If you’ve ever worked in presales, you’ll know this feeling all too well — you’re racing against a bid deadline, and you remember a perfect case study used by another region. But when you go looking for it, it’s buried deep within a maze of folders, inconsistent tags, or creative file names like “Final_V2_latest.pptx.”

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That’s the silent tax we all pay — not because knowledge doesn’t exist, but because we can’t find it when it matters most.

As someone managing a global presales knowledge library on SharePoint — filled with bid documents, success stories, and references — I’ve seen this firsthand. Each region follows its own tagging conventions, and what one person calls “Retail,” another calls “Consumer Goods.” Multiply that across hundreds of documents and multiple regions, and suddenly your “central repository” feels anything but central.

That’s where AI-powered metatagging becomes a game changer.

Why Metadata Deserves More Attention Than It Gets

In presales, speed and relevance win deals. But without consistent metadata, teams waste valuable hours recreating content that already exists.

Traditional tagging relies heavily on humans — and we’re all human. We tag differently, skip fields under time pressure, or use our own shortcuts. The result? A fragmented repository that limits search effectiveness and cross-regional collaboration.

AI changes this dynamic. It brings structure, consistency, and intelligence to something that used to depend on memory and manual effort.

AI as Your Metadata Co-Pilot

Imagine an AI assistant that understands your repository as well as you do — one that can read through bid decks, success stories, or references, and instantly assign the right tags like:

• Document Type: Bid, Case Study, Success Story
• Industry or Vertical
• Region
• Solution or Offering
• Business Challenge Addressed
• Outcome or Metrics (e.g., cost savings, efficiency gains)
• Win/Loss Status
• Recency

The process starts with a clear metadata schema — your KM DNA. Once that’s defined, AI tools powered by Natural Language Processing (NLP) and Large Language Models (LLMs) like GPT or Azure OpenAI can automate tagging at scale.

Here’s what happens behind the scenes:

• Document ingestion: AI reads through Word, PDF, and PowerPoint files.
• Content understanding: It identifies themes, regions, technologies, and business outcomes.
• Metadata generation: Tags are applied consistently, aligned with your taxonomy.
• Human-in-the-loop review: You or your KM team validate tags, feeding corrections back for continuous learning.

Over time, the AI becomes familiar with your organization’s unique language — the way you describe customers, industries, or offerings — and gets better with every cycle.

The Search Revolution: From Keywords to Context

Once your content is tagged intelligently, search transforms from a frustrating task into an intuitive experience.

Instead of typing exact keywords, users can search in natural language — “customer onboarding automation in retail” — and get results that include “digital onboarding workflow” or “client experience automation.” That’s because AI-powered search doesn’t just match words; it understands meaning.

A hybrid AI search model combines:

• Metadata-based filters for precision
• Semantic search using embeddings for contextual relevance
• LLM-driven summaries that highlight key insights from documents
  

Platforms like Azure Cognitive Search, Elasticsearch, or AWS Kendra, combined with vector databases like Pinecone or Weaviate, make this architecture achievable without overhauling your existing SharePoint setup.

The Real Transformation: From Search to Strategic Insight

When AI tagging and semantic search come together, your repository evolves into a true knowledge ecosystem.

Here’s what changes:

• Speed: Reuse winning proposals in minutes, not hours.
  
• Quality: Teams always find the most recent and relevant content.
  
• Insight: KM teams can track which regions, industries, or solutions dominate wins.
  
• Scalability: Thousands of documents can be added without increasing manual tagging workload.
  

For global teams like ours, it creates a universal language of knowledge — one that bridges silos and builds a single source of truth for all presales content.

Getting Started: The Practical Path

You don’t need to transform everything overnight. Start small — that’s how I’m approaching it, too.

1. Pick a Pilot Set: Begin with 100–200 diverse documents across regions.
   
2. Define the Taxonomy: Agree on your metadata fields and structure.
   
3. Experiment with AI: Utilize GPT-based tagging prompts or Azure Cognitive Search to automatically tag content.
   
4. Validate and Refine: Review tags, correct inconsistencies, and retrain the model.
   
5. Scale Gradually: Connect it to your repository and expand tagging across libraries.

From Custodians to Insight Enablers

As Knowledge Managers, our role isn’t to control information — it’s to make knowledge usable and valuable.

AI isn’t here to replace us; it’s here to amplify us. By letting AI handle repetitive tasks like tagging and indexing, we can focus on what truly matters — curating narratives, connecting insights, and fostering a culture where knowledge flows effortlessly.

Every document becomes a reusable asset.
Every search becomes an opportunity, and every team member becomes more confident knowing, “the answer already exists, and I can find it.”

