Making the case for a new strategic capability - and what it takes to finally see, measure, and govern everything an organisation knows.
Knowledge is the most valuable yet volatile asset in any organizational portfolio, driving innovation while demanding continuous adaptation.
The enterprise capability to identify, measure, and govern everything an organisation knows - across both explicit and tacit forms - so that knowledge becomes decision-relevant, confidence-weighted, and continuously managed over time rather than merely stored.
Every organisation runs on knowledge. The decisions that shape its direction, the expertise that defines its capability, the memory that holds its history together - all of it is knowledge. And yet no organisation can tell you what it knows, how much of it is reliable, where it is concentrated, or how fast it is disappearing.
This is not a technology failure. It is a conceptual one. Knowledge is not one thing - it is an enormous, varied universe: documents and data, yes, but also the judgement accumulated over decades of practice, the institutional memory that lives only in people's heads, the relationships that determine how decisions really get made, the patterns only visible to those who have seen enough to recognise them. No existing framework accounts for all of this. Most don't try.
I believe this gap is not incidental. It is the central unsolved problem of the knowledge economy - and the reason organisations continue to lose what they know, act on what they merely assume, and make decisions without understanding the quality of the knowledge beneath them.
The capability that closes this gap is Knowledge Intelligence: the ability to treat everything an organisation knows - in all its forms - as a governed, measurable, and decision-relevant asset. It has no established category, no recognised framework, and no dominant platform. This paper is its founding statement.
Before any intelligence framework can be built, there has to be clarity about what knowledge actually is. Not knowledge as a vague concept, but knowledge as a practical reality - the full range of what an organisation knows, holds, and depends on.
That range is vast. It spans the highly visible and the entirely invisible. And critically, the most visible part - the documents, reports, and records that fill enterprise systems - is also the smallest part.
Knowledge expressed in words and numbers represents only the tip of the iceberg. The larger part is tacit: highly personal, hard to formalise, and deeply rooted in action, experience, and individual values.
Explicit assets are visible and governable. Tacit assets are larger, harder to capture, and carry most concentration risk.
Enterprise systems manage the fraction of knowledge that has been written down. Knowledge Intelligence governs the universe it sits within.
Knowledge Intelligence sits inside the Knowledge Management lineage, but it is not a rebrand or a tool category. It is a shift in scope, in method, and in outcome.
| Dimension | Traditional KM | Knowledge Intelligence |
|---|---|---|
| Primary objective | Capture, organise, and enable retrieval and reuse of knowledge. | Measure and govern knowledge quality so it becomes decision-relevant and safe to act on. |
| Core unit | Content item - document, page, or record. | Knowledge asset - carrying explicit signals, inferred quality, and tacit risk context. |
| Trust model | Implicit trust, or manual review on a case-by-case basis. | Explicit confidence weighting with governance thresholds applied systematically. |
| Change over time | Periodic clean-up. Taxonomy decay is the norm, not the exception. | Continuous monitoring of drift, decay, and concentration. Renewal is systematic, not reactive. |
| Tacit knowledge | Addressed through cultural programmes, often separate from systems and rarely measured. | Treated as a governable estate: surfaced via risk mapping, elicitation, and network signals. |
| Decision linkage | Often indirect. Value is difficult to evidence and easy to question. | Direct. KI produces confidence-weighted insight artefacts with traceable, auditable decision inputs. |
If knowledge is so clearly valuable, why has no framework for measuring and governing it yet emerged? Not for lack of interest. Knowledge presents genuinely hard problems that no existing system has been designed to solve. The cost of that gap is well documented, even if the solutions remain absent.
Most enterprise value now sits in assets that are invisible on the balance sheet and vulnerable to silent decay.
Estimated share of S&P 500 market value represented by intangible assets, where knowledge is a core value driver.
of executives agree critical procedural and strategic knowledge walks out the door when people leave.
of leaders report they do not trust their data enough for confident decision-making at the point of action.
Knowledge value does not fail all at once. It leaks through attrition, drifts through outdated assumptions, and degrades when confidence is not governed. The governance problem is not storage. It is continuous validity.
