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Knowledge mapping is the practice of making your organisation’s collective intelligence visible. It is not about building a database. It is not about writing everything down. It is about creating a navigable map of who knows what, where critical resources live, and where the dangerous gaps are.

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We can use a map of a mall or a city as an example. The map does not replace the city, a mall, or a shopping center — it helps you navigate them easily without getting lost, and find specific shops, restaurants, or entertainment zones. A knowledge map does not replace expertise — it helps you find it.

A good knowledge map tells you three things:

•        What do we know? (The domain taxonomy — your knowledge categories)

•        Who knows it? (The expert directory — your knowledge holders)

•        Where are the gaps? (The gap register — your risk picture)

Imagine your most experienced  Proposal Manager or Lead Engineer leaves next month. Could your team still deliver with the same efficacy and timelines, especially if the KT plan is not in place? Would you even know what knowledge they left with?

Most organisations have enormous amounts of knowledge — but it is invisible. It lives in people’s heads, scattered across files, buried in email threads. Knowledge mapping makes the invisible visible. It answers three questions:

•        What do we know as an organisation?

•        Who knows it?

•        Where are the dangerous gaps?

When knowledge is mapped well, your organisation can:

•        Find the right expert in minutes, not days

•        Onboard new staff faster with clear knowledge paths

•        Reduce the impact when someone leaves — the knowledge transfer plan is already written

•        Make better decisions because the right knowledge reaches the right person at the right time

The Knowledge Mapping process does not require a special tool or an AI. All you need is a clear process, a bit of time, and people willing to be honest about what they know.

Let’s discuss the six-step mapping process as well as the cost of not mapping your knowledge and experts in the next blog.

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We are all well aware that KM is no longer about static SharePoint libraries anymore. In the AI era, governance isn’t optional — it’s survival. AI can generate, summarize, and even auto‑tag knowledge, but without clear governance, you risk misuse, non-compliance, and chaos.

Step 1: Define Governance Roles (with AI in mind)

• Human content owners: AI can draft, but human intervention and ownership are non-negotiable. Ownership means deciding what’s valid, what’s junk, and what’s sensitive.
• AI assistants: Use AI for auto‑classification, metadata tagging, and even first‑pass reviews. But AI is a tool, and humans have to be the final authority.
• Approvers: Humans still need to sign off, especially for compliance or regulatory content. AI can flag risks, but it can’t take the legal hit.

Step 2: Map the Workflow (AI‑augmented)

• Drafting: Humans or AI can create. AI helps speed up first drafts, but drafts are clearly labeled as “AI‑assisted.”
• Review & Approval: AI can highlight inconsistencies, outdated references, or compliance risks. Humans decide what passes, what upgrades, and what gets replaced or archived..
• Publishing: Automated workflows push content live, but governance rules decide visibility (global vs regional).
• Archiving: AI can auto‑detect stale content, but governance policies decide whether it’s archived or updated.

Step 3: Regional Flexibility Meets AI

AI makes centralization easier, but regulations make it harder.

We are all well aware that KM is no longer about static SharePoint libraries anymore. In the AI era, governance isn’t optional — it’s survival. AI can generate, summarize, and even auto‑tag knowledge, but without clear governance, you risk misuse, non-compliance, and chaos.

Step 4: Keep It Human‑Centric

AI can automate, but governance must stay human‑centric.

• Don’t let AI approvals replace human accountability.
• Use AI to reduce friction (auto‑tagging, reminders, archiving suggestions).
• Keep ownership visible — every piece of content should show both the human steward and whether AI was involved.

In the AI era, governance isn’t about leaving it to AI — it’s about keeping trust alive. AI can flood your KM system with content, but governance ensures it’s accurate, compliant, and usable. Think of AI as the accelerator, and governance as the brakes and steering wheel. Without both, you’re just speeding toward chaos.

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There is a pattern that Knowledge Management professionals will recognise immediately, even if they rarely say it out loud.

An organisation completes a significant programme or navigates a difficult period. The lessons are captured. People reflect honestly on what went well, what did not, and what they would do differently. The output is documented and filed.
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Two years later, the organisation faces a remarkably similar situation. A comparable decision needs to be made. And the lessons from that earlier experience are nowhere in the room.

Not because they were lost. Not because the documentation was poor. But because no mechanism existed to bring them forward at the right moment.

The lesson was captured. The decision never knew it existed.

