Explore how employees and managers perceive AI's role in knowledge management, highlighting gaps in trust and utility that affect collaboration and engagement.
Boardrooms worldwide are excited about AI’s potential in corporate intelligence. AI can quickly sift through data, helping decision-making from the moment a junior analyst uploads a market brief to when a senior executive queries a predictive model. This technology aims to speed up the process of turning raw data into actionable insights, focusing on four key knowledge management processes: acquisition, documentation, sharing, and application.
Acquisition. Machine-learning algorithms can search internal databases, external feeds, and even unstructured emails, extracting key concepts and tagging them with metadata. This creates a dynamic library that grows faster than manual systems.
Documentation. Natural-language generation can draft meeting minutes, compliance reports, and technical manuals consistently, reducing human error. Organizations using these tools report fewer revisions and quicker approvals, allowing experts to focus on analysis instead of transcription.
Sharing. AI recommendation engines deliver relevant documents to the right people at the right time, while chat-based assistants answer queries around the clock, making expertise more accessible.
Application. Predictive analytics integrate knowledge into workflows, suggesting next steps or flagging issues before they escalate. Ideally, this creates a seamless knowledge cycle that boosts innovation and competitive advantage.
Predictive analytics integrate knowledge into workflows, suggesting next steps or flagging issues before they escalate.
However, the technology’s effectiveness depends on user perception. As the Oxford Review states, “the success of AI integration … depends substantially on how users accept and perceive the usefulness of AI.” Without user buy-in, even advanced systems can fail.
Perception Gaps: Managers vs. Employees
A 2025 study by Bar-Ilan University and the University of Padova explored how employees and managers view AI in knowledge management.
Acquisition and Documentation: Managers See Gold, Employees See Tools
Both groups agree that AI excels in knowledge acquisition. Managers, focused on strategy, find AI “most useful” for faster onboarding and comprehensive competitor analysis. Employees acknowledge the speed but see the tools as “helpful but not indispensable,” believing human judgment is still necessary.
In documentation, managers value the reduced drafting time and consistent reports. However, employees worry that automated text may lack nuance, leading to a habit of double-checking that undermines efficiency.
Perceptions diverge in knowledge sharing. Employees rated AI’s usefulness lower than managers, citing distrust in algorithmic relevance. One respondent noted, “I get a lot of suggestions that feel generic,” reflecting concerns that AI may increase information overload.
In application, where knowledge turns into action, both groups rated it low, but the gap was significant. Managers believed AI could “significantly improve decision quality,” envisioning dashboards that recommend next steps. Employees, however, feared “over-reliance on black-box recommendations” that obscure reasoning and threaten their autonomy.
Implications for Engagement and Career Development These perception gaps affect collaboration and career paths.
Implications for Engagement and Career Development
These perception gaps affect collaboration and career paths. When managers promote AI tools that employees distrust, it can reduce engagement. Teams may revert to manual processes, hindering the efficiencies AI offers.
On the other hand, when employees feel empowered by transparent AI—understanding how recommendations align with their expertise—they are more likely to experiment, share insights, and upskill. The study suggests that bridging this perception gap is essential for using AI to foster career growth rather than anxiety.
The Future of Knowledge Sharing in an AI-Driven Workplace
Looking ahead, knowledge sharing will evolve due to three interconnected forces: technology, human trust, and governance frameworks.
From Face-to-Face to Algorithmic Intermediaries
Traditional knowledge exchange—coffee-break chats, brainstorming sessions, and lengthy emails—has begun to shift to AI-mediated platforms. Chatbots and virtual assistants can handle routine queries quickly, allowing senior staff to tackle complex problems. However, the human element remains vital; nuanced judgment and relational trust thrive on direct interaction.
AI-Powered Sharing Platforms: Promise and Peril
Companies are implementing AI-curated knowledge hubs that learn from user behavior, surfacing documents that likely answer questions. When effective, these systems democratize expertise, enabling a junior analyst in Nairobi to access insights like a senior manager in London. However, the Oxford Review warns that “data quality and security concerns” can undermine confidence. Poorly labeled data or lax access controls risk spreading misinformation or exposing sensitive information.
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Upskilling the Workforce for an AI-First Knowledge Culture
Transitioning to AI-augmented knowledge sharing requires new skills.
Security breaches, particularly in regulated sectors like finance and healthcare, can damage the trust AI needs. Organizations must implement strict data governance policies—clear tracking, audit trails, and role-based permissions—to ensure AI sharing does not become a liability.
Upskilling the Workforce for an AI-First Knowledge Culture
Transitioning to AI-augmented knowledge sharing requires new skills. Employees need to know how to query chatbots, interpret algorithmic recommendations, assess confidence scores, and identify anomalies. Upskilling programs that combine data analysis with domain expertise are becoming essential.
Even large financial institutions are feeling the pressure. Bloomberg reported that HSBC is considering cutting 20,000 jobs as AI takes center stage, highlighting how automation can
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