AI has the potential to change the way businesses operate.
It can help employees find information faster, automate repetitive work, analyze large amounts of data, improve decision-making, streamline workflows, and turn years of organizational knowledge into something people can actually use.
But there is a problem.
A lot of businesses are rushing to implement AI before they have addressed the operational problems that already exist inside their organization.
Poorly documented processes. Files scattered across desktops, shared drives, email, OneDrive, SharePoint, and who knows where else. Inconsistent file names. Employees with access to information they probably shouldn’t have. Years of outdated documents nobody wants to delete. No real data governance. No clear ownership of information.
And sometimes, no real idea what the company is trying to accomplish with AI in the first place.
Adding AI to that environment doesn’t automatically fix the problem.
In some cases, it simply makes the existing problems move faster.
AI Can Amplify Bad Business Processes
One of the biggest misconceptions about AI implementation is that automation automatically creates efficiency.
It doesn’t.
If your underlying business process is poorly designed, automating it can simply allow your organization to execute a bad process faster and at greater scale.
Imagine a workflow that already includes unnecessary approvals, duplicate data entry, inconsistent documentation, unclear ownership, and employees using different methods to accomplish the same task.
Now add AI.
You may be able to automate portions of that workflow, but you haven’t necessarily improved the process.
You’ve automated the dysfunction.
Before deciding where AI belongs in your organization, leadership should first understand how the work actually gets done.
Where are the bottlenecks?
Where are employees doing repetitive work?
Where are decisions being delayed?
Where does information get duplicated?
Which processes depend heavily on one employee’s institutional knowledge?
Which steps exist because they genuinely add value, and which exist because “that’s how we’ve always done it”?
AI implementation should start with business outcomes and process improvement—not with a piece of software.
Your AI Is Going to Meet Your Data
And this is where things can get ugly.
Many businesses have accumulated years of information without ever developing a real information architecture.
There might be files in:
Shared network drives.
Microsoft Teams.
SharePoint.
OneDrive.
Employee desktops.
Email attachments.
Personal folders.
Old project directories.
Cloud applications.
And sometimes several different copies of the same document in several different locations.
Now consider the file names.
Proposal-Final.docx
Proposal-Final2.docx
Proposal-Final-USETHISONE.docx
Proposal-Final-REVISED-Steve-NEW.docx
We’ve all seen it.
Humans have learned to navigate this kind of mess because someone usually knows where everything is.
AI doesn’t make the underlying mess disappear.
For AI-powered knowledge systems to become genuinely valuable, organizations need to know which information is current, authoritative, properly classified, appropriately stored, and accessible to the right people.
Otherwise, you are trying to build intelligence on top of information chaos.
AI Makes Access Control More Important, Not Less
This is one of the biggest issues businesses need to understand before implementing enterprise AI.
AI systems such as Microsoft 365 Copilot can work with organizational information available to a user through existing permissions. Microsoft specifically emphasizes governing SharePoint and OneDrive, identifying potentially overshared information, controlling access, addressing inactive or ownerless sites, and reducing unintended exposure when preparing an environment for Copilot.
In other words:
If your permissions are a disaster today, AI doesn’t magically create a good permissions model tomorrow.
Think about the environments that have evolved organically for years.
Someone needed access to a folder five years ago and still has it.
A SharePoint site was created for a project nobody remembers.
Entire departments have access to information because assigning permissions individually was inconvenient.
External sharing links were created and never reviewed.
Employees changed roles, but their access never changed with them.
Nobody is quite sure who owns certain repositories anymore.
AI makes cleaning this up more important because modern AI tools can make information dramatically easier to discover and use.
Microsoft’s own guidance emphasizes that Copilot respects existing permissions and recommends identifying overshared or sensitive content, restricting inappropriate access, and governing content throughout its lifecycle.
That’s not an AI problem.
That’s a governance problem that AI can expose.
Data Governance Cannot Be an Afterthought
Who owns your company’s information?
Which information is considered authoritative?
How long should information be retained?
Who should have access to financial information?
What about HR records?
Contracts?
Customer information?
Intellectual property?
Operational procedures?
Compliance documentation?
What happens when an employee leaves?
What happens when a document becomes obsolete?
