AI 提效神话 vs 员工更累：一份职场遥测报告的硬核拆解

Last week on Maimai, I saw a complaint: isn't AI supposed to boost efficiency? Why do I feel even more exhausted?

Today I'm going to unpack a report that speaks directly to this question.

Here is the report I'm referring to:

1. Background

The 2026 State of the Workplace report from ActivTrak reveals a structural shift reshaping the global workplace: AI has evolved from an "experimental tool" into a "work system." This amplifier effect simultaneously scales up total workload, collaboration load, and time intrusion. With hard data—80% employee AI adoption, an average of 7 AI tools deployed per company, and a 104% surge in email time—the report argues a counterintuitive conclusion: work in the AI era is not less; it is "more."

About the Publisher

ActivTrak is a workforce analytics and productivity management software company founded in 2009 and headquartered in Austin, Texas. Its core product is a 'Work Intelligence' platform, not simply monitoring software.

Its workflow can be summarized in three steps:

Collect & Integrate: Through a software 'agent' installed on employee devices, it continuously and at scale collects work activity data while protecting employee privacy.

Analyze: Through intuitive dashboards, reports, and an AI-driven conversational interface, it assesses productivity, efficiency, capacity, and resource utilization.

Optimize: It converts productivity and capacity data into concrete financial metrics, compares them against industry benchmarks, and provides recommendations for staffing decisions.

Thousands of clients already trust its products and analytical insights, backed by what it calls 'one of the world's largest behavioral datasets on workplace activity.' This means its analysis is not based on sample surveys or interviews but on aggregated workforce intelligence data from 'thousands of organizations' after anonymization. This is key to understanding the entire report—every conclusion is grounded in data, and the output takes the form of actionable improvement suggestions co-created with clients.

ActivTrak therefore believes this report better reflects the real state of work, and the industry mix leans heavily toward services, showing the full picture of AI adoption among white-collar knowledge workers. According to the pie chart, financial services (13%), professional services (12%), insurance (10%), legal services (9%), construction and engineering (9%), and cloud services (9%) together account for over 60%. These industries share common features: computer-based work, mature process digitalization, and highly trackable employee behavior—precisely the areas where generative AI is landing fastest. In contrast, labor-intensive or policy-sensitive sectors such as manufacturing (3%), logistics (1%), retail (1%), and government (<1%) hold very small shares.

This reminds readers that the report's conclusions apply primarily to knowledge-work scenarios and should not be directly extrapolated to blue-collar or field-based roles.

Publication Background

The report was released in the first half of 2026, two years after generative AI tools such as ChatGPT entered enterprises at scale. The market urgently needs to answer a key question: has AI brought real productivity change, or is it still stuck in the 'trial and curiosity' phase? ActivTrak leverages its unique workplace behavioral telemetry to provide a rare insight based on 'real usage data' rather than 'survey intent.' This report is suitable for enterprise managers who are pushing AI adoption without clear returns, AI product leaders, and researchers concerned with labor-market transformation. The new model of working alongside AI means AI is not a replacement but an 'amplifier.'

Key Terms

Before reading, it is helpful to understand the following key terms:

AI Measurement Gap: The gap between broad enterprise AI adoption and an organization's ability to assess how AI actually affects employee performance, operational strategy, and work execution patterns.

Burnout: In Chinese it is often translated as 'juan dai' (fatigue), but that can easily make it sound like a mental state. In this report, burnout purely means 'overwork' or 'work overload'—a behavioral state in which workload seriously exceeds normal time and energy capacity. Without understanding this, the rest of the report may seem contradictory. The main statistic is based on Overutilization: when employees' actual effective working hours exceed the organization's daily standard effective hours by over X%.

Capacity: Productivity / available effective work hours. The percentage of actual effective work completed relative to expected effective work hours.

Disengagement: Work detachment / passive disengagement. Employees with a negative work tendency spend more than 75% of their working time each year underutilized.

