Automated Stack Audit: Build a Flow to Detect Underused Tools and Consolidate

Cut tool sprawl and reclaim wasted SaaS spend with an automated stack audit

If your teams are juggling a dozen overlapping apps and finance still gets surprised by the monthly bills, you have a classic operational problem: tool sprawl. This guide shows how to build a repeatable automation flow that discovers underused SaaS licenses, maps functional overlaps, and produces AI-driven consolidation recommendations using internal telemetry and common connectors like Slack, GitHub, and Jira — without a six-month engineering project.

The elevator summary (what you'll get)

• Automated discovery of unused or low-usage licenses across identity, billing, and integration logs.
• Overlap mapping that scores tools by feature redundancy and team ownership.
• AI suggestions for consolidation paths and migration steps.
• Action triggers to notify owners, create Jira tickets, or initiate license reclamation workflows.

Why now? 2026 trends you should care about

In late 2025 and early 2026 enterprises moved from buying AI tools to rationalizing them. CFOs and FinOps teams put SaaS cost optimization back on the roadmap after rapid vendor churn in 2023–2025. Meanwhile, observability into application usage improved: SSO providers, MDMs, and modern identity platforms now expose richer event logs, and LLMs are production-ready for cross-system analysis. That combination makes automated stack audits not just possible, but high ROI.

High-level architecture: data-driven, connector-first, AI-enhanced

Build this flow with three layers:

1. Data collection: ingest license inventories, SSO login events, billing exports, integration/webhook activity, repo access logs, and expense reports.
2. Enrichment & analytics: join identities, calculate usage metrics, and compute redundancy scores per tool and team.
3. Action & automation: generate reports, push recommendations to Slack, open Jira tickets to remove or consolidate licenses, or run n8n/Make flows to deprovision unused seats.

Step 1 — Sources you must connect

No single source tells the whole story. Combine at least these systems to get accurate signals:

• SSO/Identity logs (Okta, Azure AD, Google Workspace): last login, MFA events, and app assignment metadata.
• Billing exports (Stripe, Billing dashboards, procurement): active seats, plan tiers, renewal dates.
• Application telemetry (API calls, webhooks, product usage events): real usage vs idle seats.
• Collaboration platforms (Slack, Microsoft Teams): channel membership and app installation counts.
• Code & project tools (GitHub, GitLab, Jira): repo contributors, ticket activity and project association.
• Expense systems (Concur, Expensify): employee-submitted SaaS expenses—useful for shadow IT detection.

Quick win connectors

• Okta/Azure AD via system logs or SCIM for user-to-app mappings.
• Stripe/Chargebee export CSVs for billing lines.
• GitHub audit logs and Jira REST API to measure application value to engineering workflows.
• Slack Enterprise Grid API for app installs and channel invites.

Step 2 — Consolidated schema & minimal data model

Normalize everything to a small schema so analytics are simple and repeatable. Store this in BigQuery, Snowflake, or a Postgres warehouse.

-- simplified schema (SQL-like)
  apps(id, name, vendor, sku, monthly_cost, features_array)
  users(id, email, department, manager)
  app_assignments(user_id, app_id, assigned_at, removed_at)
  auth_events(user_id, app_id, timestamp, event_type)
  billing_lines(app_id, date, seats, amount)
  repo_activity(user_id, repo, commits, last_commit_date)
  jira_activity(user_id, project_key, issues_created, last_activity)
  

Sample query: low-usage seats (90-day window)

SELECT a.id, a.name, SUM(b.amount) AS monthly_cost,
         COUNT(DISTINCT aa.user_id) AS assigned_seats,
         SUM(CASE WHEN auth.last_login > NOW() - INTERVAL '90 days' THEN 1 ELSE 0 END) AS active_seats
  FROM apps a
  LEFT JOIN billing_lines b ON b.app_id = a.id AND b.date = DATE_TRUNC('month', CURRENT_DATE)
  LEFT JOIN app_assignments aa ON aa.app_id = a.id
  LEFT JOIN (
    SELECT user_id, app_id, MAX(timestamp) AS last_login
    FROM auth_events WHERE event_type = 'login' GROUP BY 1,2
  ) auth ON auth.user_id = aa.user_id AND auth.app_id = a.id
  GROUP BY a.id, a.name
  HAVING (SUM(CASE WHEN auth.last_login > NOW() - INTERVAL '90 days' THEN 1 ELSE 0 END) * 1.0 / COUNT(DISTINCT aa.user_id)) < 0.2;
  

The query above surfaces apps where <20% of assigned seats were active in 90 days — a conservative cutoff for candidate reclamation.

