Frictionless automation feels efficient, but it can quietly create chaos. When systems remove effort and decisions become automatic, organizations lose visibility into how things actually work. Instead of improving control, automation can hide complexity and shift decision-making into the system itself.

Over time, this leads to unintended behaviors, misalignment, and risk—because no one is actively shaping outcomes anymore. The organization you think you designed (based on structure or intent) diverges from the one that actually operates through automated processes.

The key insight: automation doesn’t eliminate complexity—it redistributes it into hidden layers. If you don’t intentionally design governance, decision logic, and system behavior, the system will make decisions for you.

In short, friction isn’t always bad. It can act as a control mechanism. Removing it without replacing it with deliberate governance leads to invisible chaos rather than true efficiency.

Power automate warning: Do not rush to automate every workflow. Power automate warning: You might scale chaos instead of solving it. The "Digitizing Chaos" podcast explains how frictionless automation creates hidden risks. Power automate warning: Automating undefined processes can lead to errors and misinformation. You may see poor data structures cause failed flows. Power automate warning: Over-automation without human control can result in excessive notifications and incorrect updates. Power automate warning: Flows tied to personal accounts create operational risks. Power automate warning: Without error handling, failed flows erode trust. Take time to review each critical business process before you automate.

Key Takeaways

Avoid rushing to automate every workflow. Take time to understand your processes first.
Map out your processes before automation. This helps identify hidden complexities and inefficiencies.
Assign clear ownership for every flow. This ensures accountability and reduces confusion.
Implement strong error handling. Set up alerts to catch issues early and maintain trust in automation.
Review and monitor your flows regularly. Remove those that do not add value to avoid scaling chaos.
Test automations with a pilot before full-scale implementation. This helps catch problems early.
Focus on continuous improvement. Use a cycle of Plan, Do, Check, and Adjust to enhance your automations.
Ensure data security by reviewing flows for access control gaps. Protect sensitive information from unauthorized access.

Power Automate Warning: Amplifying Process Debt

The Trap of Frictionless Automation

Hidden Complexities

You may think automation always makes your work easier. The truth is, automation can hide problems that you do not see right away. When you use power automate to build flows quickly, you might skip important steps. You may not notice missing approvals or unclear rules. These hidden complexities can grow as you add more flows. Each new flow can connect to old systems or data that no one checks. Over time, your flows can become hard to manage. You may not know why a flow fails or where to fix it. This creates risks for your business.

Tip: Always map out your process before you create a flow. This helps you see where automation might hide problems.

Masked Inefficiencies

Automation can make a slow process look fast. You may use power automate to run flows that move data or send alerts. If the process has extra steps or old rules, automation will repeat those mistakes. You may not see the wasted time because flows run in the background. This can lead to process debt. You keep adding flows to fix small issues, but the main problem stays. Over time, you build a system that looks smooth but is full of hidden risks.

Provisioning Sprawl in Microsoft 365

Unmonitored Teams and Sites

Microsoft 365 lets you create teams and sites with just a few clicks. Power automate can help you set up flows that create new workspaces fast. If you do not track these flows, you can end up with many teams and sites that no one uses. This is called provisioning sprawl. You may not know who owns each team or what data they store. Unmonitored flows can create risks for your organization. You may lose control over important information.

Make a list of all flows that create teams or sites.
Review who owns each flow and what it does.

Lack of Ownership

Flows need clear owners. If you use power automate to build flows without assigning responsibility, you create confusion. No one knows who should fix a broken flow or update it when rules change. This lack of ownership can lead to process debt. Flows keep running, but no one checks if they still work. Automation should not replace human oversight. You need someone to review flows and manage changes.

Note: Assign an owner for every flow. Review flows often to reduce risks and keep automation healthy.

Common Pitfalls in Power Automate

Poor Process Design

Undefined Steps

You may want to automate a process as soon as possible. If you do not define every step, you risk creating confusion. Power automate can only follow the instructions you give. When you skip steps or leave out details, you create gaps. These gaps lead to errors that are hard to trace. You might see an error message, but not know what caused it. For example, if you forget to include a step for approval, the flow might send incomplete data. You will spend more time fixing errors than saving time.

Tip: Write down each step before you build a flow. This helps you spot missing actions and avoid errors.

Outdated Exceptions

Many organizations add exceptions to handle special cases. Over time, these exceptions can become outdated. You may forget why you added them. Power automate will still follow these old rules, which can cause errors. Outdated exceptions make your flows more complex. You will need more logic and parameters to manage them. This increases the chance of errors and makes maintenance harder. You may also lose flexibility. Hardcoded exceptions stop you from adapting to new needs. Errors can multiply, and you may miss the value of human judgment.

Outdated exceptions increase complexity and make flows harder to manage.
Maintenance costs rise as you try to keep up with changes.
You lose flexibility and may not meet new business needs.
Errors become more common and harder to fix.
Over-automation can hide the need for human review.

No Error Handling Risks

Missed Alerts

No error handling means you will not know when something goes wrong. Power automate can run many flows at once. If you do not set up alerts, you may miss important errors. For example, a flow might fail to send a report, but you will not see an error message. This can lead to missed deadlines or lost data. You need to set up alerts for every critical flow. This helps you catch errors early and fix them before they grow.

Unresolved Failures

When you ignore errors, you create bigger problems. Unresolved failures can stop your business from running smoothly. Power automate will keep trying to run the same flow, but the error will not go away. You may see repeated error messages, but not know how to solve them. This can lead to lost trust in automation. You need to check logs and fix errors as soon as possible. No error handling puts your operations at risk.

Over-Automation and Scale Issues

When Manual Steps Matter

Not every task should be automated. Some steps need human judgment. For example, you may need to review data by hand or chase signatures for compliance. Power automate cannot replace every manual task. If you automate too much, you may miss important details. You might also create errors that only a person can catch. Listing every manual step helps you decide what to automate and what to keep manual.

