed local time may suddenly shift by several hours. The underlying sequence of events, however, remains perfectly clear.

A robust time-tracking system should preserve that sequence without forcing the user to think about time zones, offsets, or daylight saving rules.

TimeSpin: Focus on the Activity, Not the Clock

This is where the difference between a traditional punch clock and modern activity-based time tracking becomes particularly interesting.

TimeSpin – Precision Time Tracking focuses on the activity rather than forcing employees to constantly interact with a running clock.

With the physical TimeSpin cube, users simply rotate the device to the side representing the activity, project, or task they are working on. The system automatically records the corresponding time.

The goal is simple:

What did I spend my time on?

The technical complexity behind that question should remain invisible to the user.

Whether daylight saving time or standard time is in effect, whether a device changes its time zone, or whether an employee is working while traveling should not complicate the basic act of recording an activity.

A professional time-tracking system should handle those conditions in the background.

The Repeated Hour Is an Excellent Software Test

The switch back to standard time is therefore a surprisingly useful test for the architecture of time-tracking software.

A robust system should be able to handle situations such as:

An employee works across the daylight saving time transition.
An activity starts during the first 2 a.m. hour and ends during the second.
Multiple devices record or synchronize time information.
A mobile device temporarily operates offline.
Data is synchronized with a server at a later point.
Employees work simultaneously in different time zones.

The key question is not what a particular device clock happens to display.

The important questions are:

When did the event actually happen?

How much time actually elapsed?

That turns what looks like a simple time-tracking feature into a classic distributed-systems problem.

Offline Time Tracking Makes the Challenge Even More Interesting

The issue becomes even more relevant for systems that do not require a permanent connection to a central server.

TimeSpin's offline time tracking is designed so that tracked time can be stored locally and synchronized later. TimeSpin's documentation confirms that the cube can record time without a permanent internet connection and transfer the stored data during synchronization.

That means correct time assignment cannot depend on having a permanent internet connection at the exact moment an activity is recorded.

A professional data model needs to preserve enough information to reconstruct, even later, when an activity occurred and how long it lasted.

Ideally, none of this complexity is visible to the employee.

They simply turn the cube and continue working.

The Clock Change Isn't Going Away — Yet

Politically, Europe's twice-yearly clock change was supposed to have disappeared by now.

The European Commission proposed ending seasonal clock changes in 2018. However, the proposal has not become final legislation. As of August 2026, the Council of the European Union states that no final decision has been taken and no timeline for such a decision has been defined. Until then, the current system remains in place.

That means Germany will once again move its clocks back on Sunday, October 25, 2026, from 3:00 a.m. to 2:00 a.m.

For people, the change can be noticeable.

For software, it should be almost boring.

And that is exactly the point.

Good Software Hides Complexity From the User

The annual clock change illustrates a fundamental principle of good business software:

Complexity doesn't disappear when you ignore it. It has to be solved in the right place.

Time zones, daylight saving rules, synchronization, offline operation, timestamps, and data consistency are technical concerns.

Users should not have to understand them simply to record what they worked on.

With time tracking, their attention should instead remain focused on the questions that actually matter to the business:

Which project consumed the most time?
How is workload distributed?
Which activities require more resources than expected?
Where are projects exceeding their budgets?
How does actual effort compare with planned effort?
Which tasks are creating bottlenecks?

These are questions for employees, project managers, and business owners.

Not:

"Which of the two 2:30 a.m. timestamps did I just mean?"

The Best Clock Change Is the One Nobody Notices

The real test of modern time-tracking software is therefore not whether it can display the correct time after the clocks change.

It is whether the user can continue working without thinking about the clock change at all.

A well-designed system separates actual time, duration, local time, time zones, and synchronization at the technical level while presenting the user with a simple experience.

That is the philosophy behind modern activity-based time tracking.

And it is why the best time change in a modern time-tracking system is ultimately the one the user never notices.

Learn more about TimeSpin

From Billable Hours to Flat Fees: Why Ending Hourly Billing Does Not Mean Ending Time Tracking

Thomas Delfing — Mon, 07 Sep 2026 08:31:26 +0000

Law Firms Are Changing Their Pricing Models — and That Makes Understanding Where Time Goes More Important Than Ever

For decades, the logic behind legal billing was relatively straightforward: time was the primary unit of billing.

A lawyer worked on a matter, recorded the hours spent on it, and — unless statutory fee regulations applied — billed those hours at an agreed hourly rate.

This model remains particularly influential in commercial law and intellectual property law. The so-called billable hour directly connects the time spent working with revenue. If a lawyer needs ten hours instead of eight to complete a task, the billable fee generally increases accordingly.

But this model is increasingly coming under pressure.

Clients are demanding greater cost transparency and predictability. Corporate legal departments are working with defined budgets. Legal Operations is becoming increasingly professionalized in the procurement of legal services. At the same time, automation and artificial intelligence can significantly accelerate certain legal workflows.

Against this backdrop, alternative fee arrangements are becoming increasingly important.

However, the billable hour is far from disappearing. Even in 2026, hourly billing remains a central part of the legal market. At the same time, recent research indicates that alternative fee arrangements are already widely used. A 2025 U.S. survey by Best Law Firms, for example, found that 72% of participating law firms offered alternative fee arrangements, with the figure rising to 90% among firms with more than 50 lawyers.

The real development, therefore, is likely to be less about completely replacing hourly billing and more about the growing coexistence of different pricing models.

For certain types of legal work, the billable hour remains useful. For others, flat fees, capped fees, retainers, portfolio pricing, and hybrid models are becoming increasingly attractive.

But this shift has an important consequence that is often overlooked:

The less working time is billed directly to the client, the more important that time can become for the law firm's internal management.

A flat fee does not eliminate working time.

It simply eliminates its direct use as the billing unit.

Why Clients Find Flat Fees Attractive

From a client's perspective, the primary advantage of a flat fee is obvious: the price is known in advance.

For example, if a law firm agrees to charge €5,000 for preparing and filing a particular application, the legal department does not have to worry about whether the final invoice will be based on 12, 18, or 25 hours of work.

This makes budgeting and cost control considerably easier.