A familiar problem with an unfamiliar name

This is one of the most persistent frustrations in Knowledge Management. It has been described in many ways: the knowing-doing gap, the lessons-learned paradox, the problem of knowledge that exists but does not travel.

What it has rarely been given is a precise structural explanation.

Lessons learned and decision-making exist in separate organisational spaces. Lessons are captured at the end of an experience. Decisions are made at the beginning of a new one. Between those two moments, there is typically no structured mechanism that asks: what has this organisation already learned that is relevant to this decision?

That gap is where significant organisational value quietly disappears.

Why existing practices do not always close it

Lessons learned processes serve an important purpose. They create structured reflection. They make tacit knowledge visible. They produce documentation that, at its best, preserves genuine organisational intelligence.

But they were designed primarily as a capture mechanism, not as a decision-support mechanism. The assumption embedded in most approaches is that if the lesson exists somewhere accessible, it will find its way to the decisions that need it.

In practice, that assumption rarely holds.

Decision-makers under time pressure do not search repositories. Teams forming around a new initiative do not systematically review what similar teams learned before them. The knowledge exists. The connection to the decision point was never made.

A practical reorientation

What is missing is not better capture. It is a deliberate connection between the moment a lesson is captured and the moment a relevant decision is being made.

This requires two things most organisations do not systematically provide.

First, lessons should be captured in a way that makes them decision-relevant — not just as a record of what happened, but as a forward-looking note for whoever faces a similar situation next. A lesson that says "stakeholder engagement was difficult" is less useful than one that says "stakeholder alignment must be established before the governance submission, not after — this assumption cost six weeks."

Second, lessons should be actively brought to decision points rather than waiting to be found — connected to governance and decision-making processes where relevant knowledge should be surfaced, not as an optional step, but as a built-in part of how important decisions are prepared

What Structured Decision Continuity adds

Structured Decision Continuity is a developing professional concept examining how organisations preserve the reasoning, context and traceability behind important decisions over time.

In the context of lessons learned, it asks one specific and practical question: was this lesson captured in a way that will actually reach the next relevant decision?

This reframes lessons learned from a retrospective activity into a forward-looking one. The lesson is not complete when it is documented. It is complete when it is connected — when there is a visible path between what was learned and the decision where that learning should be used.

A closing reflection

The question worth asking about any significant lesson captured in your organisation is not only: is it stored somewhere?

It is: will it be in the room when the next relevant decision is made?

Structured Decision Continuity is a developing professional concept examining how organisations preserve the reasoning behind important decisions over time. This article is contributed exclusively to the Knowledge Management Institute.

Disclaimer: The views expressed in this article are my own and do not represent the position of my employer or any institution I am associated with.

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Executive Functioning as the Cognitive Core of Knowledge Work

Knowledge work in defense contracting is built on the foundation of human executive functioning. Every controlled document update, chain‑of‑custody action, and compliance‑driven workflow requires the coordinated use of working memory, inhibitory control, cognitive flexibility, planning, and sustained follow‑through.

These cognitive processes form the unseen architecture of daily performance, yet they are rarely acknowledged in the design of Knowledge Management systems. In environments where precision is mandatory and errors carry contractual, operational, or security consequences, the cognitive load placed on employees is substantial. A well‑designed KM system can serve as a cognitive scaffold, reducing friction and supporting the mental processes required for reliable, repeatable, and compliant work. When KM systems ignore these cognitive realities, they inadvertently increase the likelihood of mistakes, rework, and resistance to change.

Locus of Control as the Psychological Variable Behind KM Adoption

Julian Rotter’s theory of locus of control (Rotter, 1966) provides a powerful psychological lens for understanding why some employees embrace KM systems while others resist them. Individuals with an internal locus of control believe their actions meaningfully influence outcomes; they tend to engage with KM tools, follow workflows, and take ownership of documentation. Those with an external locus of control perceive outcomes as determined by external forces, such as leadership, IT, government customers, or “the system.” Defense contracting environments, with their rigid compliance requirements and shifting government direction, often push employees toward an external locus of control simply because so many variables feel outside their influence.

