AI adoption is moving faster than most companies can manage it.
One employee uses ChatGPT. Another uses Claude. Someone else relies on Gemini. Marketing adds another AI platform. Sales experiments with another. Employees may be using free personal accounts without the company knowing exactly what information is being entered, where it goes, or how much the organization is actually spending.
The result can be what Vladimir Krstic, founder and CEO of Intrascope, describes as a new kind of AI chaos.
In this episode of the Glovisor Podcast, Vladimir explains why companies need to move beyond simply giving employees access to artificial intelligence. The next challenge is creating structure around AI—choosing the right models, controlling usage, protecting sensitive information, tracking costs, and creating a system that allows AI to become an organized business capability rather than a collection of disconnected tools.
The question for business leaders is no longer whether their companies will use AI.
It is whether they will control how AI is used.
AI Adoption Is Happening Faster Than AI Management
Vladimir built Intrascope after seeing a gap in how businesses were adopting generative AI.
Companies wanted to become more effective. They were looking for new tools and experimenting with different AI providers. But in the rush to adopt the technology, some organizations were overlooking three critical areas: context, company knowledge, and cost.
The problem is understandable.
Generative AI is relatively new, and the number of tools and models available to businesses continues to expand. Each tool can solve a particular problem, and each provider has its own strengths.
But when every department begins selecting its own AI tools independently, the organization can quickly lose visibility into what is happening.
AI adoption without a system can create complexity instead of efficiency.
Vladimir's central argument is that companies need to start treating AI like any other important business system—with structure, governance, and accountability.
What Is the Difference Between AI and Generative AI?
Before solving AI chaos, it helps to understand what businesses are actually adopting.
Vladimir explains that artificial intelligence is the broader category. Generative AI is the part of AI designed to create something, such as text, images, presentations, or other content.
That distinction matters because generative AI has made artificial intelligence dramatically more accessible to everyday employees.
Employees no longer need to be data scientists or software engineers to interact with sophisticated AI systems. They can open an account, type a prompt, and immediately begin using the technology.
That accessibility is one of AI's greatest strengths.
It can also create one of its greatest management challenges.
One Company Doesn't Need Just One AI Model
One of the most important insights from Vladimir's conversation is that businesses should not necessarily think about AI as a winner-take-all competition.
There is no single model that is automatically best at every task.
Different AI models can have different strengths. Vladimir describes using one model for web search, another for certain tasks, and another for different applications.
The objective is therefore not necessarily to choose one AI provider and force every employee and every task through it.
The objective is to match the right model to the right job.
That is where AI orchestration becomes important.
Instead of employees independently deciding which AI tool to use every time, an orchestration layer can help determine which model is appropriate for a particular request.
For a business, this creates the possibility of a more organized AI environment where multiple models can operate through one workspace.
The Right AI Model for the Right Task
Imagine a company where an employee needs to translate a document.
Does that task require the most expensive, highest-powered AI model available?
Probably not.
Now imagine another employee conducting complex research that requires significantly more sophisticated reasoning.
That task may justify a more advanced model.
Vladimir describes how Intrascope approaches this through automatic model orchestration. The system evaluates the request and selects an appropriate model rather than automatically sending every task to a top-tier model.
That distinction can have a significant impact on AI costs.
The broader business lesson is even more important: expensive resources should be used where they create additional value.
Businesses already apply this principle to employees, software, equipment, vendors, and other resources.
AI should be managed the same way.
AI Costs Can Become Invisible
One of the challenges with AI adoption is that spending can become difficult to see.
An organization may have multiple employees using different subscriptions, APIs, platforms, and services. Individual expenses may seem small, but collectively they can become significant.
Vladimir gives an example of the difference between a flat monthly subscription and usage-based AI.
A user might pay a fixed monthly fee even if they use only a small portion of the service. In a usage-based system, the company can instead pay according to actual usage.
That does not automatically mean usage-based AI will always be cheaper. The important point is visibility.
Companies need to understand what they are paying for, who is using AI, how frequently it is being used, and whether the chosen model is appropriate for the task.
Without that information, AI spending can become another form of business leakage.
A Real-World Example of AI Adoption
Vladimir shares an example involving a tax consulting company with more than 50 employees.
Before working with Intrascope, the company was not using paid AI services in a structured way. Employees were using free versions of tools such as ChatGPT and Gemini.
The opportunity was not simply to tell the company to "use AI."
The goal was to implement AI in a more structured way.
The company began using AI for tasks including communicating with clients and responding to emails. Vladimir says management could also access a dashboard showing employee AI usage and savings.
The example illustrates an important distinction.
AI transformation does not necessarily mean replacing everything a company already does.
Sometimes it means taking something employees are already doing and implementing it in a more controlled, measurable, and intentional way.
AI Governance Starts With Visibility
For business leaders, one of the most important questions is not simply:
"Are my employees using AI?"
It is:
"Do I know how my employees are using AI?"
Those are very different questions.
A company can officially approve one AI platform while employees independently use several others.
Employees may create personal accounts because they are convenient. They may use free tools because they are readily available. They may experiment with AI without considering whether the information they enter belongs inside an external system.
This creates a governance problem.
Business leaders need visibility into AI usage if they want to manage AI effectively.
That includes understanding which tools are being used, what types of tasks they are being used for, what information is being shared, and what those tools are costing the organization.
Access alone is not governance.
Your Company's Data May Be the Bigger Issue
The most serious concern raised during the conversation may not be AI spending.
It is data.
Employees routinely work with sensitive information. Tax documents, financial information, customer records, proprietary business information, internal strategies, and other confidential material can potentially find their way into AI systems.
Vladimir warns that companies need to think carefully about what happens when employees use personal or free AI accounts and upload sensitive company information.