At the same time, flat fees change the way clients think about the value of legal work.

The client is no longer primarily purchasing a certain number of legal working hours. Instead, the client is purchasing a defined service or a specific outcome.

Economically, this is a significant difference.

Under traditional hourly billing:

Fee = Time × Hourly Rate

Under a flat fee:

Fee = Agreed Price for a Defined Scope of Work

How long the law firm actually takes to deliver the service initially becomes the firm's own economic concern.

This makes flat fees particularly interesting for recurring and relatively standardized legal services.

In intellectual property law, examples could include certain filing procedures, renewals, recordals, standardized portfolio work, or clearly defined stages of legal proceedings.

Flat fees also create a different incentive for efficiency.

Under hourly billing, reducing the time required to complete a task initially means fewer billable hours. Under a flat fee, more efficient service delivery can directly improve the firm's margin.

This relationship is becoming increasingly important as Legal Tech and artificial intelligence reshape legal workflows. Market analyses are increasingly identifying a connection between AI-driven productivity gains and the growing discussion around alternative pricing models.

The Economic Paradox of Artificial Intelligence

Consider a simplified example.

A law firm charges a flat fee of €5,000 for a particular legal service.

Historically, lawyers and legal professionals required an average of 15 hours to complete the work.

Through better workflows, document automation, templates, AI-assisted research, and automated analysis, the average processing time eventually falls to nine hours.

Under the flat-fee model, revenue initially remains €5,000.

The productivity gain can therefore translate directly into higher margins for the law firm.

Under a pure hourly billing model, the same productivity gain can initially reduce billable revenue.

If the work previously required 15 hours but now takes only nine, only nine hours can generally be billed.

This creates a remarkable economic tension:

The traditional hourly model can economically penalize productivity improvements, while flat fees can reward them.

This is one reason why the growing use of artificial intelligence is changing the discussion around legal pricing.

Thomson Reuters has been highlighting for some time the growing pressure on traditional legal pricing models and the search for more value- and outcome-oriented approaches to legal fees.

However, this does not mean that every flat fee is automatically profitable.

Quite the opposite.

With a flat fee, the law firm takes on part of the economic risk that would otherwise sit with the client under an hourly billing model.

With Flat Fees, the Time Risk Shifts

Under the billable hour, the client carries a significant portion of the risk associated with unexpected increases in workload.

If a law firm needs more time to handle a matter, the fee generally increases.

With a flat fee, the situation is different.

If a service has been offered for €5,000, the initial revenue remains €5,000 regardless of whether the firm ultimately requires eight, twelve, or twenty hours internally.

This makes another variable critically important:

the variance in actual workload.

A matter that normally requires ten hours and typically varies between nine and eleven hours is relatively easy to price.

A matter that also averages ten hours but can fluctuate between three and 30 hours is significantly more risky under a flat-fee arrangement.

This distinction is particularly important in legal services.

Not every matter can be standardized.

An unexpected submission from the opposing party, a new legal issue, additional client discussions, an unusual examination report, or a technically complex invention can significantly increase the amount of work required.

This is why the definition of the scope of work is critical when using flat fees.

A flat-fee agreement should not simply state:

“We will handle the matter for €4,000.”

A more precise approach defines which services are included, how many rounds of client communication are covered, which stages of a proceeding are included, and which additional events trigger a new fee agreement.

A flat fee does not necessarily mean “everything included.”

Professionally designed flat fees require systematic scope management.

The Most Important Metric May No Longer Appear on the Invoice

This is where the role of time tracking changes.

In a traditional billable-hour law firm, time tracking has an obvious purpose:

It provides the basis for the invoice.

With a flat fee, that connection disappears.

This can lead to a dangerous conclusion:

“If we no longer bill hours, we no longer need to track them.”

From a business perspective, that would be a mistake.

The law firm still needs to know how many resources are required to deliver the agreed service.

The relevant question is no longer:

How many hours can we bill to the client?

Instead, it becomes:

How many hours can we afford to spend internally on this service while keeping the agreed flat fee profitable?

Time tracking therefore evolves from a billing tool into a management and controlling tool.

For the economic management of a law firm, this form of time tracking can ultimately become even more important than traditional billable-hour tracking.

Not Every Hour Costs the Law Firm the Same

There is another important consideration.

For a flat fee to be profitable, simply knowing the total number of hours is not enough.

It also matters who performed those hours.

A partner's hour has a different internal cost structure and opportunity cost from an associate's hour.

The working time of a patent paralegal or another specialized professional must also be assessed differently.

Consider two matters that each require 12 hours.

Matter A

8 hours performed by a partner
4 hours performed by a specialist

Matter B

2 hours performed by a partner
4 hours performed by an associate
6 hours performed by a patent paralegal

Although both matters require exactly 12 hours, their contribution margins can be significantly different.

This raises another important question:

Is each task being performed by the right person from both an economic and professional perspective?

For standardized services in particular, law firms can achieve significant efficiency gains by distributing work appropriately between lawyers, associates, paralegals, specialized Legal Operations professionals, and technical systems.

Automation and AI add another layer to this equation.

The key metric is therefore not simply working time.

It is the combination of:

time + expertise + internal cost + process + revenue.

Flat Fees Need an Internal Time Budget

Professional flat-fee pricing should therefore continue to be based on time internally.

Suppose a law firm wants to offer a standardized service for €4,000.

Based on historical data, the firm knows that an economically attractive matter must not exceed a certain level of internal resource consumption.

The flat fee therefore creates an internal time budget.

The client may never see this budget.

For the law firm's management team, however, it can be critical.

Management can then compare planned and actual resource consumption.

For example:

A matter was budgeted at 10 hours and completed in 8 hours — positive variance.
A matter was budgeted at 10 hours and required 11 hours — still within an acceptable range.
A matter was budgeted at 10 hours and required 22 hours — requires analysis.

Only a sufficiently large number of matters creates a reliable data set.

The firm may discover that the original estimate of 10 hours is realistic.

But it may also discover that actual processing time averages 14 hours.

At that point, the firm needs to respond.

It could:

increase the price,
reduce the scope of the service,
optimize the workflow,
redistribute tasks,
introduce additional automation, or
use AI more extensively.

