A friend recently shared a story that made me think about how organisations are using AI.

A team of his organisation had prepared an impressive plan for introducing a new way of working. Everything looked right: the objectives, the responsibilities, the timelines and the risks. The document was well written, carefully structured and convincing.

‍

But something was missing.

Nobody seemed to have asked whether the organisation was actually ready to put the plan into practice.

The deadlines were tight. Some responsibilities were unclear. The plan expected people to follow processes they had never used before.

The ideas were not necessarily wrong. They simply didn’t reflect how the organisation really worked. It was not tailored.

“Was it written by AI?” I asked.

“Possibly,” my friend replied. “But does that really matter?”

He had a point.

Perhaps the more important question was whether anyone had checked the plan against what the organisation already knew.

The knowledge nobody asked for

AI can help produce an impressive plan in minutes. But a plan that looks complete can make people less likely to question its assumptions, especially when it has management support.

There is also a less visible risk. When AI suggests a deadline and nobody questions it, that assumption can become part of the plan without anyone consciously validating it. It can then shape the decision, even though nobody delegated the decision to AI.

Consider a simple example.

A plan allows three weeks for testing a new process. It sounds reasonable until someone explains that the teams have never done this before and that previous projects needed much more preparation.

The knowledge to spot the problem already existed. It was simply missing from the discussion.

Making that knowledge available at the right moment is what Knowledge Management (KM) is for. KM brings previous experience, lessons learned and the people who understand how the organisation works into the discussion, while the plan can still be questioned.

A 30-minute reality check

Before approving a plan that will significantly change how people work, try three things.

1. Identify the three assumptions most likely to undermine the plan if they are wrong. Think about timelines, available people, skills or dependencies. AI can help you list them. People who know the organisation need to check them.

2. Test them against organisational experience. What evidence supports them? What happened when something similar was attempted? Who has challenged them? Can the people responsible for delivery confirm they are realistic?

3. Make responsibility clear. Who will validate each assumption? Who has the authority to accept the remaining risks?

A simple record is enough:

Assumption: [ ] | Evidence: [ ] | Validated by: [ ] | Risk owner: [ ] | Outcome / change: [ ]

Thirty minutes may not resolve every question, but it can reveal what needs checking before approval.

If an assumption does not hold up, adjust the plan, test the uncertain part first or make the risk explicit before proceeding.

Keep the record with the decision. The next team should not have to rediscover the same problem.
‍

One question before saying yes

AI has made convincing plans easier to produce. It hasn’t made organisational judgement optional.

A convincing plan is not yet a well-informed decision.

So next time an impressive plan arrives on your desk, don’t begin by asking whether AI helped write it.

Ask: Which assumption could undermine this plan, and who in our organisation would know?

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.

________________________

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.
‍

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."

_____________________________

Most KM programs fail at the same step: nobody can see what they actually have. Not for lack of content — organizations are full of knowledge.

The problem is visibility. What exists, where it lives, who owns it, who needs it, and where the gaps quietly sit.

I have put together a Knowledge Mapping Playbook:
a practical framework to map an organization's knowledge, assess it, prioritize the gaps that matter,
and turn findings into action — from SME interviews to
a governance checklist to a one-page operating model.

A map makes knowledge and gaps visible.

A playbook makes it actionable. Sharing the playbook below — curious how others are tackling this...

Knowledge Mapping and Playbook Download....
‍
‍

_______________________

Our house was divided long before the final. My son follows the French talent, my daughter has a soft spot for the Scandinavian teams, my wife supports Spain because half our friends and colleagues are Spanish.

I wanted Spain to win, and not for any of those reasons.

‍

‍

I have spent twenty years working on how organisations hold on to what they know. So I was not watching a final that night. I was watching two theories of success play each other, and I badly wanted one of them to hold.

Argentina's theory is the one most of us secretly believe in: find someone extraordinary and let them decide the night. Spain's theory is slower and far less romantic. Teach the same ideas to everyone for years, at youth level and senior level, until nobody has to be told what to do.

Argentina came at them hard: physical, direct, trying to break the rhythm and force a mistake. It is a good strategy against a team that improvises.

