IT AI adoption use cases
Use this page to scan AI adoption opportunities across the it workflow. The use cases are grouped by stage so you can decide where AI is likely to improve speed, quality, or cost before you commit to a rollout.
Plan
Review plan use cases in the it process, then pick the ideas worth testing against real work.
Benchmark-driven standard config generator
RAG system retrieves current industry benchmarks and generates recommended hardware and software standard configurations for approval.
Value drivers: Quality
Value 2/5 · Effort 3/5
Budget scenario modeler
LLM generates multiple budget scenarios with TCO breakdowns from raw business requirements and procurement history.
Value drivers: Speed
Value 2/5 · Effort 2/5
Demand and capacity forecast
ML model predicts compute, storage, and license demand 12-18 months out using historical utilization and headcount signals.
Value drivers: Cost
Value 4/5 · Effort 4/5
Requirements extraction from stakeholder input
NLP pipeline extracts structured technical requirements from unstructured interview notes, emails, and meeting transcripts.
Value drivers: Speed
Value 3/5 · Effort 2/5
Tech debt risk scoring
ML classifier scores each system on failure probability, support-end proximity, and integration coupling to prioritize refresh investment.
Value drivers: Quality
Value 4/5 · Effort 3/5
Which of these is worth automating for you?
Every use case here has a generic effort rating. Which ones pay back for your team depends on where the hours actually go today, and most teams are guessing. DoneThat reconstructs that automatically, with no timers to forget, so you can measure the baseline before committing to a project and check the gain afterward.
Select
Review select use cases in the it process, then pick the ideas worth testing against real work.
Contract risk extraction
LLM extracts and flags non-standard clauses, auto-renewal traps, and liability gaps from vendor contracts.
Value drivers: Quality
Value 3/5 · Effort 2/5
Peer review and reference mining
Retrieval agent aggregates analyst reports, G2 and Gartner peer reviews, and community forum evidence into a structured vendor brief.
Value drivers: Speed
Value 2/5 · Effort 2/5
Selection decision audit trail
LLM generates a structured, audit-ready decision memo documenting scoring rationale, alternatives considered, and risk acceptance.
Value drivers: Quality
Value 1/5 · Effort 1/5
Total cost of ownership calculator
Agentic system populates TCO models by pulling list prices, support costs, and migration estimates from vendor APIs and historical data.
Value drivers: Cost
Value 4/5 · Effort 4/5
Vendor shortlist scoring engine
LLM scores vendors against decomposed weighted criteria, including security, TCO, integration fit, and support, from RFP responses.
Value drivers: Speed
Value 3/5 · Effort 3/5
Deploy
Review deploy use cases in the it process, then pick the ideas worth testing against real work.
Change impact simulator
Graph-based AI maps CMDB dependencies and simulates downstream impact of a planned change before the change window opens.
Value drivers: Cost
Value 2/5 · Effort 5/5
Deployment runbook generation
LLM produces environment-specific runbooks from product documentation, CMDB data, and past deployment logs.
Value drivers: Speed
Value 3/5 · Effort 2/5
Post-deploy anomaly clustering
Unsupervised ML clusters error logs and telemetry spikes after deployment to surface root causes faster than manual log review.
Value drivers: Speed
Value 3/5 · Effort 3/5
Pre-flight configuration validator
AI agent checks proposed configurations against security baselines, dependency maps, and compliance policies before deployment starts.
Value drivers: Quality
Value 4/5 · Effort 3/5
Rollback decision assistant
ML model monitors deployment metrics in real time and recommends rollback with reasoning when anomaly thresholds are crossed.
Value drivers: Quality
Value 5/5 · Effort 4/5
Provision
Review provision use cases in the it process, then pick the ideas worth testing against real work.
Agentic access provisioning
AI agent fulfills access requests end to end, validating entitlement policy, calling identity APIs, and confirming delivery without human routing.
Value drivers: Speed
Value 5/5 · Effort 4/5
Entitlement anomaly detection
ML flags access requests that deviate from peer group norms or violate least-privilege rules before approval.
Value drivers: Quality
Value 4/5 · Effort 3/5
License assignment optimizer
ML matches user activity patterns to license tiers and automatically reassigns or downgrades underused seats to reduce spend.
Value drivers: Cost
Value 3/5 · Effort 3/5
Onboarding package auto-builder
LLM assembles a personalized software, hardware, and access package from HRIS role data, team context, and standard templates.
Value drivers: Speed
Value 3/5 · Effort 2/5
Provisioning request intent classifier
NLP classifier routes free-text provisioning requests to the right workflow without manual triage.
