Whenever we’re asked about the ROI of Knowledge Management, the usual responses quickly emerge — usage analytics from knowledge libraries, downloads, engagement on communities of practice, hours saved by reusing existing content, and the productivity boost from quicker access to information.

And yes, all of these are important. They’re tangible, they’re easy to track, and they speak in a language that leadership often wants to hear.

But here’s the truth we rarely talk about: some of KM’s biggest wins are the ones you can’t always measure on a dashboard.

Let’s talk about tacit knowledge — the deeply personal insights, contextual understanding, and project experiences that live in someone’s head. The kind of knowledge that disappears quietly when an employee exits, if we don’t make a conscious effort to capture it.

KM plays a powerful role here. Through knowledge harvesting, exit interviews, after-action reviews, and peer-sharing sessions, we’re able to preserve this goldmine of experience. This not only safeguards critical organizational memory but also dramatically shortens the onboarding curve for new team members. Instead of starting from scratch, they gain a fast-track view of what has worked (and what hasn’t), complete with best practices and real-world lessons learned from those who have been there and done it.

Then there’s another layer — the collaborative power of KM that rarely gets quantified but creates a massive impact. When KM teams foster communities of practice, build expert directories, or simply create spaces where people can ask questions and share ideas, something incredible happens: people connect. Silos start breaking down. A pre-sales lead in one region suddenly has access to a solution expert from another. A new joiner finds a mentor. A struggling team finds guidance. Conversations spark ideas, and ideas turn into innovation.

KM becomes more than just managing documents — it becomes about managing relationships, expertise, and trust across the organization.

So yes, keep showing those dashboards and metrics — they matter. But don’t forget to advocate for the value that can’t always be measured: the knowledge we save from being lost, the time we gift to others by preserving it, and the invisible threads of collaboration that KM quietly weaves every single day.

Because sometimes, the biggest impact we make is in the things that no one thought to measure — until they were gone like employees retired or moved out taking along their goldmine of knowledge and insights.

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Artificial intelligence (AI) systems that can independently present solutions to problems and take various actions to align with individual and corporate goals are becoming more adept every day. Advances in the last few years have brought machine learning to new levels.

While traditional AI requires commands and is task-specific, agentic AI can perform multistep processes and make intelligent decisions.

Humans are utilizing AI to help filter, retrieve and report information. The concern as people send more of their thinking tasks to computers is how it might impact personal knowledge retention and how they can continue solving problems without computer aid.

How Is Cognition Shifting and What Does It Mean for the Future of Humankind’s Brains?

Numerous agentic AI systems exist, such as those used in education technology and business. The software learns from each interaction until it can adapt and act independently.

For example, autonomous vehicles are already on the road, serving as taxis people can grab from one destination point to the next. These vehicles make driving decisions in complex situations, such as heavy traffic or pedestrian crossings. While the technology isn't perfect, it is constantly improving. Another example is AI trading bots that monitor the financial markets and make trades based on analysis.

AI helps with skills like evaluating and critical thinking. However, if misused, humans might make less spontaneous choices and fail to exercise the parts of the brain responsible for higher-order thinking.

Researchers found that AI-powered tools saved users an average of 97 minutes weekly. Many proponents of AI usage argue that people can use extra time to work on creative skills and deeper thinking. The key will be to remain aware of the potential to become too reliant on AI and intentionally develop creative thinking patterns.

How to Prevent Overreliance on Agentic AI

Researchers have studied over reliance on technology for decades. From concerns about children watching too much television to internet usage, the worries are valid. At times, software crashes, systems go down, and some businesses need employees who can think on their feet and complete crucial tasks without computer aid.

Here are the risks of using AI too frequently and how workers and leadership can reduce the impact and keep their brainssharp enough to stay ahead of the competition.

1. Automation Biases

Conversations about AI models look at how digital thinking has issues with complex topics. It may be able to solve a math problem with specific formulas and rules but often falters in a real-world scenario. The thing to keep in mind is that agentic AI is only as good as the humans coding it.