Review data by hand when accuracy matters.
Search for documents across tools before automating.
Identify tasks that need human approval.

Scaling Chaos

As you add more flows, you increase complexity. Over-automation can lead to digital chaos. Many organizations fear losing control over their automated processes. You may see more compliance risks and higher chances of errors in core business tasks. When you scale without a plan, you create more errors and error messages. Your technology estate can grow quickly, adding new systems and tools. This does not always make things easier. Sometimes, automation adds complexity instead of reducing it.

"In a constantly evolving landscape, organizations often struggle with siloed tools and technologies, which hinder their ability to streamline operations or achieve full visibility."

You need strong governance to manage your flows. Review your automation regularly. Remove flows that do not add value. This helps you avoid scaling chaos and keeps your operations efficient.

Note: Poor sharepoint list design can also lead to errors and error messages in your flows. Always review your data structure before automating.

Security and Governance in Power Automate

Data Security Challenges

Unsecured Flows

You must pay close attention to security when building automated workflows. Unsecured flows can put your organization at risk. If you do not set up proper controls, sensitive data can move outside your trusted environment. This can lead to security and data exposure. For example, you might see data leaks if someone shares information through a workflow that lacks protection. Unauthorized users may access flows without role-based control. Compliance risks also increase when you do not follow industry rules.

Imagine an employee accidentally sharing confidential client data through an automated workflow that was not secured with Power Automate governance or data loss prevention policies.

Here are some common ways unsecured flows can expose your organization:

Data leaks happen when sensitive data is shared through unprotected workflows.
Unauthorized access occurs if users gain entry to flows without proper controls.
Compliance risks grow when workflows do not meet regulations.

You need to review every flow for security gaps before deployment.

Access Control Gaps

Access control is a key part of protecting your data. Without strong control, low-level users may access sensitive workflows. This can lead to data exposure and compliance issues. Managing permissions becomes harder as you add more flows. You must set clear rules for who can create, edit, or run each workflow. Without these rules, you risk losing track of who has access to important data.

Data Security Challenge	Description
Data Leaks	Sensitive business data can be exposed through misconfigured workflows, leading to breaches.
Unauthorized Access	Low-level users may gain access to sensitive workflows without proper role-based controls.
Compliance Risks	Automated workflows may violate regulations like GDPR or HIPAA if not properly governed.
Complexity of Permissions	Managing who can create, modify, or execute workflows can become challenging without governance.

Ownership and Lifecycle Management

Assigning Responsibility

You must assign clear ownership for every flow. Flows built under personal ownership can create confusion and risk. If the owner leaves the company, no one may know how to manage or fix the flow. You should require at least one co-owner for shared flows. For critical flows, assign multiple co-owners. Use service accounts for important, long-running flows. This ensures stability and data accountability.

Best Practice	Description
Mandate co-ownership	Establish a policy requiring at least one active co-owner for shared flows.
Define criticality and ownership requirements	Classify flows based on business criticality, requiring multiple co-owners for highly critical assets.
Leverage service accounts	Use dedicated service accounts for critical, long-running flows to ensure stability.
Utilize Solutions	Package apps and flows within Power Platform Solutions for easier management and ownership transfer.
Employ a robust environment strategy	Segment environments for development, testing, and production with clear ownership expectations.
Establish regular ownership audits	Use tools to regularly audit ownership and implement attestation processes to confirm responsibility.

Lifecycle Policies

Lifecycle policies help you manage flows from creation to retirement. Good policies improve security and reduce risk. You should develop every flow in a dedicated development environment. Test flows in a staging area before moving them to production. Deploy flows through a controlled process. This reduces audit problems and makes troubleshooting easier.

Governance practices improve management by addressing compliance, security, and operational efficiency.
Policies and monitoring protect against unauthorized access and data breaches.
A strong governance framework supports secure collaboration in cloud solutions.
Implement robust data loss prevention policies to keep workflows compliant and protect sensitive data from exposure.

By following these steps, you keep your data safe and your automation under control.

Building Robust Automations

Process Mapping

Workflow Visualization

You need to see your process clearly before you automate. Start by drawing out each step and decision point. This helps you understand how work actually happens, not just how you think it happens.

Workflow visualization is crucial for identifying bottlenecks and inefficiencies before automation because it provides a clear view of how work actually happens, rather than how it is documented or perceived. This visibility allows you to pinpoint where work gets stuck, which steps take the most time, and where unnecessary rework occurs. By understanding these aspects, you can avoid automating broken processes and instead focus on optimizing workflows before implementing automation.

Bottleneck Identification

You can use several methods to find bottlenecks in your process:

Process mining helps you see real workflows and spot variations.
Value stream mapping shows how long each step takes.
Complexity/value matrix analysis helps you decide which steps to improve first.
Data readiness checks show if your information is ready for automation.

You should also review performance metrics and logs. This helps you find slow actions, inefficient loops, or outside services that slow down your Power Automate flows.

Key steps for mapping your process before automating:

Define each workflow step and decision point.
Identify what starts the workflow, like a form or document.
List actions, such as assigning tasks or sending notifications.
Set up conditions for different workflow paths.
Plan approval steps, using parallel or sequential approvals as needed.

Error Handling and Monitoring

Exception Management

You must build strong exception handling into every flow. Use Scope actions to group related steps and manage success or failure. Set up retry policies to handle temporary problems. Add a Terminate action to stop the flow if a critical error happens. Always log errors and send notifications to the right people. This makes troubleshooting easier and keeps your team informed.

Alerts and Logs

Set up alerts for every important flow. Use Compose actions to create links to failed runs. Log all errors for review. Notify stakeholders right away when something goes wrong. Test your flows with edge cases to make sure your error handling works. Good exception handling and alerting keep your automation reliable and safe.