That’s the power of intelligent knowledge management.

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Every bid — whether we win or lose — leaves behind a trail of insights: what went right, what could have gone better, and what strategies truly resonated with the client. Every bid has a few make-or-break points.

Teams meet to discuss these lessons in post-bid reviews. However, once the meeting ends, most of those valuable discussions remain trapped in transcripts, emails, or people’s memories, or in individual team channels.

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As Knowledge Managers, we often realize that these conversations are gold mines of reusable knowledge, yet we rarely have a systematic way to capture, curate, and share them.

This is where AI can step in as a silent observer, converting what’s said (and unsaid) in those meetings into structured, reusable knowledge assets that inform the next proposal.

Imagine This Workflow

• A Teams meeting is held for a review of won/lost bids.
  
• The discussion is automatically recorded and transcribed.
  
• AI processes that transcribe, extracting:
  
  • Key success or loss factors
    
  • 3 lessons learned
    
  • A summary in plain English
    
  • Action items, owners, and due dates
    
• Within minutes, a “Bid Lessons” page is created in SharePoint — complete with tags, links to the recording, and quotes from the discussion.
  
• The Bid Lead gets a Teams notification to review and approve it.
  
• Once validated, it’s published in the KM library — searchable by keywords, client name, or even “Why did we lose on pricing last year?”

That’s AI-powered KM in action: capturing tacit knowledge from human conversation and turning it into institutional memory.

Why It Matters

Traditionally, lessons learned are captured manually, often long after the project ends. By then, details fade, and enthusiasm wanes.

With AI-driven capture:

• Speed improves: knowledge is captured while it’s still fresh.
  
• Accuracy increases: the AI extracts key moments and direct quotes.
  
• Tacit insights become explicit: the nuances shared informally now become part of your corporate playbook.
  
• Searchability skyrockets: thanks to AI tagging and summaries, others can find lessons in seconds.

AI Makes It Possible — KM Makes It Valuable

AI can do the heavy lifting — transcribing, summarizing, tagging — but KM gives it meaning through:

• Governance and structure
  
• Validation and storytelling
  
• Taxonomy alignment
  
• Continuous improvement
  

Think of AI as your co-pilot for capture, not a replacement for curation.

When fully adopted, this system:

• Reduces duplication of mistakes in future bids
  
• Speeds up learning cycles across regions
  
• Enables data-driven analysis of win/loss patterns
  
• Helps new team members onboard faster with ready insights
  

In other words, your post-bid reviews evolve from routine meetings to strategic learning assets.

Just remember – You don’t need to wait for a big AI overhaul. Start small — automate one meeting’s transcript capture, generate a summary, and upload it as a SharePoint “Bid Lesson.”

Once your leaders see the immediate value, scale it across the practice.

Next in This Series

In the next AI use case, we’ll explore how AI can support content tagging and recommendation in a knowledge repository — making it easier for users to discover the right proposal templates or case studies instantly.

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"I keep hearing AI is going to take over everything — even Knowledge Management. Should we be worried?”

The fact of the matter is not at all. AI isn’t here to replace us; it’s here to make us more effective. Think of it as an extra hand that helps us do KM smarter, faster, and with greater impact.”

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Why This Matters

“But we already have repositories and portals. Isn’t that enough?”

“That’s exactly the point. Repositories are useful, but they’re not enough. Storing knowledge and creating Communities doesn’t guarantee their usage, as most KM teams struggle with KM adoption.

What really drives KM success is collaboration, networks, and processes that keep people at the center. When people can easily connect with knowledge and each other, that’s when an ecosystem comes alive. And AI is the catalyst that makes this possible.”

The KM Shift

“So how does AI change the KM landscape?”

“Here’s how AI supports it in practice:

• Repositories → Ecosystems
  Instead of static storage, AI links documents, discussions, and experts.
  Use Case: AI recommends SMEs when you search for a topic, not just files.
  
• Curation → Insight Delivery
  KM isn’t about uploading PDFs anymore; it’s about surfacing what matters.
  Use Case: AI highlights the 3 most relevant insights from a 40-page report — helping teams act, not just read.
  
• Search → Conversational Discovery
  People don’t want to “search”; they want answers.
  Use Case: A sales team asks in natural language, “Show me winning proposals in the healthcare sector,” — and AI pulls the snippets instantly.
  
• Adoption Driver → Experience Enabler
  Adoption campaigns often fail because portals feel disconnected. AI brings knowledge into the workflow.
  Use Case: An AI agent in Teams automatically shares relevant playbooks during client call preparation, eliminating the need for extra searching.

With AI, knowledge doesn’t just sit in a portal; it comes alive through people, networks, and workflows.”

