It is about changing the business.
That is where many organisations are still getting stuck.
They are approaching AI as if success comes from access. Give people tools. Stand up a platform. approve a few use cases. Publish some principles. Run some pilots.
Then wait for transformation to happen.
But access is not transformation.
And distribution is not capability.
If AI is only being added on top of the existing business, then the business has not really changed.
Real AI impact shows up when organisations change how work is designed, how decisions are supported, how services are delivered, how risk is governed, how people are managed, and how value is created.
That is a business change problem, not just a technology rollout problem.
This is why so many AI programs create noise before they create value.
The organisation talks about AI in strategic language, but the underlying operating model stays mostly intact. The same structures, same incentives, same workflows, same management assumptions, same approval bottlenecks, same disconnected delivery patterns.
In that environment, AI gets absorbed into the existing system instead of reshaping it.
And when that happens, the result is predictable:
If leaders want different outcomes, they need to stop treating AI rollout as a distribution exercise.
The question is not simply whether people have access to AI.
The question is whether the business is willing to change because of it.
That is the real divide.
Some organisations will use AI to decorate the current model.
Others will use it to redesign how the business actually works.
Only one of those paths is likely to produce lasting advantage.
]]>It succeeded because it gave code work a structure that could scale.
Version history. Branching. Merging. Review. Shared source of truth. Traceability of change. A practical way for multiple people to work on the same evolving body of logic without collapsing into chaos.
It did not remove complexity. It made complexity more governable.
That matters now, because organisations are heading into a similar problem with AI.
The issue is no longer just whether people have access to models, copilots, or agents. The issue is whether the organisation has a structure that AI can actually work with.
And in most cases, it does not.
Most organisations are not short of information.
They are short of structure.
Knowledge sits in documents, chat threads, inboxes, systems, habits, unwritten rules, local workarounds, and people’s heads. Decisions are made in one place and felt in another. Teams build fixes without knowing what already exists. Context is interpreted repeatedly, inconsistently, and often too late.
This has always been a problem, but AI makes it impossible to ignore.
AI systems need legibility. They need structure. They need to know what things are, how they relate, what matters, where authority sits, what changed, and what evidence connects one action to another.
Without that, AI does not create coherence.
It amplifies fragmentation.
One tempting response is to say: fine, let’s treat the organisation like a repository.
There is something right about that.
An organisation does contain evolving work, decisions, context, standards, dependencies, and change history. It does need traceability. It does need shared understanding. It does need a way for many contributors to work on a living system.
But if you simply start dumping organisational knowledge into an AI-accessible store, you do not get order.
You get a bigger mess.
Raw accumulation is not structure.
A company is not just a pile of content. It is a living system of meaning, action, incentives, constraints, roles, judgments, and contested priorities. If you expose all of that without an organising logic, you create sludge, not clarity.
That is why the repo analogy is useful, but incomplete.
Code works in Git because code already has strong structure.
Files have roles. Dependencies have shape. Changes can be compared. Branches have meaning. Merges can be reviewed. The system is not perfect, but it is legible enough to support disciplined collaboration.
Organisations usually do not have that level of structural clarity.
That is the gap.
If organisations are going to become AI-legible, they need more than storage. They need ontology.
They need a structured way to answer questions like:
Without this, the organisation remains a tangle of partially connected fragments.
With it, the organisation can start to become teachable to both people and AI.
KnowledgeFund should not be understood as just another knowledge management system.
It is not merely a place to put documents.
It is not a prettier intranet. It is not a smarter search bar. It is not a dumping ground for lessons learned.
The point of KnowledgeFund is to provide the organising ontology that makes an AI-legible organisation possible.
That means helping the organisation create structure around:
In that sense, KnowledgeFund is not only the store. It is the frame that makes the store meaningful.
It gives the organisation a way to place, connect, trace, and govern what it knows and what it is doing.
That is why it matters.
This problem existed before AI, but AI raises the stakes because it increases both the value of structure and the cost of incoherence.
A fragmented organisation using AI does not become aligned by default.
It becomes faster at producing disconnected output.
A company with unclear knowledge, weak traceability, political bottlenecks, and stale context will not get rescued by better prompts. It will simply produce more convincing confusion.