Responsible Scaling

Pilot First

You should always test your automation with a pilot before you scale. Run the pilot on a real sample of your workload. Compare the results with your manual process. Look for differences and find out if they come from design gaps, data quality issues, or unexpected exceptions. This step helps you catch problems early and reduces risk.

Continuous Improvement

Keep improving your automations over time. Use a cycle of Plan, Do, Check, and Adjust. Focus on fixing root causes, not just symptoms. Standardize improvements so your team can use them again. This approach boosts performance and scale assumptions for your Power Automate solutions.

Principle	Description
Clear definition of winning	Set success based on team values and priorities.
Disciplined execution	Follow a repeatable cycle: Plan, Do, Check, Adjust.
Constrained problem-solving	Address root causes for lasting improvements.
Sustained replication	Standardize and repeat improvements for better operations.

Checklist for Evaluating Processes Before Automating:

Document the problem, success criteria, exception paths, and approvals.
Assign a business and technical owner for each flow.
Set a review schedule and retirement plan.
Design security with least-privilege access and regular audits.
Use clear naming standards for flows and actions.
Align automation with your SharePoint structure for predictability.

Following these best practices ensures your automations are clear, reliable, and ready to handle growth.

You should fix your processes before you automate with Power Automate. Clear ownership, strong governance, and reliable error handling help you avoid common pitfalls. When you define roles and responsibilities, you reduce risk and improve results:

Role	Responsibility
Process Owners	Design and improve processes
Operators	Execute and monitor workflows
Governance Boards	Oversee performance and compliance

Remember, failed automation projects teach you what does not work and help you build troubleshooting skills. Use the framework above to create robust, accountable automation. Start with process clarity and make accountability your priority.

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Automation doesn't remove chaos, it scales it.

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That is the part most leaders miss

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because the screen looks cleaner

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and the build happens faster.

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The first demo usually lands well,

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a form works, a flow runs, a bot answers,

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everyone relaxes.

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But a fast build and a better business outcome

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are not the same thing.

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Low-code tools make it easy to ship something

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that feels like progress,

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even when the logic underneath is still messy

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or full of old exceptions, nobody challenged.

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That's where it breaks.

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The interface improves,

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but the operating model stays weak.

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In this episode, I want to show you

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why frictionless automation feels so convincing

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and why it hides risk instead of removing it.

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We will look at how to spot the warning signs early

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and what to change in the next 30 days

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before the mess gets harder to unwind.

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Why frictionless automation feels right and why it breaks?

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The trap starts with speed.

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When something is hard to build, people ask more questions.

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They slow down, they challenge the scope,

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they ask who owns the process,

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what happens when it fails,

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and whether the workflow even makes sense in the first place.

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But when a tool lets you build fast, doubt drops.

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The effort disappears,

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and with it, the scrutiny that used to protect

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the organization disappears too.

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That is not a tooling problem.

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It is a human one.

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You build a flow in an afternoon

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and people see a quick win.

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Leadership gets a progress story

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while the team gets that small reward loop

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that comes with visible momentum,

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something moved, something shipped,

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someone posted the screenshot, that feels good.

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It also lowers the chance that anyone asks

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the harder question,

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which is whether the process actually improved

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or just got a nicer front end.

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This clicked for me when I kept seeing the same equation

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inside organizations where a working screen

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equals a working process.

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It doesn't.

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A screen can work perfectly

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while the handoffs behind it are still confused

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and approval rights are still unclear.

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People still need side messages to get real work done.

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The automation layer can look calm

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because it hides the negotiation

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and the rework that just moved somewhere else.

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One level deeper, this is also status quo bias at work,

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redesigning how work happens is uncomfortable.

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You have to challenge roles, remove old exceptions,

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and admit that the current process may never have been good.

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Automating the mess feels easier

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because it preserves the old model.

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The names stay the same, the steps stay familiar,

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the politics stay mostly untouched.

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The organization can claim change

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without confronting what actually needs to change.

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Then automation bias kicks in.

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Once the flow runs, people start treating

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the logic as trustworthy

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just because the machine is doing it.

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Research on automation bias keeps showing the same pattern

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where people lean on automated outputs

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more than their own judgment.

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Time pressure makes that even worse.

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When a power automate flow roots approvals

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or an AI summary pulls together a decision brief,

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the fact that the system responded at all

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starts to stand in for proof that it responded well.

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There is another layer here that matters a lot

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in Microsoft 365 and Power Platform.

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These tools really do reduce cognitive load

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that is part of their value.

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Research on co-pilot and routine task support

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shows people spend less effort on things like email,

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drafting, and finding information that matters.

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But reducing effort and preserving structural integrity

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are not the same thing.

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You can make work feel lighter

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while making the underlying system weaker

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because the pain of the task drops

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before the quality of the structure improves.

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So what gets lost?

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Warning signals.

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A clumsy manual process often annoys people,

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but that annoyance tells you something.

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It tells you where ownership is fuzzy,

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it tells you where policy is unstable,

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it tells you where exceptions keep piling up.

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When you smooth all of that too early,

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you do not always remove the problem.

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Sometimes you just remove the evidence

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that is the model behind it,

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less visible effort, more hidden complexity.

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In real organizations, that pattern shows up fast.

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You can see it in collaboration provisioning,

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in approval flows, and now in AI rollout

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sitting on top of weak information structures.

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Example one, Microsoft 365 provisioning sprawl.

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Take Microsoft 365 provisioning as a starting point.

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This is one of the clearest examples

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of the gap between intent and reality

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because the goal usually starts from a good place.

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Most businesses want teams and sharepoint sites

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created faster so they can stop waiting on IT,

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cut out the endless email chains,

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and give employees a clean self-service path

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for projects or department work spaces.

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To make that happen, the organization builds a request app,

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hooks up a workflow, and adds some naming rules

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to make the whole process feel modern.