Focus Time: Deep work hours. The time employees spend on a single task or collaborative matter without interruption or attention shift.

Focus Efficiency: The share of focus time in total screen time. Average focus duration refers to the average length of sustained work without task switching (no interruptions, no attention shifts, no collaborative interruptions).

Healthy Utilization: Healthy workload. Employee daily effective work hours fall within ±1/3 of the organization's daily target effective hours.

Productive Time: Effective working time. The time employees spend on work within various business systems and platforms each day, including deep focus, collaboration, multitasking, and other effective work behaviors.

Key Findings at a Glance

As the opening of the report, this page makes a counterintuitive assertion: AI is not making work easier; it is expanding the workload. Adoption has hit 80%, but employee focus has fallen to a three-year low. The 'lighten the load' myth is being overturned by behavioral data.

1. AI Is Not Lightening the Load—It Is Amplifying the Work

The report opens by challenging the widely held assumption that AI will make workdays shorter, easier, and more controllable. Behavioral data tells a completely different story: workdays are indeed shorter, but productive hours have grown by 5%, AI tool usage time has jumped eightfold, and collaboration has surged 34%. In other words, AI has dramatically increased the 'density' of work per unit of time. Employees get more done, but their energy is consumed faster. This is an 'acceleration' rather than a 'reduction,' and companies must recalibrate their expectations of AI's value.

2. Workplaces Are Better at Preventing Burnout Than Unlocking Capacity

A more intriguing set of comparisons: on the one hand, burnout is down—a real victory; on the other hand, focus efficiency has fallen to 60%—a three-year low—and the risk of disengagement has jumped 23%.

The report draws a highly tense conclusion: employees are shifting from 'overworked' to 'chronically under-challenged rather than overextended.' When companies build strong guardrails against collapse, their ability to unleash employee potential remains stuck—a hidden blind spot in organizational capability building.

3. The Real Question for 2026: No Longer 'Whether to Use AI,' But 'Whether the Operating Model Fits the New Workforce'

The report clearly states the core challenge of 2026: the systems governing attention, focus, and employee alignment have not kept pace with the acceleration of external change. The question companies must answer has shifted from 'whether to adopt AI' and 'where and when to work' to: Is my operating model designed for a workforce whose way of working has been fundamentally transformed? And this transformation is uneven and unequal (differently—and unevenly). This is a pivotal shift from 'tool adoption' to 'organizational redesign.'

This is a wake-up call for China's AI industry.

This concept deserves deep thought from Chinese product managers: as AI takes over more execution tasks, how should the 'thinking space' and 'growth space' left for employees be redesigned? The next wave of AI products may not be decided by model capability, but by whether they can help employees find meaning and rhythm in an 'accelerated' world.

Consider these questions as you read:

Why has AI improved individual efficiency but failed to translate into organizational productivity or profit growth? Is it a management problem or a technology problem?

Under 'tool sprawl,' should companies pursue more tools or tool integration? What is the optimal number of tools?

Is the AI-driven increase in collaboration time (email +104%, instant messaging +145%) a good thing or a bad thing? Does it mean AI is creating a new 'meeting entropy' metric? Could it become a new standard for measuring the health of AI adoption? Do Chinese enterprises have similar data-collection capabilities?

Given that 95% of companies see no return on their AI investments, what role should middle managers play? Where is the boundary between technology and management?

Key Insights

1. AI Adoption Is No Longer a Question, but Governance Is Urgent

Adoption is no longer the issue: in 2023, companies used two AI tools on average; by 2025, that had grown to seven, with 83% of companies using more than six. 80% of employees already use AI (up 52% year over year), and AI's share of total working hours has jumped from 0.1% to 0.8% (an 8-fold increase). This signals that AI has upgraded from an 'optional tool' to 'workplace infrastructure,' whose entry-level status rivals that of search engines in the past.