Without time data, management is left with an impression:

“We probably make money on this flat fee.”

That is not controlling.

Portfolio-Level Analysis Becomes Especially Valuable

Flat fees do not necessarily need to be evaluated in isolation for every individual matter.

Larger client relationships can create a portfolio effect.

Imagine a law firm handling several hundred comparable matters each year for a corporate client.

Some matters are extremely simple.

Others require significantly more work than expected.

As the number of matters increases, the firm can forecast average processing costs more accurately.

The economic principle is similar to risk diversification.

Individual unprofitable matters can be offset by matters that are more profitable than expected.

Pricing therefore becomes increasingly a data-driven portfolio decision rather than simply a matter-by-matter calculation.

But this also requires historical data.

The firm needs to understand:

which types of matters consistently consume the most time,
which clients generate above-average communication cycles,
which activities can be automated,
where partner time is being used even though delegation would be possible, and
which processes consistently exceed their expected resource budget.

The larger the flat-fee portfolio becomes, the more important actual resource-consumption data becomes.

From Billing to Matter Controlling

For law firms, this represents a fundamental change in perspective.

In the traditional billable-hour environment, management focuses heavily on metrics such as:

billable hours,
hourly rates,
utilization, and
realization rates.

In a law firm increasingly using flat fees, additional questions become important.

How much did a matter actually consume in resources?

How large was the variance from the original estimate?

Which activities caused the variance?

Which matters generated a positive contribution margin?

Which clients generated an unusually high number of unplanned communication cycles?

Which processes were accelerated through automation?

And perhaps most importantly:

Which flat fees need to be adjusted during the next pricing review?

Time tracking therefore becomes part of continuous Matter Profitability Management.

A law firm should not wait until the end of the financial year to discover that a particular category of flat-fee service is economically unsustainable.

Ideally, management should be able to identify during the matter itself when work is consistently exceeding the allocated time budget.

The Most Important KPI Could Become “Time per Service”

In this environment, a relatively simple metric can become extremely valuable:

Time spent per defined service.

This enables comparisons across months and years.

The metric becomes particularly interesting when combined with artificial intelligence.

Imagine that a law firm introduces a new AI system for analyzing a particular category of documents.

Without historical time data, it is difficult to objectively determine whether the investment actually created an economic benefit.

With a historical time series, the firm can measure the change.

For example:

Before AI implementation:
Average processing time: 12 hours

Six months after implementation:
Average processing time: 8 hours

The firm can now turn the subjective statement “we are working faster” into a measurable productivity improvement.

This is particularly relevant for law firm management because technology investments are also increasing.

According to recent figures from the Thomson Reuters Institute, U.S. law firms significantly increased technology spending in the second quarter of 2026, while direct costs and overhead also increased.

Technology therefore cannot simply work.

It must create measurable economic value.

TimeSpin as a Low-Friction Tool for Time Controlling

This is where new approaches to time tracking become particularly interesting.

A system such as TimeSpin can be used in a law firm not primarily to turn every recorded minute directly into a client invoice, but to continuously capture actual resource consumption under a flat-fee model.

The physical TimeSpin cube allows different activities or matters to be assigned to individual sides of the cube. Users can switch between activities simply by turning the cube.

The objective is to make continuous time tracking as effortless as possible.

For management and controlling purposes, however, the cube itself is not the most important element.

The real value lies in the data structure created through time tracking.

When working time is consistently assigned to specific matters, clients, or activity types, planned and actual resource consumption can be compared.

For example, a firm might discover that a particular flat-fee service was originally calculated at an average of 7.8 hours but actually requires 10.6 hours.

The data may also reveal that the lawyer's share of the work is increasing, even though the original process design assumed a larger proportion of delegable activities.

TimeSpin is not intended to replace accounting software or a complete Business Intelligence system.

Cost accounting, overhead allocation, personnel cost rates, and contribution margins still need to come from the appropriate financial and management systems.

Time tracking provides one crucial input:

actual resource consumption.

And because flat-fee work no longer necessarily requires detailed time entries for client billing, the internal recording process needs to be particularly simple for employees.

Otherwise, firms risk losing precisely the data they need for their next pricing calculation.

Time Tracking Without Billing Pressure Can Produce Better Data

The change in purpose can also create a cultural effect.

In a traditional billable-hour environment, every recorded hour has direct financial relevance to the client.

This can lead to discussions about billable versus non-billable activities, minimum billing requirements, and individual billing targets.

With purely internal time tracking for flat-fee services, the purpose changes.

The recorded time is no longer primarily intended to maximize the number of billable hours.

Instead, it is intended to represent the reality of the work process.

This can enable a much more useful management question.

Not:

“Who recorded the most hours?”

But:

“How efficiently did we deliver the agreed service as a team?”

The focus therefore shifts from individual time utilization toward process productivity.

This can become particularly valuable as Legal Tech and AI take over an increasing share of standardized legal tasks.

The Risk of Creating the Wrong Incentives

However, efficiency measurement must not create a new problem.

Legal work is not industrial manufacturing. Not every legal task can or should be accelerated indefinitely.

Quality, diligence, professional responsibility, and expert review must not be sacrificed simply to complete a matter below an internal time budget.

Effective flat-fee controlling must therefore consider profitability and quality together.

A matter completed in six hours instead of ten represents a genuine productivity gain only if the quality of the legal work remains at least equivalent.

This consideration is particularly important when using generative AI.

Research into AI applications in legal services continues to indicate that performance can vary significantly depending on the task, meaning that professional validation and review of AI-generated results remain essential.

Time saved is therefore not sufficient on its own as a measure of success.

Flat Fees Need Data — Otherwise They Become a Gamble

The discussion around billable hours and flat fees therefore leads to a surprising conclusion.

At first glance, a flat fee appears to reduce the importance of working time.

In reality, something different happens.

Time disappears from the client's invoice but becomes one of the most important internal management metrics for the law firm.

A flat fee is ultimately a business forecast.

The law firm is effectively saying:

“We believe we can deliver this service profitably at this price.”

Whether that assumption is correct can only be determined by measuring actual resource consumption afterward.