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Requests go in, spaces appear, people are happy.

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At first this looks like a major win for digital maturity.

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The older laser gone, and leadership sees fewer complaints

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about access, but the moment this model breaks

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is almost never during the creation phase.

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The collapse happens later,

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when ownership, lifecycle management, and regular reviews

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fail to keep up with the sheer speed of the provisioning engine.

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Teams are now being created much faster

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than anyone can actually govern them,

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which means sites spun up for three week projects

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often sit there for three years.

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The inventory grows, but the accountability stays

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exactly where it was.

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In most organizations, this creates a very specific

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and messy pattern.

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You find inactive teams that still have live permissions,

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sharepoint sites that nobody can explain,

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and work spaces with hundreds of members,

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but no current business owner.

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Because every new initiative gets a brand new space

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instead of a better collaboration design,

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you end up with massive amounts of duplicated content.

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Search results get worse every month

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because the platform is drowning in abandoned structures

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that still look like official company records.

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And because the create button still works perfectly,

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everyone assumes the governance is working too.

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That is the trap.

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The organization solved the friction of getting access,

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but they completely ignored the actual design

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of how people work together.

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Those are two different problems.

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Faster creation helps the team start their work on day one,

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but it doesn't answer the structural questions

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about who owns the data.

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How long the space should exist?

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Or what happens when the project finally ends?

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If those decisions stay vague at the start,

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your automation is just scaling that vagueness

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across the entire enterprise.

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Leaders usually notice the damage far too late

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because it spreads quietly behind the scenes.

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Storage costs start climbing.

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Compliance questions get harder to answer,

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and people stop trusting the permissions they see.

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Findability drops as the signal to noise ratio

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gets worse every quarter,

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and the business begins paying a hidden tax

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through wasted search time and extra effort

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during every single audit.

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None of those problems ever show up

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during the initial provisioning demo.

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This example matters because it shows how easy it is

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to confuse a fast front door with a healthy house.

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The issue isn't that you automated team creation,

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but rather that you automated it

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without building any structural exits or clear ownership.

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You end up with a platform that looks incredibly responsive

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on the surface while the environment underneath

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becomes a nightmare to manage.

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Once that pattern becomes the norm,

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the next wave usually hits a much more sensitive area.

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It moves away from collaboration at the edges

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and starts affecting decision making at the core of the business,

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which is where the costs become impossible to ignore.

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Example two, power automate approval chains

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and the invisible approval collapse.

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A better place to see this risk is inside approval workflows

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where automation looks the most convincing

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while actually getting weaker underneath.

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I recently worked with a global company

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that moved their procurement approvals into power automate

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and the early results were great.

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Request moved through a standard path, managers

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got instant notifications and finance finally had visibility,

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giving leadership a perfect example of digital progress

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they could show off to the board.

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For a while that was the story,

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but the process itself was never actually redesigned.

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It was just wrapped in a digital skin.

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Over the next 18 months, that approval setup expanded

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to absorb every single historical exception

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the business had ever created.

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Regional variation stayed in place,

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legacy policies were never retired

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and special rules for specific vendors remained active

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because nobody wanted to remove a branch

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that had a political sponsor.

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Instead of simplifying how decisions were made,

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the flow became a massive container for all the ambiguity

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the business refused to resolve.

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By the time we stepped into review it,

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the company was running 27 different versions

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of the flow with over 40 exception parts.

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On paper, the procurement process was fully automated

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but in reality, the actual approval time

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had drifted from two days to nine.

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The process looked digital,

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but the outcome was objectively slower.

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That gap is where the danger lives

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because leadership could see a live dashboard,

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they stopped questioning whether the process actually worked,

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the standard response became, "We already automated that."

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And once that sentence takes root in a company,

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the internal review usually stops.

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The process enters a protected status

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where the mere presence of automation is treated

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as proof of health, even though the flow

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was just executing years of unclear handoffs

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and fuzzy ownership.

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The people on the ground felt this failure

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long before the executives did.

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Buyers started chasing approvers through side channel emails

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because the formal path took too long

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and teams began nudging each other in chat

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just to move a request an inch forward.

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Some approvals were even settled through private agreements

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before the system ever caught up

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while other requests were re-rooted manually

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because they didn't fit the rigid logic.

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The official process stayed automated,

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but the real work split into two different worlds,

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one in power automate and one in the shadows

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of inboxes and private chats.

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That is what an invisible approval collapse looks like.

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The system doesn't crash or throw an error code

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so it keeps running,

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but the trust of the employees moves entirely

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outside of the software.

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This is where most teams miss the actual point of failure.

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They spend their time looking for broken runs

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or technical connector issues,

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but the deeper problem is always structural.

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The business never settled on who should decide

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what or which exceptions were actually valid

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so they used automation to preserve uncertainty

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because that felt easier than forcing a simplification.

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This is why the exception rate

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is the only honest metric you have.

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When the reality of the work no longer fits the digital flow,

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your manual overrides and re-roots will start to climb.

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In this specific case, the exception rate hit 22%,

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which is well past the point of improving anything.

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At that level, the system is screaming at you

267
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that the logic no longer matches the work,

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but because no one was watching that signal,

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the organization just kept adding more logic.

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There was also no single person

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who actually owned the process from start to finish.

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I'd maintained the flow, but didn't own the policy

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and procurement used the tool,

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but didn't feel responsible for the technical structure.

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Technical custodians kept the automation alive

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while the underlying business problem continued to spread.

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The impact went far beyond a simple delay in approvals.

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Vendor onboarding slowed down.

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Product launches were held up by purchasing decisions

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and teams started adding buffer time to every project

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because they didn't trust the cycle.

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Drag entered the system one exception at a time

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until being slow became the new normal.

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The final sting is uncomfortable because it is so common.

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The system didn't fail, it just followed

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flawed assumptions at a massive scale.

