Hello, my name is Mirko Peters and I translate how technology actually shapes business reality.
Optimization sounds like a smart, disciplined move that defines strong leadership,
but inside most organizations it is exactly how performance becomes slower and more political over time.
When you improve one specific part without redesigning the entire system,
you don't actually create flow and instead you just end up with local excellence
surrounded by massive organizational drag.
I want to make a very direct case in this episode because you cannot fix performance
by simply polishing the individual pieces.
True performance comes from designing decision flow, data movement,
human interaction and power as a single operating model.
And only after that structure is set, does AI actually become useful.
If this system's level view helps you navigate your work,
please subscribe as this episode closes out a much larger arc.
Let me take one step back to explain why optimization became the wrong goal for so many leaders.
The optimization trap, most leaders never wake up with a plan to fragment their company,
but they often fall into a trap by making moves that sound completely reasonable on the surface.
They might tighten up a governance policy or rollout co-pilot to a specific team
and then they build a power automate flow to remove some manual work
or standardize how files are named in SharePoint.
Every one of those moves looks like a win when you view it in isolation,
but that is exactly where the danger hides.
Local intelligence is not the same thing as total system performance
and from a system's perspective, optimization usually happens only at the edge
of what a leader can actually see.
A department head focuses on their own output,
while IT improves security controls,
and meanwhile, finance pushes for reporting discipline while HR focuses on policy compliance.
Each function gets much better at protecting its own internal logic,
but an organization is not just a collection of protected silos.
The real value exists in the flow between those functions,
and if that flow is badly designed,
then every optimization starts acting like structural interference.
One team might get faster only to hand their work off into a massive queue,
or perhaps one function produces cleaner data that nobody downstream actually trusts or understands.
We see platforms get better governed while decisions wait three extra days for approval,
and departments automate tasks while the actual problem still bounds between inboxes
because nobody redefined who owns the result.
The business feels heavier and slower even though every individual team is claiming a major improvement.
You have likely seen this in organizations with very mature Microsoft 365 environments
where everything looks perfect on paper.
They have clear governance docs, well-defined team structures,
and dashboards covering every possible metric,
yet execution still feels like waiting through deep mud.
Decisions take too long, and context gets lost constantly,
which leads to people rebuilding the same information in every meeting they attend.
Reporting numbers change depending on who exported the data,
and ownership becomes a blurry mess the second a project crosses a functional boundary.
This isn't a contradiction or mistake,
it is a clear design signal that the system is doing exactly what it was set up to do.
The problem is that the system wasn't set up for the outcomes that leadership actually wants to achieve.
This matters because most organizations still mistake visible maturity
for actual operational capability,
and they assume that if the tooling looks sophisticated,
the organization must be sophisticated too.
They believe that if governance exists, then control must exist,
and they assume that if they have automation, they must have efficiency.
None of that is automatically true because performance doesn't come from the quality of isolated components.
It comes from the alignment between those components,
but that reality is hard to see because local optimization provides immediate,
tangible proof of success.
A team can point to improved SLA numbers or a reduced manual workload,
and a project sponsor can show a 100% rollout completion rate to prove they did their job.
Those are real improvements, but they are still only partial truths
because the business reality sits one level above those metrics.
Performance lives in the questions we often forget to ask,
like how long it takes to turn a signal into a concrete action,
or how many times context has to be rebuilt.
Before a leader feels safe making a choice.
We have to look at how often people ask for permission because authority is unclear,
and we have to measure how much work is happening through side channels
because the formal route is simply too slow.
That is where the health of your organization lives,
and it isn't found in a dashboard showing a completed workflow.
It is found in whether that workflow actually helped the organization move forward.
This is exactly why AI is causing so much disappointment right now
because we hear constant praise for smart models and fast outputs
yet very little changes on the bottom line.
The models aren't weak, but the organizations receiving the output are fundamentally slow.
If an AI can generate a complex answer in three seconds
while your business still needs three days to validate context and root approvals,
then your constraint isn't a lack of intelligence.
Your constraint is architecture,
and this is where optimization becomes truly dangerous
because it gives you the illusion of progress
while protecting the very structures that create the drag.
You keep improving the parts and the parts keep getting stronger,
but the whole just keeps getting heavier.
Once you recognize that pattern, the real question changes from what should we improve?
To what kind of system are we actually running?
What an organization really is.
So let's answer that question directly.
What are we actually running when we say we run an organization?
Most people point to the obvious layer first,
which usually includes the org chart,
the departments, and the leadership team.
They look at the tech stack, the policies, the governance model,
and the set of tools everyone logs into every morning.
But that visible layer is not the organization.
It is just the packaging.
The organization itself is a flow architecture.
It is the way signals move, the way decisions move,
and the way data moves through the system.
It is how context travels between people and how authority shapes
what is allowed to happen when those flows collide.
That is what produces business reality.
When a leader says they have a performance issue
or an accountability problem,
I usually take one step back because those are rarely separate issues.
They are symptoms of the same design.
From a structural perspective,
an organization runs on at least three core flows.
First, we have decision flow.
This is the path from a signal to a judgment,
then to a commitment, and finally to action.
You have to ask who sees the issue first
and who has enough context to actually decide.
We need to know who must be consulted, who can say yes,
and who has the power to stop the move entirely.
The time a decision sits in uncertainty
before something happens, matters more than most
formal operating models admit.
Strategy is not executed through intent,
but through repeated decisions made under real conditions.
Second is data flow.
This is where truth lives and how it moves,
yet we often forget to ask
who gets to interpret it.
We need to identify the source where the data is changed
and who owns the definition before duplication begins.
Often, data gets exported into slides
because the system itself cannot carry trust,
which makes data flow a political topic
rather than a technical one.
Once multiple versions of truth exist,
people stop debating reality
and start defending positions,
and that is when reporting turns into a negotiation.
Third, there is interaction flow.
This is the part many organizations underestimate
because it gets pushed into the language of culture,
but structurally it is not soft at all.
Interaction flow determines how context travels between people,
where work gets clarified,
and where ambiguity gets reduced.
It is where decisions are discussed
and where history is stored,
so that handoffs either retain meaning
or lose it completely.
Coordination alone is not enough
and you can coordinate tasks perfectly
while still failing as an organization.
If the people inside the system
do not share enough context and trust,
then every handoff becomes reconstruction work.
That is why so many organizations feel busy
and informed while acting slowly.
There is a lot of communication happening,
but the context transfer is weak.
Beneath those three flows
sits a fourth force that shapes all of them.
Power.
This is where things become real
because formal process is one thing,
but actual authority is another.
Power determines who can override a process,
delay a decision,
or ignore the agreed root without any consequences.
If the org chart says one thing,
but everybody knows a different person really decides,
then the real operating model is the power map.
Behavior is a system outcome.
People are not acting randomly.
They are responding rationally
to the environment the organization creates.
If approvals are vague, people escalate early,
and if ownership is unclear, they add more meetings.
When truth is fragmented,
people build private spreadsheets,
and when power is hidden,
they spend more energy reading politics than reading process.
That is not a motivation failure.
It is a design outcome.
Once you see the organization this way,
a lot of confusion disappears.
You stop asking why smart people produce slow results
and start asking what kind of flow architecture
would make that result inevitable.
If decisions are delayed and data is disputed,
underperformance is not surprising.
It is predictable.
Most transformation work fails because it starts
at the visible layer with a new tool or a new policy.
The visible layer is not where organizational reality begins.
It is just where it becomes visible.
If we redesign only what can be seen
without fixing the underlying flows,
we just formalize the confusion.
Before we talk about better design,
we have to deal with the assumption
that governance can rescue a misdesigned organization.
Why governance doesn't save a misdesigned system?
Governance matters, but it is not an operating model.
It is a control framework.
It tells us what should be allowed,
what should be reviewed,
and what should be protected or stopped.
That has value in environments
with high compliance pressure or regulatory risk,
but governance cannot compensate for structural confusion.
If decision parts are unclear,
governance does not create clarity.
It just adds checkpoints to unclear movement.
If ownership is weak,
governance does not create accountability.
It just creates forms that move through the same ambiguity.
When data is fragmented,
governance creates rules around that fragmentation
instead of creating truth.
I see this constantly in Microsoft 365
and power platform environments.
A company realizes things feel messy
because there are too many teams, too many sites,
and too many apps.
The response is almost always more governance
like new templates, approval boards,
and naming standards.
While these are often necessary,
if the underlying work design stays broken,
governance just formalizes the friction.
Now, the people don't just have unclear flow.
They have unclear flow with paperwork.
Governance feels like control,
which gives leadership the sense of visible action.
There is a policy and a board,
so it looks like the organization is becoming more mature.
Sometimes it is, but often it is just becoming slower
in a more official way.
Real resilience is not the same thing as administrative weight.
Too much governance often creates default no behavior.
This doesn't happen because people are resistant,
but because the system gives them no safe path to say yes.
If authority is fuzzy, they escalate,
and if risk is undefined, they delay.
When the consequences of a wrong move are visible,
but the consequences of delay are invisible,
people protect themselves with caution.
Requests sit in loops,
exceptions multiply,
and meetings become the place where real decisions happen,
because the formal route is too brittle,
then shadow governance appears.
This is the unofficial operating layer
where a trusted person speeds things up
or a side-chat settles the real decision.
That is not an exception to the system.
It is the system compensating for its own flaws.