Step 3 — Map overlaps and compute redundancy

To consolidate, you need to know which tools are functionally redundant. Build a lightweight feature matrix and a redundancy score.

1. For each app, populate features_array from vendor docs or a short crowdsourced team survey (video calls, Slack poll).
2. Compute pairwise Jaccard similarity on features to detect overlaps.

-- pairwise feature overlap (conceptual)
  SELECT a1.id AS app_a, a2.id AS app_b,
         jaccard(a1.features_array, a2.features_array) AS feature_overlap
  FROM apps a1 JOIN apps a2 ON a1.id < a2.id
  WHERE a1.vendor != a2.vendor;
  

Combine feature overlap with usage and ownership signals to rank consolidation candidates. Example weighting:

• Feature overlap: 40%
• Cost per seat & total spend: 30%
• Cross-team usage (fragmentation): 20%
• Ease-of-migration score (APIs, exports): 10%

Step 4 — AI-driven recommendation engine

Use an LLM to convert analytics into human-friendly consolidation plans. The LLM’s role is synthesis and prioritization—never blind automation for deletion. Provide structured inputs and ask for ranked actions and migration steps.

Prompt template (example)

  You are an expert FinOps/IT architect in 2026. Given the following app profiles and metrics, produce a ranked list of consolidation recommendations.

  Inputs:
  - App: {name}
  - Monthly cost: {monthly_cost}
  - Assigned seats: {assigned_seats}
  - Active seats (90d): {active_seats}
  - Feature list: {features}
  - Overlap scores with other apps: {overlaps}
  - Ease-of-migration: {ease_score}

  Output:
  1) Recommendation (Consolidate / Maintain / Investigate)
  2) Rationale (2-3 bullets)
  3) Concrete next steps (Slack message templates, Jira tickets, export commands)
  4) Estimated first-year savings
  

Run the prompt in a batch across low-usage apps and collect results in a review dashboard. Use a model with guardrails (retrieval-augmented generation using your internal data) to avoid hallucinations.

Step 5 — Automation actions (connectors and workflows)

Once you have recommendations, automate outreach and remediation using your integration platform of choice. Here are practical flows with common tools.

Example: n8n / Make-style flow

1. Trigger: daily/weekly scheduler.
2. Step 1: Run SQL job to refresh candidate list.
3. Step 2: Call LLM API to generate consolidation plan.
4. Step 3A: Post summary to #finops Slack with action buttons (approve, review, ignore).
5. Step 3B: If approved, create a Jira ticket assigned to app owner with migration checklist and rollback steps.
6. Step 4: If ticket resolves and owner confirms, call SCIM/SSO API to revoke seats or change group membership.

  // pseudocode: webhook handler after Slack approval
  POST /webhook/approval
  payload: { app_id, action, approver }
  if action == 'approve':
      createJiraTicket(app_id, owner, migrationSteps)
      scheduleDeprovision(app_id, when='30d')
  

Integration snippets

Slack message with action buttons (JSON payload):

  {
    "channel": "#finops",
    "text": "Consolidation candidate: Acme-Analytics — 80% idle seats",
    "blocks": [
      { "type": "section", "text": { "type": "mrkdwn", "text": "*Acme-Analytics* is 80% idle. Monthly cost: $12k. Action?" } },
      { "type": "actions", "elements": [
        { "type": "button", "text": {"type": "plain_text","text": "Approve"}, "value": "approve_acme" },
        { "type": "button", "text": {"type": "plain_text","text": "Investigate"}, "value": "investigate_acme" }
      ] }
    ]
  }
  

Operational governance & compliance

Automating license reclamation must respect policy, privacy, and change control:

• Require human approval from app owner before any seat revocation.
• Keep immutable audit logs: who approved, what script ran, diff of assignments.
• Mask PII when sending usage signals to external LLMs; use retrieval-augmented internal embeddings when possible.
• Define SLAs for rollback and emergency re-provisioning (e.g., 24-hour restore window).