That is why the next wave of advantage will not come only from who has access to the best models.
It will come from who can make their organisation legible enough for AI to help with real work in a coherent way.
Git gave software teams a practical way to manage a living body of change.
It made contribution, divergence, review, integration, and history workable at scale.
It did not solve every software problem. But it created a durable structural layer that modern software development could build on.
Organisations are now facing a similar need.
They need a way to make knowledge, work, decisions, and change more legible, traceable, and governable.
They need a practical structure for collective organisational intelligence.
That is why I think this may become the real role of KnowledgeFund.
What Git did for code, KnowledgeFund may need to do for organisations.
]]>For a while, much of the conversation was trapped in the shallow end: prompts, chatbots, and isolated model tricks. Then came better terms like context engineering, which helped explain that model output depends heavily on what information, memory, tools, and framing surround it.
More recently, harness engineering has emerged as a useful practical label. It captures the work of building the wrapper around an AI system: the prompts, tools, tests, retries, scripts, context loaders, verification steps, and operational scaffolding that make an agent more reliable in the real world.
That is a good term. It names something real.
But it is still not the full picture.
Because once AI starts interacting with real work, real systems, real permissions, and real people, the challenge is no longer just harness engineering.
It becomes governance.
Harness engineering is the discipline of making AI actually usable.
It is what happens when you stop treating the model like a magic box and start designing the surrounding system properly. You add better instructions. You expose the right tools. You tighten the feedback loops. You build test paths, validation scripts, memory boundaries, and execution patterns. You learn from mistakes and make them less repeatable.
This is good engineering.
It is also increasingly necessary. An agent without a harness is mostly hope. An agent with a harness can begin to behave like a working component in a broader system.
That is real progress.
The harness improves execution, but it does not define the whole system the execution belongs to.
A harness can help an agent do the right thing more often. It can reduce errors. It can improve consistency. It can make workflows faster and more repeatable.
But it does not, by itself, answer bigger questions:
These are not harness questions.
These are governance questions.
The moment AI moves beyond a toy use case, it starts shaping behaviour.
Not just model behaviour. Human behaviour. Organisational behaviour. System behaviour.
It affects what work gets surfaced and what gets ignored. It affects how knowledge is captured, translated, or lost. It affects who can act, who can approve, and who can see. It affects what becomes normal, what becomes measurable, and what becomes invisible.
This is why so many AI initiatives feel strangely incomplete. Teams improve prompts, bolt on tools, add retrieval, add agents, add orchestration, and still fail to get stable value.
They assume they are dealing with a technical performance problem when they are actually dealing with a system-shaping problem.
The harness is working on the execution path. But the organisation is still under-governed.
Governance is often misunderstood as oversight, compliance, or bureaucracy. That is too narrow.
Governance is the set of forces, rules, structures, signals, and constraints that shape behaviour in a system.
In AI, that means governance is not just the policy document sitting above the stack. It is the wider control plane that determines how the stack behaves at all.
Governance shapes:
Seen this way, prompt engineering, context engineering, and harness engineering are not alternatives to governance. They are downstream from it.
Governance is the broader pattern that gives them meaning.
Most failed AI efforts do not fail because the model is too weak.
They fail because the surrounding system is incoherent.
The AI is told to be helpful, but not given clear authority boundaries. It is connected to tools, but not to trustworthy validation. It is given memory, but not memory discipline. It is asked to move fast, but not told what must never break. It is deployed into teams, but without clear accountability for decisions and outcomes. It is measured on visible activity instead of meaningful contribution.
In that environment, better harnesses help, but only up to a point.
You can keep improving the wrapper and still get poor organisational results because the issue is not just whether the agent can act. The issue is whether the broader system gives that action coherence, legitimacy, and useful direction.
This is the real shift.
Harness engineering should absolutely continue. It is practical, valuable, and necessary. We need better wrappers, better tooling, better verification, and better operational patterns.
But we should stop pretending that this is the whole problem.
If AI is going to succeed in real organisations, then success depends on governance:
Harness engineering makes agents more effective.
Governance makes AI use coherent.
One improves execution quality. The other determines whether the execution belongs in a functioning system at all.
So yes, harness engineering is a useful term.
