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How to measure the outcomes of an AI project
Usage tells you whether AI is being used. Outcome measurement asks whether a defined task or process improved, at what cost, and with what evidence.
Steve LaBellaUpdated
Resources
Make the next AI decision a better one.
Practical guides for measuring results, understanding employee AI use, and keeping policy decisions connected to evidence.
Guides
AI value
Connect costs and results to the next investment decision.
How to build an AI spend ledger
A company AI spend ledger should reconcile provider bills, expense evidence, directory context, and usage telemetry without hiding how complete each signal is.
Steve LaBellaUpdated
How to prepare an AI usage review
A useful AI usage review gives leadership a forwardable snapshot of spend, adoption, risk, and blind spots without pretending the picture is complete.
Steve LaBellaUpdated
Guides
Shadow AI
Understand the signals, find unreviewed use, and assign the follow-up.
What is Shadow AI? Risks, examples, and discovery
Shadow AI is the use of AI tools, accounts, or features outside the organization’s approved inventory or operating process. Discovery starts by separating observable evidence from assumptions.
Steve LaBellaUpdated
How to detect AI usage without capturing employee prompts
Security teams can identify supported AI services through managed-browser hostname evidence without turning Shadow AI discovery into employee-content surveillance.
Steve LaBellaUpdated
Shadow AI discovery checklist for security teams
A defensible Shadow AI program combines scoped collection, reviewed provider coverage, identity reconciliation, owned triage, and an explicit record of what remains outside the map.
Steve LaBellaUpdated
How to find shadow AI spend
Shadow AI spend usually appears first in card and expense data, long before it appears in an IT inventory.
Steve LaBellaUpdated
Guides
Governance
Prepare the policies, reviews, and evidence your organization needs.
How to explain AI coverage to leadership
Coverage is the difference between what the company can prove, what it can infer, what it can enforce, and what remains outside the map.
Steve LaBellaUpdated
What to track for AI hiring compliance
AI hiring review starts with use-case inventory, jurisdiction scope, evidence ownership, and the distinction between system-generated records and customer-supplied artifacts.
Steve LaBellaUpdated
Consumer chatbot AI disclosure checklist
Consumer chatbot review should connect external AI experiences to disclosure text, escalation paths, owner review, jurisdiction scope, and operational evidence.
Steve LaBellaUpdated
How gateway privacy modes work
Gateway privacy modes let a customer choose what Tallin stores while keeping the operational ledger useful for spend, attribution, and policy evidence.
Steve LaBellaUpdated
Tallin vs. AI gateway tools
AI gateway tools govern traffic for AI applications a company builds. Tallin governs employee use of AI tools the company already has.
Steve LaBellaUpdated
Reference
Keep useful details close.
- Shadow AI provider coverage index
Reviewed providers, supported hostnames, and verification dates.
- Compare Shadow AI detection methods
What browser, identity, provider, network, and expense signals establish.
- AI policy template
A starting point to adapt with your policy owners and legal team.
- Coverage glossary
Definitions for evidence, controls, review states, and unresolved gaps.
Documentation
Setup instructions for connections, imports, and reports are available in your customer workspace.
Support
Contact Tallin through your workspace or the company contact page. Include the affected page and a short description, without credentials or sensitive records.
Put the next decision into practice.
Explore Tallin with sample data, or bring a project and its supporting records into your workspace.