The next phase of AI governance may not begin with lawmakers. It may begin with operational expectations that businesses can no longer ignore.
By: Jennifer Gilligan, IntegraMSP President
For many business owners, AI regulation still feels like something off in the distance.
There is an understandable assumption that formal rules, compliance standards, and enforcement mechanisms will eventually arrive through federal legislation or large-scale regulatory action. Until then, many organizations continue treating AI adoption as a largely experimental or internal operational decision.
However, the landscape is already beginning to shift.
AI governance expectations are increasingly emerging through insurers, enterprise vendors, operational frameworks, and contractual requirements long before most formal regulation fully materializes. That matters because it mirrors the exact maturity curve businesses experienced with cybersecurity over the past decade.
Years ago, cybersecurity governance was often viewed as optional operational hygiene. Multifactor authentication, endpoint monitoring, backup standards, and formal security policies were largely considered best practices rather than business requirements. Today, many organizations cannot obtain cyber insurance, complete vendor onboarding, or maintain enterprise partnerships without demonstrating those controls.
AI governance appears to be following a similar path.
Recent reporting across the insurance and compliance sectors shows that insurers, regulators, and enterprise governance organizations are rapidly shifting the conversation from theoretical AI risk toward operational accountability. The National Association of Insurance Commissioners (NAIC) continues expanding its AI governance evaluation efforts, including the development of formal AI Systems Evaluation Tools designed to assess how insurers govern and oversee AI usage operationally. (content.naic.org)
At the same time, governance organizations and compliance analysts are increasingly emphasizing that businesses will need demonstrable oversight around AI-assisted decision-making, vendor accountability, data governance, workflow documentation, human review processes, and operational transparency. This is no longer simply a conversation about whether companies use AI. The conversation is increasingly becoming about whether organizations can demonstrate responsible operational control around how AI is being used.
That distinction is important for businesses of every size, particularly small and midsize organizations that may assume these conversations only apply to large enterprises or heavily regulated industries. In reality, operational expectations often move downstream much faster than regulation itself.
Large vendors implement governance requirements. Cyber insurers revise underwriting questionnaires. Enterprise clients update contractual language. Security frameworks evolve. Vendor assessments become more detailed. Procurement standards become more restrictive. Businesses then inherit those expectations operationally, regardless of whether formal federal AI regulation exists yet.
This dynamic is already visible in the insurance industry. Multiple recent governance and compliance analyses note that regulators are increasingly focused not only on whether AI systems are used, but also on how organizations document oversight, monitor outcomes, and maintain accountability around AI-assisted processes. (waterstreetcompany.com)
At the same time, AI is rapidly becoming embedded inside the platforms businesses already use every day. Microsoft 365, Google Workspace, CRM platforms, cybersecurity tooling, workflow automation systems, and industry-specific software suites are all layering AI capabilities directly into established ecosystems. Even construction management and field-service platforms are increasingly introducing AI-assisted scheduling, reporting, and operational tools designed to improve efficiency and project visibility.
For many businesses, particularly small and midsize organizations, the safest and most practical path forward may not be piecing together disconnected public AI tools or allowing unrestricted experimentation across departments. Instead, organizations will likely be better positioned by focusing first on AI capabilities already embedded within trusted enterprise platforms that provide existing governance structures, identity management, administrative controls, audit logging, and vendor accountability.
That does not eliminate risk entirely, but it does create a more manageable operational framework than allowing uncontrolled AI adoption to spread organically throughout the organization.
This is also where businesses are beginning to confront the growing issue of shadow AI. Employees are increasingly using browser-based AI tools, AI assistants, automated workflow platforms, and public large language models independently, often without leadership visibility into where company information is being entered or how AI-generated outputs are influencing operational decisions.
As AI adoption accelerates, organizations should begin thinking practically about operational visibility and internal governance. In many cases, that may start with:
- identifying which AI tools employees are already using
- determining where sensitive business data may be exposed
- establishing internal acceptable-use policies
- defining where human review is still required
- and prioritizing approved AI tools within existing software ecosystems
Importantly, this does not mean businesses should panic or avoid AI adoption altogether. Most organizations should absolutely continue exploring how AI can improve efficiency, communication, reporting, and operational scalability. In many cases, AI will become a meaningful competitive advantage for organizations capable of implementing it thoughtfully.
However, the businesses likely to succeed long term may not simply be the organizations adopting the most AI. They will more likely be the organizations building the operational maturity, governance discipline, and visibility necessary to manage AI responsibly as expectations continue evolving.
That is ultimately why the AI governance conversation matters now, even before formal regulation fully arrives. Because by the time many businesses believe governance has become mandatory, insurers, vendors, clients, and operational frameworks may have already made it an expectation.