Since people have built-in cultures, pasts and belief systems, AI is flawed and occasionally shows biases. AI may also only have part of the information to make a decision. Leaders must never fully trust computer outputs and verify facts.

One danger is that workers accept what the computer says without double-checking whether it's factual. Users who trust outputs, fail to find other sources and don’t think critically about decisions may lessen their ability to form intricate choices.

The best way to avoid the issue is to build in cross-checks, such as having peers review one another's reports or setting a policy of always providing two sources. Leadership should encourage professionals to summarize content in their own words before turning to AI-generated summaries or starting with research in a multistep process.

2. The Power of Instant Satisfaction

Generative AI is amazing in many ways. Users can give the bot a series of commands, and it will work through them, ask for more input and create a document on the spot. It is easy to use, which makes it tempting to use it all the time for everything. This is especially true with the pressure of pending deadlines and the convenience of instant solutions.

Passively consuming information, even reports, takes away the effort needed for deep learning. Rather than wrestling with a problem and trying to figure out a solution by trial and error, people get instant answers. Quizlet, Brainscape and Traverse can be used with AI output to ensure long-term memory retention.  

One thing management can do is design workflows so users must input their ideas first or try to solve a problem before AI perfects it. Some models allow for settings where people must reflector develop a hypothesis before AI responds.

3. Zero Metacognitive Monitoring

Over time, people come to understand how they learn best. Reflecting on the most valuable lessons can increase knowledge as the learner seeks similar studies. Unfortunately, a drawback of agentic AI is that questions are answered automatically and may not factor in learning styles. Rather than allowing the user to search for a video, audio or tactile experience, the computer spits out an answer and a report.

One example can be seen at Georgia Tech, where an AI assistant the school dubbed Jill Watson responded to students' questions.While faculty had to program the responses, the lack of human interaction could allow AI tools to overlook how to present the information in favor of quick answers.

AI responses allow for personalized answers but risk reducing cognitive engagement by skipping over context. One thing schools and corporations can do to avoid a lack of awareness or misunderstanding of learning levels is to add assessment prompts. Users would review the answer and then answer a question about the topic.

Collaborative Power of AI

Agentic AI allows small businesses to catch up to big corporations. However, company leaders must use it mindfully to avoid skill loss and a future filled with employees who only know how to prompt a computer and not how to problem-solve on their mental capacity. By balancing technology with creativity, staff will find unique ideas that make the brands and out from others in the same industry. Embrace the power of AI but allow individuals to retain control of their cognitive abilities.

What started out as a novel, exciting and largely good idea - connecting with people from your past - has turned sour, nasty and toxic. Social media promised to connect the world, but instead it has fractured our attention, polarised our politics, and weaponised our insecurities. From echo chambers that radicalise users to algorithms that exploit our psychological vulnerabilities, the platforms that were supposed to bring us together have often driven us apart. Yet the solution to these digital ailments may not be in abandoning technology, but rather in embracing its next evolution: artificial intelligence.

The Diagnosis: What's Wrong with Social Media

Before exploring the cure, let’s try to understand the disease. Social media's core problems stem from its fundamental design philosophy—maximising engagement at any cost. This creates a toxic feedback loop where inflammatory content rises to the top, nuanced discussion and truth seeking get buried, and users become products to be manipulated rather than people to be served.

The symptoms are everywhere. Misinformation spreads faster than fact-checkers can respond. Young people report unprecedented levels of anxiety and depression. Political discourse has devolved into tribal warfare. Our collective attention span has shattered into fragments, leaving us  overstimulated and ironically more disconnected.

I spoke to a Gen Z woman recently, an Oxford graduate working in the city of London, she said “no matter how great a day I’ve had, when I go on social media in the evening there is always someone else who seems to be living a better life than me”. This is what happens when we engage with a business model that profits from our psychological weaknesses.And when I asked another Gen Z man, if it’s so bad why don’t you just quit it; his response was ‘I try to cut down but then when you get to the office, you’re the only one (from his generation of course) who doesn’t get the latest joke or meme etc.