When shadow governance becomes the norm,
formal governance loses all credibility.
People stop seeing it as support
and start seeing it as drag.
Governance is only critical when it is attached to clear flow.
It should protect movement, not replace it,
and it should create trust rather than distributing hesitation.
Leaders need a standard of minimum viable friction.
You should place deliberate checkpoints
only where uncertainty or material risk are actually high.
If a decision is easy to reverse,
do not govern it like a merger,
and if the people closest to the work
hold the right context,
do not pull the decision upward.
From a system perspective,
that isn't maturity, it's latency.
The question is whether your governance
supports the real operating flow
or quietly competes with it.
When governance and flow are misaligned,
the organization pays twice.
Once in delay, and once again, in compensation.
Misdesign was always expensive,
but AI is making the bill impossible to ignore.
The research signal leaders shouldn't ignore.
Let's look at the signal.
At this point, we aren't just having a philosophical debate
about better organizational design
because the market is now producing hard evidence
that misdesigned companies simply cannot capture
the value of the technology they buy.
The clearest signal is that around 95%
of generative AI pilots fail to create
any measurable impact on the bottom line.
That number matters because it doesn't actually
say AI is weak,
but rather it shows that most organizations
are trying to accelerate work inside structures
that are physically unable to absorb that acceleration.
Most people here are failure rate like that
and assume the issue must be model quality,
poor prompting, or perhaps a lack of user adoption.
When you look more closely,
the pattern is much more structural
than a simple technical glitch.
Pilots happen and the demos look impressive,
but then the value stalls somewhere between
the output and the actual business action.
Teams get excited as use cases
multiply across the department.
Yet the hard part was never actually generating
an answer from the machine.
The real challenge is integrating that answer
into real workflows,
clear ownership, and established trust boundaries.
In other words, the limiting factor
isn't model intelligence, its organizational intelligence.
There's another signal here that leaders should take seriously,
which is that only a small minority of organizations
are actually scaling AI in a meaningful way.
We have broad enthusiasm at the edge of the company,
but we see very limited structural absorption at the core.
The bottleneck is not access, it is design.
You can see the same gap when you look at leadership readiness,
where CEOs and technology leaders aren't even looking
at the same reality.
A much smaller share of CEOs acknowledge integration
as the real challenge compared to IT leaders
who are much closer to the day-to-day constraints.
This gap matters because strategy gets announced at the top,
while the friction is experienced at the bottom
and the organization starts speaking two different languages.
Leadership says they are adopting AI,
but operation says they still can't move
a decision across three teams
without rebuilding the context twice.
While the board asks for more use cases,
the people inside the system are struggling
because they don't even have an agreement
on which data source is trusted.
This isn't a communication issue, it's a design split.
The most revealing part is that workflow redesign
keeps showing up as the strongest predictor
of whether a company captures AI value.
It isn't about tool access or the number of pilots you run,
yet redesign remains one of the least adopted moves in the playbook.
Redesign is slower to announce than a pilot
and it's much harder to package or celebrate
in a steering committee meeting.
It forces leadership to confront handoffs,
power dynamics and role ambiguity,
which is work that feels less glamorous
but is exactly where the value comes from.
Organizations that actually redesign specific workflows
are seeing dramatic gains in speed and productivity.
Training cycles are being cut from days to minutes
and decision cycles are being compressed
so that reporting happens in near real time.
These outcomes didn't come from sprinkling AI
on top of old ways of working,
but from redesigning the work
so the AI had a stable path to operate inside.
If you missed that sequence, AI starts acting like an amplifier
for your design debt.
It exposes fragmented truth and reveals approval drag
much faster than a human ever could.
It scales bad handoffs and makes power misalignment
more expensive because distorted decisions
can now propagate at machine speed.
When leaders ask why they aren't seeing value,
the honest answer is usually that the value is being blocked
by the organization itself.
The people aren't incapable and the technology isn't immature
but the operating model underneath is too fragmented
to convert intelligence into action.
AI is not creating the design problem,
it is simply revealing the problem that was already there.
Once you see that, the conversation changes
because the real constraint is no longer model speed,
its organizational speed.
And decision velocity is the real performance metric.
If organizational speed is the real constraint,
then we need a better performance metric
than just output or activity.
We need to look at decision velocity
which is the time between a meaningful signal appearing
and the organization actually acting on it.
I'm not talking about when the dashboard updated
or when the AI generated a recommendation
but when that signal was judged,
committed to and translated into a real business move.
That is the metric that matters now
because AI has changed one side of the equation completely.
The system can now generate predictions,
classifications and recommendations in seconds,
effectively compressing analysis and reducing search time.
If the organization still needs three meetings
to interpret that output and two more approvals
to validate ownership,
then the actual operating speed hasn't improved at all.
The model got faster,
but the organization stayed exactly where it was.
This is where a lot of executive reporting
becomes misleading for the business.
We measure model quality and prompt volume
and those numbers can all go up
while decision velocity stays almost completely unchanged.
The business feels stuck even though the dashboard says
there is progress and from a system perspective
that makes perfect sense.
Model performance and operational performance
are not the same thing
and a model can be fast and accurate
without the business ever moving an inch.
If the surrounding architecture cannot absorb
what the AI produces,
then the value remains trapped inside the recommendation layer.
Decision velocity is a much more honest metric
because it forces us to measure the full path from signal to action.
We have to look at where the path slows down
and it's usually in very familiar places
like approval chains or unclear ownership.
Teams often wait for alignment
because no one knows whether a decision is reversible
and these aren't just minor administrative issues.
They are velocity drains that have become more obvious
because the old gap between analysis and execution was smaller.
When humans produced analysis slowly,
the rest of the organization could pretend
the pace was normal but now the front end of the process has accelerated.
The rest of the system is now exposed
because AI gives you the answer quickly
which means every structural delay after that point becomes visible.
You can no longer hide weak design
behind slow information production
and that is why so many leaders feel this tension today.
The technology isn't just ahead of the business.
It is showing the business how slow its internal decision architecture really is.
Competitive advantage is shifting away
from simple access to information or scale of execution.
Once intelligence becomes cheaper and faster,
the advantage moves toward the organization
that can convert that intelligence into action with less delay.
This is a design issue rather than a tool issue
and the winner won't be the company with the most co-pilots.
The winner will be the company where the path
from knowing to doing is structurally clean and authority is clear.
In those organizations, context travels with the work
and not every decision gets dragged into the same heavy governance gravity.
Trust in the data is high enough
that teams don't have to renegotiate reality every time they try to move forward.
If I were looking at performance today,
I would ask how long it takes to act on a known issue
or how many handoffs happened before a commitment is made.
We need to know how often a recommendation stalls
because nobody knows who actually decides
and whether that delay is caused by real risk or just simple ambiguity.
That is the real operating picture
because once you understand decision velocity,
you stop treating slowness as a vague culture problem.
You see it for what it really is, a design problem.
Decision flow, design for clarity, speed and minimal friction.
Let's start with the concept of decision flow itself
because this is exactly where performance either compounds or gets trapped.
Most organizations operate under the assumption
that decisions happen where the org chart says they should,
but that is rarely the case in reality.
In a functioning system, decisions actually happen
where context, trust, risk and authority intersect in real time
and those four elements are not always found in the same office.
You might have a senior leader who holds the formal accountability on paper
but the real judgment often sits with the person closest to the daily work.
Sometimes the opposite happens where a team appears to be empowered
but every meaningful move still gets pulled upward
because nobody actually trusts the boundaries of that empowerment.
This gap between where a decision is supposed to happen
and where it actually lands is where institutional drag begins to take hold.
When we design for flow, our first job isn't to ask who needs to be kept in the loop
but rather to ask where the decision should actually live.
We have to identify who has the best context
and who carries the ultimate consequence of the choice.
We need to know who can act without creating downstream confusion
and we must distinguish between who needs visibility
and who actually needs control.
Most organizations blur those two things together
and treat visibility like a form of entitlement.
They assume that if someone is affected by a change,
that person must also approve it or if someone is senior,
they must be the one to decide.
There is a common belief that if something feels important,
it naturally needs more friction
but from a system perspective that isn't disciplined.
That is just delay logic.
A more resilient design starts by separating decisions
into different types based on their impact.
Some choices are reversible meaning you can test an idea,
learn from the result and adapt or recover if it fails.
These decisions should move as close to the point of work as possible,
moving fast with light structure and clear intent.
Other decisions are much harder to reverse
because they affect trust, regulatory exposure
or long term operating architecture.
These deserve more friction
but it shouldn't be the kind of vague friction
that slows things down for no reason.
We want deliberate friction or what I call "minimum viable friction"
which provides just enough structure to test assumptions
and scan for impact before making a commitment.
This doesn't require 10 people in a room
or three layers of signatures born out of a fear of being blamed later.
It can be as simple as asking
"what must be true for this to work?"
and "who is materially affected by the outcome?"
That is friction in service of quality
rather than friction in service of institutional anxiety.
The problem is that many organizations
put the heaviest friction in the wrong places
leaving routine decisions stuck in approval gravity
while strategic moves happen too fast.
Teams might spend days getting permission
for a low-risk action
yet they launch high-risk changes with unclear accountability
just because the project had executive energy behind it.
That isn't a framework for success.
It is just inconsistency at scale.
To improve this flow, you need to make a few specific design moves
starting with the clear definition of decision owners.