Without that feedback loop, pricing becomes a gamble.

With it, pricing becomes a learning model.

The firm can use every matter to improve the next one.

Prices can be adjusted. Processes can be redesigned. Tasks can be redistributed. Automation opportunities can be identified.

This feedback loop is what allows flat-fee pricing to become economically sustainable over the long term.

The Future May Not Be “Flat Fee vs. Billable Hour”

The debate is therefore often framed too narrowly.

Billable hour or flat fee.

Time-based billing or value pricing.

Traditional law firm or modern law firm.

The reality is likely to be more nuanced.

Complex matters that are difficult to predict will continue to be suitable for time-based or hybrid fee models.

Standardized and recurring services, on the other hand, can increasingly be priced through flat fees, packaged services, or portfolio agreements.

Law firms therefore do not necessarily need to replace one pricing model with another.

Instead, they need to learn how to select the right pricing model for each type of legal service.

And that requires data.

One of the most important data sets will continue to be time.

Conclusion: The End of the Billable Hour Is Not the End of the Clock

Flat fees can provide significant advantages for both clients and law firms.

Clients gain greater cost predictability and budget certainty.

Law firms can standardize processes, make greater use of automation, and capture productivity gains more effectively.

Artificial intelligence can reinforce this effect by making certain legal workflows increasingly efficient.

But this opportunity also means that law firms take on greater pricing risk.

Under a flat fee, additional work does not automatically generate additional revenue.

That is why the actual working time of lawyers, associates, patent paralegals, and other professionals does not become less important.

It becomes more important.

Working time plays a significant role in determining whether a flat fee merely looks attractive in a proposal or remains genuinely profitable over the long term.

The central management question is therefore changing.

In the past, it was often:

“How many hours can we bill?”

Increasingly, it may become:

“How much time and which resources do we actually need to deliver a defined service at the agreed level of quality?”

Tools such as TimeSpin can help make this reality visible through continuous, low-friction time tracking and provide the data required for internal planned-versus-actual analysis.

This creates an apparent paradox:

The more law firms move away from the billable hour in client billing, the more important precise internal time tracking can become.

The economic success of a flat fee is not determined solely when the price is set.

It is determined afterward — minute by minute — through the actual delivery of the service.

Bremen, August 11, 2026 — Oliver Otto

Human as the Last Line of Defense — or Just a “Meatproxy”?

Thomas Delfing — Wed, 02 Sep 2026 15:40:57 +0000

Why Bill Gates’ “Human Reserved” Concept Makes Sense in Theory — but Could Fail in Practice

Artificial intelligence and robotics are fundamentally changing the world of work. Bill Gates has warned that numerous activities across fields such as law, customer service, healthcare, software development, and industry could be automated within the next decade.

His proposed counterpoint is what he calls “Human Reserved”: certain tasks should remain the responsibility of humans even when machines are technically capable of performing them.

The idea is compelling — and fundamentally problematic at the same time.

The central challenge lies in the gap between requiring human oversight in AI systems and actually creating the conditions necessary for meaningful human control.

Companies primarily adopt AI to make work faster, cheaper, and more scalable. Genuine human oversight, however, requires time, expertise, and money. If these resources are not deliberately built into the process, the human can be reduced to little more than the final approval click.

The person may remain legally or organizationally responsible while no longer understanding the recommendation well enough to challenge it.

At that point, the human is no longer the final authority.

They become a “Meatproxy”: a human proxy who gives a decision formal legitimacy even though the decision has effectively already been made by a machine.

This raises the more important question:

Does a human actually still have control — or is a human merely standing at the end of the workflow?

The decisive test is not whether a human is involved somewhere in the process.

What matters is whether that person still has the knowledge, time, authority, and practical ability to make a different decision.

What Does Bill Gates Mean by “Human Reserved”?

Gates compares his proposal to a nature preserve.

A particular area might technically be available for development. Society can nevertheless consciously decide to protect it because losing that area would be undesirable.

In the same way, certain activities could be technically automatable while still being deliberately reserved for humans.

One example Gates gives is caring for his father, who had Alzheimer’s disease. In his view, the human connection created by caregivers cannot simply be replaced by a robot.

Similarly, he asks whether a machine should ever be the one to tell a patient that their illness is likely to be fatal.

Technically, a machine could deliver that message.

From a social and ethical perspective, however, Gates argues that this responsibility should remain with humans.

It is important to note that Gates’ original essay, “The Turbulent AI Era Is Here. The Choices We Make Now Are Critical,” is more nuanced than some headlines suggest.

He is not simply arguing that entire professions should be protected from AI.

In education and psychological support, for example, he describes hybrid models in which humans retain responsibility while AI expands their capabilities.

He also acknowledges that the boundaries may differ from country to country.

A country facing a severe shortage of caregivers might evaluate care robots differently from a country that consciously chooses to preserve human caregiving as a social value.

Gates also makes an economic argument.

Certain activities could be temporarily protected if rapid automation were to eliminate large numbers of jobs while leaving affected workers with few realistic opportunities for retraining.

He has also proposed taxes or fees on robots and AI use to help fund retraining and social safety nets.

Gates therefore does not present a complete regulatory framework.

Instead, he raises a broader societal design problem.

Who decides which activities should be protected?

According to what criteria?

How do we prevent companies from circumventing such rules?

And how could such protections work in an international economy?

These questions remain open.

The Advantages of “Human Reserved”

1. Protecting Human Relationships

For some services, the human relationship is not merely the means by which the service is delivered.

The relationship itself is part of the service.

Caregiving, grief counseling, education, therapy, and delivering devastating news involve more than simply communicating accurate information.

Eye contact, compassion, situational awareness, and the willingness to take responsibility for another person have intrinsic value.

An AI system can generate language that sounds empathetic.

That does not automatically mean that a reciprocal human relationship exists between the person and the system.

If human connection is evaluated solely by how convincing a sentence sounds, we reduce something profoundly human to a user interface.

2. Protecting Normative Decisions

Many decisions cannot be reduced to calculating a single objectively correct answer.

They involve value judgments:

What level of risk is acceptable?
Which interests should take priority?
When is an exception justified?
Which long-term strategy best reflects an organization’s goals?
What outcome is socially desirable?

