Employee-Centric Knowledge Enablement: Bringing Answers To People (Not The Other Way Around)

The digital workplace is awash in noise. Employees aren't starved for information—they're drowning in it. One way to deal with this influx of information is through context-aware knowledge experiences embedded directly in collaboration tools. Instead of siloed knowledge bases, organizations could invest in AI that proactively delivers knowledge when and where it’s needed.

The Problem: Knowledge Friction

Today’s workers face unprecedented cognitive overload. Microsoft’s research shows the average employee receives around 117 emails per day and 153 Teams chats on weekdays, nearly 275 pings daily that fragment attention and force employees to “hunt” for answers across multiple platforms.

It’s no surprise that 68% of workers say, "they struggle with the pace and volume of work," while 75% already experiment with AI tools to reduce the noise.

Yet, organizational systems lag behind employee needs. A Harvard Business Review Analytic Services study found that only 3% of companies use AI for knowledge management today, and just 44% feel confident in their ability to manage enterprise knowledge effectively.

The result is what researchers call “knowledge friction,” or the wasted time and frustration caused by searching, context-switching or duplicating work because information isn't accessible in the moment of need.

The Trend: Knowledge In the Flow of Work

One of the most significant shifts in enterprise generative AI (GenAI) strategy that I'm seeing is the move away from centralized knowledge portals toward embedded, in-context experiences. Rather than requiring employees to step out of their daily workflows to consult a static knowledge base, answers now increasingly surface directly inside the systems where work happens—collaboration hubs, communication platforms, service desks, project environments and BI dashboards.

This isn't just a design preference; it reflects clear productivity gains. Research from the St. Louis Federal Reserve shows that workers using GenAI tools within their daily tasks can be up to 33% more productive per hour compared with those who don’t. Similarly, McKinsey’s "State of AI" report finds that 21% of enterprises using GenAI have already "redesigned at least some workflows" around embedded AI, with many reporting measurable revenue growth and cost reductions.

Strategically, this changes how organizations think about knowledge management and governance. It's no longer sufficient to maintain a single repository of record; leaders must instead design knowledge distribution layers that feed GenAI systems embedded throughout the enterprise. This means building semantic search, retrieval-augmented generation (RAG) and real-time analytics into the environments of a customer service agent, financial analyst or operations manager. Notably, a 2023 survey of embedded analytics adoption found that almost 38% of organizations already use embedded tools to monitor and improve productivity, although legacy infrastructure and adoption hurdles remain significant.

When knowledge is accessible in the moment of action, I've witnessed faster resolution times and higher adoption rates of AI-driven systems. Yet, the shift introduces new responsibilities, chief among them trust and provenance. As AI surfaces answers within workflows, employees must be able to understand why an answer is reliable, where it originated and whether it complies with regulatory standards.

I believe that the future of enterprise GenAI isn't a return to static knowledge portals but a distributed, embedded layer of intelligence that travels with the worker. Success will be defined by how effectively organizations combine embedded AI capabilities, rigorous governance and user-centric design to put the right knowledge in the right place, at the right time.

Technical Foundations

What makes these experiences possible is a shift in how enterprise knowledge is indexed and delivered:

• Semantic Search and Retrieval: Systems now leverage embeddings and vector search to understand meaning, not just keywords. This powers Slack’s enterprise search and Atlassian’s AI-driven Q&A.

• Context Awareness: AI assistants are tuned to interpret surrounding conversations, document history and user roles, so answers adapt to the moment.

• Governance And Trust: Enterprises demand provenance. ServiceNow and Atlassian embed citation links, while Microsoft and Zoom enforce permissions through existing identity systems.

• Extensibility: Connectors to third-party systems (e.g., CRM, ERP and ticketing tools) ensure employees get holistic answers, not just siloed responses.

A Roadmap for Executives

CIOs, CHROs and knowledge leaders can take a pragmatic approach:

1. Start in the flow of work. Choose a core collaboration layer (e.g., Slack, Teams, Confluence or Notion) and embed AI knowledge retrieval where employees already spend time.

2. Prioritize critical sources. Connect systems that drive daily productivity, such as document repositories, project tools and HR or IT systems, before chasing edge cases.

3. Instrument outcomes. Track metrics like time-to-answer, search-to-solution ratios and weekly hours reclaimed. Benchmark against published outcomes (e.g., Toshiba’s 5.6 hours per month or Prague Airport’s two hours per week).

4. Manage trust and change. Require citations, align AI permissions with identity and train employees in “prompt literacy” so adoption feels intuitive and safe.

5. Iterate across functions. Start with knowledge-heavy areas (e.g., HR, customer support and IT), then expand to R&D, marketing and finance.

The Executive Takeaway

Employee-centric knowledge enablement is no longer about building a bigger knowledge base; it’s about delivering frictionless experiences that give workers time back. Whether through Slack’s recaps, ServiceNow’s workflow automation, Confluence AI’s Q&A or Notion AI’s workspace chat, the momentum is industry-wide.

Executives should view this as both a productivity and engagement strategy. The proof points (time savings, ROI models and rising employee expectations) show the window is open now.

Sources

Fallmann, Daniel. Employee-Centric Knowledge Enablement: Bringing Answers To People (Not The Other Way Around). Forbes Technology Council, October 22, 2025. Employee-Centric Knowledge Enablement: Bringing Answers To People