Moovila CEO: MSPs Must Focus on ROI, Risk, and Reality in AI Adoption

Understanding the true business return has been a sticking point of managed service providers (MSPs) in the age of AI.

Taking a look at any AI initiative involves understanding how much error an operation can tolerate and identifying opportunities to help customers operationalize AI before fully diving in. 

To break down how to evaluate AI initiatives before they get greenlit, Channel Insider spoke with Mike Psenka, CEO of Moovila, who shared his guidance for MSPs.

When evaluating AI solutions, what criteria should channel partners prioritize before recommending them to customers?

Channel partners should start with four things: ROI, error rate, auditability, and latency. I call it keeping it real. 

If an AI solution cannot show a clear business payoff, operate within an acceptable margin of error, explain how it reached its outputs, and respond fast enough for the intended use case, it is not ready for serious deployment. 

Too many people evaluate AI based on how impressive the demo looks instead of how well it holds up in a real operating environment.

What common mistakes do you feel organizations make when adopting AI through channel partners?

The biggest mistake is chasing AI because it sounds strategic rather than because it solves a defined business problem. Another is ignoring the consequences of being wrong. 

A tool that is “mostly right” may be perfectly fine for low-risk use cases, but it can be dangerous in environments where precision matters. 

Organizations also make the mistake of buying black-box tools they cannot defend to customers, auditors, or internal stakeholders when something goes sideways. 

What challenges do partners face when scaling AI from pilot projects to full deployment?

Pilots are easy to make look good because they happen in controlled conditions. Full deployment is where reality shows up. 

Partners run into issues around inconsistent data, unclear success metrics, user adoption, and the gap between a flashy proof of concept and a solution that performs reliably every day. They also find out quickly whether the system is accurate enough for production use.

What opportunities are there for the channel in helping customers operationalize AI?

There is a major opportunity for partners to help customers move from experimentation to disciplined execution. Most customers do not need more AI ideas. 

They need help deciding where AI belongs, what type of AI fits the use case, how much error they can tolerate, and how to build workflows around it that people will actually trust and use. 

The channel can create real value by helping customers operationalize AI in a way that is measurable, governable, and aligned to business outcomes.

What role does error tolerance play when determining if an AI solution is viable for a specific customer environment?

Error tolerance is one of the most important variables in the entire decision. Every use case has a different threshold for being wrong. 

If AI helps summarize information or assist with brainstorming, the stakes are lower and some variability is acceptable. But if that same AI is making decisions that affect project schedules, resource allocations, compliance, or customer commitments, the tolerance for error drops dramatically. 

The right question is not whether the AI is impressive. It is whether the customer environment can absorb its mistakes.

How can channel partners position themselves as trusted advisors in AI decision-making?

They do it by bringing discipline to the conversation. Trusted advisors are the ones willing to slow things down and ask harder questions: What is the ROI? How wrong can this be? Can we explain it? 

They also need to be honest about where probabilistic AI belongs and where deterministic systems are the better fit. Customers do not need another cheerleader for AI. They need someone who can help them separate hype from operational reality.

Is there anything important about AI evaluation for channel partners that we didn’t cover?

The market talks about AI like it is one thing, but it is not. There are different types of AI built for different jobs. Some are probabilistic, meaning they are designed to generate likely answers, language, or recommendations based on patterns. 

Those systems can be incredibly useful for flexible tasks like summarizing information, generating content, or helping users work faster. 

Others are deterministic, meaning they operate within defined rules, logic, and constraints to produce more consistent, explainable, and repeatable outcomes. 

That distinction matters. If channel partners treat all AI the same, they will recommend the wrong solutions for the wrong use cases. 

The partners who win in this market will be the ones who understand those differences, match the technology to the business need, and help customers deploy AI with clear eyes instead of blind enthusiasm.

Channel Insider: Partner POV dives into trends, like AI adoption, that are fueling channel evolution in 2026 and beyond. Catch up on recent episodes to learn more about how providers like Logically, Harbor IT, and others are approaching the new era of managed services.