AI can structure arguments, data, and scenarios.

But the underlying values must not disappear silently into training data, system instructions, or optimization objectives.

“Human Reserved” can therefore mean something more fundamental:

The machine may provide the analysis — but humans determine which goals are worth pursuing in the first place.

3. Resilience in Exceptional Situations

Automated systems are particularly effective when handling frequent and structured cases.

Rare exceptions are different.

Changing circumstances, unusual combinations of factors, or faulty input data can create systematic errors.

Humans can identify contradictions, add missing context, and stop a process before an error spreads further.

This becomes especially important when a single error could have serious consequences — or when automation can reproduce the same error thousands of times.

4. Social Stability

Work provides more than income.

It can create:

Social belonging
Recognition
Daily structure
Professional identity
Personal meaning

The rapid displacement of large groups of workers can therefore create social costs that never appear in a conventional cost-benefit calculation.

A company can calculate savings in personnel costs without accounting for the broader social consequences of mass unemployment.

Temporarily protecting certain activities could slow the transition and create time for retraining, new business models, and policy adaptation.

5. Trust and Social Acceptance

People are more likely to accept AI when they do not feel completely powerless in relation to it.

An accessible human contact, an effective complaints process, and the ability to have an automated decision reviewed can help create trust.

But there is one crucial prerequisite:

The human must actually be able to intervene.

A human who can only confirm what the system recommends does not provide meaningful human oversight.

The Central Contradiction: Human Oversight Consumes the Time AI Was Supposed to Save

The biggest practical problem with Gates’ proposal lies in the economics of AI adoption.

AI is intended to accelerate research, analysis, writing, and decision preparation.

If a human subsequently has to reconstruct and verify every relevant step, a large portion of the saved working time disappears again.

Organizations therefore face a fundamental trade-off:

The more thoroughly a human reviews the result, the smaller the time advantage becomes.
The greater the pressure to realize the time savings, the more superficial the review becomes.
The more reliable AI appears in everyday use, the less likely people are to question it.
The fewer tasks humans perform themselves, the faster they can lose the ability to recognize rare errors.

A rule such as “The final decision must always be made by a human” does not resolve this conflict.

It can actually conceal it.

On the organizational chart, the human remains responsible.

In practice, however, the system may control most of the information, reasoning, and decision-making process.

From Decision-Maker to “Meatproxy”

A person becomes a Meatproxy when they remain formally involved but no longer exercise an independent decision-making function.

Warning signs include:

AI recommendations are almost always accepted.
The human has only a fraction of the time previously required to evaluate a case.
Users see only a summary instead of the underlying sources.
The system produces convincing explanations without clearly presenting uncertainty or counterarguments.
The human can technically reject the recommendation but must provide special justification for doing so.
Speed and case volume are included in performance evaluations, while careful review is not.
Expertise is no longer regularly applied and gradually deteriorates.
When something goes wrong, responsibility is assigned to the human approver even though that person had little control over the system, data, or model behavior.

Researcher Madeleine Clare Elish describes a related phenomenon as the “moral crumple zone.”

The term describes situations in which humans absorb responsibility for the failure of an automated system even though their actual control over that system was limited.

The human is then not necessarily protecting society from the machine.

Instead, they may be protecting the technical and organizational system from responsibility and accountability.

Automation Bias and the Loss of Human Expertise

Excessive reliance on automated recommendations is not a problem that began with generative AI.

Earlier research on automation bias showed that decision-support systems can improve overall performance while simultaneously causing users to incorrectly accept automated recommendations.

Generative AI can intensify this problem because it does not merely output a number, score, or warning.

It produces a coherent explanation in natural language.

And that explanation can sound convincing.

But plausibility is not proof of correctness.

A convincing explanation can actually increase trust even when it does not accurately represent how the system arrived at its conclusion.

The Generative Artificial Intelligence Profile published by the National Institute of Standards and Technology (NIST) explicitly warns against excessive reliance on increasingly capable AI systems. Automation bias can contribute to fabricated information, bias, and the homogenization of results.

There is another related problem: the out-of-the-loop effect.

When people stop actively performing a task themselves, they can lose situational awareness and practical skills.

When a rare system failure eventually requires them to take over, they may be least prepared precisely when their expertise is needed most.

Lisanne Bainbridge described this problem as one of the “Ironies of Automation” as early as 1983. Later research by Mica Endsley and Esin Kiris experimentally examined the out-of-the-loop problem.

The irony is simple:

The more reliably automation performs under normal conditions, the less practice humans receive for exceptional situations.

A human fallback is therefore not automatically reliable simply because a human is available.

What Does a Human Actually Need to Understand?

It would be unrealistic to expect every AI user to understand the mathematical architecture of a Large Language Model.

Users of conventional software do not understand every single line of code either.

The more important question is:

At what level does human understanding need to exist for meaningful control to be possible?

Several levels of human competence can be distinguished:

Awareness

Human capability: The user sees the result.
Is this enough for meaningful control? No.
Simply seeing an AI-generated result does not mean that the person can meaningfully evaluate or challenge it.

Plausibility Check

Human capability: Based on experience and context, the result appears reasonable.
Is this enough for meaningful control? Only for low-risk and easily reversible tasks.
This level of review can be sufficient when errors have limited consequences and can be corrected easily.

Verification

Human capability: Sources, assumptions, and critical intermediate steps can be checked.
Is this enough for meaningful control? Suitable for many medium-risk decisions.
The human must be able to verify the essential foundations of the AI-generated result.

Independent Judgment

Human capability: The person could make a reasoned decision without AI and recognize when they should disagree with the AI.
Is this enough for meaningful control? Required for consequential individual decisions.
This is where human control becomes substantive: the person is not dependent on the AI to determine the correct course of action.

System Competence

Human capability: The organization understands system limitations, failure patterns, data dependencies, and monitoring procedures.
Is this enough for meaningful control? Required for responsible overall operation.
This competence does not need to exist at the level of every individual user. It does, however, need to be clearly established somewhere within the organization.

Not every employee needs to possess every one of these levels of competence.