AI tools are becoming fragmented, expanding from two to seven, with 83% of organizations running more than six simultaneously. ChatGPT leads with 27 times the usage time of runner-up Cursor, while Microsoft (Copilot series) dominates in terms of user numbers. This 'dual-track leadership' reflects scenario divergence—ChatGPT leads in personal productivity, while Microsoft's ecosystem reigns in enterprise-grade embedding. The tool explosion brings governance challenges: data security, permission management, cost control, and usage standards all become new issues.

Depth of use and focus efficiency become a problem: the data shows that AI users have 9% more days of 'healthy usage patterns' (habits that fit a sustainable work rhythm) than non-users—a positive signal. But at the same time, AI users' average daily focus time has dropped 9%, while non-users have seen almost no change. This paradox reveals a deeper issue: while AI helps employees 'do more,' factors like frequent tool switching, prompt debugging, and result verification may silently fragment deep-focus time. AI may not make work more 'efficient,' but it may make work more 'fragmented.'

2. AI's Share of Working Hours Is Not Higher-Better; 3% of Users Hit the Productivity Sweet Spot

AI usage has an 'inverted U' sweet spot; blindly piling on usage is ineffective. Using three years of combined data (2023–2025), the report shows that when employees spend 7–10% of their working time on AI tools, productivity peaks (95%). Whether usage falls below or above this range, productivity declines. This echoes macro data from PwC's 2025 Global AI Employment Barometer: since 2022, productivity growth in AI-exposed industries has nearly quadrupled, and per-capita revenue growth has reached three times that of low-exposure industries.

In other words, AI's value lies not in 'whether to use it' but in 'whether it's used correctly.' Finding the right usage intensity is key.

Yet currently, only 3% of users truly reach the 'productivity sweet spot.' The vast majority of organizations have not crossed the 'effective threshold,' leaving huge room for optimization.

Adoption Does Not Equal Impact; Governance Gaps Are Becoming a Hidden Bottleneck to AI Scaling

IBM's 2024 CEO Study contains a striking warning: 75% of CEOs believe trusted AI is impossible without effective AI governance, yet only 39% of companies say they have mature generative AI governance capabilities. ActivTrak's customer research corroborates this contradiction: 71% of companies have already deployed or piloted AI tools in teams, but 50% are not measuring AI's actual impact on employee productivity, and 33% list 'security and privacy' as their top challenge. 'Use more, measure less, manage poorly'—this 'bare deployment' is the pain point that enterprise AI governance must confront in the next phase.

3. AI Does Not Reduce Work—It Amplifies It

AI is an 'amplifier' rather than a 'substitute': after adopting AI, time spent on all work categories increased—email +104%, instant messaging +145%, business management +94%. Nothing decreased.

This shows employees use AI to do more things, not to do the same things in less time.

High performers are the main drivers of this trend, and AI is widening rather than narrowing performance gaps.

Commentary: For domestic companies, the yardstick for measuring AI value urgently needs to shift from 'DAU and usage rate' to 'task substitution rate' and 'decision leverage.' The breakthrough direction may lie in building a unified orchestration layer combining a 'super portal + model routing' to manage a fragmented tool ecosystem, rather than simply adding more tools. In today's world of ubiquitous AI capabilities, establishing usage norms, effectiveness measurement systems, and safety guardrails is the next step to releasing real productivity.

4. Focus Has Become a Scarce Resource—Not an Episodic Risk, but a Structural Collapse

Focus efficiency refers to the share of deep, uninterrupted work time within total working time.

This figure fell from about 65% in 2023 to 60% in 2025. It may look like a mere 5-percentage-point shift, but it is actually a 'three-year continuous anomaly.'

Average single focus duration also shrank from 14 minutes 23 seconds to 13 minutes 7 seconds, a 9% decline.

Behind this are two easy-to-miss signals: first, the total amount of focus is shrinking (about 4 minutes less per day, roughly 2%); second, the depth of focus is also degrading—the length of sustained focus is becoming shorter. Deep work requires a high 'startup cost,' and fragmentation forces employees to keep starting up without ever entering flow, which may be more damaging than the decline in total time.

