Skip to content

AI Suggested Resolution (ASR) — How Enterprises Build Trust on the Path to Autonomy 

When enterprise leaders hesitate on autonomous IT, the concern is rarely about capability. It’s about accountability. 

“What happens if the system makes the wrong decision?” 
“How do we control risk?” 
“How do we satisfy auditors, regulators, and the business?” 

These are not obstacles—they are design requirements. And they are precisely why AI Suggested Resolution (ASR) has emerged as a critical bridge between manual operations and full autonomy. 

Why Jumping Straight to Autonomy Feels Unsafe 

In theory, fully autonomous remediation sounds ideal. In practice, most enterprises aren’t ready—culturally, operationally, or from a governance standpoint. 

Gartner consistently advises organizations to adopt AI in stages, emphasizing explainability, human oversight, and policy-driven control as prerequisites for enterprise-scale deployment. 

ASR exists to meet enterprises where they are—not where vendors wish they were. 

What ASR Really Changes During an Incident 

In a traditional incident, teams: 

  • Interpret noisy alerts 
  • Manually correlate logs and metrics 
  • Hypothesize root cause under pressure 
  • Debate remediation paths 

ASR restructures this process. 

By correlating telemetry, historical incidents, deployment data, and known resolution patterns, ASR generates a root cause hypothesis supported by evidence. It then proposes a step-by-step remediation plan, including: 

  • Confidence scoring 
  • Risk assessment 
  • Estimated time to recovery 
  • Validation and rollback steps 

McKinsey notes that decision-support systems significantly reduce cognitive load and error rates during high-stress scenarios. 

Instead of reacting, teams review and decide—with clarity. 

Transparency Is the Trust Multiplier 

One of the most powerful aspects of ASR is transparency. Nothing is hidden. Every recommendation is explainable. 

CIO Magazine highlights explainability as a non-negotiable requirement for AI adoption in enterprise IT, particularly in regulated environments. 

When operators understand why a recommendation is made, trust follows. And when trust follows, adoption accelerates. 

Governance Built into the Workflow 

ASR operates within explicit guardrails: 

  • What actions are allowed 
  • Under what conditions 
  • With which approvals 
  • During which windows 

ISG stresses that governance is what separates enterprise-grade AI from experimentation, enabling innovation without introducing uncontrolled risk. 

This governance-first approach ensures ASR enhances—not undermines—compliance and auditability. 

Learning Without Taking Unnecessary Risk 

Every ASR recommendation, whether approved or rejected, becomes a learning event. Successful actions are promoted. Risky ones are constrained. Knowledge artifacts evolve automatically. 

Over time, organizations don’t just move closer to autonomy—they earn it. 

The Executive Takeaway 

ASR is not a stepping stone to be rushed past. It is a strategic capability in its own right. 

It enables enterprises to: 

  • Reduce MTTR immediately 
  • Improve decision quality 
  • Institutionalize knowledge 
  • Build confidence in AI-driven operations 

Autonomy does not need to be a leap of faith. With AI Suggested Resolution, it becomes a disciplined, transparent journey—one decision at a time. 

All Insights | Next Insight