Use case

IT tier-1 help-desk auto-resolution.

From the field, AI native workflow redesign of tier-1 help-desk auto-resolution process within ITSM IT function.

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Convolving expertise

A senior Convolving delivery team partnered with the IT operations function for one sprint. Operators from our expert network – with forty combined years inside enterprise ITSM, identity, and endpoint management – reviewed the redesign at each checkpoint. Forward-deployed engineers built inside the team's ServiceNow, identity, and CMDB stack. One flat fee, artifact out, no retainer creep.

Situation

Today the IT help desk takes the volume hit on password resets, access requests, and software installs. Tier-1 agents close hundreds of tickets a day on rote work.

Knowledge bases age in confluence pages. Identity and entitlement plumbing scatters across the SaaS estate. Tier-1 agents bridge the two by hand, ticket by ticket. ServiceNow Now Assist on its own tenant resolves around ninety percent of inbound tickets; Novant Health automated sixty-three percent of incidents and cut MTTR roughly thirty percent across eighty-seven thousand predictions in four months. The legacy chatbot was a glorified search bar.

Auto-resolution <10% Tier-1 deflection on legacy bots
Time per ticket 10–15 min Agent-led tier-1 work
Self-serve trust Low Users escalate to humans by default
Engineer drag High Senior staff pulled into rote tickets

Click any node to see the activities and tools behind it. Open the canvas in fullscreen for the horizontal view.

Complication

Largest obstacles and inefficiencies.

Engineers drag into rote tier-1 work.

Senior staff get pulled into password resets and access asks at peak volume. The work that needs them sits in the queue.

Knowledge fragments across confluence pages.

KB articles age, tribal knowledge concentrates in senior reps, and turnover destroys it. Tier-1 agents stitch the two by hand.

Identity plumbing slows everything.

Access requests cross five SaaS tools, three approvers, and an entitlement matrix. The wait time dwarfs the actual change.

Resolution

The AI-native cycle.

Same five steps. Click any node to see what the redesign does in that step.

Auto-resolution 60–90% ▲ from <10% on legacy bots
Time per ticket Seconds Self-serve at point of question
Self-serve trust High Users stay with the bot by default
Engineer drag Low Senior staff back on incidents and changes
Key changes

What the redesign actually shifts.

Auto-resolution

  • Tier-1 deflection moves from under ten percent on legacy bots toward sixty to ninety.
  • Self-serve answers in seconds, with citations.
  • Common changes execute automatically with identity verification.

Engineer capacity

  • Senior staff stop draining into rote tier-1 work.
  • Tier-2 escalations arrive with structured context.
  • Engineers concentrate on incidents and changes.

Knowledge discipline

  • KB articles draft from resolved tickets continuously.
  • Stale content retires automatically.
  • Tribal knowledge stops gating service.

Audit and control

  • Every auto-action logs identity, scope, and rule.
  • Approvals route to the right human for non-standard changes.
  • Service owners read the same trail as audit.

Deploy this in your team.

The redesign above ships as a step-by-step playbook. KB ingestion spec, identity and entitlement map, deflection rule library, audit-trail schema, and the rollout cadence we use on engagements.

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