It names an important phase in the maturation of AI practice. It reflects a move away from prompt tinkering and toward system design. That is a genuine step forward.
But if we stop there, we will keep solving the smaller problem.
The bigger problem is not just how to make an agent perform better inside its wrapper.
It is how to shape the forces around AI so that behaviour, decisions, knowledge, and action remain aligned across the whole system.
That is why AI success is a governance problem.
And that is why we need to go beyond harness engineering.
]]>The pattern is familiar.
Executive vision. New tools. Pilot programs. A few enthusiastic champions. Some policy and governance overlays. Plenty of noise about transformation.
And yet, not much really changes.
Why?
Because most organisations are still treating AI like a conventional technology rollout.
That is the mistake.
AI is not just another platform to deploy. It changes how work is done, how decisions are supported, how knowledge moves, how judgment is exercised, and how capability is built.
If you use the old transformation playbook, you should expect the old transformation outcomes:
The problem is rarely lack of ambition.
And it is no longer lack of access to the technology.
The problem is that executive intent is still not being translated into changed operating reality.
If AI does not reshape workflows, decision rights, management practice, capability development, and governance at the point of work, it remains expensive experimentation.
That is the uncomfortable truth.
Many organisations do not have an AI strategy problem.
They have an execution and operating model problem.
And until that is addressed, more tools will just produce more theatre.
]]>Now it's your turn.
What have you done differently with AI?
That is the question many leaders should be asking themselves right now.
Most have already lived through the pattern once before. Big promises. New platforms. Change programs. Executive messaging. Adoption language. And then, somewhere between ambition and delivery, the whole thing thinned out into theatre, fragmentation, and underwhelming results.
Many leaders came away from that era with a quiet conviction: when my turn comes, I will do it better.
Now AI has arrived, and for many of those same leaders, it is their turn.
So what has actually changed?
If the response is still some version of tool rollout, pilot programs, innovation showcases, thin governance overlays, and vague expectations that the organisation will somehow adapt, then not much has changed at all.
And if the playbook has not changed, the outcome probably will not either.
AI does not fail only because the tools are immature or because people resist change.
It fails because executive intent still does not reliably become changed operating reality.
If workflows, decision rights, management practice, capability development, and governance at the point of work remain mostly untouched, then AI will remain expensive experimentation dressed up as transformation.
This is the uncomfortable part.
Many organisations do not need more AI enthusiasm.
They need to admit they are still using an old transformation logic for a different kind of challenge.
AI is not simply another technology deployment.
It is a test of whether an organisation can actually redesign work.
So the question is not whether you believe in AI.
The question is whether, now that it is your turn, you have done anything meaningfully differently.
]]>We are pleased to confirm that Based64 has successfully deployed the future.
The technology has landed.
The tooling is live.
The demos are strong.
The internal energy is high.
And across the organisation, people are now building apps at a remarkable pace.
This is how transformation looks.
Tom is hustling at Bubba Gump.
Tom and Gerry are still building the same apps in glorious isolation.
Felix has launched a promising pet grooming side venture.
And the bank remains on track to preserve its core pre-existing dysfunctions.
This confirms the rollout has achieved meaningful momentum.
The activity is visible.
The innovation theatre is thriving.
The screenshots are compelling.
As for whether any of it has materially changed how the business works, improved organisational capability, resolved structural issues, or connected effort to actual strategic need, that remains outside the scope of this certification.
That, unfortunately, is the joke.
A surprising number of AI rollouts now look like this.
The tools arrive.
A burst of activity follows.
People start building things.
The organisation feels modern for a moment.
But the work remains fragmented, incentives remain local, coordination remains weak, and the original business problems remain stubbornly alive.
In other words, the company has adopted the technology without changing the operating reality.
That is not transformation.
It is just a new layer of motion around the same old system.
If AI adoption mainly produces disconnected experimentation, side quests, duplicate app-building, and a fresh wave of innovation theatre, then the organisation has not become more capable.
It has just become more distractible.
This is the point many leaders still miss.
AI does not create value merely because people now have access to tools.
It creates value when the business changes, when work changes, when decision-making changes, when governance changes, and when organisational effort becomes more coherent rather than more scattered.
