Artificial intelligence entered the workplace with a compelling promise: automate repetitive tasks, reduce the time spent on operational activities, and allow people to focus on the work that truly matters. In theory, if a task that once took two hours can now be completed in just a few minutes, professionals should gain more time to work strategically, develop new ideas, and achieve better results with less effort.

However, a paradox is emerging in this transformation. If artificial intelligence is making so many activities faster, why do so many professionals feel that they are working more than ever?

A study by Jabra, reported by GlobeNewswire, highlights this growing challenge. According to the research cited in the report, 85% of professionals surveyed said they were doing more work in at least one category of activity than they had the previous year.

This finding raises an important question about one of the main expectations surrounding artificial intelligence: does working faster actually mean working less?

In practice, technology can reduce the time required to complete an individual task without reducing the overall volume of work. In fact, the ability to produce more in less time can lead to higher expectations, broader responsibilities, and additional demands on employees.

For businesses, this means that adopting artificial intelligence is only part of the equation. Companies also need to understand how work is organized, where employees spend their time, and whether efficiency gains are translating into meaningful business outcomes.

Simply knowing that a team uses AI tools or completes certain activities faster is not enough. Managers need a broader view of how work gets done, which activities consume the most time, and where opportunities exist to improve operational efficiency.

In this article, we explore why artificial intelligence can increase workloads, the factors behind this phenomenon, and how businesses can use productivity data to make better decisions. We will also examine the role of time management and how tools such as Monitoo can help organizations understand work patterns and identify opportunities for improvement.

The AI Productivity Paradox: Producing More Does Not Mean Working Less

For decades, productivity has often been associated with the ability to complete more tasks in less time. This idea has gained even more attention with the widespread adoption of artificial intelligence tools capable of summarizing documents, organizing information, generating content, analyzing data, and automating parts of business processes.

When technology reduces the effort required to perform a task, it is natural to expect the time saved to become available for other activities or to make the working day less demanding.

However, there is an important distinction between reducing the time needed to complete a task and reducing the total workload.

Imagine a professional who spends two hours preparing reports. After adopting an AI tool, the same report can be completed in just 30 minutes. At first glance, this represents a saving of an hour and a half.

Now consider what happens when the company notices this improvement and begins requesting four reports instead of one. The professional may complete each deliverable faster, but the working day is still occupied by reporting activities.

The time required for each task has decreased, but the demand has increased.

This example illustrates the productivity paradox: technology can increase production capacity without proportionally reducing the amount of work expected from employees.

What Does Jabra's Research Reveal About Increasing Workloads?

Jabra's WorkLife research examines how artificial intelligence is changing professional life, workplace collaboration, and the way people experience work.

According to the findings reported by GlobeNewswire, 85% of the professionals surveyed said they were doing more work in at least one category of activity than they had the previous year.

The research also identified increases across different types of work. Approximately 51% of respondents reported spending more time on strategic planning and analysis, while 50% reported an increase in creative work and 49% said they were spending more time on administrative tasks.

These findings broaden the conversation about AI adoption. Technology is not influencing only simple, repetitive activities. It is also changing how professionals plan, analyze information, collaborate, and develop new deliverables.

It is important to interpret these figures carefully. The research reflects the experiences reported by the participants. On its own, it does not establish that artificial intelligence is the sole cause of increasing workloads, nor does it mean every professional experiences the same situation.

Nevertheless, the findings highlight a relevant challenge for businesses: technological transformation must be accompanied by changes in the way work is organized.

Adopting a faster tool without reviewing processes, priorities, and expectations may simply increase the amount of work completed during the same working hours.

Why Productivity Can Increase While Workload Remains High

Productivity does not depend exclusively on how quickly a task is completed. It is also related to the quality of the outcome, the resources used, the complexity of the work, and the value generated for the business.

A team may produce more documents, respond to more requests, and complete more activities without necessarily improving its strategic results.

This happens because increased operational capacity can be absorbed by new demands. Instead of using the time saved to improve processes, eliminate repetitive activities, or conduct deeper analysis, an organization may fill every available moment with another task.

The result is a work environment where output increases while employees continue to feel short on time.

To understand this phenomenon, businesses need to consider at least four dimensions:

• Workload volume: How many activities are requested and completed during a given period.

• Task complexity: How much effort, expertise, and attention each deliverable requires.

• Output quality: Whether increased production maintains or improves expected standards.