We aren't talking about committees
or vague leadership alignment
but rather one specific role
that carries the responsibility to make the call.
Input can be as broad as you want
but ownership must remain singular.
Next, you have to define contributor roles
separately from approval roles
to prevent every discussion from turning into a hidden veto process.
Many people need to inform a decision
but very few should actually be able to block it.
You also need to set explicit escalation thresholds
so people know exactly what stays local
and what moves upward.
Without these markers, people will escalate
based on fear or habit
and your organizational speed will collapse.
Finally, you have to remove performative approvals
that exist only to signal a sense of control.
Everyone knows these steps add very little judgment
but they stay in place because they make the system feel governed.
In reality, they just create traffic
without adding quality,
leading people to stop trusting the formal route entirely.
None of this is about removing human judgment
which actually becomes more important as AI
becomes more available in our workflows.
But that judgment has to be structurally placed
so the organization knows exactly where discernment lives
when the data is incomplete.
Who evaluates the edge cases
and who owns the call when trade-offs are required?
That is the heart of decision design.
Once you understand flow this way,
you realize that many organizations
don't actually have a decision problem
what they really have is a data design problem
because even with clear authority,
decisions slow down when nobody agrees
on where the truth lives.
Data flow, where truth lives, and how trust moves.
Let's go one layer deeper into the infrastructure
because even when decision rights are perfectly clear,
the system breaks down if the underlying data flow is weak.
This is where many leaders confuse simple data access
with actual data design.
They assume that if a person can open a report
or export a file,
then the truth is available to them
but being available is not the same thing as being trusted.
From a system perspective,
data flow is about much more than just storage or cloud capacity.
It involves the source, the movement,
the ownership, and the timing of information
as it travels through the organization.
We have to know where the data begins
and at what point it stops being operational truth
and starts becoming a local translation.
That last part is critical
because the moment teams start translating data
for their own use without shared definitions,
organizational trust starts to fracture.
It doesn't happen all at once,
but it happens quietly as sales uses one number,
while finance and operations use two others.
When leadership asks for a single report
and gets three different stories,
the problem is no longer about reporting quality.
It is about a total loss of coherence.
This fragmented truth creates massive amounts of rework
as people build their own extracts and spend meetings,
reconciling numbers manually.
They delay action because nobody wants to move on
disputed information
and the decision flow slows down
because the organization cannot establish
a shared reality.
This is where traditional governance usually enters the chat
with metadata policies, data catalogs,
and lineage diagrams.
These tools are all useful,
but only if they actually connect to the way people work.
Data governance without usable pathways
often creates a situation where the chart says
the data has an owner,
but the business still doesn't know who to call
when a metric changes.
When I talk about a line to data flow,
I am describing something much more operational and visible.
In a well-designed system,
core business objects like customers,
projects, or orders
have real accountability behind them.
Definitions stay stable enough
that teams don't have to renegotiate basic meanings every week.
And while everyone might see the data,
not everyone is allowed to redefine it.
Trust moves through legibility.
And if people cannot trace the path of a number,
they will compensate for that lack of clarity with skepticism.
In most corporate environments,
that skepticism turns into politics very quickly.
Now, if you map that reality to the rise of AI,
you'll see that these models don't fix contradictions.
They actually amplify them.
If your environment is already full of duplicate
truth sources and unstable labels,
a tool like Copilot will simply surface
those inconsistencies faster and at a much greater scale.
The model cannot create clarity
where the architecture has preserved ambiguity,
and it will only accelerate
whatever truth structure already exists.
This is exactly why so many AI outputs feel impressive,
but ultimately unreliable when used inside a large enterprise.
The issue is rarely the quality of the sentences
the AI generates,
but rather the trust substrate underneath those sentences.
We have to be able to verify the path
and ensure that permissions reflect real accountability.
These are data flow questions, not prompt engineering questions.
If you want better performance,
you have to stop treating data as a passive asset
and start treating it as an active operating layer.
Once truth becomes fragmented,
every other flow in the system gets weaker
and your best people end up carrying context in their heads
because the architecture can no longer carry it for them.
But even with clear truth,
people still need a way to move meaning together.
Interaction model, connection over coordination.
Now we come to a layer that many leaders still try to dismiss
with soft language.
They talk about communication, collaboration and culture,
or they focus on ways of working.
While those things are important,
if you look closely, you'll see this isn't just about culture.
It is an interaction model,
and interaction models are what actually produce execution outcomes.
Most organizations today are designed for coordination.
They make sure tasks can be assigned
and meetings can be scheduled,
while ensuring messages are sent and files are shared.
They build infrastructure so comments can be added
and approvals can be requested.
This coordination infrastructure matters,
but coordination is not the same thing as connection.
Coordination moves tasks, but connection moves meaning.
That is the real distinction.
A team can be highly coordinated
and still fail repeatedly because the people inside the flow
don't share enough context to make good decisions together.
They might know exactly what to do next,
but they don't know why it matters
or what changed upstream.
They are blind to the trade-offs that were made
or the assumptions the last team was working with.
The result is that every handoff becomes thinner than it should be.
Once those handoffs get thin,
the organization starts paying a hidden tax.
People find themselves re-explaning things
and opening extra meetings,
or they ask for status updates
that should have been unnecessary from the start.
They begin using chat as a repair mechanism
because the formal work surface no longer carries enough context
to get the job done.
That isn't the case of overcommunication.
It is structural compensation.
When you map that to how we work today,
you see collaboration environments
that are dense with activity but shallow on substance.
We have teams, messages, channels and loop components
along with emails, meeting notes and planet asks.
From the outside, the system looks connected
but often it is only transaction-rich.
There is a lot of movement without much continuity
because the system was designed to help people exchange units of work
rather than preserving the meaning around that work.
Context leaks out of the system constantly.
A decision happens in a call,
but the rationale stays trapped in one person's memory
while the file is stored somewhere else
and the action is tracked in a third location.
When someone new joins the thread three days later,
the whole context has to be rebuilt from scratch.
From a system perspective, that isn't a communication failure.
It's an architectural one.
The interaction model simply fail to carry enough context
across time, roles and platforms.
This becomes even more expensive in hybrid
and asynchronous work environments.
When people aren't sharing the same room
or the same informal cues,
interaction design has to become explicit.
You can no longer rely on hallway repair
or proximity to fix the gaps
and you can't assume someone will over here
a missing piece of information to correct the flow.
The system has to do more of that heavy lifting.
A designed interaction model answers practical questions
about where decisions are discussed and documented.
It defines where rational leaves
and where exceptions should surface
while clarifying what belongs in chat
and what must survive beyond it.
These questions sound operational because they are
and when they stay unanswered,
organizations drift into a very common pattern
of being highly responsive but poorly aligned.
Everyone is available and everyone is reacting,
yet the meaning of the work fragments
across a dozen different channels.
This is why some teams look collaborative
while producing unreliable results.
They aren't lacking effort, they are lacking connective tissue.
When I talk about connection over coordination,
I don't mean replacing structure with human warmth.
I mean designing interaction so that trust
and context can move alongside the work.
This usually requires fewer surfaces and clearer rules
along with a more deliberate placement of conversation.
Not every message deserves a meeting
and not every meeting deserves a channel.
The point is to reduce the number of places
where meaning can fall apart.
Once interaction quality improves,
execution reliability improves with it.
You see fewer rebuilds, fewer duplicate conversations
and much cleaner handoffs.
The meeting load drops and context becomes more durable.
For anyone responsible for systems,
this is where it becomes relevant.
Interaction quality isn't soft overhead.
It determines whether decisions hold
and whether teams can act without constantly repairing
the flow by hand.
Power alignment, the hidden operating system.
Power is the layer most organizations feel every day
but describe the least accurately.
They prefer to talk about process, governance
and accountability yet underneath those words,
power is what actually decides what moves.
It determines who can override a decision,
who can delay a project,
and who has the standing to redefine an issue entirely.
Power is the ability to ignore the agreed route
and still be seen as reasonable.
It's the capacity to force an extra review
or create a sense of urgency
and sometimes it's the ability to make something disappear
simply by not responding to it.
If you want to understand the real operating model
of an organization, don't look at the formal process map.
Instead, look at what happens when tension appears,
when a deadline slips or a customer escalates,
or when an AI output conflicts with someone's intuition.
You have to ask who wins in that moment.
Does the documented process win,
or does the person with enough influence
to bend it take control?
That answer tells you more about the organization
than any governance deck ever will.
Power is the hidden operating system
that determines which flows are real
and which ones are just decorative.
You can have a clean approval structure on paper
but if one senior stakeholder can bypass it with a single message
then the real system is influence based
rather than approval based.
You might have role clarity in a racey matrix
but if teams still wait for someone unofficial to bless a move
then authority is not sitting where accountability sits.
Authority is sitting where political safety sits
and that misalignment is incredibly expensive.
When power and responsibility drift apart
shadow governance begins to expand.
People stop asking what the right route is
and start asking who support they need
so the project doesn't get blocked later.
This shift changes behavior immediately.
Meetings get larger and emails get more careful
while decisions are delayed until enough
invisible alignment has happened behind the scenes.
Ownership becomes symbolic because the person
named as the owner isn't the real decider.
From a system perspective this isn't just frustrating
it is a structural defect.
Access should map to responsibility
and authority should map to accountability.
Influence should not be allowed to quietly replace ownership
without being named.
