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An IT services company is ready to deliver an AI project only when it can show project-specific evidence across the entire delivery lifecycle—not merely a high maturity score or a list of familiar tools. Check for a bounded business use case, an accountable owner, lawful and usable data, secure architecture and integrations, risk controls, qualified staff, pre-defined evaluation, and a production plan covering monitoring, incidents, updates, and retirement. Use established frameworks to organize the review, then make a documented go/no-go decision for the particular client, jurisdiction, and risk level.
Define what “AI-ready” means for a services provider
Readiness is the ability to responsibly turn a client problem into a working, supportable AI service. It is not the same as having an internal chatbot, hiring a data scientist, or completing a questionnaire. A provider may be strong in model development but unable to obtain authorized data, integrate with a client’s systems, meet contractual controls, or operate the system after launch.
Assess seven connected areas:
- Business and use-case discipline: a measurable outcome, explicit constraints, and a willingness to reject AI when another approach is safer or simpler.
- Data foundations: authorized access, ownership, provenance, quality, privacy limits, retention rules, and a secure data path.
- Infrastructure and integration: an explained model and platform choice, system interfaces, security boundaries, deployment constraints, and operating-cost assumptions.
- Governance, security, and risk: decision rights, review and escalation, documentation, risk ownership, and controls appropriate to the use case.
- People and operating model: named business, technical, security, risk, delivery, and user-facing responsibilities.
- Evaluation: success measures, representative test data, acceptance thresholds, human-review rules, and evidence that the system performs acceptably before release.
- Operations: monitoring, incident response, change management, model and data updates, support ownership, and retirement criteria.
These dimensions are interdependent. Excellent model accuracy cannot compensate for missing data rights, and a secure architecture does not make an undefined business outcome a viable project.
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Require a business outcome and an accountable owner
Ask the provider to write a short use-case brief before discussing models. It should identify the process being changed, the users affected, the decision or task supported, the expected benefit, the baseline method, and how success will be measured. A business owner must be able to accept or reject the result; a technical project manager alone is not sufficient.
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Look for a statement such as “reduce average claims-triage time while maintaining the existing accuracy and escalation requirements,” rather than “deploy generative AI for claims.” The first can be tested and governed; the second is a technology aspiration.
Make constraints explicit
The brief should record prohibited data uses, geographic processing limits, latency and availability targets, budget boundaries, accessibility needs, records-retention duties, and whether outputs may directly trigger a decision. It should also state what happens when the model is uncertain or unavailable.
Ask when AI is not appropriate
A credible provider can explain why a rules engine, search system, workflow change, or conventional software would be preferable in some cases. Treat an insistence on AI for every problem as a scope and governance warning.
| Evidence to request | What it demonstrates | Warning sign |
|---|---|---|
| Use-case brief with baseline, target outcome, constraints, and named business owner | The proposal is tied to a real client need | Only model names, feature lists, or innovation language |
| Process map showing users, decisions, hand-offs, and exception paths | The provider understands where AI enters the workflow | No treatment of errors, overrides, or affected users |
| Alternative analysis covering non-AI options | Technology choice is justified | AI is presented as the default solution |
Verify data foundations before approving a design
Confirm authority, ownership, and provenance
Request a data inventory that identifies each source, owner, permitted purpose, geographic location, retention period, and access method. The provider should explain how it will obtain consent or another lawful basis where applicable, honor contractual restrictions, and prevent training or reuse outside the agreed purpose.
Provenance should be traceable enough to investigate an output. For retrieved documents, record source identifiers and versions. For training or fine-tuning material, record inclusion criteria, licensing or authorization, transformations, and removal procedures.
Test quality and representativeness
Ask how missing, duplicated, stale, mislabeled, or contradictory records will be detected. Test data should reflect the languages, formats, edge cases, and user populations encountered in production. A provider that cannot describe its data-quality tests cannot credibly promise reliable outputs.
Inspect the secure data path
The architecture should show where data is encrypted, processed, logged, cached, backed up, and deleted. Clarify whether a third-party model service can retain prompts or outputs, whether client data is used for provider training, and how secrets and privileged credentials are isolated. These points must be resolved contractually and technically rather than assumed from a vendor’s marketing description.
- Data-flow diagram with trust boundaries and external services
- Access-control model, service accounts, key management, and audit logging
- Data-quality report and representative test-set description
- Retention, deletion, backup, and cross-border-processing rules
- Documented handling of personal, confidential, regulated, or proprietary information
Examine infrastructure, integration, and model management
Demand a reasoned model and platform choice
The provider should compare candidate models or approaches against the use case’s accuracy, latency, context-window, safety, hosting, data-residency, and cost requirements. “Best model” is not a sufficient rationale. Ask what happens if the selected model changes terms, becomes unavailable, or produces a breaking version update.