• Time allocation: How working hours are distributed across execution, communication, meetings, planning, rework, and other activities.

When these dimensions are analyzed together, businesses can better assess whether technology is improving efficiency or simply concentrating more work into the same number of hours.

Why Can Artificial Intelligence Make People Work More?

There is no single explanation for rising workloads in organizations that use artificial intelligence. Several factors can overlap, including higher expectations, poorly organized processes, excessive communication, new responsibilities, and difficulties translating individual efficiency gains into broader organizational benefits.

Understanding these factors is the first step toward ensuring that technology designed to make work easier does not unintentionally make the working day more demanding.

1. Time Savings Get Filled With New Demands

One of the main reasons efficiency improvements do not always translate into more free time is the tendency to fill available capacity with additional activities.

When a team begins completing certain tasks more quickly, an organization may see an opportunity to increase output. New projects are introduced, deadlines become shorter, and additional requests appear more manageable.

This is not necessarily a bad thing. In some situations, increasing delivery capacity is precisely the goal of automation. The problem arises when demand expands without evaluating the team's actual capacity or establishing clear priorities.

Imagine a marketing team using AI to draft content, organize research, and develop campaign ideas.

Before adopting the tool, planning a campaign might take several hours. With AI, parts of the process become faster.

If the company uses the time saved to review strategy, improve campaign quality, and test new approaches, the technology can deliver significant benefits.

However, if the same team starts receiving twice as many campaigns without reducing other responsibilities, the technology may intensify the workload rather than improve working conditions.

The difference lies in how the saved time is managed.

How to address this challenge: Before increasing the volume of deliverables, evaluate which activities genuinely need to be added, which can be eliminated, and where the time saved would create the most value. The goal should be to improve efficiency sustainably, not automatically fill every available minute.

2. Expectations for Speed Keep Increasing

Artificial intelligence changes people's perceptions of what can be accomplished within a given period.

When a task becomes faster, it is natural for managers, clients, colleagues, and even employees themselves to expect similar improvements across related activities.

The problem is that not every stage of a process can be accelerated to the same extent.

An AI tool may generate the first draft of a document quickly, but checking the information, assessing risks, reviewing the content, and approving the final version still require human attention.

Similarly, generating campaign ideas is not the same as choosing the right strategy, evaluating its impact, and ensuring that execution aligns with business objectives.

When faster production is interpreted as a reason to shorten every deadline, teams can face a constant stream of urgent deliverables.

In this situation, technology improves one part of the process while expectations increase across all the others.

How to address this challenge: Set deadlines based on the entire workflow, not just the automated stage. Account for review, communication, decision-making, and task complexity when establishing realistic expectations.

3. Automation Creates New Tasks

Adopting artificial intelligence does not necessarily eliminate every activity associated with a process. In many cases, it changes existing responsibilities and creates additional steps.

A team may need to review AI-generated responses, verify sources, correct inaccurate information, adapt results to the company's context, and ensure that the data used is appropriate.

Employees may also need to learn new tools, develop better prompts, integrate systems, and document procedures.

These activities can be essential investments in quality and efficiency. However, they must be included when planning team capacity.

Consider a department that uses AI to speed up internal support requests. The tool helps prepare responses and organize information, but employees remain responsible for reviewing complex cases, correcting inconsistencies, and resolving situations that require human judgment.

If the organization counts only the time saved when preparing responses, it may overestimate the productivity improvement.

A more complete assessment considers the total time required to complete the process before and after implementation, including any new tasks introduced by automation.

How to address this challenge: Map every stage of the process, including the activities that occur after automation. Compare the total effort required to complete a deliverable before and after adopting the technology.

4. Easier Content Creation Leads to More Reviews

Artificial intelligence tools make it easier to generate multiple versions of the same material. Instead of developing a single proposal, a team can produce several alternatives in a short period.

This capability creates opportunities to test different approaches, compare ideas, and improve the quality of deliverables. However, it can also increase the number of decisions that need to be made.

The more alternatives a team produces, the more effort may be required to evaluate, select, and approve them.

The same applies to documents, presentations, reports, and analyses. Initial production may become faster, but the review process can expand if there are no clear criteria for determining which options deserve attention.

In some situations, teams end up producing more alternatives than they have the time or resources to evaluate properly.

The time saved during creation is then offset by additional review work.

