Organizations worry about the attrition rate, though they rarely worry about knowledge attrition.

When employees leave, they do not just take skills with them. They take years of context, hard-earned judgement, customer insights, relationship history, workarounds, lessons from failures, and the intuition that never made it into a document.

The result? Teams repeat mistakes, projects reinvent solutions, onboarding takes longer, and organisations lose valuable institutional memory.

The challenge is not simply capturing knowledge; that can be easily achieved. The challenge is preserving the experience and expertise of outgoing employees.

The Problem with Traditional Lessons Learned

Most organisations have a lessons learned repository and they duly promote it, most is it really used as per its worth? The fact is, it’s rarely used, because lessons learned are often treated as a project closure activity rather than a strategic knowledge asset. The outcome typically becomes a static document stored in a SharePoint site, project folder, or archive that is rarely visited.

The real value lies not in recording lessons but in making them discoverable, relevant, and actionable through socialization, by connecting people with similar skills and expertise and giving a preview and glimpse of why these matter and how and where to access the lessons and the experts.

A lesson learned six years ago becomes valuable only when it surfaces at precisely the moment someone faces a similar challenge.

Not All Lessons Learned Are Equal

To build an effective organisational memory, we need to think beyond project retrospectives.

1. Process Lessons

These focus on execution.

Examples:

• Activities that caused delays
• Processes that improved efficiency
• Governance bottlenecks
• Successful delivery approaches

These lessons help teams work smarter.

2. Technical Lessons

These capture knowledge around systems, architecture, integrations, and technology decisions.

Examples:

• Design trade-offs
• Technical debt considerations
• Architecture patterns
• Integration challenges

These lessons often become critical reference points for future projects.

3. Customer Lessons

Many of the most valuable insights come directly from client engagement.

Examples:

• Stakeholder preferences
• Communication strategies
• Change management approaches
• Business priorities

These lessons improve customer outcomes and relationship management.

4. Strategic Lessons

These focus on business impact rather than project execution.

Examples:

• Market insights
• Risk patterns
• Transformation outcomes
• Investment decisions

Strategic lessons help leaders make better decisions.

5. Tacit Lessons

This is where things get interesting.

Tacit knowledge is the knowledge people possess but rarely document.

It’s the “know-how” behind the work.

Examples:

• How an experienced project manager navigates executive stakeholders
• Which client concerns typically emerge during implementation
• Warning signs of project risk before they appear in dashboards
• Informal processes that help accelerate delivery

Tacit knowledge is often the most valuable knowledge an organisation owns and the hardest to capture.

The Tacit Knowledge Challenge

Traditional repositories capture explicit knowledge, while tacit knowledge lives in people’s heads.

Historically, organisations have tried to transfer tacit knowledge through:

• Shadowing
• Mentoring
• Communities of practice
• Interviews
• Knowledge transfer sessions

While valuable, these methods are difficult to scale after a point in time.

The challenge becomes even greater in large organisations where thousands of subject matter experts (SMEs) possess specialised knowledge spread across projects, business units, and geographies.

The question becomes: How do we connect someone who needs knowledge with someone who has knowledge?

From Knowledge Repositories to Knowledge Networks

The future of lessons learned is not just about content but more about connections.

Imagine a project manager beginning a transformation programme.

Instead of searching through hundreds of documents, the system automatically identifies and surfaces:

• Similar projects completed previously
• Relevant lessons learned
• Associated risks and mitigations
• Key project artefacts
• Subject matter experts who participated
• Individuals who solved comparable challenges

This shifts knowledge management from retrieving documents to discovering expertise.

The knowledge asset now becomes the network of people, experiences, decisions, and outcomes connected to that document.

How AI Can Help

Instead of expecting employees to search for knowledge, AI can proactively surface it.

AI-Powered Expertise Discovery

When starting a new project, AI can analyse:

• Project charter
• Industry
• Client information
• Objectives
• Technologies involved

It can then recommend:

• Previous similar engagements
• Relevant project artefacts
• Lessons learned
• Internal experts
• Communities of practice

The employee does not need to know what to search for; the knowledge comes to them.

AI-Powered Contextual Recommendations

Imagine preparing for a client workshop.

AI identifies that:

• Three previous teams worked with the same client
• Similar implementation challenges occurred
• Specific stakeholder concerns emerged repeatedly
• Particular mitigation approaches were successful

These insights can be surfaced directly within the employee’s workflow.