Critical know-how leaves with individuals unless transfer is instrumented and owned.
Previously valid knowledge becomes wrong as market, regulation, and strategy change.
Ungoverned sources receive equal weight, making low-confidence claims look decision-safe.
A decade of domain expertise, a library of validated research, and a set of informal but critical relationships - how do you compare these? How do you decide which to invest in, which to protect, which is at risk? Without a shared way of measuring knowledge, these decisions remain matters of instinct rather than evidence. Governance without measurement is just guesswork.
The enterprise content stack can only work with what has been written down and captured. The expertise in people's heads, the institutional memory embedded in practice, the relationships that determine how decisions really get made: none of these show up in any current platform. They are the largest and most valuable part of the knowledge estate, and they are entirely unmeasured. APQC's 2024 research identified tacit knowledge capture as the most urgent priority for knowledge management teams precisely because the gap is now widely felt, even if it is not yet closed.APQC, 2024
A piece of knowledge can be highly valuable in one context and irrelevant in another. Its value is also dynamic. It depreciates as conditions change, and appreciates as it becomes more applicable. Static asset valuation frameworks are not designed for assets whose value shifts continuously in response to context.
Even where knowledge has been captured, no current framework distinguishes between what an organisation knows with high confidence and what it merely believes, assumes, or has recorded without validation. All explicit knowledge is treated as equally credible. A document from five years ago carries the same implicit authority as one validated last week. An unverified assumption carries the same weight as a tested finding. This means none of it is truly governed - and when AI operates on ungoverned knowledge, low-confidence outputs become indistinguishable from high-confidence ones.
Domains where critical knowledge is held tacitly by few individuals - the invisible risk topology most organisations cannot currently see.
It has never been more necessary for knowledge managers to demonstrate the value of their organisations' investments in knowledge and learning initiatives. But achieving this is more difficult than it looks. It is difficult to build direct cause-and-effect links between specific knowledge management practices and aspects of organisational performance.
This was not a 2013 finding alone. Henley's own prior research from 2004-2005 found that even the most rigorous KM measurement frameworks broke down the moment tacit knowledge or organisational change entered the picture. The measurement gap is not waiting to be closed by better tooling. It is a structural condition that requires an architectural response.
AI can only be as good as the knowledge it runs on. Right now, many organisations have the models but not the knowledge quality or governance needed for reliable output. That is why Knowledge Intelligence is needed now.
Large organisations now hold content estates of sufficient volume and variety that patterns within them are genuinely significant. The signal-to-noise challenge is no longer a niche concern. It is a primary operational and strategic condition, and the estate is large enough that intelligence extracted from it carries real strategic weight.
AI now extracts meaning from large estates with far less manual effort. But faster processing does not fix poor sources, missing context, or stale knowledge. 78% of organisations now use AI in at least one business function,McKinsey, 2025 yet many still struggle to scale trusted use because output quality is limited by knowledge quality. The organisations struggling to realise value from their AI investment are, in most cases, struggling because the knowledge foundations beneath their AI are ungoverned.
In a fast-moving environment, wrong answers are expensive. Leaders need more than speed. They need justified confidence: evidence they can inspect, challenge, and defend before acting. 75% of leaders say they do not trust their own data enough to act on it confidently.Dataversity / Gartner, 2025 The problem is not access to information. It is the absence of any framework for knowing whether to trust it.
Generative AI did not create the knowledge problem. It exposed it. AI systems inherit the condition of the knowledge estate beneath them. Knowledge Intelligence makes that condition measurable and governable.
Knowledge Intelligence spans both the explicit and tacit dimensions of organisational knowledge: governed, confidence-weighted, and designed to support decisions that depend on the full range of what an organisation knows.
Any practical path toward this capability has to start with what is most legible: the explicit layer. Content Intelligence is the discipline of extracting meaning from an organisation's content estate at scale, assessing signal strength, evaluating reliability, and producing insight that can justify decisions rather than merely inform them. It is not the whole answer, but it is the necessary first layer.