AI as Digital Medicine

‍Artificial intelligence offers a fundamentally different approach. Rather than optimising for clicks and shares,AI can be designed to optimise for human wellbeing, understanding, and meaningful personal connection. Indeed, ChatGPT recently had individual counseling and therapy as the number one use of AI in 2025 (see graphic below. Source: HBR)

Here's how AI could help us move past toxic Social Media:
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Personalized Content Curation Beyond the Echo Chamber
Current algorithms trap users in filter bubbles by showing them more of what they already believe. AI systems can be trained to deliberately introduce intellectual diversity—exposing users to high-quality content that challenges their views while still respecting their core interests. Instead of amplifying outrage, these systems could promote curiosity and intellectual humility. This is already happening with services like “Monday” from ChatGPT, it’s a little aggressive to begin with but you ( the human) can actually guide it to your sweet spot or its better angel so to speak. And then quite bizarrely it very quickly becomes your trusted confidant.

Real-Time Context and Fact-Checking
AI can provide instant context for claims, automatically surfacing relevant background information and multiple perspectives on controversial topics. Rather than letting misinformation spread unchecked, AI systems can offer real-time corrections and help users develop better information literacy skills through gentle guidance rather than heavy-handed censorship. By the way, this is how I think organisations will tackle the thorny question of AI Governance, they will use AI to deliver the AI they want for their customers and their employees.

Mental Health Safeguards
AI can detect when users are engaging in unhealthy patterns—doom scrolling, comparing themselves to others, or consuming content that triggers anxiety or depression. Instead of exploiting these vulnerabilities, AI can intervene with compassionate suggestions: taking breaks, connecting with friends, or engaging with uplifting content tailored to their specific needs.The company that delivers this antidote to say Instagram or TikTok will win the hearts and minds and support of many parents!

Authentic Connection Over Viral Performance
AI can help users focus on meaningful relationships rather than vanity metrics. By understanding the quality of interactions rather than just their quantity, AI systems can promote deeper conversations and genuine community building over the hollow pursuit of likes and shares.

The Technical Path Forward

The infrastructure for this transformation already exists. Large language models can understand context and nuance in ways that previous algorithms couldn't. Computer vision can detect harmful content more accurately than ever before. Machine learning systems can model complex human psychology and predict the downstream effects of different content choices.

The missing piece isn't technical capability—it's incentive alignment. AI systems are only as good as the goals they're given. If we continue to optimize for engagement and advertising revenue, AI will simply become a more sophisticated tool for manipulation. But if we design AI systems with human flourishing as the primary objective, they can become powerful forces for positive change. Cue fanfare for the new tech startup that brings a form of digital Buddhism to the masses for free!

Transparency and User Control
Unlike the black-box algorithms of current social media platforms,AI systems can be designed for transparency. Users should understand why they're seeing specific content and have granular control over their experience. AI can help users understand their own psychological patterns and make conscious choices about their digital consumption. The current trend where AIs are showing chain of thought reasoning bodes well in this respect.

Community-Driven Moderation
AI can augment rather than replace human judgment in content moderation. By handling obvious cases automatically and escalating nuanced situations to human moderators with relevant context, AI can make moderation both more efficient and more thoughtful. Humans can vote for AI participation in their communities and shape the AI to be a helpful non-human member of the community with its obvious superior skills employed in the service of their needs.

Challenges and Considerations

This vision isn't without risks. AI systems can perpetuate biases, make errors, and be manipulated by bad actors. The concentration of power in the hands of AI developers raises important questions about democratic governance of digital spaces.

But these challenges aren't reasons to abandon the approach—they're reasons to approach it thoughtfully. We need diverse teams building these systems, robust oversight mechanisms, and ongoing research into AI safety and alignment. Most importantly, we need a fundamental shift in how we think about the purpose of social media platforms.

A Different Kind of Social Network

Imagine social media platforms that make you feel better about yourself and the world, not worse. Platforms that help you have meaningful conversations with people who disagree with you. Platforms that gently guide you toward accurate information and away from manipulation.Platforms that understand when you need support and connect you with help, rather than exploiting your vulnerabilities for profit.