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Once you recognize that pattern in approvals,

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the next question gets much harder.

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The same thing is happening right now in knowledge work,

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only this time the interface isn't a flow, it's AI.

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Example three, co-pilot on top of bad information architecture.

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Now take that same pattern and put AI on top of it.

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Most organizations are rolling out co-pilot

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with a very reasonable goal in mind.

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People waste too much time searching for files,

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opening endless tabs and chasing context

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just to figure out what happened in a meeting.

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Co-pilot promises relief from all of that.

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It promises less hunting, fewer window switches

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and faster answers.

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For a busy team that sounds like an obvious win,

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the tool really can reduce the manual effort of work.

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Microsoft's own studies show people spending less time

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on emails and finishing documents faster

305
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with much lower mental strength.

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That part matters, but the problem starts

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when we confuse lower effort with higher information quality.

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Co-pilot can retrieve anything it has permission to access,

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but it cannot tell you if that content is actually current,

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complete or backed by an owner who still works there.

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That's where things change.

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If your Microsoft 365 environment is a graveyard

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of duplicated files, outdated project spaces

314
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and abandoned team's channels,

315
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co-pilot isn't going to clean that up for you.

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It will just surface that mess faster than a human ever could.

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It can summarize three inconsistent documents

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into one polished fluent answer

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that feels useful in the moment,

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00:11:00,400 --> 00:11:02,240
but that professional polish often hides

321
00:11:02,240 --> 00:11:05,160
the structural weakness of the source material underneath.

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The result is a response that sounds perfectly coherent,

323
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even when the underlying data is a total wreck.

324
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This is a much harder risk to catch

325
00:11:11,640 --> 00:11:13,640
because the failure mode is so subtle,

326
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a broken approval flow eventually creates

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a visible delay that someone notices.

328
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Bad information architecture creates un-earned confidence.

329
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You ask for a summary of a client issue or a policy change

330
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and the response comes back structured and fast.

331
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That speed changes how the user behaves.

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They check the facts less, they challenge the logic less,

333
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and they move to the next task much sooner.

334
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Research on automation bias shows

335
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that when the effort of a task drops,

336
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our reliance on the machine rises,

337
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especially when we are under time pressure.

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In other words, co-pilot removes the pain of searching

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before the organization has fixed the quality

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00:11:44,400 --> 00:11:45,760
of what is being searched.

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That creates a dangerous belief trap at the leadership level.

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If AI makes knowledge easier to find,

343
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leaders start assuming the knowledge system itself

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must be in decent shape.

345
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But accessibility and integrity are two very different things.

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A messy knowledge estate can still be very accessible,

347
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but it just means it has become easier for your employees

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00:12:01,960 --> 00:12:02,920
to consume the mess.

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You see this in small quiet moments first.

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00:12:05,080 --> 00:12:07,280
Two different teams ask co-pilot the same question

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00:12:07,280 --> 00:12:08,240
and get different answers

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00:12:08,240 --> 00:12:10,560
because they are working from different document trails.

353
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A meeting summary sounds complete

354
00:12:12,160 --> 00:12:13,840
but leaves out the most important decision

355
00:12:13,840 --> 00:12:15,760
because that change happened in a side chat,

356
00:12:15,760 --> 00:12:17,320
the AI didn't prioritize.

357
00:12:17,320 --> 00:12:19,080
A policy answer pulls from an old file

358
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that was never officially retired.

359
00:12:20,920 --> 00:12:22,440
None of this feels dramatic at first

360
00:12:22,440 --> 00:12:24,280
and that is exactly why it spreads.

361
00:12:24,280 --> 00:12:26,560
One level deeper, AI shortens the distance

362
00:12:26,560 --> 00:12:28,800
between weak content and executive action.

363
00:12:28,800 --> 00:12:31,120
Decisions move faster because the context arrives faster

364
00:12:31,120 --> 00:12:32,960
but if that context is stale or onalous,

365
00:12:32,960 --> 00:12:36,640
the cycle speeds up while the information standard drops.

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The organization becomes more responsive

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00:12:38,600 --> 00:12:40,840
and less reliable at the exact same time.

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That is the uncomfortable reality of the situation.

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Co-pilot didn't create the disorder in your files

370
00:12:45,560 --> 00:12:47,680
but it did expose a brand new risk.

371
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It removed the friction that used to warn people

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when something was off.

373
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The slow search and the which version is right,

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moment were frustrating

375
00:12:54,680 --> 00:12:56,840
but they also forced a necessary pause.

376
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Once that pause disappears,

377
00:12:58,040 --> 00:13:01,000
a bad structure travels much further through the company

378
00:13:01,000 --> 00:13:02,920
before anyone notices the damage.

379
00:13:02,920 --> 00:13:05,280
Across provisioning approvals and AI,

380
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the pattern is now visible.

381
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These are different tools

382
00:13:07,800 --> 00:13:10,280
but the model underneath is exactly the same.

383
00:13:10,280 --> 00:13:13,120
The structural pattern underneath all three examples.

384
00:13:13,120 --> 00:13:15,760
Across all three cases, the pattern is identical.

385
00:13:15,760 --> 00:13:17,200
We didn't automate our processes,

386
00:13:17,200 --> 00:13:19,040
we automated our inconsistencies.

387
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That is why the surface story sounds so good

388
00:13:21,280 --> 00:13:23,320
while the actual operating story gets worse.

389
00:13:23,320 --> 00:13:26,200
Visible effort drops while hidden complexity climbs.

390
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People click less and search less at first

391
00:13:27,880 --> 00:13:30,040
but the logic and ownership that should have been cleaned up

392
00:13:30,040 --> 00:13:32,160
before the automation now sit inside the tool.

393
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They are harder to see and much easier to excuse.

394
00:13:34,560 --> 00:13:37,600
That accumulated mess is what I call process debt.

