As artificial intelligence becomes embedded across business operations, governance is re-emerging as a strategic priority, particularly for organizations operating in regulated or risk-sensitive industries.
While governance itself is not new, the scale and complexity of modern AI systems are forcing enterprises to rethink how they manage risk and accountability.
Channel Insider spoke with Anthony Habayeb, CEO of AI governance platform Monitaur, about how organizations and their channel partners should approach governance in the AI era.
Governance predates AI — but AI is changing how risk is defined
Governance refers to the policies, controls, and decision frameworks organizations use to manage risk, accountability, and outcomes across their operations.
It is not, Habayeb argues, just compliance or risk management; it’s a holistic approach to understanding what your business needs to succeed, for itself and for its customers.
As AI adoption pushes companies to rethink not just their approach to technology but the very concept of risk, governance is back in the spotlight.
“I think the reality is AI is starting to just be everywhere,” Habayeb said. “The degree of maturity is still really variable, though. Basic research tasks are very different, for example, than leveraging it for strategic business decision-making activities.”
To Habayeb, that variation means companies will have to think deeply about what their operations need to evolve into to best address not just their AI use cases but how they scale for the future, broadly speaking.
Why AI governance still starts with fundamentals
The good news for most organizations is that governance is not a new discipline.
As with other security conversations, a return to fundamentals is key, even if businesses have a new reason to address gaps.
“Governance done well is a roadmap for guiding your organization towards the best possible systems,” said Habayeb. “Most of this work will naturally align with what you need for security or risk management, and it will align with AI best practices, but this work is also just good systems thinking that every company needs.”
The fundamentals here, to Habayeb, include assessing the impact on customer experience and security, the financial implications for the business, increasingly complex, location-based regulatory and compliance needs, and more.
How organizations should evaluate AI tools against risk
As Habayeb notes, every company’s risk threshold will differ. Still, there are best practices every organization can follow as leaders virtually everywhere adopt AI within their workflows.
Habayeb recommends asking a consistent set of governance questions before adopting any AI system.
- What is the total cost of ownership for this tool?
- Will I be able to cover the computing, building, and ongoing maintenance costs associated with it?
- Is AI the only answer to this problem, or the best answer we could find?
Then, of course, are the conversations around data security and privacy, as well as general readiness, that more organizations are starting to see as necessary parts of any AI implementation.
To Habayeb and others we’ve spoken to, AI is not a catch-all solution to every problem a business faces, nor is it a miracle technology that fixes every issue an organization might identify.
As agentic AI tooling becomes more popular and other forms of AI continue to enter workflows, business leaders will need to think strategically about where and when those tools make the most sense.
“I absolutely think AI tools are incredible opportunities to make our work better, but it is ultimately just a tool. The ways that you use tools should be consistent with your business values and your approach to everything else you do,” Habayeb said.
The channel angle: what MSPs and other partners need know in 2026
For MSPs, this presents yet another AI opportunity for those working with customers as they consider governance in the way Habayeb describes.
Some providers, as we’ve covered, have already launched AI service offerings that include not just the AI tool itself but also the wraparound services required to address security and risk, which align closely with many of the governance operations clients will need.
“There’s enough agreement now that there are risks here, but still not enough agreement on how to manage that risk,” said Habayeb.
So, MSPs can take this opportunity to serve as strategic advisors to their clients and offer consulting support as they build operational strength in governance and approach AI-driven risks as needed.
For channel partners, AI governance represents a shift from tool delivery to long-term advisory and risk management services.