What Happens When AI Goes Unmanaged: Perspectives From Nitro's Leadership Team

AI adoption is accelerating across industries. But adoption without oversight creates risk. What happens when organizations roll out AI tools without clear governance, security protocols, or ways to measure success?

We asked Nitro's leadership team to weigh in on a single question: What's at stake if AI isn't managed properly?

Their answers cut across competitive positioning, security, budgeting, and employee behavior. Together, they paint a picture of why getting AI governance right matters now.

Why companies without AI governance fall behind competitors

Organizations that fail to integrate AI effectively risk losing ground to competitors who figure it out first.

"If organizations don't manage AI effectively, they risk the competition pulling ahead," says Garrett La Cava, SVP Global Sales. "If AI is not properly integrated into the layers with which they operate within their company in a way in which it's easy to access, easy to consume, easy for their employees to use it to their benefit, they're going to be left behind."

The key phrase here is "easy to use." AI tools that sit unused because they're too complicated or poorly integrated deliver no value. Employees need to be able to adopt these tools quickly and apply them to real work. Otherwise, the investment doesn't pay off, and the organization loses the productivity gains their competitors are capturing.

Why missing AI governance policies create security vulnerabilities

Poor implementation and lack of governance top the list of AI risks for many enterprises, and the data suggests most organizations aren't prepared.

"Surprisingly, 63% of companies don't actually have AI governance policies, and that opens enterprises up to a lot of risk, particularly if the products that they've chosen don't have security-first design," says Cassie Harman, Chief Product Officer.

That figure should concern any IT or security leader. Without governance policies in place, organizations have no framework for evaluating which AI tools are safe to use, how data should be handled, or who is accountable when something goes wrong.

Security-first design also matters. Not every AI product on the market treats data protection as a core requirement. Organizations need to evaluate how vendors handle data, whether information is retained or used for training, and what compliance certifications are in place.

How to budget for AI tools and measure ROI

Getting AI right requires both financial investment and a way to track whether that investment is paying off.

"I do believe if employees don't embrace this technology, they and the organizations will get left behind," says Dave Andreasson, Chief Financial Officer. "On top of that, how are you measuring the success of those projects and those tools? Is there a clear ROI that you can point to that determines whether something is kind of working or not? So I think having a handle on both of those pieces is super important."

Two things stand out here. First, employees need room to experiment. Restricting AI budgets entirely or limiting access too tightly may seem like a safe approach, but it prevents the organization from learning which tools actually deliver results.

Second, measurement matters. AI tools can generate a lot of excitement, but without clear metrics, it's hard to separate genuine productivity improvements from hype. Finance and IT leaders should be asking: How are we tracking time saved? Where are we seeing cost reductions? What workflows have actually changed?

What is shadow AI and why it's a compliance risk

One risk many organizations overlook is what happens when employees don't have access to approved AI tools. The answer: they find their own.

"The biggest risk is not providing AI tools to your organization," says John Fitzpatrick, Chief Technology Officer. "Users want to use these AI-powered tools because of the efficiency gains it gives them. And so if you don't provide them with the tools that you manage, many users will just end up using unapproved tools without enterprise-level controls, data residency, and security that your enterprise requires in order to stay compliant."

This is the shadow AI problem. Employees who see the value of AI will find ways to use it, with or without IT approval. When they turn to consumer-grade tools that lack enterprise security controls, sensitive data can end up in places it shouldn't be.

The solution isn't to ban AI. It's to provide managed tools that meet both employee needs and enterprise security requirements. That way, organizations can capture productivity benefits while maintaining control over data and compliance.

Building an AI strategy that works

The perspectives from Nitro's leadership converge on a clear message: AI management isn't optional. The risks of inaction span competitive positioning, security, financial oversight, and compliance.

Organizations that build governance frameworks, invest in security-first tools, allocate budget for experimentation, and measure outcomes will be positioned to capture AI's benefits. Those that don't will face security vulnerabilities, wasted investment, and employees working outside approved systems.

Ready to bring AI into your document workflows with enterprise-grade security? Explore Nitro AI to see how organizations work faster without compromising data protection or compliance.