395
00:13:37,600 --> 00:13:39,640
This isn't just code debt in the technical sense,

396
00:13:39,640 --> 00:13:40,640
it is process debt.

397
00:13:40,640 --> 00:13:42,800
It represents shortcuts in decision rights,

398
00:13:42,800 --> 00:13:44,320
shortcuts in review cycles

399
00:13:44,320 --> 00:13:45,960
and shortcuts in ownership.

400
00:13:45,960 --> 00:13:48,680
It is the collection of old exceptions, nobody retired

401
00:13:48,680 --> 00:13:51,640
and temporary fixes that somehow became permanent logic.

402
00:13:51,640 --> 00:13:54,720
Low-code platforms are very good at making this debt look manageable

403
00:13:54,720 --> 00:13:56,280
because the interface is friendly

404
00:13:56,280 --> 00:13:59,000
and the workflow appears understandable from a distance.

405
00:13:59,000 --> 00:14:01,920
But being readable is not the same thing as being simple.

406
00:14:01,920 --> 00:14:03,480
A visual workflow can still contain

407
00:14:03,480 --> 00:14:05,480
a massive amount of unstable logic,

408
00:14:05,480 --> 00:14:07,400
especially if it grew through constant patching

409
00:14:07,400 --> 00:14:08,920
instead of a real redesign.

410
00:14:08,920 --> 00:14:10,840
That is exactly where leaders get misled.

411
00:14:10,840 --> 00:14:13,440
The cost to build these systems keeps falling every year.

412
00:14:13,440 --> 00:14:15,840
A small team can stand something up in the weekend

413
00:14:15,840 --> 00:14:17,800
and a department can test a new idea

414
00:14:17,800 --> 00:14:19,440
without a long delivery cycle.

415
00:14:19,440 --> 00:14:22,120
All of that is useful, but the cost to verify

416
00:14:22,120 --> 00:14:24,800
whether the system still reflects the business correctly

417
00:14:24,800 --> 00:14:26,600
does not fall at the same speed.

418
00:14:26,600 --> 00:14:29,400
Verification still depends on human judgment and context.

419
00:14:29,400 --> 00:14:31,760
Someone still has to ask if the owner is current,

420
00:14:31,760 --> 00:14:33,080
if the rule is still valid

421
00:14:33,080 --> 00:14:35,160
or if the output can actually be trusted.

422
00:14:35,160 --> 00:14:36,880
What is actually happening is simple.

423
00:14:36,880 --> 00:14:38,760
It is getting cheaper to automate

424
00:14:38,760 --> 00:14:40,800
while it stays expensive to understand.

425
00:14:40,800 --> 00:14:42,080
Once that gap opens up,

426
00:14:42,080 --> 00:14:44,760
organizations drift into a very bad operating habit.

427
00:14:44,760 --> 00:14:46,440
They start to scale what they can build

428
00:14:46,440 --> 00:14:48,720
rather than scaling what they can actually govern.

429
00:14:48,720 --> 00:14:51,080
That is why I believe one leading indicator matters more

430
00:14:51,080 --> 00:14:53,920
than almost any dashboard people are currently tracking.

431
00:14:53,920 --> 00:14:55,040
The exception rate.

432
00:14:55,040 --> 00:14:57,320
If you want one honest signal for processed debt

433
00:14:57,320 --> 00:14:59,880
in an automated environment, you have to start there.

434
00:14:59,880 --> 00:15:02,000
The exception rate is the number of manual overrides

435
00:15:02,000 --> 00:15:04,320
and flow breaks divided by the total number

436
00:15:04,320 --> 00:15:05,680
of automated executions.

437
00:15:05,680 --> 00:15:08,200
This metric does not care how polished your app looks

438
00:15:08,200 --> 00:15:10,040
or whether your adoption numbers are high.

439
00:15:10,040 --> 00:15:12,480
It tells you whether reality is refusing to fit the path

440
00:15:12,480 --> 00:15:13,320
you designed.

441
00:15:13,320 --> 00:15:16,000
If you are under 5%, you are usually in a healthy range.

442
00:15:16,000 --> 00:15:18,440
Between 5 and 15%, you should start asking

443
00:15:18,440 --> 00:15:19,840
what changed in the business.

444
00:15:19,840 --> 00:15:22,880
If you are above 15%, you aren't scaling consistency anymore.

445
00:15:22,880 --> 00:15:24,320
You are scaling a mismatch.

446
00:15:24,320 --> 00:15:26,360
If you want the human version of the same test,

447
00:15:26,360 --> 00:15:28,000
just ask a simpler question.

448
00:15:28,000 --> 00:15:30,440
How many teams messages does it take to finish one automated

449
00:15:30,440 --> 00:15:31,200
process?

450
00:15:31,200 --> 00:15:33,000
The side channel always tells the truth.

451
00:15:33,000 --> 00:15:35,040
If people still need private chats, nudges,

452
00:15:35,040 --> 00:15:37,160
and workarounds to get the outcome they need,

453
00:15:37,160 --> 00:15:39,520
then the automation didn't actually remove the debt.

454
00:15:39,520 --> 00:15:40,760
It just relocated it.

455
00:15:40,760 --> 00:15:43,680
This is also why governance is so widely misunderstood.

456
00:15:43,680 --> 00:15:45,080
Most people hear the word governance

457
00:15:45,080 --> 00:15:46,680
and think of delays, forms, and committees

458
00:15:46,680 --> 00:15:47,840
that block progress.

459
00:15:47,840 --> 00:15:49,480
But the research on structured governance

460
00:15:49,480 --> 00:15:51,040
for platforms like Power Platform

461
00:15:51,040 --> 00:15:52,560
points in the opposite direction.