How to address this challenge: Define quality criteria, limit the number of versions required at each stage, and establish who is responsible for the final decision. Technology should make it easier to identify the best solution, not endlessly multiply the options.

5. Poor Prioritization Turns Efficiency Into Overload

Even an efficient team can struggle when every request is treated as urgent.

Artificial intelligence can accelerate specific tasks, but it does not automatically resolve conflicting priorities, planning problems, or uncertainty about business objectives.

When there is no clear order of importance, time savings can disappear as employees switch constantly between projects.

A professional begins a report, interrupts the work to respond to a request, joins a meeting, and returns to the document only to discover that another task has become the top priority.

Although individual activities may be completed quickly, the overall workday becomes fragmented.

Workload can also increase when different managers assign tasks to the same employee without considering existing responsibilities.

In this context, the problem is not necessarily a lack of speed. It is a lack of coordination.

How to address this challenge: Establish clear goals, organize requests by priority, and evaluate available capacity before accepting new commitments. Work management practices need to evolve alongside technology.

6. More Tools Can Mean More Interruptions

Digital transformation often involves adopting multiple platforms for communication, task management, data analysis, and automation.

Each tool may solve a specific problem, but an increasing number of systems can also make information harder to find, require frequent switching between environments, and complicate request tracking.

Constant notifications, messages across different channels, and frequent updates can interrupt periods of concentration.

Artificial intelligence may speed up one activity while also making it easier for new requests to arrive.

An employee finishes one task and receives a message about another. While organizing the new request, an update in another system demands attention. When the employee returns to the original task, they need to reconstruct their train of thought.

These interruptions may seem insignificant when considered individually. However, when repeated throughout the day, they can make sustained concentration more difficult.

How to address this challenge: Review communication channels, reduce unnecessary notifications, and establish appropriate times for responding to messages and requests. Whenever possible, centralize information and avoid workflows that require constant switching between tools.

7. Teams Have No Time to Learn and Adapt

Implementing new technology requires an adjustment period.

Even when a tool appears intuitive, professionals need to understand its capabilities, recognize its limitations, develop effective workflows, and learn how to evaluate its results.

If a company expects immediate improvements without reserving time for training, employees may be forced to learn new systems while maintaining all their existing responsibilities.

This creates an additional layer of work: alongside completing their usual tasks, professionals must figure out how to perform them differently.

Insufficient training can also lead to inappropriate use of tools, more corrections, and reduced confidence in the results.

For this reason, AI adoption should include practical guidance, quality standards, and time for responsible experimentation.

How to address this challenge: Plan implementation in stages, identify training needs, and track the difficulties users encounter. Learning time should be treated as part of the transformation, not as an invisible additional responsibility.

8. Companies Measure Output but Ignore the Effort Behind It

One of the simplest ways to evaluate a team is to count how many tasks it completes during a given period.

Although this metric can be useful, it does not explain how the work was performed or how much effort was required to achieve the result.

A team completing one hundred simple activities cannot automatically be compared with another completing twenty complex assignments.

It is also possible to increase output while overtime, rework, or review requirements continue to grow.

When an organization looks only at the volume produced, it may conclude that a team has become more productive without recognizing that the improvement comes with a more intense working day.

The same problem occurs when managers use isolated indicators to compare departments with different responsibilities.

A meaningful assessment should consider context, task complexity, available resources, and the results achieved.

How to address this challenge: Combine quantitative indicators with information about quality, time invested, rework, and goal achievement. No single metric should be treated as a complete representation of professional performance.

What Does Workplace Productivity Mean in the Age of AI?

Workplace productivity describes the relationship between the results achieved and the resources used to produce them. These resources can include time, people, tools, knowledge, infrastructure, and financial investment.

In practice, this means that producing more is not the only way to improve productivity.

A business can also become more productive by reducing errors, eliminating unnecessary steps, improving deliverable quality, organizing processes, and making better use of existing capacity.

Artificial intelligence expands these opportunities by allowing organizations to automate parts of certain activities and accelerate access to information. However, the benefits depend on how these capabilities are used.

A tool that reduces the time required for a task creates an opportunity. The actual improvement depends on what happens to the process afterward.

If the time saved is used to eliminate rework, improve decisions, and serve customers more effectively, the technology can generate additional value.

If it is consumed by unnecessary tasks, interruptions, and unprioritized demands, the overall efficiency gains may be limited.

Efficiency, Effectiveness, and Productivity: What Is the Difference?