AI does not magically solve alignment.
It can, and will, amplify chaos if the organisational knowledge body is poor, fragmented, political, or stale.
So if you are seeing these kinds of results, it may be a sign that your governance needs some care and attention.
Until then, Based64 will continue to look very advanced.
Right up until the moment someone asks what actually got better.
]]>Any given framework would typically provide a quicks pathway to adoption as to aid framework adoption. Additionally, it would provide a level of educational opportunities that would enable users to achieve better understanding and provide the skill to tailor the Framework as needed.
Some of the reasons why frameworks fail:
Let's discuss these in some detail.
Aggregated nature of a framework means that it's an iceberg of knowledge, and it represents the cumulative knowledge it's author's as well as knowledge of giants on which shoulders those authors stood. These knowledge icebergs if not approached correctly, will seal your faith which is an unfortunate nature of the unknown.
Organisations are also icebergs of knowledge and represent the cumulative knowledge of all their participants, and the more brilliant they appear, the more knowledge and data-hungry they are. Organisations leverage a multitude of structural societal frameworks and employ humans to add value and help to deal with exceptions. This means that essentially organisation leverages societal structures to achieve an exception that would be mutually beneficial.
Let's explore these concepts further when a new employee joins an organisation they bring with them a wealth of experience and unique perspective that is mutually helpful when integrated. This integration occurs slowly, which is the primary reason for success, as both organisation and employee explore alignment between their knowledge structures and have time to adjust. The best outcome is when both employee and organisational knowledge structures are similar and align without exceptions.
Knowledge structure alignment exceptions occur when either of the structures does not align cleanly and in all cases employee would be in a position to decide their actions. An employee can either use their influence to alter the organisations or strive for other goals. Most mutually beneficial goals are when employees choose to influence the organisation as it provides growth not only to employee and organisation but also for other employees within the organisation.
The scope and incentives of the initiative by the employee mean that the alignment of changes would either be local or organisation-wide, and though this process moulding and enriching of both knowledge structures occur. Throughout this process, the employee plays the role of a change agent whose incentive is to ensure the Framework and their knowledge is tightly integrated into the organisation. This, in essence, provides a way for an employee to leave an imprint on the organisational knowledge structure.
When it comes to aligning frameworks to organisations, essentially the same process occurs. The biggest difference in alignment of a framework to an organisation comes from the scope of impact and implementation timelines. The scope of impact for Framework tends to always be organisation-wide, even if they are appear localised appreciation of the Framework and its purpose needs support from adjacent participants as in to support and encourage the change efforts.
To join an organisation and a framework as knowledge icebergs intimate of both has to occur to ensure close alignment, any areas that don't align would create tension and friction that would long term reverse the alignment and organisation would reject the Framework. This is not a negative outcome as it would allow the organisation to grow and understand what in fact does not work so that the organisation can align to something that does.
In the majority of situations, the rejection is done at a social level as frameworks tend to prescribe a particular operational method. Technical systems can cause failures in operation models, but those reasons explicit, structurally evident and have a degree of predictability. Failures that stem from social rejection are much harder to identify, evaluate and predict. Passive aversion towards change is a catalyst for the slow erosion of progress, its undetectable until its too late and even in the retrospective are hard to identify.
The structural systems of an organisation once they are established do not have an ability self-change, social layer, on the other hand, is in a constant state of change. Within an organisation, the social layer is the sole mechanisms for dealing with the unknown and adjusting organisational structures to fit. This means attempts to introduce any frameworks into an organisation would need tangible social reasons for all participants.
Typically in an organisation, maintaining information is a role dedicated to a set of specialised roles that act as eyes and ears for the organisation, these roles are typically the translators between groups of people. Job for the roles that do translation is to package the knowledge and information from one side and make it relevant to another side and vice versa. These roles are typically supported with specific tools and enable some collaboration and presentation of their content to the greater community. These tools are either formal modelling suites that require foundational training and specialisation to use or could be a collection of ad-hoc material compiled overtime to provide input in a social context.
Even when roles are formally incentivised to maintain quality of information and data, they are still dependant on the organisational capacity to produce quality data that can be used as-is without translation. In the absence of that those roles are left extending the knowledge gap by filling in the blank, this done from conceptual, logical, exception handling and social where its no new reusable information is created.
