Art of the Possible · Workshop with Lenovo

From reactive ITSM to autonomous resolution — what's now possible at Lenovo

Complementing your ServiceNow OTSM with AI-powered correlation, prediction, and resolution.

×

25 June 2026

3:00 – 4:30 PM · 90 minutes

A working session, not a pitch

We'll show you what's possible, explore your priorities, and leave with a concrete POC plan for 1-2 use cases.

01

Context & architecture

How DevRev layers on ServiceNow OTSM — integration, data sync, and the AI engine.

~20 min

02

AI capabilities deep-dive

Problem correlation, knowledge-driven resolution, ML-based prediction, and voice-bot.

~30 min

03

Q&A & discovery

Your questions, your priorities — which problems matter most to your team.

~20 min

04

POC proposal

Concrete next steps — pick 1-2 use cases, define scope, and agree on a 4-week pilot.

~20 min

One ask

Questions will spark as we go — please jot them down. We've set aside time so every one gets a proper answer.

The challenge today

Where your SRE team feels the pain

Before we show what's possible, let's ground in the reality your team faces every day.

Pain point

Reactive incident handling

Tickets pile up, L2/L3 engineers spend hours correlating manually across systems and equipment to find root cause.

Pain point

Siloed knowledge

ServiceNow's knowledge graph exists but isn't connected to real-time telemetry, equipment docs, or historical resolution patterns.

Pain point

Alert fatigue

Too many alerts, not enough correlation — engineers drowning in noise instead of acting on signal.

DevRev doesn't replace ServiceNow — it adds the intelligence layer that makes it autonomous.

The platform

Data in. Intelligence out. Actions taken.

Computer unifies your ServiceNow data, telemetry, and documentation into one permission-aware memory, then reasons and acts across all of it.

Live with you today Now possible

Same incident · same window Memory filters the noise so engineers see only signal

Raw alerts · all noise 4,200

Computer · correlated signal 12

Memory deduplicates, correlates via CMDB topology, and filters before surfacing to your team — 99% noise reduction, and faster as your infrastructure scales.

Advanced problem correlation · L2 / L3

One root cause. Multiple symptoms. Found in seconds.

1

47 tickets land

Across server, network, and storage teams

→

CMDB topology scan

Computer correlates via dependency mapping

→

3

Root cause found

Single upstream switch failure · rack C-4

→

4

All 47 linked & resolved

Resolution pushed to all affected tickets

Correlation evidence Signal vs noise

● Switch C-4 port flap · 09:12:01 CMDB: 47 CIs downstream Pattern match: INC-6221 DNS latency spike · unrelated

Seconds

to root cause — not hours of manual correlation across systems.

47 → 1

incident grouping — one fix resolves all affected tickets.

4 hrs saved

per L3 engineer per incident — freed for proactive work.

Knowledge-driven resolution

Step-by-step guidance. From docs, not guesses.

1

Incident lands

INC-8834 · server rack B-12 intermittent power faults

→

Reads docs & history

Equipment manuals + historical fixes scanned

→

3

Resolution surfaced

Matching fix from March 2026 · step-by-step

→

4

KB article auto-created

Resolution applied & knowledge base grows

Sources used Knowledge graph

● Equipment Manual v3.2 · rack power section Similar fix: INC-7721 · March 2026 CMDB topology · rack B-12 dependencies Firmware update log · already current

FCR +40%

First-call resolution up — engineers get the right answer on the first try.

L2 esc. -60%

L2 escalations down — knowledge-driven guidance keeps it at L1.

KB grows

Every resolution auto-creates a knowledge article for the next engineer.

ML architecture · Incident forecasting engine

Three layers: Predict. Monitor. Act.

A trained ML pipeline that forecasts incident probability, monitors drift in real-time, and triggers autonomous action — deterministic where it matters, intelligent where it helps.

Design principle Deterministic where it matters, intelligent where it helps

Forecast numbers and alert thresholds are pure code — reproducible, auditable, never hallucinated. The AI agent only interprets and explains pre-computed results. Model retrained monthly; thresholds auto-tune based on operator feedback.

Trend detection & predictive insights

See it coming. Act before it breaks.

ML models trained on your telemetry detect recurring patterns, anomalies, and emerging risks — giving your team a window to act before failure hits.

VP Infrastructure — asks"Which equipment clusters are showing anomaly patterns this month?"

Computer · analyzing telemetry…

Equipment health · anomaly report

Clusters by risk score, last 30 days

Sourced

Anomaly score per equipment cluster

Storage-A

Net-Core

Compute-B

Storage-C

Net-Edge

Compute-D

Clusters above failure threshold3 at risk

Common patternStorage controller degradation

Predicted failures next 14 days5 assets

Predictive insights, surfaced

3 clusters

showing anomaly patterns matching prior outages — your risk list.

14-day window

predicted failures give your team time to act before customer impact.

Storage

the top emerging risk area — controller degradation is the leading indicator.

Who's in the scenario

R

Raj

SRE on-call engineer

Wants instant context when paged — no time to read dashboards at 2 AM.

P

Priya

Incident commander

Needs the escalation chain to work automatically — every second counts.