This creates a difficult challenge because employees may not even realize they are creating a risk.
They may simply think:
"I need help summarizing this."
"I need to respond to this email."
"I need to analyze this document."
The AI tool makes the task easier.
But the organization needs to know what information was provided to the system and how that information is handled.
Data protection therefore needs to become part of the AI conversation from the beginning.
SaaS or Private AI Infrastructure?
Vladimir describes two broad approaches organizations can consider.
For some businesses, a SaaS-based AI platform may provide what they need. A smaller team working on routine tasks may not need its own private infrastructure.
For larger organizations or businesses handling highly confidential information, the need for greater control can change the equation.
Intrascope can provide private systems deployed on a company's own infrastructure and connected to models designed for that organization.
The advantage Vladimir emphasizes is control.
A private AI environment can give an organization greater control over where its data resides and how its AI infrastructure is managed.
That does not mean every company needs a private AI system.
The important lesson is that the choice should be intentional.
Businesses should evaluate their size, data sensitivity, usage patterns, regulatory environment, costs, and operational requirements before deciding how much infrastructure and control they need.
AI Implementation Is a Business Strategy—Not Just a Technology Decision
It is tempting to treat AI as an IT project.
Install the tools.
Train employees.
Start using them.
Move on.
But Vladimir's perspective suggests that AI adoption needs to involve much more of the organization.
AI affects productivity.
It affects costs.
It affects data.
It affects workflows.
It affects customer communication.
It affects employee behavior.
It can influence how decisions are made and how work gets completed.
That makes AI implementation a business strategy issue, not merely a technology issue.
Leaders need to ask where AI creates measurable value and where it introduces unnecessary complexity.
They also need to decide who owns AI governance and how usage will be monitored.
Entrepreneurs Have Too Many Ideas—The Challenge Is Choosing
Vladimir's entrepreneurial journey provides an interesting connection to the AI discussion.
He started his first company at 20 and describes having an internal drive to do something beyond the routine of a traditional job.
But entrepreneurship creates another problem: too many possibilities.
As Joe points out during the conversation, founders often have tremendous creativity and energy. That can generate a constant stream of ideas.
Vladimir's response is especially relevant to both entrepreneurship and AI adoption:
You have ideas, but you need to choose wisely.
The same principle applies to AI.
Companies can adopt hundreds of AI tools.
They do not need to.
The objective is not to use the most AI possible.
The objective is to use the right AI in the right places to produce meaningful business results.
AI Chaos Is Often a Decision-Making Problem
At first glance, AI chaos appears to be a technology problem.
But underneath it is often a decision-making problem.
Who chooses the AI tools?
Who approves them?
Who monitors them?
Who determines which models should be used?
Who is responsible for sensitive information?
Who tracks costs?
Who decides whether an AI tool actually improves productivity?
Without clear answers, companies can end up with technology scattered across departments without a coherent strategy.
The technology may be sophisticated.
The system around the technology may not be.
That is why AI governance matters.
The Future of AI May Require More Control, Not Less
Vladimir believes businesses are still early in the AI adoption cycle.
As companies become more dependent on AI, the importance of controlling their infrastructure may increase.
For some organizations, that could mean investing in private infrastructure or locally deployed models. For others, it may mean using SaaS platforms with stronger governance and centralized management.
There is no single solution for every business.
But the underlying principle is consistent: companies need to know what they are using, why they are using it, what it costs, and what happens to their data.
The organizations that answer those questions early will be better positioned to make deliberate decisions as AI continues to evolve.
Take Back Control Before AI Controls the Business
The most important message from Vladimir's conversation is not that every company needs Intrascope.
It is that every company using AI needs a strategy.
AI should not simply spread organically across an organization because employees discover tools on their own.
Business leaders should create a framework for adoption.
They should identify the problems AI is supposed to solve. They should determine which tools and models fit those problems. They should establish expectations around sensitive data. They should monitor usage and costs. And they should continually evaluate whether the technology is producing the expected results.
AI can create extraordinary leverage for a business.
But leverage without control can also create risk.
The goal is not to eliminate experimentation. Entrepreneurship requires experimentation.
The goal is to create enough structure that experimentation produces learning rather than chaos.
The Entrepreneur's AI Lesson: Choose Wisely, Then Scale
Vladimir began his entrepreneurial journey because he wanted something more than repeating the same work every day. That same mindset is visible in the way he approaches AI.
Technology creates possibilities.
Entrepreneurship is about deciding which possibilities are worth pursuing.
For companies adopting AI, that means resisting the temptation to chase every new tool simply because everyone else is using it.
Instead, ask better questions.
What problem are we solving?
Which model is appropriate?
What data is involved?
What will it cost?
Who should have access?
How will we measure the result?
And perhaps most importantly:
Who is responsible for making sure the system stays under control?
For entrepreneurs and business leaders working to build more efficient, scalable organizations, that kind of structured thinking is at the heart of sustainable growth. Explore more business growth, personal development, communication, and leadership resources at Glovisor.
Final Takeaway: AI Adoption Without Governance Creates Chaos
AI is not going away.
The number of models, tools, platforms, and applications available to businesses will continue to grow.
That makes the challenge for leaders increasingly clear.
The future is not simply about adopting AI faster.
It is about adopting AI intelligently.
The companies that thrive with AI will need more than access to powerful models. They will need systems for choosing the right tools, protecting data, managing costs, monitoring usage, and aligning technology with actual business objectives.
As Vladimir's experience with Intrascope demonstrates, the next stage of AI adoption is not just experimentation.
It is orchestration.
It is governance.
It is control.
And for business leaders, taking control of AI may be the difference between using technology as a competitive advantage and allowing technology to become another source of organizational chaos.