Once hidden influence becomes stronger than formal design
clarity collapses.
This gets much worse in platform environments
like Microsoft 365 or Power Platform.
In these systems access isn't a side issue
it is executable power.
The system is defined by who can see data
who can automate a flow and who has the right
to approve or trigger an action.
These are power questions expressed through digital permissions.
When permissions and accountability are misaligned
the digital environment starts reflecting
that organizational distortion.
The wrong people store the work
while the right people find they cannot act.
Sensitive decisions begin to travel through side channels
an automation gets built by those with access
rather than those with responsibility.
Leaders then wonder why the environment feels fragmented
but the reason is that the power model was fragmented first.
This is also why AI raises the stakes so significantly.
When power is unclear in a manual system
the damage is usually slow and localized.
However when AI and automation enter the picture
scale multiplies the distortion.
A weak approval logic becomes a scaled approval logic
and an unclear ownership boundary
becomes a machine assisted ambiguity generator.
AI doesn't remove power issues.
It just makes their cost visible much faster.
Power alignment has to be treated as design work
rather than an uncomfortable cultural topic we avoid.
It is political but it is also architectural.
We have to be clear about who has authority,
who has access and who carries the consequence of a failure.
If those answers are hidden performance will always be unstable
because people will optimize for survival instead of flow.
They will escalate early and copy everyone on emails
or they will delay commitment and build private buffers
to protect themselves.
This isn't a people problem, it is a system outcome.
The goal isn't to eliminate power which is a fantasy but to align it.
We need to make it visible where authority really sits
and match permissions to accountable roles.
By removing the gap between formal ownership
and actual control we reduce single points of failure.
Once power alignment improves the organization
becomes easier to trust and that trust reduces drag
across every other part of the system.
Now let's map that to what leaders often call transformation.
Why most transformation programs stall?
Now let's map those system principles to what leaders usually call transformation.
Most of these programs stall for one simple reason.
They try to swap out the tools without changing
the operating logic those tools are entering.
You can have a rollout that looks successful on a spreadsheet
while producing almost zero change in your actual business reality.
The platform is live and the licenses are assigned
but the organization still feels exactly the same as it did before the investment.
Training sessions happen and governance boards meet yet
the needle doesn't move because deployment is being mistaken for true redesign.
A new tool doesn't magically become a new operating model
just because you made it available to everyone on the payroll.
Copilot doesn't change how decisions move through your hierarchy
and power automate won't fix a culture of unclear ownership.
A modern internet cannot repair a fragmented truth
just as a collaboration platform
won't automatically create better human interaction.
These tools simply give your existing organization a new surface
to express the same old design,
sometimes faster and occasionally with better branding
but it is still the same design.
This is why so many transformation efforts end up increasing visible activity
while leaving the structural friction completely untouched.
We see a few specific patterns behind this failure.
Starting with pilot logic that lacks any real architectural follow-through.
A small team proves a use case where reporting gets faster
or onboarding gets smoother and suddenly everyone in leadership gets excited
but that pilot usually sits inside a protected pocket of clarity
that doesn't exist in the wider messier business.
Once that solution needs to cross functions or handle shared data dependencies
the gains collapse because the surrounding organization was never redesigned
to absorb the change.
The second pattern involves process mapping
based on intended workflows instead of lived behavior
which is a massive trap for most architects.
Organizations love to document how work is supposed to happen
by defining which roles have myths in which system updates the record.
It looks clean on a slide but the real work often happens in the shadows of that diagram.
The exceptions get handled in a quick chat,
missing context gets rebuilt in an unscheduled meeting
and the actual decision waits for someone who isn't even shown on the official map.
When transformation is built around procedural fiction,
reality eventually pushes back and leaders often mistake that friction for employee resistance.
Usually it isn't resistance at all, it is simply the system refusing to behave like a map
that doesn't match the terrain.
The third pattern is about incentives and this matters more than many leaders are willing to admit
during a board meeting.
If your organization still rewards effort and compliance theatre over actual results
then your transformation will naturally optimize for visible busyness.
People will attend every training and generate every required dashboard
yet they won't actually improve the business outcomes
because the reward structure is attached to participation rather than performance
the system learns to appear transformed without actually becoming more capable.
Finally, a fourth pattern emerges where exception handling is completely ignored
during the design phase.
The standard happy path is designed beautifully
but real organizations don't run on standard paths
they run on variation and judgment calls made under pressure.
If your transformation only accounts for the perfect scenario
the very first missing data point will push people back into manual coordination
and political fallbacks.
Once that happens often enough trust in the new model disappears
and people return to their old survival habits
like side channels and informal approvals.
The transformation technically exists on the IT roadmap
but the real operating model stays exactly where it was 10 years ago.
This is why stalled transformations
leave a footprint of more dashboards and more meetings
but significantly less clarity and less trust.
From a system perspective that isn't a failure of adoption
it is a case of misaligned redesign.
The organization changed the layer people could see
while leaving the underlying flow architecture mostly untouched
if we want to understand what real transformation looks like
we have to stop looking at launch plans
and start looking at live behavior.
The most useful work doesn't happen during implementation
it happens during observation.
From fragmented to designed before the redesign
let's make this concrete because these concepts can sound abstract
until you see them playing out inside a real environment.
I'm thinking of one specific organization
that looked incredibly mature from the outside
they had a strong Microsoft 365 footprint,
clear investment in governance,
and a power platform environment that was already in active use.
They had documented processes and security controls
representing a serious effort from both IT and business leaders
to create a sense of order.
No one would have described this company as careless
and if anything they looked more disciplined than most of their competitors.
This wasn't a story about chaos.
It was a story about fragmentation,
wearing the expensive clothes of corporate maturity.
The people inside the system were highly competent
their platform choices were reasonable
and their intent was genuinely good.
Despite all of that,
the business kept feeling slower than it should have
and that slowness wasn't happening in one dramatic place.
It was happening everywhere in small ways
that added up like a decision pausing
because a team lacked the context that was never carried forward.
A report would circulate through the office
then immediately lose credibility
because the receiving group used a different source
and a different definition.
An automation might remove one manual step
but the exceptions it created still had nowhere clean to go
within the existing structure.
Ownership looked clear inside a specific department
yet it dissolved the moment
where across the boundary into another territory
the system started showing up in the language people used
with constant complaints about too many meetings
and repeated escalations.
Everyone could see the friction
but they were naming the problem
based on the specific angle they happened to own.
When organizations are fragmented,
every function tends to diagnose the issue locally
rather than looking at the whole system.
IT says the issue is uncontrolled growth
while operations claims the problem is a lack of process discipline.
Business teams complain about slow support
leaders point to a lack of accountability
and the users just say the tools are too complex.
All of those observations can be true
at the same time while still missing
the structural reality of the situation.
In this case the dominant explanation from leadership
was adoption assuming that if people just use the tools better
things would improve.
They believe that more consistency
or another automated workflow
would finally fix the underlying drag on performance.
That logic is common because it protects the basic architecture by
assuming the design is right and the people are the problem.
When you looked at the actual business reality
that explanation didn't hold up
because the environment had already been optimized
with more controls and more structure.
The gains were strangely local
meaning teams got better at managing their own silos
but the organization didn't get better at moving as a whole.
Local optimization had increased administrative sophistication
without producing any kind of integrated performance.
The tech stack looked mature
but the operating model stayed fragmented
and once that happens, the people inside the system begin compensating.
They carry context manually and remember what the process
forgot building side channels just to preserve some sense of momentum.
They join extra meetings because the formal flow
no longer gives them enough confidence to act without checking three times.
The organization keeps functioning
but it does so at a massive cost because the tools didn't fail.
The design did.
The people were acting as human middleware
between misaligned parts which is the phrase I keep coming back to
when I see these systems.
Human middleware consists of highly capable people
stitching together a system that looks complete on paper
but cannot carry authority on its own.
From the outside, leadership saw a mature digital workplace.
But from the inside, the people doing the work
were still bridging gaps by hand.
That is the before state we have to understand
if we want to build something better.
It isn't dysfunction in the obvious sense
it is a well-intentioned environment that has become structurally heavy
without becoming structurally clear.
Once that became visible to us,
the work stopped being about implementation
and became an observation of how the system actually lived
from fragmented to designed seeing reality.
The next step in this journey
wasn't about another software rollout,
a new set of governance documents
or a loud internal adoption campaign
instead the real work started when we decided to look at
what was actually happening on the ground,
which sounds simple until you realize
how many official stories organizations tell themselves
about how work moves.
In the official version, a request enters the system
and approval happens at a specific desk
and the record gets updated before the handoff is complete
and the workflow closes.
It looks clean, rational and perfectly presentable
on a slide deck,
but formal process maps usually describe
how leadership intends for things to work
rather than how they are actually executed
by the people doing the job.
So instead of asking how the process was supposed to function,
we started asking a much more direct question,
what actually happens from the moment work
enters the system to the moment something real gets done?
That shift in perspective changed everything
because once we started tracing actual human behavior,
the hidden architecture of the organization
became visible almost immediately.
We discovered that decisions were not really waiting
where the documentation said they were
but were instead stuck in places
where context had to be rebuilt from scratch.
A team would receive a request without enough rationale
so they would pause, ask clarifying questions,
schedule a call and recreate the missing history
before they could finally decide.
On paper, this looked like a short and simple approval step,
but in reality, it was a massive context recovery loop
that drained time and energy from everyone involved.