AI-Powered Knowledge Graphs

One of the most exciting opportunities lies in creating organisational knowledge graphs.

Knowledge graphs connect:

• Projects
• Clients
• Deliverables
• Lessons learned
• Risks
• SMEs
• Communities
• Business capabilities

Instead of a collection of disconnected documents, organisations gain an interconnected knowledge ecosystem.

Why Metadata Becomes the Foundation

Many organisations view metadata as an administrative burden.

In reality, metadata is the backbone of AI-powered knowledge discovery.

Without metadata, AI can search; with metadata, AI can reason.

Consider a lesson learned document. It’s simply a text doc without metadata; however, with metadata, it’s a pathway to

• Client name
• Industry
• Project type
• Business capability
• Geography
• Technology stack
• Risks
• Authors
• SMEs
• Outcomes
• Project lifecycle phase

Good metadata transforms isolated information into connected organisational intelligence; with the help of AI, tagging and content surfacing becomes so much easier.

Beyond Lessons Learned: Building an Organisational Memory System

The future is not another lessons learned repository, but an organisational system that combines:

• Content – Lessons learned, playbooks, templates, project artefacts.
• Context – Metadata, business taxonomy, project characteristics.
• Connections – Relationships between projects, clients, risks, decisions, and outcomes.
• Communities – Experts, practitioners, and communities of practice.
• AI – Intelligent discovery, recommendation, summarisation, and expertise location.

Together, these elements create a living knowledge ecosystem rather than a static archive.

The most valuable knowledge in any organisation is rarely stored in documents.

It exists in experiences, conversations, decisions, and the judgement of people who have solved problems before.

As experienced employees move on and organisations continue to transform, the challenge is no longer simply capturing lessons learned, but ensuring that those lessons remain accessible, discoverable, and connected to the people who need them most.

In the age of AI, the question is no longer:

“How do we store knowledge?”

The real question is:

“How do we ensure the right knowledge, from the right experience, reaches the right person at the right moment?”

Because when knowledge remains connected to context, expertise, and people, organisational memory doesn’t walk out the door. It continues to create value long after individuals have moved on.

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Many organizations still operate without a formal Knowledge Management (KM) practice and it often shows:

Important knowledge lives in inboxes, chat threads, personal files and in the heads of a few key people. When that knowledge is not captured, organized and shared. Organizations lose time, repeat mistakes and become dependent on individuals instead of building collective capability.

Setting up knowledge management is not just about storing documents. It is about creating a culture and system where knowledge is easy to find, trust, reuse and improve.

A strong KM practice can help organizations:
- reduce duplication of work
- speed up onboarding and training
- improve decision-making
- preserve expertise when people move roles or leave
- increase consistency, quality, and collaboration

For organizations without KM today, the biggest risk is not just inefficiency, it is knowledge loss.
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The good news is that KM does not have to start big. It can begin with simple steps: identifying critical knowledge, creating a shared structure, defining ownership and making knowledge easy to access.

Organizations that invest in KM build stronger teams, smarter operations and a more resilient future.

"Knowledge management is not a nice-to-have. It is foundational."

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Change Management helps people move through the change. Knowledge Management helps people learn through the change

Every organization today is somewhere on the curve of change, especially with the heavy focus on AI adaptation, digital transformation, and constantly evolving ways of working. But there is one question I believe organizations often overlook:

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Where does Knowledge Management fit into the change journey?

We often talk about Change Management and Knowledge Management as two separate disciplines. In reality, I see them as two parallel processes that need to move together.

Change creates the disruption while KM helps people navigate that disruption.

Change creates uncertainty. Knowledge creates clarity.

Whenever a new technology, process, operating model, or organizational structure is introduced, people naturally have questions.

What is changing?
Why are we changing it?
How will this impact my role?
What worked for others?
Where can I find the information I need?

This is where KM can play a much bigger role than simply being a repository of documents.

KM can become the connective tissue of the change journey.

It can bring together the knowledge, experiences, conversations, and lessons that help people move from uncertainty to understanding—and eventually to adoption.

And this is where I see four important KM interventions.

1. Leaders need to walk the talk

Change cannot be communicated only through emails, presentations, and training sessions.

People watch what leaders do to understand and accept the gravity of change.

If leaders expect teams to share knowledge, collaborate and adopt new ways of working, they need to demonstrate those behaviours themselves.

This is where KM can support leadership.

