The conversation around AI in business tends to move between two extremes. On one side, the promise, productivity gains, competitive advantage, and transformation. On the other side, warnings, data breaches, compliance failures, and tools that do more harm than good.
Neither extreme is particularly useful for a business owner in Lancaster County trying to make a practical decision. What is useful is an honest look at the specific risks that apply to businesses about your size, in your industries, and what it takes to address them before they become problems.
That is what this blog is about.
Risk 1: Your Data Ends Up Where You Did Not Intend
This is the risk that generates the most concern, and rightly so.
When employees use general-purpose AI tools for work tasks without an approved policy in place, your organization’s data leaves your control. A team member summarizing a client proposal in ChatGPT. A manager drafts a financial memo with a free AI writing tool. An employee using a browser-based AI assistant to clean up a sensitive internal document.
In each of these scenarios, the data entered into the tool is processed on external servers. Many consumer AI platforms retain that data and use it to improve their underlying models. The client details, financial figures, and operational information your employee typed in may be incorporated into a dataset that has no obligation to protect your business or your clients.
For businesses in healthcare, where HIPAA governs how patient information is handled, this exposure is not just a business risk. It is a compliance violation. For insurance agencies, financial services firms, and legal practices handling client data under regulatory frameworks, the same principle applies.
The protection against this risk is a combination of policy and platform selection. A documented AI policy that specifies which tools are approved and what data categories are off limits for AI processing closes the behavioral gap. Platform selection, specifically, choosing tools like Microsoft Copilot that operate within your existing environment rather than sending data to external servers, closes the architectural gap.
Risk 2: Permissions Gaps Become Visible in the Worst Way
This is the risk that catches most businesses off guard, because it is a consequence of deploying AI on top of an environment that was never designed to have an AI searching across it.
Microsoft Copilot accesses content based on user permissions. If a user has access to a file or a folder, Copilot can surface that content in response to that user’s queries. In a well-configured Microsoft 365 environment where permissions reflect actual business intent, this is the right behavior. In an environment where permissions have accumulated organically over years, broad access granted for convenience, former employees not fully offboarded, sensitive files in widely accessible locations; this behavior creates problems.
A manufacturing firm’s operations manager asking Copilot about recent project communications should not receive content from a confidential HR matter. A financial services employee asking about client account history should not see records from accounts that are not theirs.
These are real world consequences of activating an AI tool that searches your entire environment without first reviewing whether that environment’s access controls reflect what you actually intend.
The fix is a permissions audit before Copilot deployment, a systematic review of who has access to what, and whether that access matches the business’s actual requirements. Permissions management is identified as the single most important prerequisite for a safe and successful deployment. It is not optional.
Risk 3: AI Output Gets Used Without Adequate Review
Copilot drafts. It summarizes. It generates. It does not verify.
The output Copilot produces requires human review before it is used, and that review needs to be applied with the understanding that AI can produce errors that sound authoritative. A document drafted with a factual inaccuracy. A summary that mischaracterizes the content of the original. A data analysis that draws a plausible sounding but incorrect conclusion from the underlying numbers.
For most business tasks, this is manageable with the right review process in place. For tasks with higher stakes, client-facing communications, compliance documentation, and financial reporting, it requires more deliberate oversight.
The risk is not that Copilot makes mistakes. All tools make mistakes. The risk is that businesses deploy AI without establishing clear protocols for how outputs are reviewed before they are used. That gap is what turns an AI-assisted error into a client relationship problem or a compliance issue.
The solution is user training that is specific to the workflows where AI assistance is being applied, not a one-page guide about what Copilot can do, but practical guidance on where review is non-negotiable and what the standard for that review looks like.
Risk 4: Adoption Happens Without Governance
This is the risk that connects all the others. When AI tools are deployed without a governance framework, a clear policy on which tools are approved, what data they can access, how outputs should be handled, and what the process is when something goes wrong, employees make their own decisions.
Some of those decisions will be reasonable. Others will create exposure that surfaces long after the initial decision is made.
Research consistently finds that organizations with documented AI governance frameworks report significantly lower rates of unauthorized tool adoption and data exposure incidents than those without. The governance framework does not have to be complex. It must exist before broad AI deployment, not as an afterthought once problems emerge.
Managing Risk Is Not a Reason to Wait
Managing AI risk is not a reason to avoid AI adoption. It is a prerequisite for adoption that delivers value without creating the kind of problems that offset that value.
The businesses that are getting real returns from Copilot in Central PA and across the country are the ones that address these risks before deployment rather than after. They conducted a permissions audit. They established a governance policy. They selected platforms that operate within their existing security environment. They trained their teams on what appropriate use looks like and where review is required.
None of that is a complex or expensive project with the right partner involved. But all of it must happen in the right sequence for the deployment to deliver what it is capable of.
Join Us June 24th: AI and Copilot 101 Webinar
On June 24th at 11:00 AM ET, TCW-GAV is hosting a free live webinar where Steve Walter and Julie Hodges from Microsoft will address these risks directly, what they are, how they apply to Central PA businesses, and what the practical steps are to address them before deployment.
Register for the June 24th AI and Copilot 101 Webinar
TCW-GAV: Risk-Aware AI Adoption for Central PA Businesses
At TCW-GAV, we approach AI adoption the same way we approach every technology decision for our clients — with a clear-eyed assessment of the risks before making any recommendation. Our Copilot readiness work for Central PA businesses includes:
- Data governance and permissions review before any AI tool is activated
- AI policy development that covers approved tools, data categories, and output handling
- Security configuration assessment aligned to Copilot deployment requirements
- User training designed around your team’s specific workflows and review obligations
- Ongoing support as the AI landscape continues to evolve
The Risk Conversation Is the Starting Point
An honest assessment of the risks is not a reason to wait. It is the foundation for moving forward confidently.
Register for the June 24th webinar and bring the risk questions you have been sitting on. That is exactly what the session is designed to address.