Although these concepts are related, they represent different dimensions of management.

Efficiency concerns the use of resources. A process is more efficient when it achieves a result using less time, effort, or resources without compromising necessary requirements.

Effectiveness concerns achieving the intended objectives. A team may complete many tasks quickly without necessarily achieving the desired outcome.

Productivity relates the results produced to the resources used. Its evaluation depends on the context and the metrics selected.

Consider a sales team that uses artificial intelligence to prepare proposals.

If the tool reduces the time needed to create each proposal, it creates an opportunity to improve efficiency.

If the proposals continue to address customer needs and contribute to commercial objectives, the team is also making progress toward effectiveness.

If the team achieves better results using the same amount of resources, its productivity may have improved.

On the other hand, if the tool increases the number of proposals produced but the proposals are low quality or fail to address customer needs, the additional output does not necessarily represent a productivity improvement.

This distinction is essential for preventing businesses from confusing speed with results.

How Can You Tell Whether AI Is Improving Productivity?

The answer requires comparing the situation before adopting the tool with the results observed afterward.

The first step is to define the problem the organization wants to solve.

Is the objective to reduce the time needed for a task? Decrease errors? Speed up customer service? Improve analytical quality? Reduce rework?

Each objective requires appropriate metrics.

If the goal is to reduce execution time, the company can compare the average duration of an activity before and after implementation.

If the intention is to improve quality, it can assess error rates, the volume of corrections required, or compliance with established standards.

If the goal is to increase operational efficiency, it can examine the relationship between completed deliverables and the resources used.

It is also important to consider indirect effects. A tool may accelerate one stage while increasing the time needed for review or approval. Whenever possible, evaluation should therefore cover the entire process.

Comparisons should account for changes in workload volume, task complexity, employee experience, and other factors that may influence results.

The objective is not to prove that technology works in every situation. It is to understand the conditions under which it delivers measurable benefits.

How to Measure Productivity Without Confusing Activity With Results

One of the challenges of modern management is turning large volumes of operational information into useful insights for decision-making.

Knowing that an employee spent several hours connected to a computer, used different applications, or responded to many messages does not, by itself, reveal the quality of the work performed.

Likewise, a professional who spends time concentrating on a complex task may display a very different activity pattern from someone whose role primarily involves customer service and communication.

Metrics must therefore be interpreted according to each role's responsibilities and objectives.

Working Hours

Tracking working hours helps businesses understand how the workday is distributed and identify patterns that deserve further analysis.

This information can support capacity planning, the evaluation of periods with higher demand, and the identification of situations where particular activities consume more time than expected.

However, working hours should not be interpreted in isolation as a measure of productivity.

Working longer does not necessarily mean producing better results. Similarly, reducing the time required to complete an activity can represent a legitimate efficiency improvement.

Analysis should consider deliverables, task complexity, quality, and operational context.

Time Spent on Activities and Applications

Understanding how time is distributed across activities and applications can help identify opportunities for improvement.

For example, a team may spend a significant portion of its working day manually updating information that could be organized more efficiently.

Another department may use several different systems to complete stages of the same process, creating frequent context switching and making requests harder to track.

Analyzing these patterns can reveal opportunities to simplify routines, integrate tools, and reduce unnecessary operational activities.

It is important to avoid making automatic assumptions about the value of an activity based solely on the application being used. The same program can support productive or unproductive activities depending on the context.

A metric provides a starting point for investigating a process, not a definitive answer about an employee's performance.

Inactivity Time

Periods without recorded activity can have different explanations, including breaks, in-person meetings, reading, planning, work performed away from a computer, or time spent thinking through a solution.

For this reason, inactivity should not automatically be interpreted as a lack of productivity.

When analyzed appropriately, these data can help identify operational patterns, understand how the workday is distributed, and investigate potential bottlenecks.

Interpretation should take into account the role, work routine, and limitations of the data being used.

Output Volume and Quality

The number of completed deliverables is a useful metric when activities can be compared meaningfully.

However, it is also necessary to assess the quality of the results.

A team that increases output while reducing errors and rework may be demonstrating a significant improvement in efficiency.

If output increases but corrections also become more frequent, the organization should investigate whether faster execution is compromising important stages of the process.

Combining these indicators provides a clearer understanding of the balance between speed and quality.

Productivity Metrics Should Work Together

No single metric can represent the full complexity of work.