Leader conversations, knowledge-sharing sessions, town halls and storytelling can create visibility around the change while also creating space for people to ask questions and share concerns.

Sometimes, a 30-minute conversation with a leader can achieve what a 30-page communication document cannot.

Because change is not just about information, it is about trust.

2. Collaboration becomes more important when change accelerates

One of the biggest risks during transformation is the creation of knowledge silos.

One team runs a successful pilot.

Another team faces the same challenge six months later.

A lesson has already been learned—but nobody knows where it is.

This is where KM can turn individual experiences into organizational knowledge.

Communities of Practice, cross-functional forums, collaboration spaces, and knowledge-sharing platforms can help connect people who are solving similar problems.

The objective is not to create more content.

The objective is to make sure that knowledge does not remain trapped in pockets of the organization.

3. Lessons learned should not become lessons forgotten

This is probably one of the biggest opportunities for KM during transformation.

Organizations run pilots. They experiment. Some initiatives succeed. Others don’t.

But what happens afterwards?

Too often, the project closes, and the learning disappears with it.

A strong KM approach can capture:

• What worked?
• What didn’t?
• What surprised us?
• What would we do differently?
• What can another team reuse?
• What should we avoid repeating?

The real value of lessons learned isn’t in documenting them.

It is in reusing them.

That is when a lesson moves from being project knowledge to becoming organizational knowledge.

4. Don’t underestimate the power of informal KM

One thing I have learned through my KM journey is that not all knowledge sharing happens inside a repository.

Some of the most valuable knowledge emerges through conversations.

A Knowledge Café.

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A Leader Talk.

A storytelling session.

A small group discussing what went wrong in a pilot.

A colleague sharing how they solved a problem that another team is now facing.

These informal spaces are particularly valuable during change because they create room for something that formal communication often doesn’t:

conversation.

People can express resistance.

They can challenge assumptions.

They can share experiences.

And they can hear how others are navigating the same change.

Resistance isn’t always a barrier that needs to be eliminated.

Sometimes, it is simply a signal that people need more context, clarity, or involvement.

From Resistance to Adaptation

If we look at the change curve—Status Quo → Resistance → Acceptance → Adaptation—KM can play a role at every stage.

At Status Quo:
KM helps people understand why change is happening by making knowledge and context accessible.

At Resistance:
KM creates spaces for questions, conversations, experiences, and concerns.

At Acceptance:
KM enables learning, training, peer sharing, and access to practical knowledge.

At Adaptation:
KM captures new experiences, lessons, and emerging practices so that they can be shared and scaled.

And this is where the relationship between Change Management and Knowledge Management becomes particularly interesting.

Change Management helps people move through the change.

Knowledge Management helps people learn through the change.

When the two work together, change doesn’t have to feel like something being done to people.

It can become something people learn, experience, and evolve through together.

Perhaps we need to stop thinking of KM as something that comes after transformation.

The repository shouldn’t be populated only after the project is complete.

We shouldn’t capture lessons only at the end.

Training shouldn’t be the only knowledge intervention.

And KM shouldn’t simply be the place where people go to find a document.

KM needs to be part of the change journey from the beginning.

Because every transformation creates knowledge.

The question is whether that knowledge is captured, connected, shared, and reused—or whether it disappears into project folders, individual inboxes, and people’s memories.

A final thought

Change is inevitable, but the way organizations learn through change is a choice.

For me, this is where KM’s real opportunity lies.

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Ekta Sachania has over 15 years of experience in learning and talent development disciplines, including knowledge management, content management, and learning & collaboration with expertise in content harvesting, practice enablement, metrics analysis, site management, collaboration activities, communications strategy and market trends analysis. Demonstrated success in managing multiple stakeholder expectations across time zones and exhibiting good project management skills, by successfully developing and deploying projects for large audiences.  Ability to adapt and work in emerging areas with fast-shifting priorities.  

‍Connect with Ekta at LinkedIn...

A practical reflection on Syntax, SharePoint, content curation and the changing role of the Knowledge Manager

One of the most time-consuming parts of Knowledge Management is often not creating knowledge; it is getting existing content into a shape where people can actually find, trust and reuse it.

Before content can become useful organisational knowledge, someone has to find it, understand it, clean it, categorise it, tag it, check it, and move it to the right place.

Recently, I explored an AI automation use case using Syntax with SharePoint. What interested me was not simply that AI could read documents. It was the possibility of using AI to take on the repetitive first layer of content curation—while keeping the Knowledge Manager firmly in the loop.