However, the organization must clearly define who possesses which capabilities and where responsibility lies.

For example, a caseworker might be responsible for the substantive decision, while another team monitors model quality, data protection, security, and systemic bias.

The person who ultimately clicks “Approve” should not automatically become the person who bears responsibility for the entire decision-making process.

Responsibility should follow actual control.

If a person does not have the necessary expertise, information, time, or authority to challenge an AI-generated recommendation, assigning formal responsibility to that person does not create meaningful human oversight.

The Risks of a Permanent “Human Reserve”

Gates’ proposal protects important values, but it could also create new problems.

Higher Costs and Lower Availability

Human services are limited and expensive.

In regions facing a shortage of doctors, a high-quality AI-assisted initial consultation may be better than no consultation at all.

The same applies to education, psychological support, and caregiving.

If a service is reserved exclusively for humans, access to that service could actually become worse.

The humane solution is therefore not automatically the one with the highest proportion of human labor.

Sometimes the human benefit comes precisely from AI making a service affordable and available.

Protecting Existing Professions Instead of Human Values

Professional groups could use “Human Reserved” to protect themselves from competition and technological change.

In that case, the rule would protect neither dignity, relationships, nor safety.

It would protect existing market positions.

Inefficient processes could remain in place even when automation would clearly benefit customers and society.

We therefore should not necessarily protect entire professions.

We should protect specific relationships, decisions, and areas of responsibility.

Humans Are Not Automatically Better

Humans make mistakes too.

They overlook information, make inconsistent decisions, and are influenced by bias, fatigue, incentives, and personal interests.

A decision is not automatically better simply because a human made it.

For some tasks, a properly tested automated system may be more consistent, fair, and reliable than an unstructured human decision.

The goal should therefore not be:

Human versus machine.

Instead:

The right capability for the appropriate level of risk.

International Competitive Disadvantages

If one country requires human involvement while another fully automates the same process, significant cost differences can emerge.

Companies may move activities abroad or purchase automated services from other countries.

Gates himself identifies this as an unresolved problem.

Without international coordination, reserving certain activities for humans could either become a competitive disadvantage or a largely symbolic rule that is difficult to enforce.

New Forms of Social Inequality

Two opposing developments are possible.

Wealthier customers could receive personal human services while everyone else is directed toward cheaper AI alternatives.

Conversely, mandatory human involvement could make services so expensive that lower-income people lose access.

A meaningful framework therefore needs to ask more than:

“Is a human involved?”

It must also ask:

“Who has access to human support — and who can afford it?”

Four Possible Operating Models

1. Full Automation

How it works:

AI performs the task from beginning to end with little or no human involvement.

Main advantages:

Maximum speed
High scalability
Consistent results

Main risks:

Errors can spread quickly.
Humans have little control over individual decisions.

Suitable for:

Routine tasks that are standardized, measurable, and easy to reverse.

2. AI Recommendation + Human Approval

How it works:

AI provides a recommendation that is then reviewed and approved by a human.

Main advantages:

Easy to integrate into existing workflows
A human remains formally involved

Main risks:

Humans may simply confirm what the AI recommends.
Responsibility may remain with the human even though the AI has effectively made the decision.

Suitable for:

Tasks where the human has sufficient time, expertise, and authority to genuinely review the recommendation.

3. Human Judgment First, AI Second

How it works:

The human makes an initial assessment before seeing the AI recommendation. The AI then provides a second opinion.

Main advantages:

Preserves independent human judgment
AI can challenge or supplement the human assessment

Main risks:

Requires more time
Can result in duplicated work

Suitable for:

Rare or particularly consequential decisions where independent human judgment is critical.

4. Risk-Based Human Oversight

How it works:

The level of human involvement is determined by the potential consequences of an AI error.

Main advantages:

Maximum control where it matters most
Avoids unnecessary human effort for low-risk tasks

Main risks:

The risk classification itself may be incorrect or incomplete.

Suitable for:

Most real-world AI applications within organizations.

The Best Approach?

In practice, organizations probably should not require a human to approve every single AI decision.

Instead, it makes more sense to match the level of human oversight to the level of risk.

Low-risk tasks can be largely automated.

Medium-risk tasks can combine AI recommendations with meaningful human review.

For high-risk decisions, independent human judgment should be preserved.

The goal is not to involve humans everywhere. The goal is to keep humans genuinely in control where it matters.

A Practical Model for Human Oversight

Green: Automation with Monitoring

Green-category tasks have limited impact, easily recognizable errors, and simple ways to correct mistakes.

Examples:

Formatting
Preliminary tagging
Duplicate detection
Internal drafts
Routine classification

AI can operate largely autonomously here.

Organizations should nevertheless maintain logging, quality metrics, regular sampling, and a simple mechanism for reversing incorrect results.

Yellow: Verifiable Decision Support

Yellow-category tasks can have meaningful but generally reversible consequences.

AI generates a recommendation.

The human checks the critical facts and sources rather than reconstructing every technical processing step.

The system should provide at least:

Original sources
Key assumptions
Known uncertainties
Potential counterarguments
Missing or contradictory information
The potential consequences of an incorrect decision

A merely convincing explanation is not enough.

Red: Independent Human Judgment

Red-category decisions have irreversible or difficult-to-reverse consequences, involve loss of rights, significant financial impact, health risks, or particularly important ethical or personal questions.

Here, the human should ideally form an initial judgment before seeing the AI recommendation.

AI can then serve as:

A research assistant
A second opinion
A source of counterarguments
A consistency check

Depending on the consequences, additional safeguards may be necessary, such as the four-eyes principle, documented reasoning, and separation between recommendation and approval.

The lower time savings at this level are not a design flaw.

They are the price of genuine human oversight.

Seven Requirements for Preventing the Meatproxy Effect

1. A Dedicated and Funded Oversight Budget

Review requires working time.

If that time is not explicitly planned and funded, human oversight exists only on paper.

Organizations should define for each risk category what level of review is realistically required and who is responsible for providing it.

2. Access to Primary Sources

Users must be able to move from an AI-generated answer to the underlying documents and data relevant to the decision.

Another AI-generated summary is not an independent source.

