Organisations rarely struggle because they have no knowledge. More often, the knowledge they need is dispersed across teams, embedded in day-to-day practice and held by experienced people who may not realise how much they know.

The usual response is to introduce a system: a new knowledge base, learning platform, AI tool or governance framework. Yet technology alone does not make people share what they know, learn from one another or change how they work.

That was the central message of Professor Naoki Ogiwara’s session on the Japanese approach to knowledge-powered growth at Knowledge Summit Dublin 2026. Drawing on the work of the late Professor Ikujiro Nonaka and on case studies from Fujifilm Medical Systems and Hitachi-GE Nuclear Energy, Ogiwara offered a different sequence for change:

Bring the right people together. Give them room to act. Learn from what happens. Then build the structures and technology that will help the new practice spread.

For training, learning and knowledge managers, this is more than an interesting philosophy. It is a practical way to avoid expensive programmes that look good on paper but fail to become part of everyday work.

 

Lesson 1: Shift the focus from managing knowledge to creating it

Many knowledge initiatives begin with storage: Where will the content live? How will it be categorised? What platform should we use?

The Japanese tradition described by Ogiwara begins somewhere else. It asks how people create new knowledge together.

In this view, knowledge is human, not simply data. Its tacit dimension lives in experience, judgement and relationships as much as in records or repositories.

This distinction matters. A repository can hold what is already known and documented. It cannot, by itself, surface the judgement of an experienced engineer, the instinct of a successful salesperson or the local workaround that prevents a recurring problem. That knowledge emerges through observation, dialogue, experimentation and reflection.

The first design question should therefore be:

What interactions need to happen for useful knowledge to emerge?

Only then should we ask how technology can capture, organise and extend it.

In our knowledge-risk work at Slick+, we start with the work itself – the critical decisions, recurring problems, handovers and expertise held by too few people – before deciding whether the answer is peer video, mentoring, formal training or better use of an existing system.

 

Lesson 2: Create the conditions for knowledge exchange – not just another channel

A central concept in Nonaka’s work is ba. In everyday Japanese, the word refers to a space or setting. Nonaka gave it a deeper organisational meaning: the shared time and space in which people create knowledge together. It can be a physical place, a virtual space or a recurring social practice. What defines it is not the location but the quality of the interaction.

A meeting does not automatically become ba. Neither does a Teams channel, community platform or workshop. People need enough trust, shared purpose and psychological permission to discuss what really happens – including mistakes, uncertainties and practices that have never been written down.

AI can analyse information and organise explicit knowledge, but it cannot create ba. Trust, shared purpose and meaning are built between people.

This helps explain why in-person gatherings continue to matter, even when almost any information can be exchanged online. Informal conversations, shared experiences and time spent together build relationships. Those relationships make it easier to exchange the valuable knowledge that people rarely put into an email or formal report.

Slick+ was built on the same premise: technology should not replace the conversations in which knowledge is created; it should help the learning from those conversations travel across shifts, sites and time.

For a learning or knowledge manager, improving ba might mean:

• observing a task alongside an experienced employee;
• running a small peer conversation around a real operational problem;
• creating a regular cross-shift or cross-site practice-sharing session;
• giving people a safe way to share failures as well as successes; or
• allowing enough time for questions, reflection and follow-up.

The lesson is simple: do not measure the quality of a knowledge environment by the number of channels created. Measure it by the quality of the conversations and actions it enables.

 

Lesson 3: Observe expertise in context before trying to document it

Fujifilm Medical Systems faced a profound business shift as demand for traditional X-ray film declined. Its salespeople could no longer succeed primarily by selling equipment; they needed to become solution providers who could understand and respond to more complex customer needs.

The organisation did not begin by asking its strongest salespeople to write a manual. Much of their expertise was tacit: they used it instinctively and could not necessarily explain it in response to a direct question.

Instead, colleagues accompanied and observed high-performing salespeople in their work. Follow-up conversations helped both the observers and the experts recognise what was happening: the questions being asked, the signals being noticed, the decisions being made and the principles guiding those decisions.

The process moved through three forms of ba:

1. Observation: see expertise being applied in its real context.
2. Expression: bring people together to articulate patterns, principles and reasoning.
3. Practice: create repeated opportunities for others to try, discuss and refine the approach.

The result was the “FMS Way for Sales”: not a rigid procedure, but a set of guiding principles for a more consultative, solution-oriented way of working. The approach was reinforced through manager workshops and regional sessions where teams discussed good practice, failures and current challenges. It later extended beyond sales to service and support functions.

The broader lesson is important: if you want to capture how good work is really done, go to the work. Interviews and documents are useful, but they often reveal only what people remember to say – not what they actually notice and do.

 

Lesson 4: Capture judgement, not just steps

Traditional training is often designed around the standard process: the correct sequence, approved procedure or expected behaviour. That is necessary, particularly in regulated and safety-critical work, but it is rarely sufficient.

Competent performance also depends on context:

• What should I pay attention to here?
• How do I recognise that the situation is changing?
• Which mistakes are most likely?
• When does the standard response need to be escalated?
• What would an experienced person check before acting?

These questions surface the thinking behind the task. They turn knowledge capture from a content exercise into a learning exercise.

The most useful output may not be a comprehensive manual. It may be a short set of principles, a demonstration, a worked example, a decision guide or a collection of real scenarios that helps others make better judgments.

 

 

Lesson 5: Start with a few “dynamos,” not an organisation-wide rollout

Ogiwara used the term dynamos for the small number of people who become catalysts for change. They are curious, credible and prepared to try something new before every detail is settled.

