By Evan Reiss
AI is accelerating content creation, but organisations are discovering that trust, validation, and oversight are becoming the new bottlenecks to productivity.
Artificial intelligence has rapidly moved from experimentation to everyday business use. Across industries, employees now rely on AI to draft documents, summarise information, analyse data, and automate routine tasks. The promise is compelling: faster work, greater efficiency, and higher productivity.
Many organisations are already seeing these benefits. Yet a growing body of evidence suggests that the story is more complicated than productivity metrics alone indicate.
As AI adoption matures, businesses are encouraging a new challenge. While AI can dramatically accelerate content creation, the time required to verify, validate, and approve AI-generated outputs is emerging as a significant new category of work. In many cases, work is not disappearing; rather, it is being redistributed.
This shift is giving rise to what can be described as the verification economy: an environment in which trust, oversight, and validation become increasingly important contributors to organisational performance.
The Productivity Paradox
The narrative surrounding AI has largely focused on efficiency gains. Organisations invest in AI because they expect employees to complete tasks faster, produce more outputs, and reduce operational costs.
Research certainly suggests that many workers feel more productive when using AI. However, a closer examination reveals a more nuanced reality.
Foxit’s recent State of Document Intelligence research found that while 89% of executives and 79% of end users report productivity improvements from AI, much of the time saved is being consumed elsewhere in the workflow.
Executives reported saving an average of 4.6 hours per week through AI, but spending 4 hours and 20 minutes validating outputs. End users reported saving 3.6 hours per week while spending 3 hours and 50 minutes reviewing AI-generated content. The result was a net productivity gain of just 16 minutes per week for executives and a net loss of 14 minutes for end users.
These findings highlight a growing disconnect between perceived productivity and realised productivity. AI can produce content in seconds. The challenge is determining whether that content can be trusted.
Verification Is Becoming a New Form of Work
Historically, creating information was often the most time-consuming part of knowledge work. AI has dramatically reduced that burden. However, organisations are discovering that the responsibility for accuracy has not disappeared. It has simply moved.
Employees increasingly spend time reviewing outputs, checking facts, validating sources, ensuring compliance, and refining content before it can be used in real-world settings. In regulated industries such as finance, healthcare, legal services, and government, these review processes are often mandatory.
This dynamic is reshaping the nature of knowledge work itself. Where employees once spent most of their time creating and processing information, many now find themselves primarily verifying and approving it. The cognitive task has shifted from doing to reviewing — a change that is less visible in productivity dashboards but acutely felt by workers on the ground. A related concern is the accumulation of what might be called knowledge rot: as AI generates content at scale, outdated or unreliable information can quietly embed itself in workflows, relied upon by both humans and AI systems as though it were current and correct.
This helps explain why many organisations have not yet achieved the transformative productivity gains often associated with AI adoption. Faster creation does not automatically translate into faster outcomes if verification requirements expand at the same pace.
The Growing Trust Gap Inside Organisations
Verification is fundamentally a trust issue. While AI systems have become remarkably capable, they can still generate inaccuracies, fabricate information, and produce outputs that appear convincing despite containing errors. As a result, employees often approach AI-generated content with caution.
This caution carries real weight. A Deloitte survey of more than 11,000 knowledge workers found that 60% reported AI adoption had resulted in heavier rather than lighter workloads. The burden of oversight is not evenly distributed, either: employees closest to day-to-day document workflows bear a disproportionate share of the verification effort, while the benefits of speed are often most visible to those furthest from it.
The cognitive toll of sustained AI oversight is also becoming measurable. Research from Boston Consulting Group, published in Harvard Business Review earlier this year, found that workers whose AI tools required high degrees of direct monitoring expended 14% more mental effort and reported 19% greater information overload than those with minimal oversight requirements. Critically, those experiencing the highest cognitive burden made major errors at a rate 39% higher than their less-overloaded peers — a finding that challenges the assumption that more AI always means fewer mistakes.
Interestingly, confidence in AI appears to vary significantly depending on organisational role. Foxit’s research found that 60% of executives describe themselves as highly confident in AI-generated outputs, compared with only 33% of end users. Just one in ten end users reports being extremely confident in AI accuracy. This creates an important organisational challenge.
Senior leaders often evaluate AI through strategic outcomes, productivity metrics, and investment priorities. Employees, meanwhile, experience the day-to-day realities of reviewing outputs, identifying inaccuracies, and managing risk. When these perspectives diverge, organisations risk overestimating the maturity of their AI initiatives.
Successful adoption requires more than deploying technology. It requires establishing realistic expectations about where AI adds value, where human oversight remains essential, and how accountability should be distributed throughout the workflow.
Why Productivity Alone Is No Longer Enough
As verification becomes a larger component of work, organisations may need to rethink how they measure AI success. Traditional measures such as output volume, task completion speed, and return on investment remain important. However, they may no longer provide a complete picture.
A workflow that generates content faster but requires extensive review may not deliver meaningful business value. Similarly, an employee who produces more output but spends increasing amounts of time validating information may not experience a genuine productivity improvement. This is helping drive interest in broader frameworks that capture the human impact of AI adoption.
Increasingly, organisations are evaluating factors such as employee confidence, capability development, trust, and satisfaction alongside conventional productivity measures. This shift reflects the emergence of Return on Employee (ROE) as a complementary way to assess AI investments.
The goal is not simply to increase output. It is to improve outcomes while enabling employees to work effectively and confidently.
Reducing the Cost of Trust
The next phase of AI adoption will be defined by how efficiently organisations can establish trust in content. Reducing verification burden requires a combination of technology, process, and organisational design.
AI systems must become better integrated into existing workflows. Greater transparency into how outputs are generated can help users assess reliability more quickly. Domain-specific models and better contextual training can improve output quality from the outset, reducing the need for extensive review.
Equally important are workflow changes that embed verification earlier in the process rather than treating it as a final checkpoint. Clear accountability, structured review processes, and well-defined governance frameworks can significantly reduce friction.
Perhaps most importantly, organisations must continue investing in human skills. As AI becomes more capable, critical thinking, judgment, and problem-solving become increasingly valuable differentiators.
The future of work is unlikely to be fully automated. Instead, it will depend on effective collaboration between human expertise and machine intelligence.
A Final Word
The conversation around AI has largely focused on what the technology can create. The next conversation should focus on what organisations can trust.
The rise of the verification economy reflects a broader reality: AI is changing work, but not necessarily reducing it. Verification, validation, and oversight are becoming central components of modern workflows.
The organisations that realise the greatest value from AI will not simply be those that generate content faster. They will be those who minimise the cost of trust while preserving the human judgement that turns information into action.
