An AI readiness baseline is a dated record of what the organization intends to do, which systems and information are involved, who is accountable, what controls are operating, and what evidence supports production use. It gives leaders a reliable starting point for decisions and gives support staff something more useful than a collection of meeting notes, vendor promises, and undocumented experiments.
The baseline should be specific to an approved use case. A generic statement that the company has clean data or secure technology is not enough. The record needs to show the workflow, data lineage, user and administrator access, model or service configuration, expected behavior, evaluation results, human decision points, monitoring, continuity, cost ownership, and unresolved risk. Another qualified person should be able to trace the evidence and reproduce important tests.
ALLMSP builds and maintains AI readiness baselines for businesses in Lawrenceville, Suwanee, Gwinnett County, Metro Atlanta, and across Georgia. Our in-house team can inventory AI use, correct access and data problems, configure controls, test workflows, document decisions, and operate the resulting environment.
Record the evidence behind every approved AI use case
- Purpose and boundary: State the intended outcome, permitted users, prohibited uses, systems, data, customers, and decision limits.
- Ownership: Name business, process, data, technical, security, privacy, support, financial, and acceptance owners.
- Technical inventory: Document models, services, accounts, agents, connectors, sources, destinations, versions, and dependencies.
- Control evidence: Link approvals, access reviews, configurations, contracts, evaluations, logs, incidents, training, and recovery tests.
- Known limitations: Describe weak data, uncertain outputs, excluded scenarios, manual steps, vendor dependence, and residual risk.
- Change history: Track what changed, why, who approved it, how it was tested, and whether the baseline remains valid.
Inventory the complete AI-enabled business system
Begin at the business event that starts the work and follow it through every system. Record the application or form that supplies the request, identity provider, data repositories, retrieval service, model endpoint, prompt or instruction, automation platform, human review queue, destination system, reports, and logs. Include service accounts, API keys, browser extensions, custom agents, plug-ins, mobile access, exports, and manual copies. Shadow AI often appears at the edges rather than in the approved platform inventory.
For every component, record owner, purpose, environment, vendor, subscription, administrative role, data classification, integration method, support path, renewal, and recovery dependency. Capture versions and configuration dates where behavior can change. Compare inventory with identity, finance, browser-management, cloud, endpoint, and network evidence. Remove unused access and disconnected trials rather than leaving them available for future confusion.
- Use-case record: Document trigger, users, inputs, processing, output, decision, destination, measure, and approval.
- Data lineage: Trace authoritative source, transformations, retrieval, model input, output storage, retention, and deletion.
- Identity path: List users, groups, administrators, service identities, connectors, tokens, and emergency access.
- Vendor dependence: Record service terms, configuration, regional processing, limits, updates, support, export, and exit options.
- Hidden use: Check extensions, personal accounts, unmanaged applications, copied data, unofficial agents, and abandoned pilots.
The baseline should describe the system that actually handles work, including the quiet dependencies that do not appear on an architecture slide.
Attach control evidence and repeatable evaluation results
Connect each important requirement with evidence. A policy may establish intent, but the baseline should also show the platform setting, access review, approved group, data-loss prevention rule, administrator record, logging destination, user acknowledgement, or test result that demonstrates operation. Record exceptions with an owner, reason, expiration, compensating control, and decision authority. Avoid screenshots when an export, configuration record, log, or API result provides stronger and more maintainable proof.
Maintain a protected evaluation set for the use case. Include correct ordinary examples, difficult exceptions, sensitive inputs, misleading instructions, conflicting sources, unsupported questions, and failure of an upstream system. Record expected behavior and scoring guidance before each release. Save model or service version, prompt version, retrieval sources, settings, date, evaluator, result, correction needed, and acceptance decision so later performance claims can be compared honestly.
- Access evidence: Keep group membership, role assignment, administrator approval, review date, and removed access.
- Data evidence: Keep classification, source owner, quality result, approved purpose, retention, and deletion method.
- Evaluation evidence: Keep test cases, expected outcomes, scoring, versions, failures, corrections, and final acceptance.
- Operating evidence: Keep logs, alerts, reviews, incidents, user feedback, costs, support trends, and outcome measures.
- Continuity evidence: Keep fallback instructions, backup or export details, dependency tests, recovery results, and reconciliation.
Defensible does not mean collecting every possible artifact. It means keeping the evidence needed to explain, test, support, and govern the use case.
Maintain the baseline as technology and work change
Assign a review frequency based on consequence and rate of change. A customer-facing agent connected to business systems may need frequent operational review, while a narrow internal drafting aid may need less. Trigger an immediate review after a model or vendor update, prompt change, new integration, data-source change, permission change, incident, material error, ownership transfer, regulatory requirement, or expanded user population. Record whether prior evaluation remains valid.