Check out the detailed PDF Overview...

The use case: from SharePoint folders to curated knowledge

Think about a SharePoint folder with hundreds of files accumulated over time: proposals, case studies, presentations, solution documents, meeting notes, templates, project material, drafts, and older content. Manually, a KM professional may need to open individual files, identify the content type, understand the subject, apply metadata, identify possible restrictions, and decide where the content belongs.

With an AI-assisted workflow, the model changes:

SharePoint folder → AI processing → classification and metadata suggestions → governance flags → human review → curated repository

What AI can help automate

• Identify likely content type—for example, case study, proposal content, solution, SOP, meeting notes or thought leadership.
• Suggest categories based on the organisation’s taxonomy.
• Generate or recommend metadata such as practice, industry, geography, solution, technology and content type.
• Surface potentially duplicate, outdated or low-value content for review.
• Flag content that may contain confidential, client-sensitive, copyright-protected, or proprietary/IP material, subject to appropriate rules and validation.
• Route approved content to the appropriate SharePoint location through workflow automation.

Is it 100% accurate? No—and that is okay.

Based on my exploration, I would describe the initial output as roughly 70% accurate/usable rather than 100%. The exact result will vary with the quality of the taxonomy, instructions, examples, content, and workflow design.

For me, the important point is that AI does not have to be 100% accurate to create value. If it can remove a large part of repetitive discovery, sorting, classification, and tagging, it gives the KM team more time for the work that requires judgement.

AI is only as good as the KM context we give it

This is where the Knowledge Manager’s role becomes even more important. If we expect AI to recognise reusable content, confidential information, copyright restrictions or organisational IP, we have to define what those things mean in our environment.

The KM team provides the taxonomy, content definitions, metadata rules, examples, and governance principles. In other words, we are not simply asking AI to read content. We are teaching it how our organisation manages knowledge.

The model I believe works: AI + human review

I do not see the process as:

AI → Publish

I see it as:

AI → Process → Recommend → Flag → Human Review → Publish

The final responsibility for trusted knowledge should remain with the KM professional or designated content owner. AI can accelerate the work, but the human layer validates accuracy, relevance, reusability, confidentiality, IP, taxonomy, and content quality.

Beyond folders: connecting Forms, Power Automate and SharePoint

The bigger opportunity is to move from one-off content harvesting to continuous knowledge capture. For example, a simple Microsoft Form can capture meeting notes, project decisions, lessons learned, challenges, solutions, and reusable assets. Power Automate can trigger processing, and AI can structure and classify the information before it reaches the KM review queue.

A possible flow is:

Capture → Structure → AI classify/tag → Flag → KM review → Store → Reuse

A use case I see for bid and proposal teams

Bid teams work under pressure and repeatedly need similar knowledge: case studies, credentials, solution descriptions, previous responses, differentiators, delivery models, industry examples, and reusable proposal language.

If content is continuously classified and tagged, the bid team can move beyond searching for filenames and folders. The longer-term goal is to search by intent and meaning—for example, asking for examples of how the organisation helped healthcare clients improve customer experience through digital transformation.

This is where KM can shift from being a repository service to becoming a business enablement capability.

Manual vs AI-assisted curation

What happens to the Knowledge Manager?

This is the question AI naturally raises. My view is that the role does not disappear—it moves up the value chain.

• Less time on repetitive content administration.
• More time on knowledge strategy and architecture.
• More focus on taxonomy and governance.
• More attention to knowledge gaps and content lifecycle.
• More SME engagement and adoption.
• More time to measure reuse and business impact.
• A new responsibility for designing, testing, and improving AI-enabled knowledge workflows.

My biggest takeaway

AI does not replace the Knowledge Manager. It changes what the Knowledge Manager should spend time doing.

The future of KM may be less about manually managing every document and more about designing intelligent knowledge flows that can discover, classify, curate, flag, and route information—while humans provide the judgement, governance, and accountability.

Perhaps the future is not Human versus AI. It is Human + AI = Intelligent Knowledge Management.

We have spent years asking people to contribute knowledge and use repositories. The next opportunity may be to build systems that capture and process knowledge as part of the work people are already doing.

The question is no longer only, ‘Where is the knowledge?’ It is becoming, ‘How intelligently can we move knowledge to where it is needed?’

Also, I have attached my practical guide draft that I have been leveraging to automate content discovery, classification, tagging, governance, and routing.