Value drivers: Speed
Value 1/5 · Effort 1/5
Support
Review support use cases in the it process, then pick the ideas worth testing against real work.
KB article auto-generation
LLM drafts KB articles from resolved ticket data and agent notes, flagging gaps where no article exists.
Value drivers: Quality
Value 2/5 · Effort 1/5
Proactive incident detection
AIOps platform correlates metrics, logs, and events across the stack to detect and alert on emerging incidents before users report them.
Value drivers: Quality
Value 5/5 · Effort 5/5
Real-time agent assist
LLM surfaces relevant KB articles, similar resolved tickets, and suggested next steps to the support agent during an active session.
Value drivers: Speed
Value 3/5 · Effort 2/5
Sentiment-based escalation trigger
NLP model scores ticket sentiment and detects frustration signals to auto-escalate before SLA breach or churn risk materializes.
Value drivers: Quality
Value 2/5 · Effort 2/5
Tier-1 autonomous resolution agent
Agentic AI resolves password resets, VPN issues, and software installs without human touch, deflecting 50-80% of L1 volume.
Value drivers: Cost
Value 5/5 · Effort 4/5
Upgrade
Review upgrade use cases in the it process, then pick the ideas worth testing against real work.
Patch prioritization by risk
ML model ranks outstanding patches by exploitability, asset criticality, and exposure to prioritize sequencing.
Value drivers: Quality
Value 4/5 · Effort 3/5
Post-upgrade performance baseliner
ML establishes pre-upgrade performance baselines and automatically flags regressions against them after cutover.
Value drivers: Cost
Value 2/5 · Effort 4/5
Regression test case generator
LLM generates regression test cases for critical workflows from system documentation and past incident data before an upgrade ships.
Value drivers: Speed
Value 3/5 · Effort 2/5
Upgrade communication drafter
LLM produces audience-appropriate upgrade notices, including a technical runbook for IT and a plain-language notice for end users, from a single change record.
Value drivers: Speed
Value 1/5 · Effort 1/5
Upgrade readiness assessor
AI agent cross-references CMDB dependencies, patch history, and vendor compatibility matrices to produce a per-asset upgrade readiness score.
Value drivers: Quality
Value 4/5 · Effort 3/5
Replace
Review replace use cases in the it process, then pick the ideas worth testing against real work.
Data migration risk classifier
ML classifier flags high-risk data elements for migration, including sensitive, orphaned, and format-incompatible records, before migration scripts run.
Value drivers: Quality
Value 3/5 · Effort 3/5
Decommission dependency mapper
Graph AI scans integrations, scheduled jobs, and API calls to surface hidden dependencies on a system marked for replacement.
Value drivers: Cost
Value 4/5 · Effort 4/5
Replacement timing optimizer
ML model combines failure probability, support-end dates, utilization rates, and capex cycles to recommend optimal replacement timing per asset.
Value drivers: Cost
Value 5/5 · Effort 4/5
RFP response summarizer
LLM ingests vendor RFP responses and produces a structured comparison summary with gap analysis against stated requirements.
Value drivers: Speed
Value 1/5 · Effort 1/5
User adoption impact predictor
LLM analyzes change history and user segment data to predict adoption friction and recommend targeted enablement interventions.
Value drivers: Quality
Value 2/5 · Effort 2/5
Retire
Review retire use cases in the it process, then pick the ideas worth testing against real work.
Data retention and deletion policy enforcer
AI agent classifies data on retiring systems by retention obligation, routes records to archive, and flags items requiring secure deletion.
Value drivers: Quality
Value 4/5 · Effort 4/5
Institutional knowledge extractor
LLM mines documentation, tickets, and runbooks tied to a retiring system to produce a structured knowledge transfer document before decommission.
Value drivers: Quality
Value 3/5 · Effort 2/5
License reclamation detector
ML identifies licenses, subscriptions, and entitlements tied to a retiring asset and surfaces them for reassignment or cancellation.
Value drivers: Cost
Value 3/5 · Effort 2/5
Retirement cost-benefit reporter
LLM aggregates support costs, incident rates, and stranded license spend into a retirement ROI report for sign-off.
Value drivers: Cost
Value 1/5 · Effort 1/5
Secure wipe verification auditor
AI cross-checks wipe logs against asset inventory and compliance standards, generating an audit-ready certificate of data destruction.
Value drivers: Quality
Value 2/5 · Effort 3/5
Which of these is worth automating for you?
Every use case here has a generic effort rating. Which ones pay back for your team depends on where the hours actually go today, and most teams are guessing. DoneThat reconstructs that automatically, with no timers to forget, so you can measure the baseline before committing to a project and check the gain afterward.