When KM systems are opaque, overly complex, or inconsistently applied, they reinforce this external orientation and create a sense of learned helplessness. Conversely, when KM systems are transparent, predictable, and cognitively supportive, they strengthen the internal locus of control by helping employees feel capable, informed, and in command of their work. This psychological shift is not trivial; it directly predicts whether employees will adopt new processes, maintain accuracy, and sustain engagement over time. (Albrecht et al., 2023)

Viktor Frankl and Meaning as a Driver of KM Engagement

Viktor Frankl’s work adds a deeper dimension by emphasizing that meaning is a psychological resource essential for human motivation. Frankl argued that people can endure almost any “how” if they understand the “why,” and this principle applies directly to KM adoption (Frankl, 1946). Employees do not engage with documentation, workflows, or repositories simply because they exist; they engage when they understand why these structures matter. In defense contracting, the “why” is profound: KM protects mission integrity, ensures audit readiness, preserves chain‑of‑custody accuracy, and safeguards national security.

When KM leaders communicate this meaning clearly, employees shift from passive compliance to active stewardship. Frankl’s insights remind us that meaning reduces ambiguity, strengthens an internal locus of control, and supports executive functioning by giving employees a coherent narrative for their actions. (Gillette, 2024) In this sense, meaning is not philosophical; it is operational. It is the psychological anchor that transforms KM from a bureaucratic requirement into a mission‑aligned practice.

The Interaction of Locus of Control and Meaning in Defense Contracting

The relationship between locus of control and meaning is especially important in defense contracting, where employees often feel constrained by external requirements. When individuals perceive that they have no influence over outcomes, their motivation declines, their cognitive engagement narrows, and their willingness to adopt new systems diminishes. (Chipperfield et al., 2016) Frankl’s emphasis on meaning provides a counterweight to this dynamic.

When employees understand the purpose behind KM processes, they begin to reclaim a sense of agency even within a highly regulated environment. Meaning reframes compliance from something imposed to something chosen. It shifts the external locus of control to an internal one by showing employees how their actions contribute to mission success (Spector, 1982, pp. 482-497). This psychological shift is essential for sustaining high‑quality documentation, accurate workflows, and consistent adherence to contract requirements.

KM Design That Supports Executive Functioning in Defense Contracting

Designing KM to support executive functioning requires intentionality and an understanding of cognitive architecture. (Designing a Knowledge Management System for Distributed Activities: A Human Centered Approach, 2005, pp. 355-380). Defense contracting environments are cognitively demanding, and KM must function as a cognitive exoskeleton that reduces load rather than adding to it (Bequette et al., 2020). This means structuring information in ways that align with working memory limits, reducing task switching by integrating tools, providing retrieval cues through consistent naming conventions and metadata, and offering visual structure through dashboards and status indicators. These design choices directly support the brain’s executive functions, making it easier for employees to initiate tasks, maintain accuracy, and complete workflows without unnecessary strain. (Langer et al., 2020) When KM systems are designed with cognitive architecture in mind, they transform from repositories into enablers of reliable performance.

KM Design That Strengthens Internal Locus of Control

Equally important is designing KM to strengthen the internal locus of control. Employees must feel that they can influence their outcomes, even within the constraints of government contracting. KM systems can foster this sense of agency by offering transparent workflows, allowing personalization of dashboards or views, involving users in the creation of taxonomies or SOP updates, and providing immediate feedback that confirms successful actions. When employees understand the rationale behind processes and see how their contributions improve accuracy, compliance, or mission readiness, they experience a shift from “I have to do this” to “I can do this.” (Spector, 1982, pp. 482-497) This psychological shift is essential for adoption, accuracy, and long‑term engagement. In this way, KM becomes not just a technical system but a psychological intervention that strengthens autonomy, competence, and ownership.

Final Thoughts: KM as a Cognitive‑Behavioral System

All these points show that KM is not just about technical processes; it shapes how people think, act, and feel about their work. Frankl teaches that people work harder when they know why their effort matters. Rotter’s work shows that people use systems more when they feel they have some control. If KM systems help people remember steps and show that their actions matter, employees will use them more, make fewer errors, and be more willing to try new things. In defense contracting, where accuracy is essential, using KM systems designed with psychology in mind directly supports better work and improves employees' job satisfaction.

References

(2005). Designing a Knowledge Management System for Distributed Activities: A Human Centered Approach. International Journal of Human-Computer Studies 62(3), pp. 355-380. https://doi.org/10.1016/j.ijhcs.2004.11.001

Albrecht, S. L., Furlong, S. & Leiter, M. P. (2023). The psychological conditions for employee engagement in organizational change: Test of a change engagement model. Frontiers in Psychology 14. https://doi.org/10.3389/fpsyg.2023.1071924

Bequette, B., Norton, A., Jones, E. & Stirling, L. (2020). Physical and Cognitive Load Effects Due to a Powered Lower-Body Exoskeleton. Human Factors 62(3). https://doi.org/10.1177/0018720820907450

Chipperfield, J. G., Perry, R. P., Pekrun, R., Barchfeld, P., Lang, F. R. & Hamm, J. M. (2016). The Paradoxical Role of Perceived Control in Late Life Health Behavior. PLOS ONE 11(3). https://doi.org/10.1371/journal.pone.0148921

Frankl, V. E. (1946). Experiences in a Concentration Camp. International Journal of Psycho-Analysis, 27, 57–60.