That distinction matters because the delay wasn't caused
by a lack of will or a slow employee
but was a direct result of the flow design itself.
We saw the same pattern in the data
where reports looked stable until they crossed team boundaries
and people started checking the numbers against their own local extracts.
Different teams were using different versions of the same business objects
not because they wanted fragmentation
but because the system gave them no single operational path
they could actually trust under pressure.
To survive, they built compensations
like manual exports, private trackers and reference files
which meant that what looked like wasteful duplication
was actually a necessary trust workaround.
Then we found the hand of dead zones,
those places where work was technically transferred
but structurally abandoned because no one person clearly owned the next move.
Everyone was included in the email chain
but no one was truly accountable
so the item sat in a procedural fog
until someone with enough seniority or memory eventually pushed it forward.
From the outside, this looked like slow execution
but from the inside, it was an ownership vacuum
that forced people to work harder just to keep things moving.
We also found the same approval logic appearing multiple times
across a single flow where different people were asked to validate
slightly different versions of the same thing.
This didn't happen because each review added new judgment
but because nobody had enough confidence in the upstream decision
to let the work travel cleanly without checking it again.
Approval had become a form of structural reassurance
and reassurance is incredibly expensive
especially when the organization pretends it is a form of control
what made this phase of the project important
was the total removal of blame
which was critical because if you look for underperformance
at the individual level, you will miss the systemic pattern every time.
The people inside the system were not the problem
in many cases they were the only reason the system still functioned at all.
They were remembering the context the tools failed to preserve
translating between teams with different definitions
and spotting exceptions before the workflows could break.
They were using meetings, chats and personal relationships
as a form of structural compensation
which isn't inefficiency in the human sense
but rather a form of resilience at the human layer
because the design layer is incomplete.
This matters for leaders because once you see reality clearly
the narrative changes from why are people creating workarounds
to what kind of design makes those workarounds necessary for basic continuity.
Once that question is visible
redesign stops being a theoretical exercise
and you can finally point to the exact spots
where context collapses, trust breaks, and ownership disappears.
You see where power quietly overrides the process
and where the people inside the system are keeping the whole thing alive by hand.
That was the turning point
because once reality became visible
nobody needed another maturity story
they needed a redesign story that moved from describing symptoms
to changing the structure.
From fragmented to designed, what changed?
Once the reality of the system was visible
the work didn't actually get bigger but it did become much sharper.
This is an important distinction
because many organizations respond to these moments
by launching a massive transformation machine
full of work streams, steering groups
and complex change roadmaps.
In our case the useful move was the exact opposite
focusing on reducing the number of moving parts
and tightening the architecture to make the real path easier to see and carry.
The first major change was in the decision parts
where we stopped treating every decision as if it had the same weight and impact.
We separated decisions by type, moving reversible choices closer to the actual work
with clearer owners and fewer approval points.
A reversible or high impact decisions kept more friction
but it became a deliberate form of friction
designed to test assumptions
rather than a habit inherited from old processes.
This alone reduced the surprising amount of drag
because people no longer had to guess whether they were allowed to move.
They knew exactly where they stood
and where to escalate if they couldn't move alone.
The second shift focused on data ownership
where instead of trying to solve trust issues
with more reporting, the organization started aligning ownership
around core business objects.
This changed the conversation from who built this report
to who owns the meaning of this number
and which source is the operational truth.
Once those answers were clear the duplication started falling away
not because we banned spreadsheets
but because fewer people needed private reconciliations just to feel safe enough to act.
That is the fundamental difference between control and design.
One punishes the work around
while the other removes the reason for the work around to exist in the first place.
Then we looked at the interaction layer
which wasn't about telling people to communicate better
but about being explicit about where context actually belongs.
We defined what gets decided in meetings versus what stays in chat
and we ensured that rationale lived in a place
where someone entering the project late
could recover the state of the work without reopening the whole discussion.
This redesign sounds small until you see the effect
because once context starts traveling with the work
the coordination load on the team drops almost immediately.
There are fewer repair meetings, fewer duplicated explanations
and fewer moments where progress depends entirely on the memory of the most informed person in the room.
We then connected this to permissions and access
treating it as an operating model decision rather than just a simple admin task.
We mapped access directly to accountable roles
asking who should be able to trigger a flow, who should approve
and who should see sensitive context based on their actual responsibility.
This mattered because once access reflects responsibility,
the platform becomes structurally cleaner
and people stop improvising around blocked paths or overpowered roles that shouldn't exist.
We also removed a lot of overlapping procedural language,
cutting out parallel routes and duplicate controls
that were only there to compensate for a lack of trust.
This made the operating model easier to read
and readability is vital because if people cannot understand the path
they cannot execute it with any level of confidence.
The redesign didn't add sophistication,
it removed ambiguity which is why the result felt different
very quickly without being more digital or more governed.
It was simply more coherent, allowing the existing tools
like Microsoft 365 and the Power Platform to support a cleaner flow
instead of competing with one another for attention.
From a system perspective, that is the only change that matters.
Finding a better fit between the flow of decisions,
the flow of data and the rules of interaction.
Once that fit improved, the people inside the system no longer had to keep the whole thing alive
through constant structural compensation.
That is the moment when the outcomes finally started to change
because the system was finally designed to sustain the work
rather than drain the people doing it.
So, from fragmented to design, what improved?
So, what actually got better?
It wasn't a magical overnight transformation
and it certainly didn't happen because the leadership team
stumbled upon some perfect management framework.
The real shift was much more fundamental.
What improved was the direct relationship between effort and outcome,
which is always the first sign that the system is finally being designed correctly.
Before we started the redesign, people were working themselves to the bone
just to keep things moving at all.
After the changes took hold, that same level of energy
started producing much cleaner progress.
Instead of wasting hours on constant translation,
endless reassurance and fixing broken handoffs,
the team could finally focus on actually moving the business forward.
The first thing to speed up was decision making.
This didn't happen because everyone suddenly became more courageous.
It happened because the system stopped treating every single choice like a high-stakes crisis.
Teams started acting within much clearer boundaries.
Escalation points became obvious
and fewer decisions sat rotting in hidden cues while waiting for an unofficial blessing.
When that latency drops, the entire rhythm of the company changes.
And when latency drops, something else usually follows.
Escalation volume starts to plummet.
That might sound like a minor detail, but it's a massive structural win.
Constant escalation is almost always a symptom of a deeper problem,
signaling that local authority is weak or that people simply don't trust the path they're on.
Once the flow of decisions became clear, fewer issues had to be kicked upstairs
just to gain a sense of legitimacy.
Ownership also became something people could actually use.
I'm not talking about ownership as a slogan or a line in a job description.
I mean, usable ownership.
Most organizations already have plenty of racey models
and governance charters gathering digital dust,
but usable ownership means every person knows exactly who decides,
who contributes and who deals with the consequences when things change.
That clarity lowers the coordination load immediately.
We saw fewer check-ins just to confirm who was responsible,
fewer reply-all messages sent as professional protection,
and far fewer situations where everyone was involved,
but nobody was truly accountable.
Then we saw the data effect.
Once the core business objects had clear owners and fewer conflicting parts,
the quality of reporting improved in a very practical way.
It wasn't that every metric suddenly became perfect,
but teams finally stopped arguing about which version of reality was the right one.
Reconciliation efforts dropped reporting disputes vanished
and downstream action improved because trust arrived much faster than before.
This is easily one of the most overlooked gains in any redesign project.
Better data trust isn't just a win for the analytics team.
It completely changes your operational confidence.
People move significantly faster when they no longer have to renegotiate the basic facts
before they can even start responding to them.
The way people interacted also got a lot lighter.
I don't mean softer or more polite.
I mean, the actual weight of collaboration decreased.
We had fewer repair meetings because the context of a project was actually surviving the handoff
from one person to the next.
Someone entering a decision late could catch up on the logic
without forcing everyone to restart the entire conversation from scratch.
Chad stopped being a place where people went to recover lost information
and became a useful support surface instead.
Documentation finally started carrying the continuity
that used to live exclusively inside the heads of a few reliable veterans.
That shift reduced a massive amount of dependency risk,
and why does that matter so much?
Because concentrated memories are a single point of failure.
If your execution depends on the same three informal translators or context holders
every time we're crosses a department line, your organization isn't resilient.
It's just being held together by a few overloaded humans.
Better interaction design creates redundancy.
And redundancy is exactly what gives the system its structural resilience.
Now map all of that to AI.
This is where the improvement became strategically visible for the board.
AI outcomes improved not because we bought better models
because the organization underneath finally stopped fighting the automation.
The outputs had clear roots into action.
Permissions were aligned with real roles
and the trust in source data was finally there.
Human judgments stayed in the loop where it actually mattered
rather than being used to manually fix data errors.
AI stopped behaving like a chaos multiplier
and started acting like a genuine accelerator
that is a very different role for technology to play
and is the biggest lesson from this entire case.
They didn't get better results by stacking more tools on top of a mess.
They got better results by redesigning how the work actually moved through the pipes.
This is the part I think leaders really need to sit with for a moment.
Most organizations are still trying to buy performance through isolated, shiny improvements.
Another dashboard, another automation, or another governance layer.
But in a complex environment, performance doesn't come from what you stack on top.
It comes from how you design the flow underneath.
Once that flow is clean, speed improves naturally.
Clarity and trust follow right behind it.