3. The Right and Ability to Disagree

Anyone expected to reject an AI recommendation needs both the professional expertise and organizational authority to do so.

Disagreement should not automatically be treated as a performance problem.

There must also be a clear escalation path for difficult cases.

4. Preserve Human Expertise

Regular manual work, blind tests without AI recommendations, training, and collaborative analysis of errors can help preserve expertise.

A fallback system that is never practiced is not a reliable fallback system.

5. Independent Quality Measurement

Organizations should not measure AI success solely by how many cases are completed faster.

They should also monitor:

Error types
Corrections
Complaints
Near misses
Differences between human and AI decisions
Human performance without AI assistance

A consistently near-100% approval rate can be a warning sign.

It may indicate excellent model performance.

But it may also mean that humans are no longer genuinely reviewing the results.

6. Visible Limitations Instead of Artificial Certainty

AI systems should clearly communicate uncertainty, missing information, and known limitations.

Explanations should help users evaluate an answer — not merely convince them to trust it.

7. Responsibility Must Follow Actual Control

Responsibility should be distributed according to actual influence across:

System operators
Developers
Data providers
Executives
Subject-matter experts
Decision-makers

The person at the end of the workflow should not automatically become the liability sink for failures that originated elsewhere in the system.

Regulation Alone Is Not Enough

The European Union AI Act follows a similar principle for high-risk AI systems.

Human oversight is intended to enable people to understand a system’s capabilities and limitations, account for automation bias, correctly interpret outputs, override or disregard outputs, and stop the system when necessary.

The voluntary AI Risk Management Framework developed by the National Institute of Standards and Technology (NIST) also emphasizes clearly defined roles, competency requirements, training, risk-based resources, and continuous monitoring.

These requirements are important.

But they do not resolve the underlying economic conflict.

No law or guideline automatically creates:

Expertise
Review time
Organizational authority
The willingness to challenge an AI system

An organization can formally assign a human a role in a process while simultaneously designing that process so that the person has virtually no realistic option other than confirming the result.

Effective human oversight is not a function of an approval button.

It is a property of the entire operating model.

Example: AI in IP Management Software

Consider software for managing patents and trademarks.

A sensible division of responsibilities could look like this:

1. Identifying Document Types and Suggesting Keywords

AI role: Largely automated processing
Human role: Conduct spot checks and make simple corrections

2. Summarizing File Contents

AI role: Generate a draft with source references
Human role: Review the content before external or strategic use

3. Structuring Research Results

AI role: Organize results and identify similarities and gaps
Human role: Assess relevance and legal significance

4. Assessing Deadlines or Legal Status

AI role: Prepare information and flag inconsistencies
Human role: Perform a binding review using reliable official and rule-based sources

5. Recommending Whether to Maintain or Abandon an IP Right

AI role: Present costs, portfolio context, and different scenarios
Human role: Make an independent business and legal decision

6. Communicating Negative Procedural Decisions

AI role: Prepare the wording
Human role: Take responsibility for the content and personally handle sensitive communication

Why Software Design Matters

Software design is critical.

A system that saves time by hiding context weakens human oversight.

A system that saves time by structuring sources, contradictions, and decision-relevant information, on the other hand, can strengthen it.

That distinction is fundamental.

Conclusion: Human Oversight Is Not a Free Feature

Bill Gates is right to raise a question that is often overlooked in the AI debate:

Not everything that can technically be automated should automatically be automated.

Human relationships, normative judgment, responsibility, and social stability have a value that cannot be fully expressed through processing time and cost.

However, the “Human Reserved” concept remains incomplete unless it is connected to the economic reality of organizations.

Companies adopt AI because they want to save time.

Meaningful human oversight costs time.

If organizations are unwilling to pay that price, the human becomes a Meatproxy: approving decisions they can no longer independently evaluate while assuming responsibility for systems they did not design.

The solution is neither unrestricted automation nor the universal requirement for a human to click “Approve.”

What we need is a deliberate distribution of responsibility:

Automation for reversible, measurable routine tasks.
Verifiable AI assistance for medium-risk decisions.
Independent human judgment for irreversible and normative decisions.
Human relationships where the relationship itself is part of the service.

“Human Reserved” should therefore not become a protected zone for entire professions.

It should become a protected space for human judgment, responsibility, and relationships.

Because meaningful human oversight requires time, expertise, authority, and money.

If organizations promise meaningful human control, they must provide all four.

Otherwise, the human is not the last line of defense.

They are merely the last signature.

Working Time Law in Germany: Time Tracking, the 8-Hour Day & Digital Solutions

Thomas Delfing — Mon, 24 Aug 2026 07:26:02 +0000

Working time law in Germany is changing. The debate over the traditional eight-hour working day is gaining momentum while employers simultaneously face increasing requirements to document working hours.

At first glance, these developments appear contradictory:

Employees and employers want greater flexibility.
European and German case law requires reliable working time records.
Modern work is increasingly remote, project-based, and distributed across time zones.

But these trends do not necessarily conflict. In fact, accurate time tracking may be what makes greater flexibility possible.

The Eight-Hour Day Is Under Pressure

Germany's traditional working time framework is based on the Arbeitszeitgesetz (Working Time Act, ArbZG).

Under Section 3 ArbZG, employees generally may not work more than eight hours per working day. The limit can be extended to ten hours if the average working day remains within eight hours over a six-calendar-month or 24-week reference period.

The underlying purpose is employee health protection.

However, the modern workplace increasingly operates differently.

Knowledge workers may have periods of intense concentration followed by quieter periods. Project teams may work according to deadlines rather than fixed schedules. Remote workers may distribute their working hours throughout the day, while international teams routinely collaborate across multiple time zones.

This raises an important question:

Should working time regulation continue to focus primarily on the length of an individual working day, or should it place greater emphasis on overall workload and rest periods?

The European Framework

German working time law operates within the framework of the EU Working Time Directive 2003/88/EC.

The Directive establishes minimum requirements relating to:

daily and weekly rest periods,
breaks,
annual leave, and
maximum weekly working time.

One of its key principles is the 48-hour average maximum working week, including overtime.

The European Court of Justice (CJEU) significantly strengthened the importance of working time recording in its landmark CCOO judgment of 14 May 2019, Case C-55/18.

