Miro Finds AI Productivity Gains Are Not Reaching Teams

AI adoption is improving individual employee productivity, but organizations are struggling to translate those gains into better collaboration, decision-making and business outcomes. New research from Miro surveyed more than 9,500 business leaders and employees, finding that 83% believe AI has improved their individual productivity, while only 56% report improvements in output quality. The findings highlight a growing challenge for enterprise HR and technology leaders: managing the organizational and human impact of AI adoption as employees face information silos, increased workloads, fragmented collaboration and growing volumes of AI-generated content.

Miro identifies the productivity gap in enterprise AI adoption

Enterprise AI adoption is creating a productivity paradox. Employees are completing individual tasks faster, but organizations are not consistently seeing improvements in the quality of work, decision-making or collaboration.

Miro’s new report, The State of Intelligent Collaboration, examines this gap through a survey of more than 9,500 leaders and employees across North America, Europe and Asia-Pacific. The research covers product development, IT, operations and other business functions, exploring how AI is changing workplace practices and organizational collaboration.

According to the findings, 83% of respondents believe AI has improved their individual productivity, while 73% report faster output. However, only 56% say AI deployment has improved work quality. More than half also report no improvement in decision-making, while 60% say collaboration between colleagues and teams has not improved.

The results suggest that individual AI productivity and organizational performance are developing at different speeds. For HR departments, this creates a workforce management challenge that extends beyond introducing new tools or providing employees with AI training.

Workplace silos limit the benefits of AI

One of the central issues identified in the report is the fragmented way employees use AI.

Miro found that 69% of respondents said most AI-assisted work takes place in private sessions rather than shared collaboration environments. Meanwhile, 49% of enterprises struggle with information silos that prevent employees from accessing shared context across systems.

The visibility and governance challenges are also significant. Forty percent of leaders rate visibility into organizational AI usage as poor or nonexistent, while 58% report compliance and security concerns without unified governance.

For HR and IT leaders, these findings point to a need for more coordinated AI adoption strategies. Employees may use AI assistants to generate reports, analyze information, develop presentations or accelerate project tasks, but the resulting knowledge can remain disconnected from team workflows.

This fragmentation makes it harder for organizations to understand how AI-generated work contributes to shared objectives. It can also create inconsistent practices around data handling, accountability and the review of AI-generated decisions.

AI-generated content is increasing employee workload

The research also highlights the human cost of fragmented AI adoption. Although automation is intended to reduce repetitive work, employees can face additional responsibilities when AI-generated material requires extensive checking, editing and correction.

Miro found that 59% of respondents are reviewing larger volumes of AI-generated output, while 55% report an increase in what the company describes as “AI slop” — low-quality AI-generated material that creates additional review and rework.

Context switching is another concern. Forty-four percent said it had increased over the previous year. Meanwhile, 60% worry that role boundaries are becoming blurred, and 47% fear that related roles could merge as AI changes how responsibilities are distributed.

The findings also raise questions about workplace culture. Forty-four percent reported concerns about declining collaboration, while one in five said work feels lonelier because of increased AI usage.

For HR leaders, these developments create new priorities around employee wellbeing, job design and workforce development. AI implementation needs to account for the time employees spend reviewing machine-generated work, validating information and coordinating tasks across teams.

The findings also suggest that conventional productivity measurements may be insufficient. Measuring the number of tasks completed or the speed of output without accounting for review effort and cognitive load could overstate the benefits of automation.

AI workhubs emerge as an enterprise technology category

Miro identifies AI workhubs as a potential response to fragmented enterprise collaboration. The concept involves bringing collaboration tools, organizational information and AI agents into shared environments where employees can work with consistent context and oversight.

The survey found that approximately two-thirds of enterprise decision-makers plan to implement an AI workhub within 12 months.

This reflects a broader enterprise technology shift toward integrating AI assistants and agents into existing workflows rather than deploying isolated tools for individual tasks. Microsoft, for example, is advancing human-agent collaboration through its Microsoft 365 ecosystem and Copilot capabilities.

Microsoft’s 2026 Work Trend Index offers supporting context. Its research found that organizational factors, including workplace culture, management support and talent practices, accounted for more than twice the reported AI impact of individual factors such as employee mindset and behavior.

The implication for HR technology providers is that successful AI transformation requires more than access to advanced models. Organizations need employee training, clear responsibilities, shared workflows and governance frameworks that establish how people and AI systems work together.

Miro’s research positions collaborative workspaces as one possible foundation for this transition. However, the effectiveness of AI workhubs will depend on their ability to integrate with existing enterprise systems, protect sensitive information and provide measurable improvements in decision quality.

HRTech implications: redesigning work around people and AI

The report highlights a growing intersection between HR technology, enterprise collaboration and AI governance.

HR departments will increasingly need to understand how AI changes job responsibilities, team structures, employee workloads and the skills required for effective human-AI collaboration. Learning and development programs will also need to move beyond basic AI literacy toward critical evaluation, workflow design and responsible AI use.

For technology providers, the opportunity lies in connecting collaboration environments with workforce development and organizational intelligence. Platforms that help enterprises understand how employees use AI, share knowledge and make decisions could become important components of the broader HRTech ecosystem.

Miro’s research is based on self-reported survey responses, so it does not independently establish that AI caused the reported changes in collaboration or wellbeing. Nevertheless, its findings highlight an important issue for enterprise leaders: individual productivity gains do not automatically translate into stronger organizational performance.

The next phase of workplace AI adoption may therefore depend less on how many AI tools employees receive and more on how effectively organizations redesign work around shared knowledge, human judgment and accountable automation.

Market Landscape

The enterprise AI market is moving from individual AI assistants toward integrated platforms that support collaboration between employees, enterprise systems and autonomous agents.

Three developments are shaping this transition:

• Collaborative AI: Enterprises are looking beyond individual productivity tools toward shared AI environments that preserve organizational context.

• AI governance: Security, compliance, visibility and accountability are becoming essential requirements for scaling enterprise AI.

• Workforce transformation: HR leaders must address changing job responsibilities, employee skills, cognitive workloads and the impact of automation on workplace culture.

Miro’s findings align with Microsoft’s 2026 Work Trend Index, which emphasizes organizational readiness, management support and workplace culture as important factors in realizing AI’s benefits.

The emerging AI workhub category could create competition among collaboration platforms, enterprise productivity providers and AI workspace vendors. However, integration capabilities, governance and measurable improvements in team outcomes will determine which platforms deliver sustainable value.

Top Insights

• Miro surveyed more than 9,500 workers and leaders, finding that individual AI productivity gains are not consistently translating into organizational performance.

• Although 83% reported improved individual productivity, only 56% experienced better output quality following AI adoption.

• Information silos and fragmented AI workflows remain significant obstacles, with 69% reporting that AI-assisted work happens primarily in private sessions.

• AI-generated content is increasing review workloads, with 59% reporting more output to review and 55% experiencing additional AI slop.

• Approximately two-thirds of enterprise decision-makers plan to implement AI workhubs within 12 months, according to the survey.

  Join thousands of HR leaders who rely on HRTechEdge for the latest in workforce technology, AI-driven HR solutions, and strategic insights