Who is responsible for reviewing sensitive information?
If nobody can confidently answer those questions, the organization probably isn’t ready to turn AI loose across its information environment.
Data governance doesn’t mean making it impossible for employees to get anything done.
Good governance does the opposite.
It creates enough structure that employees—and eventually AI systems—can find and use information confidently without unnecessarily exposing the organization to risk.
File Structure Suddenly Matters Again
There is sometimes an assumption that AI will make organization unnecessary because employees will simply ask AI to find whatever they need.
That’s backwards.
AI makes a well-organized information environment even more valuable.
Consistent naming conventions, defined document ownership, sensible folder and site structures, metadata, retention policies, lifecycle management, version control, and access governance all contribute to a stronger organizational knowledge foundation.
Microsoft similarly recommends keeping organizational content current and well governed when preparing SharePoint and OneDrive for Copilot and AI agents.
The objective isn’t simply having prettier folders.
The objective is knowing:
What information do we have?
Where does it belong?
Who owns it?
Who should be able to access it?
Which version should we trust?
That becomes increasingly important as AI becomes another way employees interact with organizational knowledge.
Don’t Buy AI Until You Know What You’re Trying to Accomplish
This may be the most important part.
“We need AI” is not a business strategy.
Neither is:
“Our competitors are using it.”
Neither is:
“Microsoft added Copilot.”
Neither is:
“The CEO saw a demonstration at a conference.”
Before investing heavily in AI, leadership should be able to articulate what business problem the organization is trying to solve.
Maybe the objective is reducing the time required to review contracts.
Maybe it’s improving access to institutional knowledge.
Maybe it’s automating repetitive document processing.
Maybe it’s helping leadership analyze financial scenarios.
Maybe it’s reducing administrative work for employees.
Maybe it’s improving customer service.
Maybe it’s accelerating a specific operational workflow.
Those are business objectives.
“Implement AI” isn’t.
Kraken’s approach to AI strategy starts with discovery—understanding existing workflows and identifying where AI can deliver meaningful impact—before designing and implementing AI systems around those processes.
Technology should follow the business objective.
Not the other way around.
Before AI, Build the Foundation
For many organizations, the best first step in an AI initiative may have very little to do with AI.
It may involve cleaning up SharePoint.
Fixing permissions.
Reviewing identity and access controls.
Removing obsolete information.
Creating file-naming standards.
Documenting critical processes.
Establishing information ownership.
Defining retention requirements.
Improving cybersecurity controls.
Creating acceptable-use policies for AI.
Identifying sensitive information.
Building a governance structure.
Determining which business processes should be improved before they’re automated.
And deciding what leadership actually expects AI to accomplish.
None of that is particularly flashy.
But it is what turns AI from an interesting technology experiment into a sustainable business capability.
AI Readiness Is Really an Operational Maturity Question
The companies that get the most value from AI won’t necessarily be the companies that buy the most AI tools.
They will be the companies that build the strongest foundation underneath them.
They understand their processes.
They know where their information lives.
They control access to sensitive data.
They maintain authoritative organizational knowledge.
They have appropriate cybersecurity controls.
They establish governance.
They understand their business objectives.
And then they use AI to amplify an organization that already knows where it is going.
That’s an important distinction.
Because AI is an amplifier.
Give it well-designed processes, trustworthy information, appropriate permissions, good governance, and clear business objectives, and it can amplify the strengths of your organization.
Give it chaos?
It can amplify that too.
Start With Clarity Before You Start With AI
Before making a major AI investment, leadership needs a clear understanding of the organization’s current technology environment, cybersecurity posture, operational maturity, data practices, risks, and AI readiness.
That’s exactly why we built the Kraken Strategic Technology Assessment.
The assessment helps leadership understand where the organization stands today, identify priority risks and opportunities, evaluate AI readiness, and develop a practical roadmap aligned with the business.
Then, when the organization is ready to move from assessment to implementation, Kraken’s Fractional AI Officer Services provide ongoing executive-level AI strategy, implementation, workflow development, knowledge systems, and optimization.
Because successful AI adoption isn’t about turning on another piece of software.
It’s about building an organization that is ready to use it.
Before you go all in on AI, get your house in order.