Automate insights, not blind deletion. Use AI to recommend and engineers to validate.

Measuring success: KPIs and savings calculations

Track these KPIs to prove impact:

• License reclamation rate: reclaimed seats / identified idle seats.
• Monthly recurring cost (MRC) reduction: baseline MRC vs MRC after 90 days.
• Time-to-remediation: days from detection to seat reclamation or migration.
• False positive rate: revocations that required rollback.

Simple ROI example:

  baseline_monthly_saas = $50000
  identified_idle = $6000
  reclaimed = 0.7 * identified_idle = $4200
  annual_savings = reclaimed * 12 = $50,400
  

Real-world checklist: what to build first (90-day plan)

1. Week 1–2: Inventory — export billing & SSO data into your warehouse.
2. Week 3–4: Basic analytics — run low-usage queries and verify with owners.
3. Week 5–6: Feature mapping — populate features_array for top 50 apps.
   • Use a Slack survey and a short CSV ingestion to speed data capture.
4. Week 7–8: LLM-based recommendation prototype; restrict outputs to structured JSON.
5. Week 9–12: Automate notifications and Jira ticketing; pilot with one department and measure KPIs.

Case study: mid-market SaaS company (hypothetical)

A 600-person SaaS scale-up had 38 paid collaboration tools. After connecting Okta logs, Stripe billing, and GitHub activity, they identified 7 apps with >75% idle seats. Using an automated flow and staged approvals, they reclaimed 420 seats in 90 days and cut annual SaaS spend by ~$120k. Key to success: owner approvals, a migration playbook for two high-value tools, and a monthly report that kept stakeholders aligned.

Common pitfalls and how to avoid them

• Pitfall: Relying only on billing exports. Fix: correlate with SSO and activity logs to avoid false positives.
• Pitfall: Over-automating seat removals. Fix: require app-owner confirmation and implement soft-disable (suspend) first.
• Pitfall: Feature taxonomy mismatch. Fix: standardize a small controlled vocabulary for features and map vendor terms to it.

Advanced strategies for 2026 and beyond

As internal observability matures, consider these advanced approaches:

• Behavioral baselining: use ML to classify users into power, regular, and dormant, then classify seats accordingly.
• Cross-vendor migrations: create migration templates (export/import scripts) for common pairs (e.g., two analytics platforms) to reduce switching friction.
• Automated shadow IT detection: combine expense reports, domain-based app signups, and web proxy logs to find off-book tools.
• Budget-based guardrails: integrate with procurement to prevent duplication during renewals using webhook checks at purchase time.

Security, privacy & model governance

When using LLMs in 2026, follow model governance best practices:

• Prefer closed/private LLM or on-prem embeddings for sensitive telemetry.
• Log prompts and model outputs for auditability and to reduce drift.
• Obfuscate user identifiers before sending telemetry off-platform.

Actionable takeaways (one-page checklist)

• Connect SSO + billing + activity logs into a central warehouse.
• Run a 90-day low-usage query and validate with app owners.
• Build a simple feature matrix and compute overlap scores.
• Use an LLM to convert analytics into prioritized consolidation plans (use RAG).
• Automate Slack notifications and Jira tickets; require owner approval before deprovisioning.
• Measure reclaimed spend, time-to-remediation, and false-positive rate.

Closing: why automated audits win

Manual SaaS cleanups are slow and political. Automated stack audits reduce friction by surfacing objective data, scoring overlap, and producing reproducible recommendations that teams can act on. In 2026, with richer identity logs and reliable LLMs, an integration-first approach delivers fast wins in cost reduction and operational clarity — and it scales as your stack grows.

Next step (call to action)

Ready to build your first automated stack audit? Start by exporting your SSO and billing data into a temporary analytics workspace and run the low-usage query above. If you want a reference flow or a checklist template to run a pilot, get our downloadable 90-day implementation pack and Slack + Jira templates — or book a short workshop with our automation engineers to tailor the flow to your environment.

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