The nuclear-energy case study showed why this matters. Following the Fukushima disaster, the industry experienced long shutdowns, fewer opportunities for on-site learning and an urgent need to transfer expertise from veteran engineers to a younger generation. There was also a demographic gap: many experienced specialists and newer employees, but fewer people in between to bridge the transfer.

Rather than beginning with a fully designed corporate programme, the company selected a small group of engineers and gave them responsibility for exploring the problem alongside their existing roles. They learned from organisations including Boeing, NASA and APQC, then used that learning to propose an approach suited to their own environment.

Early action created the evidence for a more formal commitment. Management subsequently supported a dedicated knowledge team and a wider set of practices.

This sequence is the reverse of many transformation programmes. It does not wait for perfect governance before anyone can act. It creates a bounded space in which trusted people can learn what is likely to work.

The 5S and Kaizen influence behind the Slick+ Planner feature is deliberate: prioritise the knowledge that matters, organise who will capture it, standardise what proves useful and keep improving the library rather than trying to build it all at once.

 

Lesson 6: Use small wins to attract the next wave

An early experiment does not need to transform the organisation. It needs to produce a credible signal that something useful is happening.

When a small success becomes visible, it does two things. It recognises the people who took the initial risk, and it invites a second group to join. That second group is essential: change starts to move from an isolated initiative towards a broader practice.

In the nuclear-energy example, knowledge maps helped identify critical areas of expertise and prioritise what needed attention first. Study sessions, on-the-job learning, World Cafés and an annual knowledge award created multiple ways to participate. Visible involvement from the company president signalled that knowledge work was part of the organisation’s core business – not a side project owned only by the KM team.

This suggests a more useful set of early measures than content volume alone:

• Did the experiment solve or reduce a real operational problem?
• Did someone reuse knowledge they could not previously access?
• Did an expert make an important part of their judgement more visible?
• Did another team ask to take part?
• Did a manager change a routine or decision because of what was learned?

Slick+’s work with a frontline logistics company followed that same path: a focused pilot produced more than 100 short videos across ten categories; the next step was to curate what proved useful, close the gaps and structure the hub around how teams actually work before considering wider expansion.

These are signs of movement. They show that knowledge is beginning to travel.

 

Lesson 7: Build governance and technology around proven behaviour

The familiar change model assumes that people must first adopt a new mindset, which will then change behaviour, culture and results. Ogiwara challenged that sequence.

Mindset does not necessarily change first – action does. Through action, people learn. Through learning and reflection, their mindset shifts. When enough people begin to change their behaviour and beliefs, the culture follows.

That leads to a more practical sequence:

1. Plant the flag. Define a meaningful challenge and a shared direction.
2. Rally the dynamos. Bring together the credible, motivated people who can begin the movement.
3. Trigger action. Give them room to run a bounded experiment with genuine trial and error.
4. Reflect and shift mindset. Identify what worked, what failed and what the experience changed.
5. Celebrate small wins. Recognise the dynamos, make the value visible and build confidence in the approach.
6. Let the majority join. Use visible evidence and peer credibility to invite wider participation.
7. Shape the environment for lasting change. Introduce the right roles, processes, incentives, governance and technology based on evidence from practice.

Governance still matters. So do standards, platforms, AI and measurement. The point is timing: these should reinforce behaviours the organisation has begun to understand, rather than locking everyone into assumptions made before the work started.

 

 

A 30-day experiment for learning and knowledge teams

The principles can be tested without launching a major transformation programme.

Week 1: Choose one consequential problem

Select a specific knowledge risk or performance challenge, such as repeated errors during shift handovers, slow onboarding for a specialist role, inconsistent troubleshooting or reliance on one experienced employee.

Define the operational outcome you want to improve. Avoid beginning with a content target such as “create 20 resources.”

Week 2: Find the knowledge and observe the work

Identify two or three credible practitioners – the people colleagues already turn to. Observe the task or decision in context. Ask them to explain what they notice, where people tend to go wrong and how they adapt when conditions change.

Week 3: Express and test the knowledge

Turn what you learn into the lightest useful format: a short demonstration, a set of guiding principles, a scenario, a checklist with explanation or a brief peer session. Test it with a small group of intended users.

Week 4: Review the evidence

Ask what changed. Did people perform the task more confidently? Were fewer explanations repeated? Did the resource answer a real question? What knowledge is still missing? Who wants to participate next?

Use those answers to decide what to refine, what to scale and what supporting technology or governance is actually needed.

 

What this means in the age of AI

AI can make existing knowledge easier to transcribe, translate, tag, find, summarise and reuse. That is valuable. But it does not remove the need to create the conditions in which people are willing and able to share what they know.

As Ogiwara put it, AI is strongest at automating what is already known. The more strategic challenge is surfacing the unknown: the unspoken judgement, emerging practice and new insight that appear when people tackle real problems together.

For training, learning and knowledge managers, the opportunity is not to choose between people and technology. It is to get the sequence right.

Start with a problem that matters. Bring together the people closest to it. Observe the work. Make the hidden knowledge visible. Test it in practice. Then use technology to help the learning travel further, faster and more reliably.

That is how a knowledge initiative stops being a library of good intentions and starts becoming part of how the organisation works.

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Source note

This article is based primarily on Professor Naoki Ogiwara’s session, “Unlocking Change: The Japanese Approach to Knowledge-Powered Growth,” at Knowledge Summit Dublin 2026.

The article interprets the session for a training, learning and knowledge-management audience rather than serving as a verbatim account.

For further context, see the Knowledge Summit Dublin speaker programme, this overview of Ikujiro Nonaka’s work on knowledge creation and ba, and Hitachi’s account of its knowledge-management approach to technical knowledge transfer.