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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A few years ago, a Knowledge Management team I am aware of spent considerable effort documenting the lessons from a major programme. They interviewed people, captured the key insights, organised them clearly and stored everything in a system that was genuinely accessible.

Six months later, the organisation launched a similar programme.

Almost none of the captured lessons informed the new decisions being made.

Not because the knowledge was hidden. Not because people were indifferent. But because there was no real connection between what had been captured and the moment when decisions were actually being made. The knowledge existed in one place. The decisions happened somewhere else.

Capture is not the same as use

Knowledge Management has made genuine progress over the past two decades. Organisations capture more than they used to. They share more. They structure experience into frameworks, lessons and repositories that previous generations did not have.

But capturing knowledge and ensuring it reaches decisions are two different things.

A lessons-learned repository is not, by itself, a decision input. An expertise directory does not automatically put the right knowledge in front of the right person at the right moment. A documented best practice does not guarantee it shaped the choice that was made.

The path between what an organisation knows and what actually influences its decisions is often shorter in theory than in practice.

Why this gap is structural, not a failure

It is worth being precise about this, because Knowledge Management professionals sometimes hear this observation as a criticism. It is not.

KM was largely designed to solve a real and important problem: organisations were losing knowledge they needed to retain. The response — capture it, structure it, share it — was the right one.

But there is a second problem that sits adjacent to the first: even when knowledge is captured, it may not reach the decisions it was meant to support.

That is not a KM failure. It is a structural gap between two things that are related but not automatically connected — knowledge management and decision-making.

Closing that gap requires something more deliberate than a better repository or a more comprehensive lessons-learned process. It requires thinking about the path from knowledge to decision as something worth designing.

What that connection looks like in practice

It does not need to be complicated.

For an important decision, it is worth asking explicitly: what does the organisation already know that is relevant here? What lessons exist? What expertise is available? What have we learned from similar situations?

And then — crucially — making sure the answers actually reach the people making the decision, at the moment they are making it, in a form they can use.

This is different from storing knowledge well. It is about ensuring that knowledge travels to the right moment.

It also means preserving, after the decision, what knowledge was used and how it shaped the reasoning. Because that connection — between knowledge and decision — is exactly what tends to disappear over time, as last week's article explored.

Why this matters now

Decisions are where knowledge either proves its value or quietly fails to.

An organisation can have an excellent KM function and still make decisions that ignore what it knows. It can have well-maintained repositories and still repeat avoidable mistakes. Not because KM is not working, but because the link between knowledge and decision was never explicitly made.

Structured Decision Continuity examines that link — not as a criticism of Knowledge Management, but as an extension of what KM can offer when it is connected more deliberately to the decisions it is meant to support.

The question worth asking is not only: do we capture our knowledge?

It is: does our knowledge reach our decisions?

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This article is part of a monthly series on how organisations can preserve the reasoning behind important decisions, explored through the lens of Structured Decision Continuity.

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We talk a lot about KM implementation. But how many of us have stopped to ask — is our KM framework upgraded and evolved for the AI era?

I created a KM roadmap a while back to help KM folks like me have a structured approach to knowledge management. The six steps — from defining objectives to measuring outcomes — remain as relevant as ever. But times have changed, and so has our KM roadmap.

AI is no longer a future consideration. And if our KM systems are not designed with AI in mind, we are leaving enormous value on the table.

So I went back to my original framework and asked one simple question at every step: where can AI make this smarter, faster, and more impactful?

Here is what that looks like:

When you define objectives, AI can analyse patterns across your organisation to predict which knowledge gaps are causing the most friction — before your customers even tell you.

When you identify knowledge sources, AI can crawl across your systems, documents, and conversations to surface the knowledge that already exists but nobody can find.

When you choose your KMS, look beyond traditional systems. AI-native platforms with smart search, auto-tagging, and content recommendations are now the baseline, not the premium.

When you design your KM plan, let AI do the heavy lifting on categorisation, taxonomy suggestions, and flagging content that has gone stale or outdated.

When you train for cultural shift, AI can create personalised learning paths so every team member gets the knowledge most relevant to their role — not a one-size-fits-all training deck.

When you measure and evaluate, AI dashboards can track not just knowledge usage but also real CX outcomes — CSAT, first-contact resolution, average handling time — connecting your KM investment directly to business results.

This is not about replacing the human side of knowledge management. It is about amplifying it by using AI as your assistant..

Your people still drive the culture. Your experts still create the insight. AI simply helps you do more with what you already have by giving you time to focus on what matters and freeing up your time for things that you can automate.

If you are a KM professional thinking about where to focus your energy this year, start here. Not by overhauling everything — but by adding the AI layer, one step at a time.

I would love to know — which of these six steps do you think AI can impact the most in your organisation? Drop your thoughts in the comments.

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Artificial intelligence is reshaping how organizations handle information and influence decisions. Many treat it as a replacement for Knowledge Management, assuming better answers will follow.

The real challenge is how people think, question, and decide together with AI, which makes Conversational Leadership a practical discipline for responsible judgement and action.

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Artificial intelligence is reshaping how organizations handle information and what we often call knowledge. It is tempting to see it as a replacement for Knowledge Management, a more capable system that finally delivers what earlier approaches struggled to achieve. In one sense, that is understandable. AI can capture, retrieve, and synthesize information at a scale and speed that traditional repositories, taxonomies, and search tools never managed.

But if that is all we mean by Knowledge Management, then we have reduced it to something quite limited.

The deeper ambition was never just better storage or faster access. It was always about better judgment, better learning, and better decisions in situations that are often messy and uncertain. The challenge was never simply information. It was how we make sense of it together.

AI changes the terrain. It does not just store or retrieve information; it can participate in our flow of thinking. It can reframe questions, suggest connections, and influence what we notice. When we begin to think with AI rather than only use it as a tool, the line between information and knowledge becomes less clear.

AI works with representations of the past. It does not experience the present as we do, and it does not bear responsibility for what follows. That remains with us.

This matters because AI outputs often feel fluent and convincing. The risk is not that we know too little, but that we accept too quickly. We may find ourselves agreeing without fully examining what is being suggested or overlooking what is missing.

As AI strengthens the informational backbone of organizations, the real work shifts. It moves toward interpretation, alignment, and responsible action. It asks more of us in how we question what we see, how we surface assumptions, and how willing we are to stay with uncertainty rather than close things down too quickly.

Conversation becomes central here, but not just any conversation. Many organizational conversations reinforce existing patterns, avoid challenge, or defer to authority. For conversation to be useful in this context, the conditions need to support curiosity, allow for doubt, and enable thinking things through together without rushing to agreement.

This is where Conversational Leadership comes in, not as a role or a position, but as a practice. It is about creating the conditions in which people can think together more carefully, especially when the issues are complex and the answers are not obvious.

In the age of AI, that practice extends to how we engage with the technology itself. If AI becomes part of how thinking happens in organizations, then it also becomes part of the conversation. It needs to be questioned, tested, and worked with, not simply accepted.

Seen this way, AI is not an oracle that provides answers, but a participant in a broader system of sense making. It can extend our thinking, but it does not replace our responsibility for judgment, ethics, or action.

So, the question is less about what AI can do, and more about how we respond to it. Knowledge Management, in this light, becomes less about systems and more about our collective ability to make sense of things together in environments where AI is always present.

The tools will continue to evolve. The need to think well together, and to take responsibility for what we decide and do, remains a human concern.

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