Only then does your technology finally have something stable enough to actually accelerate.
To make this useful for you, we need to turn this story into a practical model
you can apply to your own system.
Step one, see reality.
If you want to apply this to your own organization,
the first step isn't actually redesign.
It's its reality.
I start there because most redesign work is doomed before it even begins.
It fails because it starts from a place of aspiration
rather than honest observation.
Leaders will describe the intended process.
Teams will point to the approved workflow
and platform owners will show you the configured environment.
All of those descriptions can be technically true on paper
while having absolutely nothing to do with how work actually moves.
The first discipline of a systems thinker is to stop asking what the process should be
and start asking what the organization is actually running right now.
That means you have to follow the work itself.
Don't look at the slide deck, the policy manual, or the architecture diagram.
Just look at the work.
Pick one high friction flow that has a visible impact on the business.
Maybe it's a customer escalation, a budget approval, or a cross-functional change request.
It needs to be something painful enough that people feel it every day
and important enough that leadership can't just dismiss it as noise.
Then you trace it from end to end.
Where does the work actually begin?
And who is the first person to touch it?
You need to see what information arrives with that task and what gets lost along the way.
Find out where the work pauses and where someone has to rebuild the context
from their own memory or a private chat thread.
You are looking for the exact spot where the formal route ends
and the unofficial shadow route begins.
This level of observation is mandatory
because system truth lives in the gap between the documented path and the lived path.
And here is a critical rule.
Do this without an ounce of blame.
If you find people relying on side channels, messy spreadsheets,
or undocumented workarounds, do not treat that as a personal failure,
treat it as evidence.
Those workarounds are telling you that the formal system wasn't strong enough to carry the work to the finished line.
The workaround isn't the problem.
It's a signal and a predictable system outcome.
As you look, a few things will become visible very quickly.
You'll see the real decision paths, not who's on the chart,
but who the work is actually waiting for.
You'll see the real collaboration routes,
which usually happen wherever the time pressure is highest.
You'll discover the real data dependencies by seeing which numbers people actually trust
enough to put their names on.
This is also where you see how exceptions are handled.
Most flows look fine until something unusual happens,
like missing information or conflicting sense of ownership.
That is where the real design of your company reveals itself.
I would map this out in the planist terms possible.
Ask yourself where the item stopped and why it had to stop.
Identify who had to get involved, who wasn't part of the formal plan.
Figure out which system held the context and which one dropped it.
Most importantly, look for where people switched from following a process to relying on personal trust.
When a flow depends on specific people to translate or manually reconnect the pieces,
you aren't looking at a stable operating model.
You are looking at human compensation for a broken architecture.
This is why observation is a thousand times more valuable than broad documentation at this stage.
You aren't trying to create a massive inventory of every process in the company.
You are trying to establish enough operational truth to say,
"This is how work really moves, this is where the friction lives,
and this is where our design and our reality diverge."
Once you can see that, the entire conversation changes.
You stop arguing about adoption in the abstract
and you stop assuming that another software rollout will fix the underlying rod.
You stop coaching people to perform better inside a structure that was literally designed to make them fail.
Now you have a visible pattern.
Friction stops being emotional language and starts being structural evidence.
That is the move you have to make, because if you cannot see the real path,
you cannot redesign the real system.
You will only ever optimize the official story
and official stories are usually where performance goes to hide.
Step 2, identify friction.
Once you've made the reality of your workflow visible,
the next step is to identify friction.
But we have to do this the right way.
I'm not talking about friction as a general mood or a vague complaint
that people are tired and overwhelmed.
While those feelings are usually true, they are symptoms of a deeper structural issue
rather than the root cause itself.
From a system's design perspective, friction is much more precise than a bad vibe.
It is the measurable resistance inside a flow that slows down movement,
increases the effort required for simple tasks
and forces people to manually compensate just to keep the wheels turning.
This level of precision matters because if you mislabel every problem
as resistance to change or skill gaps,
you end up coaching your people to work around fundamental design flaws.
That is one of the most expensive mistakes I see leaders make
and it's a massive drain on organizational energy.
So what does this friction actually look like when you're standing inside an organization?
It usually shows up in a few repeatable patterns that are easy to spot
once you know what to look for.
You'll see delays where works it's untouched, duplicate efforts across different teams
and constant waiting states that kill momentum.
You might notice people constantly reconstructing context
or performing repeated validations
because nobody trusts the data they were handed.
These are the footprints of a broken system.
A request enters the queue and then waits two days before anyone even looks at it
or a report gets rebuilt three times by three different teams.
Using slightly different definitions, you might see meetings that exist solely
to restore a shared understanding that should have traveled with the work in the first place.
Sometimes an extra approval is added simply because one group doesn't trust the
previous group's judgment and that is a clear signal of structural friction.
Now I want to be clear, not all friction is bad.
That distinction is vital for building a resilient system
because some resistance actually serves a purpose.
If a decision is hard to reverse or carries a high level of material risk,
you actually want a bit of resistance there to prevent a single point of failure.
This is where the concept of "minimum viable" friction becomes a tool for quality.
A short assumption check or a visible judgment statement can improve the final outcome
without completely collapsing your momentum.
But destructive friction is a different animal entirely.
It adds significant cost to the process without adding any real confidence to the result.
It slows down routine work, multiplies unnecessary handoffs
and encourages political safety behaviors where people ask for more meetings
just to cover their tracks.
The structure gets weaker, so people compensate by adding more eyes and more layers
which only makes the system more fragile over time.
Your job as a leader is to separate the useful friction from the destructive drag.
The easiest way to do that is to ask one simple question.
What is this specific step protecting us from?
If the answer is clear, specific and proportionate to the risk,
the friction might be doing useful work for the organization.
But if the answer is vague, historical or purely political,
then you are looking at pure drag that needs to be removed.
This is where redesigning your business reality starts to get practical.
You stop saying this process feels heavy and start saying
this step duplicates a review we already did.
You can point to an approval that adds no new judgment or a cue
that only exists because ownership of the task is unclear.
That level of specificity changes the entire conversation
from a complaint into a technical problem we can actually solve.
It also gives you concrete signals that you can measure over time.
Cycle time is a big one.
Not how long the formal manual says a task should take
but how long it actually takes from entry to action in the real world.
You should also watch for overwrite frequency, which is how often
the formal process gets bypassed because someone with enough
influence forces are faster route.
If the official design is constantly being ignored,
it's a sign the system isn't trusted.
Support load and exception volume are also critical indicators of friction.
If people are repeatedly asking for manual intervention
or if the work constantly falls off the standard path,
you don't have a people issue, you have a flow issue.
The process isn't stable enough to automate
because it relies on human rescue to function.
When you see a handful of overpowered admins or one manager
who has to bless every move, you found a bottleneck disguised as control.
Friction isn't some abstract concept.
It leaves a physical footprint in your rework, your escalations,
and the volume of compensating behavior your team needs just to survive the week.
Once you can see that footprint clearly,
the pain stops being a mystery.
You can finally stop trying to motivate people to survive a broken system
and start redesigning the flow, so the system actually works for them.
Step 3 - redesign flows.
Once you've identified where the friction lives, your next move is redesign.
I want to be very specific here.
I'm talking about redesign, not optimization.
Optimization assumes the current path is basically correct
and just needs to run a little bit faster,
but redesign asks if this is even the right path to achieve the outcome we want.
Many organizations avoid this question
because it feels disruptive to the status quo.
They would much rather tune a few handoffs,
add a fancy new automation, or tighten an approval process,
then rethink the entire structure.
But if your flow is carrying too many transfers and too much ambiguity,
then all you're doing is making a weak root slightly more efficient.
From a system's perspective, that isn't an improvement.
It's just structural compensation with better branding.
Redesign starts by aggressively simplifying the root.
You want fewer handoffs, fewer decision points,
and fewer places where the context of the work can fall apart.
Every time work changes hands, you aren't just transferring a task.
You are transferring meaning, responsibility, and timing.
If those three things don't move together perfectly,
the next person in line inherits confusion.
And they slow down because the architecture handed them on certainty instead of clarity.
That's why your first redesign question should always be about the minimum.
Ask yourself, what is the minimum number of transitions required for this work
to reach a reliable outcome?
Don't worry about the maximum number of stakeholders who might want visibility.
Focus on the leanest path to the result.
That shift in perspective changes everything about how you build your infrastructure.
The next principle is to place context as close to the action as possible.
If a decision maker has to hunt through Slack, old emails,
and various dashboards just to understand a request,
your flow is badly designed.
Context should arrive with the work.
The history, the assumptions, and the rationale should all be right there.
This reduces the coordination tax that drains so much productivity in modern business environments.
You also have to stop designing only for the happy path where everything goes perfectly.
Most process efforts fail because they build elegant models for ideal conditions
and then break the moment reality hits.
Real work is messy and full of exceptions,
so if your redesign cannot absorb variation,
it's just a pretty diagram, not a functional system.
Exceptional handling, knowing exactly where a case goes when data is missing or rules conflict
must be part of the architecture from day one.
In my experience, a good redesign should actually create fewer pathways rather than more.
When organizations face complexity,
they often react by layering on more local workarounds and special branches for every edge case.
Very quickly the system becomes unreadable and unreadable systems are impossible to scale
because nobody knows which path is real under pressure.
The better move is to create one clear primary path
and a small number of explicit exception routes that everyone can see.