Task duration shows how much time was recorded. Output volume indicates how much was produced. Quality helps determine whether the result meets expectations. Context explains why certain patterns may occur.

When these types of information are analyzed together, managers can build a more complete picture of operations.

This approach also reduces the risk of encouraging undesirable behavior, such as prioritizing easy tasks simply to increase output counts or accelerating processes without considering quality.

The purpose of measurement should be to understand how work gets done and identify opportunities to improve results, not to turn a single number into an absolute judgment of an employee.

How Monitoo Helps Businesses Understand Where Time Goes

When a company adopts artificial intelligence, automates processes, or expands its digital tools, understanding how time is distributed becomes even more important.

Efficiency improvements can be significant, but they should be accompanied by information that helps managers evaluate how work routines are changing.

This is where Monitoo can support productivity management.

Monitoo brings together information related to website and application usage, time spent using those tools, and operational indicators. It presents this information through dashboards, reports, and visualizations that help managers better understand how work is distributed.

Instead of relying exclusively on general impressions about daily routines, businesses can use organized information to investigate patterns and identify opportunities for improvement.

Understand How the Workday Is Distributed

One of the most important management questions is: where does the team's time go?

Answering this requires more than knowing the total length of the workday. Managers also need to understand how different activities, tools, and processes contribute to the overall routine.

With organized information about application and website usage, managers can investigate usage patterns, identify activities that account for a significant portion of recorded time, and evaluate whether the observed distribution aligns with departmental objectives.

These data can help generate more specific questions.

Is a team spending too much time on operational activities? Are there processes that require frequent switching between systems? Could certain routines be simplified? Is the adoption of a new tool accompanied by noticeable changes in how work is organized?

Data do not replace contextual analysis, but they can help identify where further investigation is worthwhile.

Identify Opportunities to Reduce Operational Waste

Not every loss of time is related to a lack of individual effort.

In many cases, time is lost through processes that require unnecessary steps, poorly integrated systems, duplicate requests, or manual activities that could be simplified.

By examining usage patterns and operational indicators, businesses can identify situations that deserve attention and evaluate possible changes to existing routines.

For example, if an administrative activity consumes a significant amount of a team's time, it may be worth investigating whether the process could be standardized or certain stages automated.

If several employees experience similar difficulties with particular tools, the organization could evaluate whether additional training or workflow improvements are needed.

The opportunity lies in turning information into useful questions and then testing solutions appropriate to the situation.

Use Metrics to Make Better-Informed Decisions

Productivity management becomes more consistent when decisions are supported by organized information and clear criteria.

Reports and dashboards can help managers track changes in metrics, compare periods, and identify patterns that may be difficult to detect through isolated observations.

This information can support discussions about operational capacity, task distribution, and opportunities for improvement.

However, results must be interpreted responsibly. Differences between teams may reflect different roles, projects, periods of higher demand, or specific characteristics of their activities.

Data should therefore serve as analytical and planning tools, complemented by information about processes, objectives, and employees' needs.

Turn Analysis Into Continuous Improvement

Measurement only creates value when it contributes to decisions and concrete actions.

After identifying a pattern, the organization needs to investigate possible causes, define an intervention, and monitor the results.

If the problem is related to a manual process, the solution may involve automation or simplification.

If it is associated with unclear priorities, planning practices may need to be reviewed.

If time is being lost because employees struggle to use certain tools, training and documentation may help.

Monitoo can support the understanding of operational patterns that guide these investigations. However, the actions selected should reflect the team's actual context and the organization's business objectives.

The goal is not simply to observe work. It is to understand operations more clearly and create the conditions for greater efficiency.

Conclusion: Saving Time Is Not Enough. You Need to Know What to Do With It.

Artificial intelligence is changing how businesses produce content, analyze information, and organize their activities. By automating certain stages, technology can reduce operational effort and expand execution capacity.

But these benefits do not automatically guarantee a less demanding workday.

As Jabra's WorkLife research, reported by GlobeNewswire, indicates, many professionals say they are experiencing an increase in workload amid the expansion of artificial intelligence.

This reinforces the importance of looking beyond speed. Businesses need to understand how demands are changing, which activities consume working hours, where bottlenecks exist, and whether available resources are being used effectively.

For organizations, the challenge is not simply to get teams to produce more. It is to create the conditions for technology to contribute to better results, more efficient processes, and better-informed decisions.