Extending incentives beyond central authority in organisations are not feasible as it distracts others from their core activities. Furthermore, any form of generating and maintaining non-social information silos outside of main control function becomes a form of side-hustle. It's it a hard job to manage information without the support, and it's just easier to create social process gates to avoid the hassle.
These and other factors place an organisation future at the mercy of social consequence, where its social selection of people that help along the journey defines its success. There is no quick solution; it means that organisations need to be very thorough when introducing frameworks into their organisations.
Organisations are like people; their ability to understand a framework depend on their historical experience and acquired knowledge, so "uploading" framework knowledge into an organisation does not work like in the Matrix. GXP will explore this "upload" notion and how this could be possible in organisations of the future.
]]>Existing patterns and frameworks focus on educating individual parts of the organisation in order to mature the organisation as a whole. Primarily this education efforts usually occur at a point in time where an organisation needs a boost to mature past current state. These education efforts are an oversight approach when applied and practised produces the desired outcome.
Fundamentally all organisations are socio-technical entities and at their very nature are very complicated at best. Maturing organisations is a process for developing the social and technical layers; this requires architecting and communicating of change across the organisation and its where the challenges start.
In organisations miscommunication is pathological, and it is prevalent in all types of socio-technical organisations startups, government and corporates. The primary reason for this is audience conflict, where teams loyalty at lower levels often erode its ability to deal with external ideas, other groups and its allegiance to the organisation as a whole.
These organisational communication issues further amplified by scale and velocity at which an organisation tries to move. In an organisation, geographic, corporate, and domain silos provide a ground on which miscommunication impeds communication and collaboration.
Socio-technical organisations need to promote empathy in an organisation that allows teams to respect each other's contributions as well as maintain the shared purpose of organisational goal. Organisations that enable teams to embrace the shared language and establish mutual regard towards other groups would develop organisational cohesion that will further reinforce organisational success.
Significant social issues in organisations that impact performance fall into the following categories:
One of the approaches to deal with social issues is to encourage cohesion across teams. For organisations to establish organisational cohesion, all participants need to feel that their contribution is valuable and equally important. Enabling valuable contribution can be achieved by providing tailored experience at the team level, in turn, enabling teams to achieve their goals while ensuring that all groups are working off the same shared knowledge. Establishing a shared knowledge base and providing tailored experiences for groups creates a common purpose, facilitates communication and enables cooperation, all of this enabling coordination across the organisation.
Organisations are socio-technical platforms that attempt to facilitate socially driven change to technical systems created by the evolution of that organisation. Coordinating social change has become difficult to achieve without shared knowledge, that can be adopted and tailored though experiences for individuals that make up those organisations. By doing this, organisations will provide a method that will ensure that everyone involved feels that their contribution is valued and is equally important.
Establishing a shared knowledge that whole organisation use as a base for decisions requires a new type of governance platform. A new governance platform that will provide a foundation for defining governance models so that they can be leveraged by AI to provide direction and insights while maintaining human social participation. This platform will provide a central core to each organisation and will support all interactions across organisations and will enable orchestration of organisational systems and will enhance social participation.
Organisational complexity creates a paradox of organisational deafness, where the sheer amount of noise drowns individual ability to get a message heard, let alone have that message make an impact. This primarily because organisations are unable to listen and engage with all inputs to that organisation. This further eventuates the paradox leads to organisations to adopt the path of less resistance approach though out its delivery.
Forces that bring about change to an organisation are both external and internal. External forces bring about changes that an organisation is unable to control, and it relies on its social ecosystem to predict and counter all external effects. Organisational internal social ecosystem translates external input and converts these into internalising actions that adjust organisational structures—the more efficient this process, the more effective the organisation as a whole.
Organisations internal social ecosystem efficiency is coupled with the effectiveness of that organisation; therefore, organisations that focus on the health of their social ecosystem would see positive outcomes for the organisation as a whole. The ability of an organisational social ecosystem to actively listen and synchronise changes needed to the organisation is largely rendered impractical due to the complexity of organisations. This leaves a minority of inputs that are leveraged for establishing a direction for change.