462
00:15:52,560 --> 00:15:54,440
Organizations with stronger governance actually

463
00:15:54,440 --> 00:15:56,200
report faster time to production

464
00:15:56,200 --> 00:15:58,040
because governance exposes ownership gaps

465
00:15:58,040 --> 00:16:00,120
and life cycle risks before they spread.

466
00:16:00,120 --> 00:16:02,840
The belief that mature automation means removing all friction

467
00:16:02,840 --> 00:16:03,680
is just wrong.

468
00:16:03,680 --> 00:16:06,600
Some friction is just noise and you should definitely remove that.

469
00:16:06,600 --> 00:16:08,560
But some friction carries a vital signal.

470
00:16:08,560 --> 00:16:10,200
It tells you where your model no longer

471
00:16:10,200 --> 00:16:11,920
fits the actual work being done.

472
00:16:11,920 --> 00:16:13,720
When you erase that signal too early,

473
00:16:13,720 --> 00:16:16,320
you lose the feedback you need to keep the system honest.

474
00:16:16,320 --> 00:16:19,120
Once you see that, the next move becomes much clearer.

475
00:16:19,120 --> 00:16:21,320
The answer isn't to slow everything down to a crawl.

476
00:16:21,320 --> 00:16:24,120
The answer is to put friction back into one specific place

477
00:16:24,120 --> 00:16:26,600
where it creates visibility instead of waste.

478
00:16:26,600 --> 00:16:30,000
The 30-day fix inject friction where it creates insight.

479
00:16:30,000 --> 00:16:31,520
So what do you do with this?

480
00:16:31,520 --> 00:16:33,240
The answer isn't more bureaucracy

481
00:16:33,240 --> 00:16:35,720
and it definitely isn't a giant redesign program that

482
00:16:35,720 --> 00:16:37,720
stalls your progress for six months.

483
00:16:37,720 --> 00:16:39,200
You need a small control surface.

484
00:16:39,200 --> 00:16:41,480
You need to add it fast, specifically in the places

485
00:16:41,480 --> 00:16:43,560
where your system currently hides too much.

486
00:16:43,560 --> 00:16:45,480
I call this a friction injection layer.

487
00:16:45,480 --> 00:16:48,160
The goal here isn't to slow down work across the board,

488
00:16:48,160 --> 00:16:50,440
but to place short deliberate checkpoints

489
00:16:50,440 --> 00:16:52,560
where they can expose ownership and drift

490
00:16:52,560 --> 00:16:54,680
before those problems start to compound,

491
00:16:54,680 --> 00:16:56,080
start with ownership tagging.

492
00:16:56,080 --> 00:16:58,440
Every flow, every automated intake,

493
00:16:58,440 --> 00:17:00,480
and every co-pilot facing knowledge pattern

494
00:17:00,480 --> 00:17:02,760
needs a named business owner instead of just a technical

495
00:17:02,760 --> 00:17:03,440
maintainer.

496
00:17:03,440 --> 00:17:05,000
That is a hard distinction to make.

497
00:17:05,000 --> 00:17:07,200
While platform teams can support the automation,

498
00:17:07,200 --> 00:17:09,760
they cannot be the ones who silently inherit accountability

499
00:17:09,760 --> 00:17:12,280
for business logic they don't actually control.

500
00:17:12,280 --> 00:17:14,080
If nobody in the business owns the outcome,

501
00:17:14,080 --> 00:17:15,400
the system will keep running

502
00:17:15,400 --> 00:17:17,560
while the process behind it slowly degrades.

503
00:17:17,560 --> 00:17:20,400
Next, you have to put expiry and review rules in place.

504
00:17:20,400 --> 00:17:22,920
A massive amount of low-code mess survives simply

505
00:17:22,920 --> 00:17:25,960
because nothing ever expires, which means teams remain open

506
00:17:25,960 --> 00:17:28,840
and flows remain live long after the original business need

507
00:17:28,840 --> 00:17:29,880
has changed.

508
00:17:29,880 --> 00:17:31,640
You should set a simple review rhythm

509
00:17:31,640 --> 00:17:33,120
where any workflow or automation

510
00:17:33,120 --> 00:17:36,080
showing no meaningful activity for 90 days triggers a review.

511
00:17:36,080 --> 00:17:38,240
This doesn't require a massive board meeting.

512
00:17:38,240 --> 00:17:40,840
It just requires a single decision to keep, simplify,

513
00:17:40,840 --> 00:17:42,640
archive or remove the asset.

514
00:17:42,640 --> 00:17:44,640
That one move creates structural exits

515
00:17:44,640 --> 00:17:47,000
and that is exactly what most fast automation setups

516
00:17:47,000 --> 00:17:48,080
are missing today.

517
00:17:48,080 --> 00:17:50,440
Then you need to make exception logging the default.

518
00:17:50,440 --> 00:17:52,200
This matters because leaders usually only

519
00:17:52,200 --> 00:17:53,480
see the successful runs.

520
00:17:53,480 --> 00:17:55,280
Even though the rest of the organization

521
00:17:55,280 --> 00:17:57,800
feels the manual work happening around the edges,

522
00:17:57,800 --> 00:17:59,960
manual overrides and human fixes happen

523
00:17:59,960 --> 00:18:01,760
every time the official path stalls.

524
00:18:01,760 --> 00:18:03,600
And if those moments stay invisible,

525
00:18:03,600 --> 00:18:05,680
your process dead stays invisible too.

526
00:18:05,680 --> 00:18:07,720
When every override becomes a data point,

527
00:18:07,720 --> 00:18:09,400
you stop arguing from anecdotes

528
00:18:09,400 --> 00:18:12,240
and you finally start seeing the true shape of the mismatch.

529
00:18:12,240 --> 00:18:14,320
After that, add lightweight approval

530
00:18:14,320 --> 00:18:16,920
for new automation patterns rather than every individual build.

