Editor’s note: The following is a guest post by Rick Mutzel, manager of technology at managed IT and security service provider Omega Systems.
A big mistake organizations make is assuming AI is a blanket solution to an undefined problem. It’s a mindset that has urgent implications for regulated industries.
Healthcare and financial services firms are dangerously underestimating the risk of misapplying AI by treating the technology as merely automation. It’s an approach to AI that leads organizations to move quickly on deployments with plans to figure out governance later.
That approach was manageable with traditional automation. Current AI models, however, create operational, security and compliance risks that quietly compound inside client environments. Breaking this news to clients will be a defining challenge for managed service providers moving forward.
Automation is rules-based and thrives on predictability. Give it a well-defined, consistently executed process and it will drive efficiency. AI is adaptive and data-driven. It generates outputs based on patterns and probabilities rather than predefined rules. That makes it powerful in ways automation never was, but also less predictable.
That difference matters because the two types of technologies fail in fundamentally different ways.
A healthcare provider using rules-based automation to route prior authorization requests will see the failure immediately when a claim is misrouted or rejected. The same provider using AI to summarize patient records may receive an output that omits a medication interaction but nonetheless appears to be complete. The risk compounds until it surfaces in a patient encounter or an audit.
The same dynamic applies in financial services, where AI-generated compliance summaries can miss material requirements without producing any visible error.
The consequences are already measurable. IBM found organizations experienced an average of 54 AI-related incidents last year, with 17% classified as high-severity. Organizations that embedded governance directly into their AI systems experienced 25% fewer incidents.
The risk isn't theoretical. Ford recently rehired more than 300 veteran engineers after AI-driven automation couldn't replicate the tacit, decades-earned expertise its quality checks depended on — a reminder that institutional knowledge doesn't automate as cleanly as many leaders assume.
MSPs are now being asked to help clients navigate this environment — not just implementing AI, but governing it.
The MSP adivisory role
MSPs manage the systems, workflows, users and security environments clients depend on every day. This gives them a view across the business that no software vendor or AI platform provider has. And it’s exactly what regulated organizations need right now.
When an organization enables an AI tool inside their CRM, or a department starts processing sensitive communications through an AI platform, the risk does not stay contained. It touches data governance, access controls, regulatory obligations and downstream workflows that no single internal team is evaluating in full.
Many organizational leaders know this, according to a DDN report published earlier this year. The survey of 600 IT and business leaders found that 72% of organizations rely on third-party expertise to build and manage their AI infrastructure. Only 12% depend solely on in-house talent. For most regulated organizations, the MSP is the partner making those calls.
That's a different role than MSPs have historically played. Keeping systems running was the baseline. Now clients are asking how AI should be governed, where it can be trusted to act independently and where human oversight needs to stay in the loop.
A healthcare practice needs to know whether an AI-powered clinical documentation tool meets HIPAA requirements before it's live — not after. A financial services firm needs documented governance around AI-generated client recommendations to satisfy audit and regulatory expectations.
Answering those questions positions the MSP as a partner that can help an organization grow without introducing risks it isn’t prepared to manage.
From IT services to AI governance
Most organizations aren't asking the right questions before they enable AI. Are the underlying processes mature enough to support it or will AI simply inherit the inconsistencies that already exist? What can AI be trusted to act on independently, and where does it still need human approval?
Those questions determine whether an AI deployment creates value or compounds risk — and answering them is where the MSP advisory conversation should start.
For MSPs working with regulated clients, that conversation should cover five areas:
- Understand the client: Evaluate the organization's regulatory requirements, unique operational environment and risk tolerance before recommending AI or automation solutions.
- Establish governance: Define policies for AI use, data handling, accountability and human oversight, while ensuring AI vendors meet the organization's security and compliance standards.
- Focus on value creation: Start with the business problem, not the technology. Rather than adopting AI for its own sake, identify where it creates measurable value and where traditional automation is the better fit. That discipline extends to cost. Running a routine lookup through a large AI model burns tokens — and budget — that a standard search or existing system could resolve for a fraction of the price. Overuse isn't just inefficient, it's a governance blind spot in its own right.
- Close the pilot gap. Most AI pilots look promising. Few deliver sustained value. The gap is almost always the same: the pilot ran in isolation, disconnected from the workflows and governance requirements that determine whether AI performs in production. Move clients from proof-of-concept into integrated, governed workflows before considering the deployment complete.
- Treat adoption as risk mitigation. Governance frameworks only work if people use them. In regulated environments, employees who don't understand how an AI system reaches its outputs will route around it. Staff education isn't a soft addition to an AI rollout. It's risk mitigation.
AI is changing what organizations need from their MSPs. The challenge is no longer simply deploying and supporting technology, but helping clients govern, secure and apply it responsibly.
The organizations that get AI and automation right will not necessarily be the ones that move fastest. They will be the ones that treat adoption as a security, governance and business-risk decision from the start.