The ability of an organisation to actively listen and act on all inputs would provide a method to synchronise changes need to achieve effectiveness.
The case for a new governance platform is all too apparent across all industries and social divides. Oldways of doing things within organisations cannot keep up with breadth, depth and scale of new age demands. The complexity of organisational dependencies, social ways of working and technology ecosystems create a foundation for unpredictable delivery.
Governance Experience Platform (GXP) is a platform that enables the development of Organisational Experiences that leverage foundational knowledge and structures that self-reinforces its foundations. GXP is a platform that will provide the foundations on which organisations can create organisational experiences that harness the shared knowledge into a shared data structure that further reinforce organisations foundations.
Organisations need to be able to create experiences that are efficient, effective and personalised to an individual level, crossing domain, organisational and geographic boundaries. GXP focus is to provide a foundation for creating experiences that facilitate social and procedural governance patterns, that can demonstrate and elevate the contribution of each individual to the social organisation journey.
Humanity needs a way to share governance models from small businesses and communities to conglomerates and countries. Current methods of sharing governance models rest on ad hoc consumer-driven approach. The breadth of governance landscape prevents correlation between governance models at any level. GXP will provide a mechanism to share organisational experiences across boundaries without compromising shared knowledge of those organisations.
GXP will enable organisational governance and provide a pathway to establish trusted relationships with governments that provide stewardship in shared societal responsibilities with organisations. Establishing a non-invasive pathway for governments to contribute to organisational governance would facilitate cohesion between organisations without removing the free-market competition fundamentals that drive economies where those organisations operate.
]]>Any given framework would typically provide a quicks pathway to adoption as to aid framework adoption. Additionally, it would provide a level of educational opportunities that would enable users to achieve better understanding and provide the skill to tailor the Framework as needed.
Some of the reasons why frameworks fail:
Let's discuss these in some detail.
Aggregated nature of a framework means that it's an iceberg of knowledge, and it represents the cumulative knowledge it's author's as well as knowledge of giants on which shoulders those authors stood. These knowledge icebergs if not approached correctly, will seal your faith which is an unfortunate nature of the unknown.
Organisations are also icebergs of knowledge and represent the cumulative knowledge of all their participants, and the more brilliant they appear, the more knowledge and data-hungry they are. Organisations leverage a multitude of structural societal frameworks and employ humans to add value and help to deal with exceptions. This means that essentially organisation leverages societal structures to achieve an exception that would be mutually beneficial.
Let's explore these concepts further when a new employee joins an organisation they bring with them a wealth of experience and unique perspective that is mutually helpful when integrated. This integration occurs slowly, which is the primary reason for success, as both organisation and employee explore alignment between their knowledge structures and have time to adjust. The best outcome is when both employee and organisational knowledge structures are similar and align without exceptions.
Knowledge structure alignment exceptions occur when either of the structures does not align cleanly and in all cases employee would be in a position to decide their actions. An employee can either use their influence to alter the organisations or strive for other goals. Most mutually beneficial goals are when employees choose to influence the organisation as it provides growth not only to employee and organisation but also for other employees within the organisation.
The scope and incentives of the initiative by the employee mean that the alignment of changes would either be local or organisation-wide, and though this process moulding and enriching of both knowledge structures occur. Throughout this process, the employee plays the role of a change agent whose incentive is to ensure the Framework and their knowledge is tightly integrated into the organisation. This, in essence, provides a way for an employee to leave an imprint on the organisational knowledge structure.
When it comes to aligning frameworks to organisations, essentially the same process occurs. The biggest difference in alignment of a framework to an organisation comes from the scope of impact and implementation timelines. The scope of impact for Framework tends to always be organisation-wide, even if they are appear localised appreciation of the Framework and its purpose needs support from adjacent participants as in to support and encourage the change efforts.
To join an organisation and a framework as knowledge icebergs intimate of both has to occur to ensure close alignment, any areas that don't align would create tension and friction that would long term reverse the alignment and organisation would reject the Framework. This is not a negative outcome as it would allow the organisation to grow and understand what in fact does not work so that the organisation can align to something that does.