Spain didn't improvise. What struck me was how little visible communication there was. Nobody shouting instructions, nobody reorganising anything. Everyone already knew where everyone else would be. My wife asked, around the hour mark, why nobody was doing anything spectacular. That was the point. Nobody needed to.

Remove one Spanish player and the shape held. Remove Argentina's best and the theory collapses. That difference is not luck. It is twelve years of the same knowledge being passed to the next group, and the next.

I have watched organisations lose that same match. Not on a pitch, but in a meeting room, eighteen months after someone resigned. You know the person: Tom. The one who knows why the framework was designed that way, who remembers what was agreed with the regulator in 2019 etc etc. They are not listed anywhere as critical infrastructure. They just are.

Three things worth doing before that Tuesday:

Write down why, not just what. Most decision records confirm something was approved. Almost none explain what made it sensible at the time.

Name your single points of knowledge. If this person were unavailable for three months, what stops? Most teams have never made that list.

Debrief the near-misses. They hold the most useful knowledge and the least defensiveness.

Knowledge Management is not about producing heroes. It is about building organisations that can afford to lose them.

Every organisation loses its best people eventually.

_____________________________________________________

You probably remember someone like him: Tom, the senior policy expert who spent two decades at the institution. Tom remembered which decisions had been revisited three times before reaching their final form. Tom carried in his head the assumptions behind frameworks that now looked obvious but had been genuinely contested when they were designed.

Tom is "The Organisational Memory" :)

When he retired, the organisation held a warm farewell.Three months later, his former team faced a decision that depended on understanding why a key position had been taken eight years earlier. They found the approval. The minutes. The final document.They could not find the reasoning.It had retired with him.

‍The gap existing frameworks do not close
In a previous article, I introduced Structured Decision Continuity through the story of Sophia: every senior expert whose absence only becomes visible when the system that replaced her produces an answer that looks correct and is not.

The diagnosis is this: organisations do not fail because they lack knowledge. They fail because the continuity between their knowledge and their decisions is not governed.

SDC names that gap. At the centre of SDC sits the Control Layer, a practical model that examines whether the path from knowledge to decision is strong enough to survive over time.
‍

The Four Conditions
The Control Layer has four conditions. Each addresses a failure that senior professionals recognise but few organisations name explicitly

‍Validation
Was the knowledge behind this decision reliable and valid in context?

Validation ensures that information, analysis and expertise were sufficiently checked; not just accurate in isolation, but appropriate for the specific decision being made.

The failure mode is familiar: a recommendation sound in theory but built on data that no longer reflected organisational reality. Before closing any significant decision, one question is worth asking: what are we taking for granted that we have not actually tested.

‍Context
Will someone encountering this decision in three years understand the conditions that shaped it.

Context is the set of circumstances and constraints that existed at the moment a decision was made. It is why the decision made sense then; even if it looks different now.

Context is the component most commonly lost. It lives informally in the people who were present. When those people retire or move on, the context goes with them unless it has been deliberately preserved.‍

Alignment
Was this decision coherent with strategy and governance and is that coherence still visible?

The failure mode here is subtle. A decision can be fully aligned at the moment it is made and look disconnected two years later, not because it was wrong, but because the strategy moved and no one recorded what the decision was originally aligned to.‍

Traceability
Can someone follow the path from knowledge to decision and from decision to consequence?

This is the expert's failure. The decision was defended procedurally. But the reasoning left with the person who held it. Traceability ensures that reasoning does not have to leave when the expert does.‍

Four Questions Worth Asking

For any decision that will carry long-term consequences, four questions are enough:
- Was the knowledge behind this decision sufficiently checked?
- Is the context preserved?
- Is the alignment with governance and strategy visible?
- Can the path from knowledge to decision be followed later?

If the honest answer to any of these is uncertain, something is worth capturing before the people who hold the answer have moved on.‍

A Closing Thought

The expert who retired took something with him that no record system was designed to preserve. Not his knowledge; much of that existed in documents. What left with him was the reasoning that connected that knowledge to the decisions that had shaped the institution.

The SDC Control Layer does not prevent people from leaving. It ensures that when they do, the reasoning behind their most important decisions does not leave with them.

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

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.

_______________________