Use a compact decision register to keep changes accountable. State the proposed change, reason, expected benefit, affected components, risk, test plan, rollback, approvers, implementation date, result, and unresolved issues. Retire the associated accounts, connectors, data copies, documentation, monitoring, and subscriptions when a use case ends. A stale baseline is dangerous because it gives false confidence that controls still match the live environment.
- Review calendar: Set operational, access, security, data, outcome, vendor, cost, and executive review intervals.
- Change gate: Require impact analysis, test cases, owner approval, rollback, communication, and post-change evidence.
- Drift detection: Watch source quality, permissions, prompt behavior, model results, exceptions, usage, and cost.
- Incident link: Connect support and security events with affected versions, evidence, correction, retest, and communication.
- Retirement: Remove access, integrations, stored data, credentials, licenses, monitoring, and outdated instructions.
A maintained baseline shortens troubleshooting, improves leadership decisions, and allows the organization to expand AI without losing track of what it has already approved.
AI readiness assessment, correction, and governance from ALLMSP
ALLMSP can build the baseline and complete the technical work it reveals. We inventory approved and unapproved AI use, map data and integrations, review identities and administrators, configure security controls, create evaluations, test continuity, improve documentation, train users, and establish monitoring and recurring reviews.
Our in-house team connects AI governance with managed IT and cybersecurity operations, which makes evidence easier to maintain after the initial assessment. Organizations in Gwinnett County and throughout Georgia can work with the same ALLMSP team for discovery, remediation, rollout, support, and future changes.
- Discover: Find the live use cases, tools, identities, information, integrations, owners, costs, and unsupported experiments.
- Prove: Link requirements with configuration, access, evaluation, logging, training, recovery, and decision evidence.
- Maintain: Monitor drift, control changes, review outcomes, correct findings, and retire unused technology cleanly.
Primary resources for defensible AI governance
A useful baseline can align with established AI, security, privacy, and secure-design guidance while remaining proportionate to the use case.
- NIST AI Risk Management Framework 1.0. Defines voluntary outcomes for managing AI risk and trustworthiness across design, development, deployment, use, and evaluation.
- NIST Generative AI Profile. Describes generative-AI risks and actions that can inform evaluation records, incident planning, and continuing oversight.
- NIST Privacy Framework. Supports decisions about data processing, governance, control, communication, and protection when AI uses personal or sensitive information.
- CISA Guidelines for Secure AI System Development. Provides secure-design considerations for development, deployment, and operation, including accountability and customer security outcomes.
AI readiness baseline FAQs
What is an AI readiness baseline?
It is a dated, evidence-backed record of an approved AI use case, its purpose, owners, systems, data, access, controls, evaluation results, limitations, support, continuity, costs, and change history.
How is a baseline different from an AI policy?
A policy sets organization-wide expectations. A baseline shows how those expectations and platform-specific requirements are applied and tested for one live use case.
Which AI tools should appear in the inventory?
Include approved platforms, embedded assistants, agents, extensions, APIs, models, automation tools, connectors, personal or trial accounts using business data, and services that store or receive AI output.
What is data lineage for an AI use case?
Data lineage traces information from its authoritative source through transformations, retrieval, model input, human review, output storage, downstream action, retention, and deletion.
What evidence is stronger than a screenshot?
Prefer configuration exports, role assignments, access-review results, logs, policy records, test reports, versioned instructions, API results, tickets, approvals, and recovery evidence when the platform provides them.
How often should the baseline be reviewed?
Choose a schedule based on consequence and change rate, then review immediately after material changes to models, prompts, sources, permissions, integrations, users, policies, vendors, or observed performance.
What should happen when an evaluation fails?
Record the failed case, affected version, consequence, owner, containment, correction, retest, acceptance decision, and whether production use or expansion should pause.
How should an AI use case be retired?
Disable access and automation, revoke credentials, export or dispose of data as required, remove integrations, stop subscriptions, update documentation and monitoring, and preserve required decision or audit records.
Can ALLMSP correct the problems found during the assessment?
Yes. ALLMSP can complete the inventory, data and access cleanup, security configuration, integration changes, evaluation, documentation, training, monitoring, and support with its own team.
Where can Georgia businesses receive an AI readiness assessment?
ALLMSP works with businesses in Lawrenceville, Suwanee, Gwinnett County, Metro Atlanta, and throughout Georgia, including organizations with remote employees or multiple locations.
