'Explainability' in GenAI: What does it mean?

As AI becomes more prevalent in MSP operations, the concept of "explainability" has emerged as a critical consideration. But what does it really mean, and why should MSPs care about whether their AI can explain its decisions?

Defining AI Explainability

AI explainability refers to the ability of an artificial intelligence system to provide clear, understandable reasons for its decisions and recommendations. Think of it as the AI's ability to show its work—just like you'd want a student to explain how they solved a math problem.

Why Explainability Matters for MSPs

Client Trust and Transparency

When your AI system recommends a specific solution or identifies a potential issue, your clients want to understand why. Explainable AI allows you to provide clear reasoning:

• "The AI flagged this ticket as high priority because it detected keywords associated with business-critical systems"
• "The recommendation is based on similar issues in your environment and industry best practices"
• "The system identified these three factors that indicate potential hardware failure"

Compliance and Auditing

Many industries have regulatory requirements for decision-making processes. Explainable AI helps MSPs:

• Document the reasoning behind AI-driven decisions
• Provide audit trails for compliance reviews
• Demonstrate due diligence in automated processes
• Meet data protection and privacy regulations

Engineer Confidence and Learning

Your technical team needs to trust AI recommendations. Explainable systems help by:

• Showing the reasoning behind ticket prioritization
• Explaining why certain solutions are suggested
• Helping engineers learn from AI insights
• Building confidence in AI-driven processes

The Black Box Problem

Many AI systems, particularly deep learning models, operate as "black boxes"—they provide outputs without explaining how they reached their conclusions. This creates several challenges for MSPs:

Risks of Black Box AI

Types of AI Explainability

Global Explainability

Understanding how the AI system works overall—its general approach to decision-making, what factors it considers most important, and how it weighs different inputs.

Local Explainability

Understanding why the AI made a specific decision in a particular case—what factors influenced this specific recommendation or classification.

Counterfactual Explanations

Explaining what would need to change for the AI to make a different decision—"If the ticket had included these keywords, it would have been classified as high priority instead."

Practical Examples in MSP Operations

Ticket Prioritization

Solution Recommendations

Time Entry Automation

Evaluating AI Solutions for Explainability

When evaluating AI tools for your MSP, ask these key questions:

The Balance: Accuracy vs. Explainability

There's often a trade-off between AI accuracy and explainability. Very complex models might be more accurate but less explainable, while simpler models are easier to understand but might be less precise.

For MSPs, the sweet spot usually involves:

• Choosing AI that's "accurate enough" for the task with good explainability
• Accepting slightly lower accuracy for significantly better transparency
• Using ensemble approaches that combine explainable and complex models
• Implementing human oversight for critical decisions

Building Explainability into Your AI Strategy

1. 1. Define Explainability Requirements: Determine what level of explanation you need for different use cases
2. 2. Choose Appropriate Tools: Select AI solutions that meet your explainability requirements
3. 3. Train Your Team: Help engineers understand how to interpret AI explanations
4. 4. Document Decisions: Create processes for recording AI-driven decisions and their reasoning
5. 5. Regular Review: Periodically audit AI decisions to ensure explanations remain accurate

The Future of Explainable AI

As AI technology evolves, we're seeing improvements in explainability techniques. New approaches are making it possible to maintain high accuracy while providing clear explanations. For MSPs, this means future AI tools will be both more powerful and more transparent.

Remember: The best AI system is one your team trusts and understands. Explainability isn't just a nice-to-have—it's essential for successful AI adoption.