Gillette, H. (2024). Logotherapy: Finding Meaning in the Face of Extreme Distress. Healthline. https://www.healthline.com/health/logotherapy

Langer, M., König, C. J. & Busch, V. (2020). Changing the means of managerial work: effects of automated decision support systems on personnel selection tasks. Journal of Business and Psychology 36. https://doi.org/10.1007/s10869-020-09711-6

Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs: General and Applied, 80(1), 1–28. https://doi.org/10.1037/h0092976

Spector, P. E. (1982). Behavior in Organizations as a Function of Employee Locus of Control. Psychological Bulletin 91(3), pp. 482-497. https://doi.org/10.1037/0033-2909.91.3.482

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In many organizations, presales Knowledge Management still means one thing:

A repository of old proposals, case studies, solution documents, and reusable RFP content.

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But the fact of the matter is that today’s presales environment is very different. Teams are working across regions, deal cycles are faster, solutions are becoming more complex, and customers expect industry-specific answers almost immediately.

This is where I believe KM needs to evolve beyond just maintaining a proposal library.

Because modern KM is not only about storing documents.

It is about connecting:

• people
• expertise
• lessons learned
• competitive intelligence
• best practices
• delivery experience
• reusable knowledge

at the right time.

The Real Problem Isn’t Missing Content

Most organizations already have huge amounts of content.

The real problem is:

• Knowledge is scattered
• SMEs are overloadedThe
• content is duplicated
• Lessons learned stay inside teams
• New joiners struggle to navigate information
• Nobody knows who has solved a similar problem before

So even with thousands of files available, teams still recreate work.

I have personally seen proposal teams spend days searching for information that probably already existed somewhere inside the organization.

What Modern Presales KM Should Actually Do

A mature KM framework should help teams:

• quickly identify reusable assets
• connect with experts who handled similar projects
• access industry insights
• find delivery-backed case studies
• reuse lessons learned
• collaborate globally
• reduce dependency on tribal knowledge

The value of KM increases when it starts connecting knowledge and people instead of simply storing it.

One of the Most Underrated Areas — Expert Discovery

This is something many organizations still miss.

Sometimes the biggest challenge during a bid is not content.

It is finding the right person.

• Who worked on a similar healthcare transformation?
• Who handled a migration project in Europe?
• Who understands a specific compliance requirement?

A good KM framework should help teams quickly discover expertise, rather than relying on informal networks or endless Teams messages.

This becomes even more critical in global organizations.

Where AI Can Change the Game

This is where things get exciting for KM.

AI can help presales teams move from manual searching to intelligent discovery.

Imagine asking:

“Show me similar manufacturing transformation proposals for Europe.”

Or:

“Who worked on cloud migration deals for telecom clients?”

Instead of searching folders manually, AI can surface:

• relevant content
• SMEs
• past proposals
• case studies
• lessons learned

AI can also help with:

• content tagging
• duplicate detection
• summarization
• semantic search
• proposal recommendations
• governance automation

The opportunity is massive if organizations use AI strategically within KM.

Before Setting Up a KM Team, Start Here

One mistake many organizations make is starting with technology first.

KM is not successful because of SharePoint, Copilot, or any platform alone.

It works when there is:

• clear governance
• strong taxonomy
• leadership support
• contribution culture
• defined ownership
• business alignment

The first step should always be understanding:

What business problem are we solving through KM?

• Faster proposals?
• Better collaboration?
• Reducing rework?
• Capturing delivery knowledge?
• Improving onboarding?

The answer defines the KM strategy.

Final Thought

The future of presales KM is not about building larger repositories.

It is about building connected knowledge ecosystems that help people collaborate faster, learn more quickly, and respond more intelligently.

Organizations that continue treating KM as only a document library may struggle to scale effectively.

But organizations that use KM strategically — especially with AI — can create a significant competitive advantage in presales.

In my next blog, I’ll dive deeper into:

“How AI is Transforming Presales Knowledge Management Beyond Search and Storage”

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