Finally, you must design for redundancy where it actually matters for the business.
If only one person can unblock a critical flow,
or only one team understands how a specific route works,
you've built a single point of failure.
Redesign is about removing that concentration of risk
so that your performance isn't just about speed,
but about speed that survives under pressure.
The goal of this entire step isn't to make the work look elegant on a slide deck.
It's to make the movement of work clearer, thinner and more reliable
under the real world conditions your team faces every day.
When you achieve that, people stop spending all their energy navigating the route
and start using that energy to actually drive the outcome.
But remember, even the best redesign flow will collapse if the power and access still sit in the wrong places.
Step 4. Align access and ownership.
We've reached the point where most redesign efforts quietly fall apart
even when the workflow itself looks cleaner on paper.
The reason is simple. Access and ownership remain misaligned.
If you don't fix that structural gap, your new design won't last
because while flow is about movement, authority is about power.
You can simplify a route, cut out handoffs and bring context closer to the action,
but if the people responsible for the outcome can't actually act inside the system,
the organization will slide right back into its old broken habits.
The result is a system defined by waiting, escalating and creating manual workarounds.
People end up asking for permission from managers who don't carry any of the actual consequences,
which is why matching permissions to accountability is the most critical part of this step.
You have to align them directly rather than loosely.
Access isn't just a technical checkbox for the IT department, it is operational power.
When we look at a system, we have to ask who can create the record,
who can change the status and who has the right to approve an exception.
These aren't just administrative settings, they are fundamental design choices
that dictate how work actually happens.
If the person who is supposedly accountable for a result cannot move the work forward,
their ownership is fake.
Conversely, if someone can intervene in a process without carrying the risk of the outcome,
your control model is distorted.
The goal here isn't to give more people more access,
because that's usually how fragmentation starts to scale.
Instead, you want to create a sharp, clean relationship between a person's role
and their ability to take action.
I like to break this down into four distinct categories, who decides, who advises,
who executes and who audits.
These four functions should never blow into each other by accident.
And while they need to interact, they shouldn't collapse into a muddy permission set
where everyone participates, but nobody truly owns the result.
In my experience, Microsoft 365 and Power Platform environments are incredibly revealing
because the platform mirrors organizational ambiguity with painful honesty.
If your ownership structure is vague, your permissions will be vague,
and if power is driven by politics.
Access becomes a political tool.
When leadership avoids making hard calls about authority,
the digital environment fills up with shared mailboxes, broad member rights, and unofficial automation.
From the outside, this might look like a collaborative culture,
but from a system perspective, it's just unresolved ownership expressed through software.
To fix this, you have to ask some uncomfortable questions about who is truly accountable for business outcomes.
You need to find out if those roles have the system access they need to do their jobs,
or if people are holding access without any corresponding responsibility.
Often you'll find that approvals are being done out of habit rather than necessity,
or that employees are building workarounds because the right action is blocked by a legacy permission model.
There is one more question that matters more than most teams realize
where has the organization confused visibility with control?
These are not the same thing and failing to distinguish between them slows everything down.
A leader might need to see a flow without being the one who approves it,
just as a governance team, might need to audit a process without being part of the daily handoff.
When these lines are blurred, you end up with too many editors and too many people copied on emails for safety,
which isn't resilience, it's just diffusion.
Diffusion is the fastest way to kill accountability,
so the best move in a redesign is usually subtraction.
You want to remove those zones of ambiguous ownership
and cut out the unnecessary layers of approval that don't add value.
Give the accountable roles the rights they need to act limit override permissions
where they are actually justified and make your escalation paths explicit.
When you document responsibility in the language of action,
asking who can trigger a flow or who can be audited afterward,
you rebuild trust across the entire organization.
Once authority finally sits where the consequence sits,
the platform stops feeling like a confusing maze of permissions.
It starts behaving like a coherent operating model,
which is the real shift required for a system to scale.
Only after you've achieved this structural clarity,
does automation become a true force multiplier instead of just amplifying the existing chaos?
Step 5. Layar AI and automation last.
Now we finally get to the part that everyone wants to lead with AI,
co-pilots and the promise of total automation.
It's tempting to start here because these tools offer incredible speed,
but they should always be the final layer of your architecture.
AI is not a repair tool that fixes broken processes.
It is an acceleration layer that makes whatever you already have move faster.
If your decision paths are a mess,
AI will simply help you make bad decisions at a higher velocity.
If your data is fragmented and your permissions are politically distorted,
automation will scale that distortion across your entire enterprise.
When you ask an agent to operate in an environment where exception handling is vague,
that agent will hit a wall of uncertainty and fail more often.
The system isn't broken in that scenario,
it's doing exactly what it was designed to do,
but it's being pushed to a speed,
the underlying organization can't actually support.
Most AI disappointments don't happen because the technology is bad.
They happen because the organization tried to use AI to pay off design debt.
That debt doesn't just go away when you add intelligence,
it actually compounds and becomes more expensive to manage.
When I say you should layer AI last,
I'm talking about the sequence of the work.
You have to make the flow legible and the ownership clear
before you ever touch an automation tool.
Once you've stabilized the data path and defined where human judgment still matters,
you can finally decide what should be automated and what should be augmented.
This is a vital distinction
because stable repeatable tasks are great for automation,
while high variability work with real consequences is better for augmentation.
If you ignore this difference,
you'll either automate something unstable
and create a nightmare of manual exceptions
or you'll waste human talent on work the system could have handled alone.
In a well-sequenced system,
you know exactly where the work starts,
who owns the next move and which data source holds the truth.
You understand which cases can flow straight through
and which ones require a human to pause and review the edge cases.
This creates a structural boundary where AI has a safe place to land
and human in the loop stops being a feel-good phrase
and starts being a strategic decision.
Human judgment stays where risk and values require interpretation
while the machine handles the patent recognition and repetitive execution.
The real question for leaders isn't about how fast they can adopt AI
but rather what kind of organization they are asking that AI to accelerate.
AI cannot create clarity where none exists.
It only reveals the quality of your existing architecture.
I've seen companies roll out co-pilot into messy environments
where truth was contested and rights were unclear
and while individuals got slightly faster,
the organization as a whole didn't get any smarter.
That isn't a digital transformation,
it's just velocity without alignment.
The strategic move is always architecture first followed by design,
then optimization and finally automation.
When you let AI sit on top as the final multiplier of a coherent operating model,
it stops being a distraction or a workaround for a broken structure.
It becomes a force multiplier for clarity and that is the exact moment
when your technology stops fighting the business and starts reinforcing it.
What this means for Microsoft 365 co-pilot and power platform.
So what does all of this actually mean?
If your daily environment is built on Microsoft 365 co-pilot and the power platform,
it means you have to stop thinking about these tools as simple productivity products
because they aren't just software packages you install to check a box.
They are operating surfaces that expose exactly how work moves through your company,
where context lives and who actually has the power to act.
When you look at these platforms, they reveal whether your organization has clean relationships
between information, responsibility and action,
or if things are just tangled together.
This is exactly why Microsoft 365 maturity can be so misleading for leadership teams.
An organization can have teams, sharepoint, power automate,
and co-pilot running with strong security controls and active governance
yet still remains structurally slow.
None of those technical milestones guarantee coherence because they only prove that capability is available
which is never the same thing as having a high quality operating model.
If you are leading in this space, the question isn't whether you've deployed the platform well
but rather what kind of organizational logic that platform is currently reinforcing.
Let's look at Microsoft 365 more broadly as an operating substrate
where your business reality lives.
It is the place where communication happens, files move and decisions are discussed,
and the permissions within it shape who has access to vital context.
When M365 feels noisy, fragmented or politically heavy,
that usually isn't a tool problem but an organizational design problem showing up through the software.
Too many teams channels often reflect unclear boundaries,
while too many chats carrying key decisions show that your interaction design is weak.
The platform isn't causing this behavior out of nowhere.
It is simply making the underlying business reality visible to everyone.
Now map that same logic to co-pilot which only becomes truly valuable
when your context and truth sources are aligned enough for AI assistance to land inside a coherent flow.
If the underlying environment is clean,
co-pilot can reduce search time and help people recover their state quickly
which is incredibly useful for daily operations.
But if your source landscape is contradictory and your collaboration patterns vary rationale in private channels,
co-pilot will still generate output, it just won't reliably generate organizational clarity.
That is the trap where leaders see individual productivity gains
and assume the whole organization is transforming when really people are just producing faster
inside a fragmented architecture.
Success with co-pilot isn't mainly a question of how well you can write a prompt
but a question of how you design your information.
You have to ask if the tool can access the right context
and if the human receiving the output can actually act inside clear decision boundaries.
If those structural pieces are missing, the value stays local
and never scales to the rest of the business.
This brings us to Power Automate
which is where fragmentation gets dangerous because the tool scales so beautifully.
While it can scale efficiency, it can also scale brittleness
if the process underneath is unstable or dependent on invisible human judgment.
When an unstable workflow gets automated,
the manual effort doesn't actually disappear.
It just moves into exception handling admin intervention and support tickets.
The flow might run technically while failing operationally,
creating a false sense of maturity that eventually erodes trust across the team.
Power Platform governance matters here too, but only if it supports the flow of work instead of just slowing it down.
Good governance should clarify ownership and make approved pathways easier to follow
than shadow IT solutions.
If your governance adds delay without adding any clarity,
it isn't strengthening the platform, it's just increasing latency.