Content Intelligence is a pipeline, not a feature. Raw content enters, passes through meaning extraction and confidence weighting, and emerges as a governed insight - or is withheld when confidence is insufficient. Every output carries a weight. The system governs what each confidence level is permitted to influence.
The most difficult part of Knowledge Intelligence is tacit knowledge: what people know but have not written down. This cannot be fully captured. It can be managed through a metadata-first approach that does not require blanket reading of private content - reducing tacit risk without eliminating the need for human judgement.
Every knowledge asset within a KI framework produces a single composite indicator - the Knowledge Health Score - combining five dimensions into one signal that tells the organisation whether to trust, review, or retire what it knows.
A governance signal that continuously combines five dimensions, then classifies each asset into an explicit operating state: trust, review, or retire.
Every knowledge management system ever built has the same structural failure: a taxonomy that someone designed, published, and then watched decay. As the organisation changed, the classification didn't. Knowledge accumulated in categories that no longer fit, under concepts that had been superseded, in structures built for a version of the business that no longer existed. Maintaining it required manual effort that no one had time for. So it was left. And the estate quietly became ungovernable again.
A Knowledge Intelligence framework breaks this cycle through a fundamentally different model. Classification is not designed once and imposed on the estate - it emerges continuously from the estate itself. The intelligence engines that process incoming knowledge read patterns across the full estate, identify where concepts are converging or diverging, detect when new themes are forming before they have been named, and update the taxonomy accordingly. The result is a classification system that evolves in step with the organisation - not months or years behind it.
Humans set the parameters, review proposed changes, and retain authority over the taxonomy's direction. But the intelligence layer does the continuous work of observation and proposal. The taxonomy becomes a living model - not a published artefact that someone has to remember to update.
Knowledge Intelligence is not a philosophy - it is a capability with a defined architecture. At its core is a deceptively simple idea: every piece of knowledge an organisation holds should exist as a governed asset with a known identity, a measurable quality, and a traceable history. Not just documents. Not just data. Everything the organisation knows, in whatever form it takes.
This is what a Common Knowledge Asset is: a governed record defined by eight core attributes, organised into three operating dimensions. Ingestion, classification, and baseline scoring are automated. People step in for exceptions: conflicts, threshold breaches, and high-impact disputes.
Confidence is calculated from clear inputs: source quality, corroboration with other trusted assets, recency relative to domain change, usage in successful decisions, and detected conflicts. This is a governance signal, not a sentiment score. Confidence, coverage, and consistency are treated as separate governance signals. Confidence asks whether an asset is trustworthy, coverage asks whether relevant assets exist, and consistency asks whether key assets agree with each other.
Eight governed attributes surrounding one computed record. Hover segments or legend items to inspect each attribute.
Every knowledge asset carries a structured record across eight governed attributes, organised into three operating dimensions. Together this model shows what the organisation knows, how reliable it is, and who is accountable when confidence drops.
A Common Knowledge Asset tracks knowledge through its entire life - from ingestion to retirement - updating its health score at every stage.
The value of Knowledge Intelligence becomes concrete when anchored to decisions that were previously unavailable, or available only through costly, one-off, and unreliable manual effort.
Themes are typically only recognised once someone names them. Knowledge Intelligence enables early visibility of persistent, converging signals across both the documented content estate and tacit knowledge networks, allowing leaders to see change forming before it becomes obvious - without being asked to react to noise.
When critical expertise is held by one person, or institutional memory exists nowhere but in the minds of a small team, the organisation is carrying knowledge risk it cannot currently see or measure. Nearly half of executives already recognise this: 48% agree that employees take critical procedural and strategic knowledge with them when they leave.KMWorld, 2024 Knowledge Intelligence makes this risk visible and measurable before it becomes a crisis - treating knowledge concentration the same way a finance team treats exposure in a portfolio.
Organisations accumulate frameworks and models that persist long after their usefulness declines. Knowledge Intelligence provides visibility into how concepts perform across the full knowledge estate, in documents, in practice, and in expert judgement, enabling deliberate and evidenced evolution rather than conceptual drift.