Raj → Priya · escalation

Use case · Critical incident voice-bot

Critical alerts deserve a voice, not just a notification

When a P1 hits, Computer doesn't just page — it calls the on-call engineer with a spoken incident summary, recommended first steps, and intelligent escalation if there's no response.

01

Automated call-out

P1 detected — voice call to on-call with spoken incident summary and recommended first steps.

02

Intelligent escalation

No acknowledgment in 5 min — next in the chain gets called, with full context preserved.

03

Hands-free triage

Engineer responds by voice — Computer logs actions & updates ServiceNow automatically.

Live demo scenario

P1 network outage — automated call to Raj

P1 detected in ServiceNow→ Voice call with spoken RCA summary→ Recommended first steps→ Raj acknowledges by voice

Also handles P2 alerts via SMS · Scheduled maintenance reminders · Post-incident debrief scheduling

Integration · ServiceNow OTSM

Bidirectional sync — complement, don't replace

DevRev connects to your existing ServiceNow OTSM, enriches data with AI, and syncs results back — no rip-and-replace, no migration.

Connected systems

Bidirectional data flow

DevRev enriches data syncs back real-time, bidirectional

Integration capabilities

1 Incidents sync bidirectionally

Create in ServiceNow, enrich in DevRev with AI correlation and context, resolve back to ServiceNow — single source of truth preserved.
ServiceNowDevRev

2 CMDB topology imported

Dependency mapping powers the correlation engine — Computer understands which CIs affect which services.
ServiceNow CMDB

3 Knowledge articles flow both ways

DevRev auto-creates KB articles from resolved incidents — ServiceNow surfaces them to engineers on the next similar issue.
ServiceNow KBDevRev KG

4 Telemetry streams in real-time

No batch, no lag — live metrics and logs from Datadog, Prometheus, CloudWatch feed directly into the correlation engine.
DatadogPrometheusCloudWatch

Now's the time

Your questions

Everything you noted along the way — let's work through it together before we turn to your priorities.

Discovery · Your priorities

Now — where could this help your teams most?

The heart of the day. Let's go around the room, lane by lane.

Integration

ServiceNow ↔ DevRev

What ServiceNow data first?
Which direction — push, pull, or both?
What timeline to production?

Correlation & RCA

Find root cause

Which equipment clusters first?
Which ticket types to correlate?
What CMDB topology to import?

Knowledge & Resolution

Guide engineers

Which documentation sources?
Which teams to enable first?
What runbooks to ingest?

Predictive & Voice

Prevent & alert

Which metrics to watch?
Who gets called on P1?
What failure thresholds?

…and to make it concrete — three places we could start →

Where we could start

Three places to start — pick the pilot

Mapped to your priorities. Which one do we go deep on today?

01 Quick win

ServiceNow + DevRev integration pilot

foundation · 2 weeks

"Start with incident sync and see enrichment in action within 2 weeks."

Computer does it

Bidirectional incident sync, CMDB topology import, and AI-enriched context on every ticket — visible immediately in ServiceNow.

→ Proves the integration works before scaling to correlation and prediction.

⚡ 2-week time to value

SOwner · SRE Lead

02 High impact

Problem correlation for top-5 equipment clusters

reduce L3 escalation time

"Reduce L3 escalation time by correlating across CMDB topology."

Computer does it

Correlate incidents across your top-5 problem equipment clusters — surface root cause in seconds, link all related tickets automatically.

→ L3 engineers spend time fixing, not hunting across systems.

📈 4 hrs saved per L3 per incident

IOwner · IT Ops Manager

03 Strategic

Predictive alerting for storage infrastructure

prevent outages proactively

"ML models trained on 6 months of telemetry to predict failures 14 days ahead."

Computer does it

Train anomaly models on your historical telemetry — predict storage controller failures with a 14-day lead time for proactive maintenance.

→ Move from reactive to predictive — fix it before the customer feels it.

🛡️ 14-day early warning window

VOwner · VP Infrastructure

Proposed next step

A 4-week POC — pick 1-2 use cases

Scoped, time-boxed, and measurable. We prove value on your data, your team, your infrastructure — then decide together.

WK 1

Integration & data onboarding

Connect ServiceNow OTSM via AirSync. Import CMDB topology. Ingest 6 months of incident & telemetry history.

WK 2

Model training & tuning

Train ML models on your data. Configure correlation rules for chosen equipment clusters. Set alert thresholds.

WK 3

Shadow mode & validation

Run alongside ServiceNow in shadow mode. Validate predictions against real incidents. Tune with your SRE team's feedback.

WK 4

Results & go/no-go

Present measured outcomes (MTTR reduction, false positive rate, engineer time saved). Decide on production rollout.

Success criteria

Measurable from day one: MTTR reduction, L3 escalation reduction, noise-to-signal ratio improvement, and engineer satisfaction. No black-box outcomes — full transparency on model accuracy.

The takeaway

Let's prove it.

DevRev sits alongside ServiceNow — adding intelligence, correlation, and autonomous resolution where it matters most. 4 weeks to measurable outcomes.