This isn't utopian fantasy—it's an achievable goal with the AI tools we have today. The question isn't whether we can build better social media platforms with AI, but whether we have the will to do so.

The antidote to social media's poison isn't to abandon digital connection altogether. It's to build digital spaces that serve human needs rather than exploit human weaknesses. AI, designed with wisdom and deployed with care, can be the medicine our digital society desperately needs.

The choice is ours: we can continue letting algorithms optimize for engagement at the expense of our wellbeing, or we can harness AI's power to create online spaces that make us more connected, more informed, and more human. The technology is ready. The question is whether we are.

Communities of Practice (CoPs) have become strategic assets in knowledge-driven organizations, helping teams innovate faster, share expertise and drive continuous improvement. However, translating the value of these communities into clear business terms is still a complex task. While leaders understand their role in fostering collaboration and breaking down silos, many struggle to quantify their impact on key outcomes like productivity, cost savings and innovation. Without the right measurement approach, CoPs risk being seen as soft initiatives rather than drivers of tangible value.

Aligning CoP Value With Business Outcomes

Linking CoPs' activities to relevant outcomes that drive growth and performance is crucial to securing lasting support. This is beyond a best practice. In fact, 78% of leaders say capability building is vital to their organizations’ long-term growth, which underscores the strategic importance of knowledge sharing.

Measuring CoP success requires embracing amulti-dimensional return on investment (ROI) approach. It must blend quantitative metrics — like cost savings or cycle time reduction — with qualitative gains, such as enhanced collaboration or innovation culture.Knowledge management professionals can strengthen this analysis by borrowing rigor from corporate valuation methods. Applying frameworks like ROI and earnings capitalization to determine a company’s market value helps present CoP impact in terms that resonate with corporate leaders and chief financial officers.

Methodologies for Quantifying CoP Value and Impact

Measuring the value of CoPs requires more than counting participation or activity levels. Advanced methodologies help capture how CoPs influence enterprise outcomes, drive innovation and contribute to growth.

Engagement Metrics

Engagement metrics give valuable insights into the health and vitality of CoPs. By tracking active participation rates, contribution ratios, and attendance at events and sessions, organizations canassess how invested members are in sharing and applying expertise.

Content creation and consumption trends further indicate whether community members actively generate and use valuable knowledge to inform their work. These signals help determine whether a CoP fosters meaningful connections and drives sustained value.

Balanced Scorecard Approach

Balanced scorecards offer a powerful way to map CoP activities to key performance indicators across critical dimensions. These include financial impact, learning and growth, and customer or internal process outcomes.

This approach tracks direct results and evaluates the brand’s capacity to innovate and improve through ongoing learning and adaptation. A holistic and repeatable measurement framework helps knowledge management professionals demonstrate how CoPs contribute to strategic priorities and long-term value.

Case-Based ROI Calculation

Building ROI cases around specific CoP initiatives allows entities to showcase how targeted knowledge-sharing efforts drive real results. Professionals can demonstrate value by focusing on concrete outcomes such as process improvements, new product ideas or cost savings from shared learning.

Strengthening these cases with before-and-after data or counterfactual analysis provides a more accurate picture of the CoP’s contribution. This method proves especially effective when piloting new CoPs or when incremental value needs to be highlighted to secure continued leadership support and investment.

Value Network Analysis

Value network analysis models how knowledge flows, relationships form, and influence spreads within and beyond a CoP. This approach captures intangible values such as faster problem-solving, broader expertise diffusion and sparks of innovation traditional metrics may miss.

It also helps measure how participants convert what they know into tangible outcomes and intangible contributions that benefit the wider organization. Visual tools like network maps and influence diagrams make these insights easy to communicate. They build stakeholder buy-in and enhance the storytelling necessary to secure continued CoP support.