531
00:18:16,920 --> 00:18:18,720
This is where most governance programs go wrong

532
00:18:18,720 --> 00:18:20,840
because they turn every small change into a gate,

533
00:18:20,840 --> 00:18:23,640
which eventually forces people to root around governance completely.

534
00:18:23,640 --> 00:18:24,440
You don't need that.

535
00:18:24,440 --> 00:18:25,480
You need patent review.

536
00:18:25,480 --> 00:18:28,400
If someone wants to introduce a new category of approval logic

537
00:18:28,400 --> 00:18:30,280
or a new AI-supported workflow,

538
00:18:30,280 --> 00:18:31,840
that should pause briefly for a check.

539
00:18:31,840 --> 00:18:33,880
However, repeating an already approved patent

540
00:18:33,880 --> 00:18:36,640
inside clear boundaries should never be slowed down.

541
00:18:36,640 --> 00:18:40,000
That balance matters because your job isn't to create drag.

542
00:18:40,000 --> 00:18:41,760
Your job is to move friction upstream

543
00:18:41,760 --> 00:18:44,160
before hidden complexity spreads downstream

544
00:18:44,160 --> 00:18:46,280
into your operations or your audit logs.

545
00:18:46,280 --> 00:18:48,200
The leadership upside here is actually bigger

546
00:18:48,200 --> 00:18:49,520
than just gaining control.

547
00:18:49,520 --> 00:18:52,080
You get cleaner visibility into what is actually running

548
00:18:52,080 --> 00:18:53,920
and you get much clearer accountability

549
00:18:53,920 --> 00:18:55,600
the moment something starts to drift.

550
00:18:55,600 --> 00:18:57,560
This creates better signals for simplification

551
00:18:57,560 --> 00:18:58,800
instead of endless patching.

552
00:18:58,800 --> 00:19:01,840
It also creates the conditions for faster delivery later on.

553
00:19:01,840 --> 00:19:04,440
Mostly because your teams will stop stacking new automations

554
00:19:04,440 --> 00:19:07,120
on top of structures that nobody fully trusts.

555
00:19:07,120 --> 00:19:09,720
From there, the operating rhythm stays simple.

556
00:19:09,720 --> 00:19:11,480
You review the exception rate monthly

557
00:19:11,480 --> 00:19:14,080
and if it crosses the warning range, you investigate.

558
00:19:14,080 --> 00:19:15,600
If it crosses the chaos threshold,

559
00:19:15,600 --> 00:19:18,160
you stop scaling that process and simplify the work

560
00:19:18,160 --> 00:19:20,400
before you automate anything else around it.

561
00:19:20,400 --> 00:19:21,360
That is the discipline.

562
00:19:21,360 --> 00:19:22,560
It is an anti-automation.

563
00:19:22,560 --> 00:19:24,840
It's just a refusal to confuse execution speed

564
00:19:24,840 --> 00:19:26,400
with actual process health.

565
00:19:26,400 --> 00:19:28,040
If you are wondering where to start this week,

566
00:19:28,040 --> 00:19:31,080
go audit the last five automations your organization shipped.

567
00:19:31,080 --> 00:19:32,200
Ask four questions.

568
00:19:32,200 --> 00:19:33,600
Who owns the business outcome?

569
00:19:33,600 --> 00:19:35,000
When does this expire?

570
00:19:35,000 --> 00:19:36,440
Where are the exceptions being logged?

571
00:19:36,440 --> 00:19:39,000
And finally, what manual workaround still exists

572
00:19:39,000 --> 00:19:40,200
outside the system?

573
00:19:40,200 --> 00:19:41,920
Those four questions will tell you very quickly

574
00:19:41,920 --> 00:19:43,360
whether you actually built relief

575
00:19:43,360 --> 00:19:47,080
or if you just buried a problem under a smoother interface.

576
00:19:47,080 --> 00:19:47,880
The shift is simple.

577
00:19:47,880 --> 00:19:49,440
You have to stop treating low-code speed

578
00:19:49,440 --> 00:19:51,400
as evidence that a process is healthy.

579
00:19:51,400 --> 00:19:53,640
The mature move isn't removing every bit of friction.

580
00:19:53,640 --> 00:19:56,720
It's keeping the friction that still tells you something useful.

581
00:19:56,720 --> 00:19:58,200
This week, pick one live automation

582
00:19:58,200 --> 00:19:59,760
and check the owner, the expiry date,

583
00:19:59,760 --> 00:20:01,960
the exception data and the side channel work.

584
00:20:01,960 --> 00:20:04,000
If the exception rate is above 15%,

585
00:20:04,000 --> 00:20:06,800
you need to pause expansion and simplify the process first.

586
00:20:06,800 --> 00:20:10,600
Subscribe to M365FM for more on system design

587
00:20:10,600 --> 00:20:12,960
and leave a review if this exposed a blind spot

588
00:20:12,960 --> 00:20:14,080
in your strategy.

589
00:20:14,080 --> 00:20:15,560
You can also find me on LinkedIn

590
00:20:15,560 --> 00:20:17,000
to share the process in your business

591
00:20:17,000 --> 00:20:19,720
that looks automated but still leaks manual work.

Mirko Peters

Founder of m365.fm, m365.show and m365con.net

Mirko Peters is a Microsoft 365 expert, content creator, and founder of m365.fm, a platform dedicated to sharing practical insights on modern workplace technologies. His work focuses on Microsoft 365 governance, security, collaboration, and real-world implementation strategies.

Through his podcast and written content, Mirko provides hands-on guidance for IT professionals, architects, and business leaders navigating the complexities of Microsoft 365. He is known for translating complex topics into clear, actionable advice, often highlighting common mistakes and overlooked risks in real-world environments.

With a strong emphasis on community contribution and knowledge sharing, Mirko is actively building a platform that connects experts, shares experiences, and helps organizations get the most out of their Microsoft 365 investments.

Digitizing Chaos: The Psychological Trap of Frictionless Automation