5 Ways AI Lends a Hand in KM

Here are five big ones:

1 –  Content Intelligence – Auto-tagging, duplicate detection, and gap analysis.
2 – Knowledge Discovery – Conversational search that feels like asking a colleague.
3 – Personalization – Role-based feeds and recommendations.
4 – Tacit Knowledge Capture – Summaries and insights from meetings and calls.
5 – Proactive Delivery – Knowledge appearing in Teams, Slack, or CRM when you need it.

Steps for KM Leaders: to Start Leveraging AI

Keep it simple and build momentum:

1. Start small — pilot one AI use case (like auto-tagging).
   
2. Co-create with SMEs and users to build trust.
   
3. Embed AI into daily workflows — not another portal.
   
4. Measure & showcase quick wins (time saved, reuse rates).
   
5. Scale gradually across teams, functions, and regions.
   

AI won’t replace Knowledge Managers. It makes us more strategic. We move from managing repositories to curating experiences. From being content custodians to becoming AI-enabled change leaders.

AI doesn’t replace KM discipline. It helps us finally deliver on the promise of KM: knowledge that is living, connected, and impactful.

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Part 2 of the Series: Life-Saving Power of KM in Healthcare.  See Part 1: "When Systems Fail: What a Crisis Teaches Us About Knowledge Management"

In my last blog, I spoke about the life-saving potential of Knowledge Management (KM) in healthcare—how a centralized, intelligent, and global knowledge repository can bridge information and infrastructure gaps that can cost lives. But how about if this knowledge system could think, learn, and assist in real time?

This is where Artificial Intelligence (AI) and KM converge, creating a powerful alliance that can transform healthcare as we know it.

From Knowledge Access to Knowledge Intelligence

A well-structured KM system gives doctors access to case studies, treatment protocols, and medical insights. But it still relies on human effort to search, interpret, and apply that knowledge.

Now, with AI embedded into this system, it automatically surfaces the most relevant insights, analyzes patterns across millions of data points, and even predicts potential risks before they manifest.

This isn’t just information at your fingertips. This is intelligence at the point of care.

Real-World Examples of AI-Powered KM in Healthcare

Let’s explore how this can play out:

1. Centralized Diagnostic Assistance

A hospital chain implements a KM system that houses historical patient data, lab results, imaging records, and treatment outcomes. AI runs over this repository to identify common symptom patterns.

• A physician enters symptoms into the system.
• AI cross-matches it with past cases and suggests probable diagnoses.
• The system also flags potential red alerts—like when mild chest pain mirrors patterns seen in early cardiac distress.
  

Result? Faster, more accurate diagnosis—especially for rare or easily misdiagnosed conditions.

2. Virtual Symptom Triage

In rural clinics or during telehealth consultations, AI-powered KM systems can act as virtual assistants.

• Patients input symptoms into a chatbot interface.
• AI uses KM data to suggest next steps: self-care, consult a GP, or immediate ER visit.
• It can even provide local language support and health literacy tips.

This reduces the burden on doctors and ensures timely intervention for critical cases.

3. Personalized Treatment Pathways

A cancer treatment center uses KM to store anonymized treatment plans, drug combinations, and recovery timelines. AI analyzes these to recommend personalized care pathways based on age, genetic profile, co-morbidities, and more.

This enables precision medicine, backed not just by evidence but by intelligent insights.

4. Predictive Public Health Surveillance

On a population level, AI-enabled KM systems can spot emerging disease trends. For instance:

• A spike in respiratory symptoms was logged in one region.
• AI correlates this with air-quality data and flags possible outbreaks or environmental hazards.
• Authorities receive alerts and initiate preventive measures.

This is how KM and AI can shift healthcare from reactive to predictive.

In this evolved landscape, the role of a Knowledge Manager becomes even more strategic.

• Curating with AI: Use AI to auto-tag and classify content, reduce duplication, and highlight knowledge gaps.
• Analysing Trends: AI helps KMs spot patterns across data sets—be it treatment efficacy, regional symptom clusters, or frequently missed diagnoses.
• Enabling Decision Support: AI tools can suggest knowledge assets based on clinician behaviour, context, or patient condition—delivering knowledge before it’s even requested.

With AI, KM moves from being a repository to being a real-time decision-enabler.

The Future Is Intelligent, Not Just Informed

Healthcare today doesn’t just need more data—it needs smarter systems. Systems that learn from every patient, every symptom, every outcome, and feed that intelligence back into care.

When AI meets KM, we don’t just centralize knowledge—we activate it.

In the final part of this series, I’ll explore the challenges, ethics, and future roadmap for integrating AI with KM in healthcare. Because while the potential is immense, so is the responsibility.

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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.