Metrics for Assessing CoP ROI

Knowledge management professionals must track data points to reveal how CoPs influence daily operations and long-term outcomes. Here are metrics to consider when assessing the ROI:

●     Content activity trends: Volume and growth of content creation and consumption over time

●     Knowledge reuse: Knowledge is applied in new contexts through citations, solution adoption or process improvements

●     Cross-unit collaboration: Frequency of cooperation and referrals between different teams or departments sparked by CoP interactions

●     Process efficiency gains: Reduction in time-to-solution, shorter cycle times or faster onboarding linked to CoP contributions

●     Cost savings: Measurable reductions in expenses through shared learning, improved processes or avoided duplication of effort

●     Revenue impact: Influence of CoP-driven innovations or process improvements on revenue growth or customer outcomes

●     Employee development: Gains in competency development and retention of high-value talent

●     Innovation outcomes: Number and quality of new ideas, products, patents, or process enhancements emerging from CoP discussions

●     Cultural impact: Stronger knowledge-sharing culture and enhanced organizational learning agility

Implementing Tracking Mechanisms

Implementing robust tracking mechanisms allows organizations to capture and communicate the actual value of CoPs. Knowledge management can harness enterprise analytics and well-structured metadata to monitor activity systematically. AI memory systems further elevate this effort by storing, retrieving and utilizing insights, which enhances business intelligence and drives more informed decision-making.

Monitoring participation and knowledge flow through collaboration tools and event platforms offers additional layers of insights. Blending quantitative data into clear ROI narratives helps stakeholders see how CoPs support strategic goals, including boosting product innovation. Visual reporting brings these insights to life to guide the continuous improvement of CoP initiatives across the company.

Turning Communities of PracticeInto Strategic Business Drivers

Advanced ROI measurement transforms CoPs from nice-to-haves into demonstrably valuable assets that drive business outcomes.Knowledge management professionals should embrace an iterative and pragmatic approach to measurement, continuously refining their methods to capture tangible and intangible impacts.

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Artificial Intelligence (AI) and Knowledge Management (KM) create a powerful symbiotic relationship that enhances how organizations capture, organize, and utilize knowledge. This relationship works bidirectionally, with each discipline strengthening the other. Let's explore how...
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How AI Enhances Knowledge Management

• Knowledge Discovery: AI algorithms can identify patterns and connections in vast data repositories that human analysts might miss. This applies to both structured and unstructured data.
  
• Knowledge Organization: AI can automatically categorize, tag, and structure information based on content and context. This applies to new and legacy content.
  
• Knowledge Retrieval: AI-powered search tools can understand natural language queries and provide contextually relevant results.
  
• Knowledge Transfer: AI can personalize knowledge delivery based on individual learning styles and needs.
• SECI: AI can take the traditional SECI model to completely new levels

How Knowledge Management Strengthens AI

• Training Data: Well-managed knowledge bases provide high-quality, structured data for AI training.
  

• Domain Expertise: KM captures the tacit knowledge of experts that informs AI development
  

• Contextual Understanding: KM provides the organizational context necessary for AI to make relevant recommendations.
  
• Validation Framework: KM practices establish metrics and processes to evaluate AI outputs.
  
• AI Use Cases: Good Knowledge Management especially when deployed through an AI Centre of Excellence helps design, deliver and deploy the most valuable AI use cases ‍

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Practical Applications

Knowledge Capture and Organization
AI tools automatically extract information from documents, conversations, and digital interactions, then organize this content within knowledge management systems. For example, meeting transcription AIs can capture discussions and automatically categorize action items, decisions, and key insights. AI’s can repurpose content in muli-modal formats to suit different generations in the workplace.
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Intelligent Knowledge Retrieval
Modern knowledge management platforms use AI to power semantic search, enabling users to find information based on meaning rather than exact keyword matches. These
systems can understand queries like "customer cancellation policy updates" and return relevant documents even if they don't contain those exact terms.

Knowledge Gap Identification
AI analyzes knowledge usage patterns and identifies areas where organizational knowledge is incomplete or outdated. This allows KM professionals to prioritize knowledge acquisition efforts.