The executive implication is straightforward.
Stop buying isolated gains and stop treating M365 as a bundle of apps or co-pilot as a miracle layer.
You need to start designing a platform supported operating model
where decision flow is clear and access reflects real accountability.
Then the Microsoft stack becomes powerful,
not because the tools got smarter, but because the organization became coherent enough to use them.
The metrics of a designed organization.
If we are serious about designing organizations,
then we also need to get serious about what we actually measure.
This is where most leadership teams drift back into old habits by talking about transformation
while only measuring license adoption or meeting counts.
Those things are signals, but they aren't proof
that the organization is becoming structurally better or more resilient.
You need metrics that describe how the organization behaves as a system
rather than just reporting that activity happened.
The first category you should focus on is decision flow,
specifically looking at decision latency.
You want to measure how much time passes between a signal,
the judgment, the commitment and the final action.
It's not about when the request was logged or when the deck was presented
but when the organization actually moved.
You should also track escalation frequency to see how often work gets pushed upward
because local authority or confidence is missing.
If your escalations are high,
it's a sign the system doesn't trust its own design
and your reversible decisions are likely facing the same friction as irreversible ones.
The second category is data trust
because if people don't trust the data path, they will manually rebuild the truth
every time the pressure rises.
You can measure this by looking at duplicate source creation
where teams create local versions of key data just to feel safe enough to act.
Look at the reconciliation effort as well,
calculating how much human time is spent,
comparing reports and resolving definition disputes.
If teams can't trace where a metric came from or who owns it,
then your trust is weak even if your dashboards look polished and professional.
Next we have to look at interaction quality
and I mean quality in a structural sense rather than an emotional one.
Track how many handoffs require a full re-explanation
and how much response lag appears when work crosses a departmental boundary.
A great signal here is documentation reuse.
If people keep asking questions that should be answerable from the recorded path,
your interaction model is leaking meaning.
When meetings exist mainly to restore missing context that should have been there already,
your system is failing to sustain itself.
The fourth category is power alignment,
which is the one most organizations avoid
because it gets politically uncomfortable very quickly.
You need to track approval concentration to see how many critical flows
depend on a very small number of people
which reveals your single points of failure.
Watch for override patterns
where the formal process gets bypassed by influence or urgency
because if overrides are common, your official model isn't the real one.
You also need to spot access mismatches where people hold permissions without responsibility
or where accountable roles lack the access they need to actually do their jobs.
These metrics matter because they shift the conversation away from performance,
theatre and toward structural reality.
An organization can look digitally mature on paper
and still have terrible decision latency
or spend huge amounts of time reconciling the truth.
The point isn't to look modern, the point is to become structurally capable
by measuring the things that reveal if flow is cleaner and trust is stronger.
If I were starting with a simple scorecard,
I would want to see decision latency, escalation rates
and reconciliation effort visible every single week.
These numbers tell you whether the organization is becoming easier to move through
which is the only test that actually matters.
It's not about whether the platform is busy
or if the licenses are assigned
but whether the business can act with clarity under pressure.
Mature organizations aren't defined by how much process they have on the books.
They are defined by how much coordinated movement their design allows when things get difficult.
If you audited your organizational design the same way you audit your technical systems
you might find that your current structure is actually designed to slow you down.
The counterintuitive truth about high performance.
So here's the counterintuitive part of the whole equation.
The organizations that look the most advanced on paper
are not always the ones that actually perform the best in the real world.
In fact, I've seen that sometimes the exact opposite is true.
The more complex your environment becomes,
the more dangerous that kind of superficial sophistication gets for the business.
You can have a modern platform stack and strong governance language
while keeping AI pilots in motion and cross-functional steering groups on the calendar.
You might have detailed reporting and a polished transformation narrative
yet still find yourself slower, heavier and less reliable than a simpler organization
with better structural alignment.
And why is that?
It's because sophistication is not the same thing as coherence.
A lot of leaders still assume that performance improves whenever you add more capability to the mix.
They add more tools, more controls, more specialist functions and more dashboards
until the system is buried under process layers and departmental optimization.
That feels disciplined and it certainly feels mature
but if those additions aren't aligned into one operating logic
they don't increase performance.
Instead they just increase internal drag.
The truth isn't that advanced organizations are inherently bad
but rather that advancement without alignment becomes expensive very quickly.
Every extra layer creates another point where work can pause
meaning can split and responsibility can blur until power distorts the path entirely.
That is why I keep coming back to the concept of design.
High performance is not the outcome of endless improvement activity.
It is the outcome of architectural fit.
When the decision flow fits the pace of the business
and the data flow fits the need for trust, performance rises even if the environment isn't flashy.
Interaction must fit the complexity of coordination and power must fit
the people accountable for outcomes for the system to actually work
when those things don't fit performance falls even if the organization looks digitally impressive from the outside.
This is a hard truth for leadership teams because it challenges a very common instinct
to optimize whatever is visible.
If approvals feel slow, they optimize approvals and if meetings feel heavy
or reporting feels messy they try to optimize those specific points.
Even when AI value is weak, the instinct is to optimize prompting, training
or use case selection to fix the immediate gap.
But here's the thing, those interventions might help locally.
But they won't solve the deeper issue when the organization is fragmented at the design level.
You cannot optimize your way into coherence because you have to design for it from the ground up.
That shift in perspective changes how we think about high performance entirely.
High performing organizations are not just better managed collections of parts.
They are aligned systems designed to reduce unnecessary interpretation at the point of action.
They are designed so people know where truth lives and routine decisions
don't have to pretend to be strategic theatre.
These systems ensure that technology reinforces the path instead of exposing the confusion inside it
which is why alignment beats sophistication every time complexity rises.
Complexity multiplies the cost of every inconsistency, turning a small annoyance into a massive structural failure.
One week handoff in a small organization is annoying,
but that same handoff in a highly connected AI accelerated organization
becomes a destructive chain reaction.
An unclear owner in a simple process creates a delay,
while an unclear owner in an automated cross-platform flow creates stalled work and a total loss of confidence at scale.
If complexity is increasing then structural resilience matters more than any isolated best practice you could implement.
They use the phrase structural resilience because it describes the actual requirement for a modern business to survive.
Can the organization keep moving clearly under pressure and speed
or does it collapse into meetings and manual repair the moment an exception occurs?
Can it handle acceleration without multiplying confusion across the entire stack?
That is what high performance really means now, not maximum activity or looking transformed,
but being structurally capable.
Once you see that the sequence of work becomes much clearer.
Design first, optimize second, and automate last.
That is the order you have to follow.
If you reverse it, you usually end up amplifying instability,
but if you respect that order, then optimization finally lands in the right place.
It sharpens a coherent path instead of just decorating a broken one
and automation finally becomes a strategic tool that accelerates a system that already knows how to move.
The most effective organizations are not the ones doing the most.
They are the ones with the cleanest fit between architecture and business reality.
Where leaders should start on Monday.
So if you are leading a team, a function, or a platform environment,
where do you actually start on Monday morning?
You don't start with a transformation office, a new AI pilot,
or a broad maturity assessment that produces 47 recommendations nobody can absorb.
You start with one flow, specifically one high friction decision flow,
with a visible business impact that your organization feels every single week.
Maybe it's a budget approval that always stalls or a customer issue that keeps escalating,
or perhaps it's a reporting cycle that triggers arguments instead of action.
You might have a project intake path that looks governed,
but moves like wet concrete.
So just pick one and do something very simple.
Map the real path, not the official one,
and sit with the people inside the flow to trace what actually happens.
You need to see where it starts, where it pauses,
and who really makes the final decision when things get complicated.
Ask which data gets questioned, where chat replaces the official process,
and where someone with unofficial authority steps into change the route.
That exercise alone will tell you more than another status tech ever could,
and then you can identify three specific things, one ownership ambiguity,
one truth conflict, and one interaction bottleneck.
That is enough to get started.
You do not need a full organizational redesign to begin this process.
You just need one piece of visible reality you are willing to treat seriously.
Redesign that one flow by clarifying the decision owner,
reducing one unnecessary approval, and moving context closer to the point of action.
Remove one duplicate report, and align one permission with one accountable role
to see how the system responds.
That may sound small, but it isn't, because once one important flow becomes clearer,
the people inside it feel the difference immediately.
They experience less waiting, less guessing,
and less of that protective communication that eats up so much time.
This creates credibility, which is something most transformation programs struggle to produce
because they focus on announcements rather than results.
The system starts behaving differently,
and that is the kind of momentum you want, a designed system outcome,
rather than a corporate slogan.
Once you have one success, you can repeat the method.
See the reality, identify the friction, redesign the flow,
and align access before layering automation last.
This sequence scales because it is based on operational truth instead of blind optimism.
If you are working in the Microsoft 365 world specifically,
this approach is even more practical.
Choose one flow already touching teams, sharepoint, power automate, and approvals,
because those environments already contain all the evidence you need.
You do not need more abstraction, you just need better observation of how work actually moves.
So here is the question I would bring into the office on Monday,
where in our organization is important work still being held together by human compensation,
find that point of failure where people are working around the system just to get things done.
That is where your design work should begin.
Adding more capability to a fragmented model won't build a better organization
because real growth only happens when you design how work, truth, and authority actually move together.
If this perspective changed how you see performance, I'd appreciate a review or a connection on LinkedIn.
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