In the majority of situations, the rejection is done at a social level as frameworks tend to prescribe a particular operational method. Technical systems can cause failures in operation models, but those reasons explicit, structurally evident and have a degree of predictability. Failures that stem from social rejection are much harder to identify, evaluate and predict. Passive aversion towards change is a catalyst for the slow erosion of progress, its undetectable until its too late and even in the retrospective are hard to identify.
The structural systems of an organisation once they are established do not have an ability self-change, social layer, on the other hand, is in a constant state of change. Within an organisation, the social layer is the sole mechanisms for dealing with the unknown and adjusting organisational structures to fit. This means attempts to introduce any frameworks into an organisation would need tangible social reasons for all participants.
Typically in an organisation, maintaining information is a role dedicated to a set of specialised roles that act as eyes and ears for the organisation, these roles are typically the translators between groups of people. Job for the roles that do translation is to package the knowledge and information from one side and make it relevant to another side and vice versa. These roles are typically supported with specific tools and enable some collaboration and presentation of their content to the greater community. These tools are either formal modelling suites that require foundational training and specialisation to use or could be a collection of ad-hoc material compiled overtime to provide input in a social context.
Even when roles are formally incentivised to maintain quality of information and data, they are still dependant on the organisational capacity to produce quality data that can be used as-is without translation. In the absence of that those roles are left extending the knowledge gap by filling in the blank, this done from conceptual, logical, exception handling and social where its no new reusable information is created.
Extending incentives beyond central authority in organisations are not feasible as it distracts others from their core activities. Furthermore, any form of generating and maintaining non-social information silos outside of main control function becomes a form of side-hustle. It's it a hard job to manage information without the support, and it's just easier to create social process gates to avoid the hassle.
These and other factors place an organisation future at the mercy of social consequence, where its social selection of people that help along the journey defines its success. There is no quick solution; it means that organisations need to be very thorough when introducing frameworks into their organisations.
Organisations are like people; their ability to understand a framework depend on their historical experience and acquired knowledge, so "uploading" framework knowledge into an organisation does not work like in the Matrix. GXP will explore this "upload" notion and how this could be possible in organisations of the future.
]]>Copy-paste governance approach does not work. This approach refers to an attempt to replicate the governance approach from one context to another. In the organisational context, this means copying/mimicking a governance pattern of another organisation.
Organisational governance patterns are evolutionary; they are outcomes of micro transformations performed by the organisation over numbers of years. They are representative of the social and technical structures as well as the evolution journey of that organisation.
Organisational governance is the historical record of the organisation evolution; it a combination of persistence and maturity of each part of the organisation over time. Thus simply copying governance pattern of an organisation in time to another organisation does not produce the same results.
Organisations are complex socio-technical organisms, and applying externally conceived governance patterns would mean that all of the parts that make up organisation would need to go through a transformation on a maturity curve that was followed by the organisation from which governance pattern being copied.
Across all industries, organisations use oversight as the primary method of leadership and stewardship, and this approach is well defined by oversight governance. In summary oversight governance means that all of the corrective actions are applied post specific events; it's a reactive command and control approach. This approach works when the central command has ample experience to deal with issues as they arise. When things get too complicated, this approach leads to failure, in best cases, adding to the command experience and allowing a clean to restart.
Across all industries, the old way of helping organisations mature is not scaling; this is primarily due to the level of complexity that has evolved in social and technical layers of organisations. Tried and tested societal patterns for managing change are tested as socio-technical complexity increases. Existing models are complex and primarily used as an oversight stewardship approach, usually as a reaction to the organisation in need of help. The situation further complicated by barriers for knowledge sharing.
A better approach would be to provide a governance foundation as a form of a scaffold for new organisations so that as they evolve, they unlock and leverage parts of the framework that matches their maturity. This approach would provide a mechanism that would minimise routine transformations that are influence organisations of today.
Furthermore, this approach would provide a method of sharing and leveraging knowledge across organisations, as well as provide a central holistic knowledge area for contributors to focus their attention without creating unnecessary deviations. At the same time providing a platform for sharing patterns and maintaining the visibility of contribution by the authors.
]]>This site will aim to collate relevant knowledge that provides a foundation for developing governance frameworks.
As well as providing insight on available governance frameworks, a new concept's for governance will be explored.
Welcome to the journey!
]]>