Collaboration and Meetings Are Exploding, Systematically Eroding the Soil for Deep Focus

Collaboration time has surged 34%, equivalent to an extra 1 hour and 3 minutes per day spent on communication and coordination, reaching 52 minutes. Meanwhile, multitasking has risen 12% (+10 minutes) to 1 hour and 33 minutes. Combined, this means employees spend nearly 2.5 hours per day in a state of being 'interrupted' or 'switching between threads.'

Here is a truth most professionals overlook:

Meaningful deep work requires at least 20–25 minutes of 'flow startup time.' Thirteen minutes of focus is simply not enough to solve any complex problem. 'Seemingly busy' collaboration is hollowing out an organization's 'thinking capacity.'

'The Foundation of Sustainable Output' Is Being Hollowed Out—The Most Critical Signal

AI does relieve employees from repetitive work, but at the long-term cost of 'cognitive fragmentation'—a cost that is invisible in the short term but has unknown effects on long-term innovation quality. The report uses data to sketch a paradox of 'short-term boom, long-term overdraft': per-unit output is rising, but the 'infrastructure' of productivity—focus duration, focus depth, and continuous thinking ability—is steadily deteriorating.

This is like a server forced to overclock: it looks impressive on benchmark scores in the short term, but hardware wear is accelerating. For any organization that depends on knowledge work, this 'focus deficit' will erupt within the next 2–3 years in the form of 'declining decision quality,' 'innovation exhaustion,' and 'employee burnout.'

5. Disengagement and Engagement Risk Surge

The report highlights a critical group: employees who are not fully activated.

When employees are in an 'underutilized' state for more than 75% of their working time—not because they are burned out, but because they are chronically 'not busy, not challenged, not growing'—the expansion of this group is also a warning sign. It means AI has not made work more meaningful; instead, it has exposed large gaps in job design.

The report's core conclusion about Disengagement Risk is that it is replacing Burnout as the top employee risk leaders face.

From 'overworked' to 'bored': 'Work is no longer as challenging or interesting as before.'

The core driver is 'underutilization': the report attributes this sense of disengagement to employees being in a state of 'underutilized' or 'underchallenged' for long periods. The capacity freed by AI does not automatically flow to high-value work. The report highlights a logical trap many companies overlook: when AI reduces employee overload, the freed time does not naturally transform into more meaningful work. 'Without deliberate design, freed capacity drifts toward disengagement.' In other words, AI is not a magic potion that 'automatically makes work more meaningful.' It requires proactive management and repeated emphasis: the real leverage point of AI is not tool deployment but organizational redesign.

The economic cost of 'disengagement' is far beyond intuition—mid-market companies lose $200–350 million a year. McKinsey's estimates show that disengagement and employee attrition cost a typical mid-sized S&P 500 company about $228 million to $355 million in productivity each year. Combined with Gallup's data—62% of global employees are 'not engaged' (doing only the minimum) and 15% are 'actively disengaged'—this means the vast majority of the world's workforce potential is being systematically wasted. This elevates 'disengagement' from a soft issue to a hard financial risk.

In summary, the report depicts a contradictory state of 'doing more, but investing less.' AI tools improve work efficiency but fail to optimize work organization, causing efficiency gains to be consumed by fragmented work patterns and intensifying employee disengagement.

Finished reading? A bit confused? Is work more tiring or less tiring?

One moment it says burnout is down, working hours are shorter, and employees are under-activated; the next it says work has not decreased, start times have moved earlier, and weekend work has increased. It's easy to feel dizzy.

Let's sort it out: employees are online longer, but longer online time does not mean more engaged. They are more often interrupted by fragmented and collaborative matters, while deep involvement time decreases. In other words: the body is present, but the soul is drifting.