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Knowledge management usually splits knowledge into two buckets:

• Explicit knowledge — things that can be written down: procedures, checklists, reports, data. This travels well. You can email it, file it, search it later.
• Tacit knowledge — the experience-based, contextual, often unconscious knowledge that lives in someone’s head. The “why we don’t do it that way,” the pattern recognition built from years of doing the job, the judgment calls. This is exactly what the TV protagonist was worried about losing.

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The sad truth is that most organizations are very good at capturing explicit knowledge and very bad at capturing tacit knowledge. A project closes, a case is transferred, an employee leaves — and the parts of the story that mattered most, the ones that would help the next person avoid the same mistake, quietly walk out the door with the person who knew them.

Knowledge that exists but never moves has no operational value. A lesson sitting in someone’s head, or buried in a report nobody reads, is functionally the same as a lesson that was never learned at all.

Why aviation had no choice but to solve this

Most industries can afford to lose a little context in a handoff. Aviation can’t. When the cost of an unlearned lesson is measured in lives, “we’ll write it up eventually” isn’t good enough. That pressure is exactly why aviation has built one of the most mature knowledge-management ecosystems of any industry — worth studying even if you’ve never set foot in a cockpit.

A few of the mechanisms worth knowing about:

Confidential reporting systems. In the US, the Aviation Safety Reporting System (ASRS) is a voluntary, confidential channel that lets pilots, air traffic controllers, cabin crew, dispatchers, and maintenance staff report near-misses and close calls in the interest of improving safety. Crucially, it’s run by NASA rather than the FAA, which gives it the neutrality people need to actually be honest, since NASA has no enforcement power over them. Report something within the right window, and you get limited immunity — the system is built on the premise that a mistake reported openly teaches the whole industry more than a mistake punished quietly. It was created in direct response to a fatal 1974 crash where investigators found that similar warning signs had existed before, but nothing was systematically capturing or circulating them.

Operator-level programs alongside the national one. Airlines run their own internal version, the Aviation Safety Action Program (ASAP), through formal agreements between the airline, employee unions, and regulators. This captures the tacit, day-to-day knowledge — the near-misses that never make headlines — before it’s lost to memory or turnover.

A shared global taxonomy. Different countries and airlines used to describe incidents differently, which made it hard to compare data or spot patterns across borders. ICAO and the European Commission have worked to promote a single shared repository and a common categorization scheme so that all aviation accidents and incidents worldwide can be reported the same way. That sounds like a bureaucratic detail, but it’s the difference between a lesson staying local and a lesson becoming global. Standardizing the “language” of the report is what lets a hazard identified in one country prevent an accident in another.

Institutionalized feedback loops. Incident data doesn’t just sit in a database — it feeds back into training curricula, cockpit procedures, aircraft design, and regulation. Crew Resource Management training, now standard worldwide, exists largely because of hard lessons from accidents where the technical flying was fine but communication and hierarchy in the cockpit weren’t. The knowledge didn’t just get recorded; it got re-injected into the system that produced the next generation of pilots.

The pattern underneath it all

Strip away the aviation-specific detail, and the model is transferable to almost any team, industry or project:

1. Make capturing knowledge low-friction and safe. People share the messy, honest version of what happened only when they trust it won’t be used against them.
2. Capture context, not just conclusions. A checklist item (“check altitude clearance”) is explicit knowledge. The story of why that checklist item exists — the confusion, the assumption that went wrong — is the tacit knowledge that actually changes behavior.
3. Standardize how knowledge is described, so it can be compared, searched, and aggregated across teams instead of staying siloed in one person’s notes.
4. Close the loop. A lesson learned that never gets fed back into training, onboarding, or process design is just an interesting anecdote. It has to change what the next person does.
5. Treat the handoff itself as a risk point. The TV show’s protagonist was right about one thing: transfer is where knowledge degrades. Overlap periods, structured debriefs, and “why” documentation — not just “what” documentation — are how you protect against that.

Knowledge existing in an organization is necessary but not sufficient. What determines whether it actually prevents the next mistake is whether it’s captured with enough context, shared without fear, standardized enough to travel, and looped back into the system before the next person needs it. Aviation didn’t get this right because it’s a more disciplined industry by nature — it got it right because the cost of getting it wrong left no other option. That’s the real lesson for the rest of us: build the system as if the stakes were that high, before something forces you to.

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