The Court held that employers must have an objective, reliable, and accessible system for measuring employees' daily working time.

This is important because time tracking is not treated as an administrative objective in itself.

It is a mechanism for ensuring that existing working time protections can actually be enforced.

Germany's Working Time Recording Obligation

The German Federal Labour Court (Bundesarbeitsgericht, BAG) subsequently addressed the issue directly.

In its decision of 13 September 2022 (1 ABR 22/21), the BAG held that employers are already required to establish a system for recording working time under Section 3(2) No. 1 of the Occupational Health and Safety Act (Arbeitsschutzgesetz, ArbSchG).

The practical consequence is significant:

German employers cannot simply treat working time recording as optional because a comprehensive amendment to the Working Time Act has not yet been enacted.

The legal discussion is therefore no longer primarily about whether working time should be recorded.

The more relevant question is:

How should working time be recorded in a modern workplace?

Flexibility vs. Time Tracking: A False Contradiction?

The call for more flexible working hours can initially appear incompatible with precise time recording.

It is not.

In fact, the relationship can be exactly the opposite.

Trust-Based Working Time Does Not Mean No Time Tracking

A common misconception is that mandatory time tracking automatically eliminates trust-based working time.

It does not.

Trust-based working time is fundamentally about how an employer manages employees, not whether working hours exist as a documented dataset.

An employer can trust employees to organize their work independently while still requiring working hours to be recorded.

The distinction is important:

Documentation does not have to mean surveillance.

A well-designed system can record working time without monitoring every aspect of an employee's behavior.

Time Tracking as a Workplace Tool

Traditional time tracking systems are sometimes perceived as administrative or controlling.

This is particularly problematic when employees have to interrupt their work to enter data manually.

Modern approaches take a different direction.

Instead of asking employees to treat time tracking as an additional administrative task, the technology can become part of the workflow itself.

This is particularly relevant for project-based organizations.

Why Project-Based Work Changes the Equation

In industries such as:

software development,
consulting,
law,
engineering,
marketing,
creative agencies, and
professional services,

working time is often directly connected to projects.

Time data can support:

project billing,
cost allocation,
profitability analysis,
resource planning,
workload management, and
project forecasting.

This means time tracking does not necessarily exist because management wants to monitor employees.

It can exist because the organization needs to understand where its resources are actually going.

Haptic Time Tracking: Making Time Tracking More Natural

One interesting development is the combination of digital time tracking with physical, haptic interaction.

For example, TimeSpin approaches time tracking through a physical interaction model rather than relying exclusively on conventional applications or web interfaces.

A physical object can be associated with a specific project or task. Changing the physical position or orientation can then be used to switch between activities.

This creates a different interaction model:

physical action → activity change → digital time record

The potential advantage is simplicity.

Instead of opening an application, finding the correct project, navigating through menus, and entering a time entry after the fact, the employee can make a deliberate physical interaction at the moment the activity changes.

Why this matters

Immediate recording can help reduce the problems associated with retrospective time entries.

For example:

Less administrative friction — changing activities can become almost instantaneous.
Fewer forgotten entries — the recording happens closer to the actual event.
Better project data — activities can be associated with projects as work occurs.
Higher acceptance — interaction can feel like part of the workflow rather than a reporting obligation.
Better visibility — organizations receive more consistent data about how working time is distributed.

The broader principle is worth considering:

The best time tracking system may be the one employees barely notice using.

What Should a Legally Robust Time Tracking System Provide?

Technology alone does not guarantee compliance.

A modern working time system should address several fundamental requirements.

1. Objectivity

The system should create records in a way that minimizes manipulation and provides trustworthy information about actual working time.

2. Reliability

Records need to remain available and protected against technical failures or data loss.

3. Accessibility

Employees should be able to access and review their recorded working time.

4. Data Protection

Working time data is personal data.

Organizations therefore need to consider the requirements of the General Data Protection Regulation (GDPR), including principles such as purpose limitation and data minimization.

5. Practical Usability

A technically compliant system that employees consistently avoid, forget, or circumvent is not a particularly effective solution.

The human factor matters.

Legal compliance and user experience should therefore be designed together.

The Future of Working Time in Germany

The debate around the future of the eight-hour day is unlikely to disappear.

The German working world is becoming more flexible, but employee protection remains a central legal objective.

The likely direction is therefore not necessarily:

less regulation → less documentation

but rather:

more flexible working models → better documentation → greater legal certainty

This is an important distinction.

A flexible working time system needs reliable data to demonstrate that flexibility remains within the applicable legal framework.

The Bigger Picture: From Time Control to Time Intelligence

The future of time tracking may ultimately be about more than simply recording when someone starts and stops working.

Accurate time data can become an operational resource.

Organizations can use it to understand:

which projects consume the most resources,
where teams are overloaded,
which activities are profitable,
where processes create unnecessary administrative work, and
how work is actually distributed.

In this context, time tracking moves from being a compliance tool toward becoming a business intelligence and workflow tool.

That shift could significantly change how employees perceive the technology.

Conclusion

The debate about the eight-hour working day and mandatory time tracking does not have to produce opposing camps.

Greater flexibility and accurate time recording can reinforce each other.

If working hours become more flexible, organizations need reliable information to demonstrate compliance with working time and rest requirements.

At the same time, time tracking needs to become less intrusive and more integrated into everyday work.

This is where modern approaches—including project-based and haptic interaction models—can play an interesting role.

The future of working time may therefore not be about choosing between flexibility and time tracking.

It may be about using better time tracking to enable greater flexibility.

Key Takeaways

Germany's working time framework is increasingly being challenged by modern work patterns.
The CJEU and German Federal Labour Court have established the importance of working time recording.
Flexible working hours do not necessarily conflict with time tracking.
Trust-based working time can coexist with documented working hours.
Project-oriented time tracking can provide both compliance and operational benefits.
Haptic solutions such as TimeSpin offer an alternative to purely app- or browser-based time recording.
Effective systems should combine objectivity, reliability, accessibility, data protection, and usability.
Better time tracking may ultimately be a prerequisite for more flexible working models.