“It does not do to leave a live dragon out of your calculations,
if you live near him.”— J.R.R. Tolkien, The Hobbit
At Integrated IT, we have been working through our own internal AI adoption strategy for a little over a year and a half. Recently, I reflected on where we started, and how that starting point compares to where many business leaders I speak with are as they begin exploring AI.
Like us, nearly everyone has heard the same quiet but persistent message: use AI or fall behind. Competitors are touting how AI will skyrocket profitability. Market peers are adding flashy AI call-to-action boxes to their websites. Clients are asking, and in many cases expecting, when and how much cost savings will materialize once AI is adopted.
The Push to Adopt
The narrative is remarkably consistent. AI is framed as a productivity shortcut, a competitive necessity, and the inevitable next step. And to be clear, AI does offer real benefits.
It is also hard to argue with the numbers. Recent reports consistently note that the percentage of companies with fully modernized, AI-led processes has nearly doubled year over year, and that these organizations significantly outperform peers across revenue growth, productivity, and their ability to scale generative AI use cases. (source, source, source)
That story is compelling, and it explains why so many organizations feel pressure to move quickly. What is missing from the conversation, though, is whether those organizations are prepared for what comes with that speed.
The Overlooked Risk
The problem is not the push to adopt AI. The problem is that adoption is often happening faster than governance, risk management, and control structures can mature to support it.
For us, the question is not whether to adopt AI. It is whether AI is being adopted in a way that preserves control, accountability, and regulatory defensibility. As productivity pressure increases, organizations are being exposed to new categories of risk that many cannot yet fully quantify, monitor, or explain. That gap matters.
There are early warning signs that many organizations are underestimating the downstream impact of AI adoption:
“As organizations rapidly adopt generative and agentic AI across their operations, an important counter-movement is emerging: insurers are apparently seeking to limit or exclude coverage for AI-related risks. This shift would mark a pivotal moment for corporate risk management, one that many companies may not have yet fully appreciated.” (source)
This is a concrete indication that risk is being re-priced faster than many organizations realize.
And the risks themselves are neither abstract nor just Dilbert-speak.
These are issues security teams are already contending with:
- AI providers may use customer data to train and refine their models, often with limited transparency.
- Threat actors are actively working to bypass AI platform safeguards designed to prevent data leakage.
- AI integrations can expose far more data and system access than organizations intend, especially through APIs.
- Productivity pressure drives users to share sensitive company and regulated data with AI tools, frequently without realizing the downstream impact.
In practice, this rarely looks reckless. Users share PII, PHI, credentials, intellectual property, internal documents, and regulated data with AI tools, often without malicious intent and often without a clear understanding of where that data goes, who can access it, or how long it persists.
This is where the risk stops being theoretical and becomes personal. That medical record uploaded for summarization or advice is no longer fully under your control. That contract shared for a quick summary or risk assessment now exists on infrastructure you do not manage, protected by controls you did not design, and governed by terms you may not have read closely enough.
More Deliberate Adoption
AI introduces new data flows, new integrations, new third-party dependencies, and new accountability questions that become increasingly difficult to unwind once embedded into daily operations.
The belief that risk can be addressed after jumping head-first into AI adoption is a dangerous one. Like living next to a dragon, ignoring it does not make it less real. It just makes the consequences harder to contain.
Handled carefully and deliberately, AI can be an extraordinary asset. But realizing its benefits without creating unnecessary exposure requires discipline, governance, and a willingness to slow down just enough to get it right the first time.
If this resonates and you want to discuss your AI posture, let’s connect.
AI is already inside your organization. The question is whether the foundation supporting it was built for this pace.
Read this practical guide for security and IT leaders navigating AI adoption without losing control.
Download our guide and gain a clearer path forward.