This requires clear objectives, well-defined priorities, quality assessments, training, and responsible interpretation of productivity metrics.

It also requires recognizing that productivity does not mean keeping every minute occupied. Effective management must account for the complexity of work, the need for concentration, planning, and the quality of deliverables.

With organized information about daily work patterns, tools such as Monitoo can help businesses better understand how time is distributed and identify opportunities for improvement.

That is why the most important question is not simply how much time artificial intelligence has saved.

The real question is what the business is doing with that time.

Because working faster creates an opportunity. Turning that opportunity into sustainable results is a management decision.

Frequently Asked Questions About AI and Productivity

Does Artificial Intelligence Really Improve Productivity?

Artificial intelligence can improve productivity by automating tasks, accelerating process stages, and making information easier to access. However, the outcome depends on the type of activity, the quality of implementation, and how the resulting improvements are used.

To evaluate the impact, businesses should compare results before and after adopting the technology, considering time, quality, rework, and resources used.

Why Are People Working More Despite Using AI?

Possible reasons include higher delivery expectations, increased workloads, new review tasks, and a lack of changes in how work is organized.

When time savings are filled with additional activities, production capacity can increase without reducing the overall workload.

What Does Jabra's 85% Finding Mean?

According to the research findings reported by GlobeNewswire, 85% of professionals surveyed said they were doing more work in at least one category of activity than they had the previous year.

The research collected responses from knowledge workers across several markets. The finding reflects participants' reported experiences and does not, by itself, establish that artificial intelligence is the sole cause of increasing workloads.

How Do You Measure Team Productivity?

Productivity can be assessed using metrics related to completed deliverables, time invested, quality, rework, and the use of available resources.

Metrics should be selected according to each team's responsibilities. Ideally, multiple data points should be combined to avoid conclusions based solely on task volume or working hours.

Is Tracking Working Time the Same as Measuring Productivity?

No. Time tracking provides information about the duration and distribution of activities, but it does not independently represent productivity.

Interpreting these data requires consideration of job objectives, task complexity, output quality, and other relevant factors.

How Can Businesses Identify Time Wasters?

The first step is to analyze workflows and understand how working time is distributed. Businesses can then investigate repetitive manual tasks, frequent interruptions, unnecessary process steps, and difficulties using tools.

Operational data can help identify patterns, but organizations should investigate the underlying causes before making changes.

Can Monitoo Help Businesses Manage Productivity?

Monitoo provides information about website and application usage, time spent using those tools, and operational indicators, organized through dashboards and reports.

These resources can help managers understand activity patterns and identify opportunities for improvement. The data should be interpreted in the context of different roles, with transparency and clear criteria.

How Can Businesses Prevent AI From Increasing Workload?

Businesses can start by defining clear automation objectives and evaluating which activities should be simplified, eliminated, or prioritized.

It is also important to adjust expectations, account for review effort, provide training, and monitor indicators that help assess both results and workload changes.

Does Completing More Tasks Mean Higher Productivity?

Not necessarily. An increase in completed tasks may represent an improvement, but it should be analyzed alongside quality, task complexity, and resources used.

If output increases while errors, rework, or required effort also increase, the result should be investigated before being considered a productivity improvement.

What Is the Main Lesson for Businesses Adopting AI?

The main lesson is that technology must be accompanied by a management strategy.

Automating tasks is only one part of the transformation. To achieve sustainable results, businesses also need to organize processes, establish priorities, track meaningful metrics, and understand how saved time is being used.

References and Sources

• Jabra Research Institute. WorkLife Research. Research on artificial intelligence, collaboration, and the changing nature of work. Official Jabra website: https://www.jabra.com/thought-leadership/worklife

• GlobeNewswire. Report on Jabra's WorkLife research and the productivity paradox. Verify the original publication and report details before publishing: https://www.globenewswire.com/

• Google Search Central. SEO Starter Guide. Recommendations on titles, content, links, and how pages appear in search results: https://developers.google.com/search/docs/fundamentals/seo-starter-guide

• Google Search Central. Creating Helpful, Reliable, People-First Content. Guidance on creating useful, trustworthy content focused on readers' needs: https://developers.google.com/search/docs/fundamentals/creating-helpful-content

Editorial note: Attribute the 85% statistic to Jabra's research as reported by GlobeNewswire. The examples throughout this article are illustrative. Productivity findings should be interpreted in the context of each organization and its operational requirements.