Personalized Knowledge Delivery
‍AI-powered recommendation systems deliver relevant knowledge assets based on an individual's role, projects, and past behavior. For example, when an employee works on a specific client proposal, the system automatically suggests relevant past proposals, market research, and expert contacts. This is the new world of mass customisation. 

Knowledge Transfer and Retention
‍When experienced employees leave, AI can help preserve their knowledge by analyzing their digital footprint, documenting their expertise, and creating training materials for successors.

AI and Knowledge Management Evolution: From ANI to AGI to ASI
As artificial intelligence evolves from Artificial Narrow Intelligence (ANI) through Artificial General Intelligence (AGI) to Artificial Superintelligence (ASI), its relationship with Knowledge Management (KM) will transform dramatically. Let's explore how this partnership might develop across these evolutionary stages.

Present Day: ANI and Knowledge Management

Currently, we operate in the ANI era, where AI excels at specific tasks but lacks broader understanding:

• Specialized Knowledge Processing: ANI systems like GPTs provide domain-specific analysis.
  
• Semi-Automated Knowledge Workflows: KM systems use ANI to automate portions of knowledge workflows while still requiring human oversight for context, quality control, and strategic decisions.
  
• Knowledge Discovery Assistance: ANI helps identify patterns and connections in data, but humans must interpret significance and take action.

The Transition to AGI and Knowledge Management
As we move toward AGI—systems with human-like general problem-solving abilities— the relationship deepens:
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Enhanced Knowledge Contextualization
AGI will understand not just information but its context within organizational ecosystems. It will connect disparate knowledge areas, discovering insights that cross traditional domain boundaries.

Knowledge Co-Creation
Rather than simply organizing existing knowledge, AGI will actively participate in knowledge creation (Agentic AI) :

• Contributing novel perspectives to innovation processes
  
• Identifying blind spots in organizational thinking
  
• Suggesting alternative approaches based on cross-domain learning
  

Self-Organizing Knowledge Systems
AGI-powered KM systems will:

• Autonomously restructure knowledge taxonomies as organizational needs evolve
  
• Predict future knowledge requirements and proactively gather relevant information
  
• Identify emerging knowledge patterns before they become obvious to human observers
  

Intelligent Knowledge Transfer
AGI will revolutionize knowledge transfer by:

• Creating personalized learning pathways that adjust in real-time based on learner responses
  
• Translating complex expertise into formats appropriate for different skill levels
  
• Simulating expert reasoning to teach not just what is known, but how experts think

The Speculative Future: ASI and Knowledge Management
If ASI—intelligence far surpassing human capabilities—emerges, the relationship with KM would fundamentally transform:
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Knowledge Superintelligence
ASI might:

• Anticipate knowledge needs far in advance of human awareness
  
• Develop entirely new knowledge frameworks beyond current human conceptualization
  
• Independently identify and fill critical knowledge gaps across organizational and societal levels

Practical Implications for Organizations
‍The ANI to AGI Transition Period Organizations should prepare by:

• Developing hybrid human-AI knowledge workflows that leverage the strengths of both
  
• Creating knowledge governance frameworks that maintain human values while benefiting from AI capabilities
  
• Investing in explainable AI to ensure knowledge processes remain transparent and trustworthy

Knowledge Management Infrastructure Evolution
Organizations will need:

• More sophisticated knowledge representation systems capable of handling multi-dimensional relationships
  
• Ethical frameworks for managing AI contributions to organizational knowledge
  
• New roles for human knowledge workers as partners rather than managers of AI systems

Preserving Human Knowledge Value
Even as AI advances, organizations must:

• Maintain spaces for human intuition, creativity, and wisdom that complement AI capabilities
  
• Ensure critical ethical and contextual knowledge remains central to decision processes
  
• Develop new forms of human expertise focused on guiding and collaborating with advanced AI

The evolution from ANI to AGI to ASI will transform knowledge management from a primarily human-directed activity to an increasingly collaborative and eventually AI-led function, raising profound questions about the nature of knowledge, expertise, and human-AI collaboration in organizational contexts.