No current framework distinguishes between what an organisation knows with high confidence and what it merely assumes. 75% of leaders say they do not trust their own data for decision-making.Dataversity / Gartner, 2025 Knowledge Intelligence makes confidence a first-class governance dimension, ensuring the knowledge underpinning high-stakes decisions can be interrogated, not assumed.
Internal knowledge - both explicit and tacit - drifts relative to external reality. Knowledge Intelligence treats external context as weighted evidence, allowing internal understanding to be reinforced, challenged, or updated deliberately rather than by crisis. This applies to documented knowledge and to the assumptions embedded in expert practice.
A strategic capability without a measurement framework is an act of faith. The most common reason Knowledge Intelligence programmes lose momentum is not that they fail to deliver - it is that no one agreed in advance how to recognise success. The organisations that have deployed this capability at scale share a common discipline: they defined what they were trying to move, established a baseline before they began, and tracked a small number of meaningful metrics rather than a large number of available ones.
The framework below organises measurement into four tiers. Together they show quality, operational effect, financial efficiency, and estate health. Each metric is grounded in operational signals rather than abstract sentiment.
No single metric tells the full story. Retrieval success without freshness tracking means you are measuring how well people find information that may be wrong. Freshness SLA without reuse rate means you are maintaining a library no one is reading. The four tiers work together: knowledge quality sets the standard, operational impact proves workflow value, financial efficiency proves cost impact, and estate health protects long-term reliability.
Start with three or four metrics that cover all tiers, and establish baselines before deployment. The most common error is attempting to measure everything at once and measuring nothing with rigour.
Knowledge Intelligence is a strategic capability, which means it needs strategic ownership. Three shifts in thinking are necessary for it to take root and build value over time.
Organisations need to start treating knowledge - in all its forms, tacit and explicit - as something with a measurable state, a direction of travel, and a governance obligation. That means investing in the frameworks that make it possible to see and manage the full knowledge universe, not just the documented surface of it.
Governance needs to go beyond compliance and cover the quality of understanding - whether the knowledge being relied upon is current, validated, and well-founded. Confidence must become a first-class concern with clear ownership and accountability at the executive level.
The most valuable intelligence is not what the current state is, but what has changed and why. Leaders need to invest in understanding over time - including the ability to see how the organisation's knowledge estate is evolving, where it is concentrating, and where it is at risk of being lost.
A case this clear deserves an honest account of what stands in the way. Knowledge Intelligence is not a difficult idea to understand. It is a difficult capability to build - and organisations attempting it will encounter resistance that is predictable, significant, and worth naming directly.
If source quality degrades or conflicts are ignored, confidence scores drift and low-quality assets begin influencing decisions without detection.
If exceptions do not have named owners and defined response windows, issues accumulate and teams stop trusting governance decisions.
If contributors cannot see the impact of sharing, contribution drops and tacit concentration risk rises. This also increases dependency on external vendors for knowledge the organisation already holds.
Organisations have spent decades building systems to manage what they've written down. Those systems are valuable and will remain so. But they address only the visible surface of what organisations actually know - leaving the vast majority of their knowledge unmeasured, unmonitored, and ungoverned.
Knowledge Intelligence changes that. Not by adding another layer to the existing content stack, but by building a genuinely new capability - one that treats the full universe of what an organisation knows as something that can be classified, measured, and governed. That requires a different way of thinking about knowledge itself: not as a category of documents, but as everything an organisation knows, in every form it takes.
This capability does not sit apart from the broader digital and AI agenda - it is its foundation. Every AI strategy depends on the quality of the knowledge it operates on. Every data governance programme leaves the largest part of the knowledge estate untouched. Every transformation initiative runs on assumptions that have never been tested. Knowledge Intelligence is what changes the foundation, not just the surface.
The conditions have converged. The knowledge estates are large enough. The AI capability is sufficient. The decision stakes are high enough. The cost of ungoverned knowledge is no longer acceptable.
This paper is a founding statement, not a final answer. The work of building the capability and proving its value is what comes next - and it's work I intend to do through the Kore platform I am building.
Kore is the platform I am building to operationalise Knowledge Intelligence with measurable governance, trusted retrieval, and stronger decision confidence.