If you say it's tiring, it seems AI has taken over the overwork of past code-crunching. If you say it's not tiring, you seem to be working all day. And you have lost a sense of meaning and achievement.

Take programmers: in the past, their dream was to reduce meetings, collaboration, documentation, and reporting so they could focus entirely on writing code. For them, uninterrupted coding allowed flow state, a sweet reward after a series of meetings, chasing others for interfaces and integration, and debating requirements—even if it meant working late.

But now, the coding they once enjoyed most is done by AI, leaving only the tasks they never liked. The boss thinks they have a lighter load, but that seemingly tiring yet deeply engaged state was the idyllic retreat in the past...

In the past, they were tired but engaged. Now, the work they were engaged in has turned into fragmented tasks they dislike, and the heart is tired. Therefore: the body is less tired, but the mind is more exhausted.

3. The Report's Recommendations: What Do They Mean? What Can We Do?

Based on over 443 million hours of behavioral data accumulated over three years, ActivTrak's Productivity Lab points out that in 2026, AI application is no longer the core issue; organizations face the challenge of building 'AI orchestration' capabilities. At the same time, declining employee engagement and attention depth are becoming more severe workplace crises than burnout.

1. The Center of AI Discussion Has Shifted from 'Whether to Adopt' to 'How to Orchestrate'

After three years of accumulation, 88% of employees are already using AI tools, and 95% of organizations have completed some form of AI rollout. This means the 'whether to use AI' debate is outdated. The dividing line in organizational capability has shifted toward 'operational discipline' and adoption rate. The report's key judgment: when AI occupies 7–10% of total working time, the organization is in the 'optimal zone.' This zone is a blind spot that most enterprises have not identified, let alone actively managed. Currently, the average number of AI tools deployed per company has reached seven and is still growing. The report is clear: AI governance is no longer a future topic but a current reality that must be addressed.

2. The Employee Engagement Crisis Is Replacing the Burnout Crisis

The core workplace narrative in the post-pandemic era has been 'burnout,' but the data reveals a more dangerous truth: among at-risk employees, the share in a state of 'disengagement' has surpassed those at burnout risk, and it has grown 21% in one year. These employees are not 'lying flat' and waiting to leave; they are 'silent capital' whose capacity is severely underestimated and not yet fully deployed. In terms of workload management, organizations have focused on balancing workload but rarely invest effort in capability redeployment. The report asserts that whether an organization can identify these hidden capacity troughs will determine the next round of competitive gaps.

3. Focus Will Shift from a 'Natural State' to a 'Resource That Must Be Managed'

The report notes that focus efficiency has declined for three consecutive years, a structural shift driven by the combination of collaboration tools, AI platforms, and hybrid work models. In 2025, the average deep focus time was just 13 minutes and 7 seconds, down 9% from 2023, and deep, sustained focus is becoming increasingly rare. The report is explicit: if companies treat focus management as a 'personal matter' for employees, the core indicators that predict long-term performance will continue to erode. Organizations that systematically overhaul meeting norms, promote async workflows, and set protected time blocks will pull ahead significantly in the next phase.

The report's proposed 'optimal AI share of 7–10%' range means that over-reliance may actually dilute organizational effectiveness. This contrasts sharply with the tendency of many domestic companies to blindly pursue 'all-in AI.' At present, when China's leading companies evaluate AI implementation, metrics still remain at the level of 'usage rate' and 'activity rate.' Cases that truly establish 'AI effectiveness evaluation' and 'AI orchestration governance' frameworks are still quite rare. In addition, the judgment that 'focus is a managed resource' is especially a warning for the domestic context: the proliferation of collaboration tools such as Feishu, DingTalk, and WeCom has not improved deep work efficiency; instead, it has amplified attention fragmentation. Those companies that take the lead in building 'focus management mechanisms' at the organizational level—such as regulating meeting hours and setting default deep-work windows—may gain an advantage in the next round of productivity competition.