Trace every integration
Review how the system will authenticate to client applications, validate inputs, handle timeouts, enforce authorization, and write results back. Define idempotency, retries, rate limits, queueing, and rollback for operations that change records or trigger actions. For retrieval-augmented systems, inspect indexing, document refresh, access filtering, and citation behavior.
Separate prototype infrastructure from production infrastructure
A notebook or demonstration environment does not prove production readiness. Ask for an environment plan covering development, test, staging, and production; infrastructure-as-code or equivalent repeatability; observability; disaster recovery; and capacity assumptions. Include estimated inference, storage, network, licensing, support, and human-review costs, with the pricing period and usage assumptions stated.
Plan for model and data change
Model management includes version pinning, approval for upgrades, regression tests, rollback, prompt or configuration change control, and records of which version generated each consequential output. Data pipelines need equivalent controls for schema changes, source drift, and index refreshes.
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The NIST AI Risk Management Framework (AI RMF) is voluntary guidance intended to improve the incorporation of trustworthiness considerations into AI design, development, use, and evaluation. It is useful for structuring questions, but it is not a certification of an IT services company. NIST reports that AI RMF 1.0 is being revised and that an April 7, 2026 concept note addresses a critical-infrastructure profile; verify the current status before relying on a version in a contract.
Use the lifecycle functions as review prompts
- Govern: identify policies, roles, accountability, documentation, and escalation.
- Map: describe the context, users, affected groups, intended and foreseeable uses, and potential harms.
- Measure: test performance, security, privacy, robustness, fairness, and other properties relevant to the use case.
- Manage: prioritize risks, apply controls, monitor residual risk, respond to incidents, and improve or retire the system.
NIST’s AI RMF Playbook explicitly says, “The Playbook is neither a checklist nor set of steps to be followed in its entirety.” Use it to expose missing questions, then tailor the controls to the client’s sector, contract, geography, and impact level. Confirm binding legal and regulatory requirements separately; voluntary guidance does not replace them.
Assign decision rights
Obtain a responsibility matrix naming the business owner, product or service owner, technical lead, data owner, security reviewer, privacy or legal reviewer, risk owner, operations lead, and incident commander. It should state who can pause deployment, approve a model change, accept residual risk, notify affected parties, and authorize retirement.
Review threat and misuse scenarios
Require testing for prompt injection, data exfiltration, unauthorized tool use, malicious or poisoned documents, insecure dependencies, model abuse, and denial-of-service conditions where relevant. For generative systems, include fabricated content, sensitive-data disclosure, and unsafe or discriminatory responses. Controls should include least privilege, input and output filtering where appropriate, isolation of tools, logging, rate limits, and human escalation.
Verify people and the operating model
OECD workforce guidance distinguishes capability needs for general employees, leaders, and digital and data professionals. A delivery team therefore needs more than technical model-building skills.
| Role group | Evidence of capability |
|---|---|
| Leadership and account owners | Can set risk appetite, fund operations, resolve scope conflicts, and accept or reject residual risk. |
| Digital and data specialists | Can build data pipelines, evaluate models, secure integrations, manage versions, and investigate failures. |
| Project and product staff | Can translate user needs into acceptance criteria, coordinate reviews, document decisions, and manage change. |
| General employees and affected users | Know appropriate use, verification duties, escalation routes, privacy expectations, and limitations of AI outputs. |
| Operations and support | Can monitor service health, triage incidents, execute rollback, communicate outages, and maintain runbooks. |
Ask for named people, availability during the project and after launch, relevant experience with comparable risk and integration complexity, and a staffing plan for leave or turnover. Generic résumés or a single part-time specialist are weak evidence for a system that requires continuous oversight.
Require evaluation before deployment
Define acceptance criteria in advance
Evaluation should specify the task, test population, metrics, thresholds, confidence or abstention behavior, and conditions under which a human must review an output. For a classification system, this may include precision and recall by important subgroup; for a generative system, it may include groundedness, refusal behavior, citation accuracy, latency, and structured human ratings. Select measures that reflect the business harm of false positives and false negatives.
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Use representative and adversarial tests
Keep a test set separate from development data. Include rare but consequential cases, ambiguous requests, language or formatting variants, unauthorized requests, and known failure modes. Record model, prompt, retrieval, data, and configuration versions so results can be reproduced.
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Plan human review and acceptance
Specify who reviews uncertain or high-impact outputs, the maximum response time, what information the reviewer sees, and how overrides are recorded. A “human in the loop” is not a control if the person lacks authority, time, training, or a meaningful way to disagree.
Assess production monitoring, incidents, and maintenance
Ask to see a draft runbook, not just a promise to monitor the system. It should cover:
- Availability, latency, throughput, cost, and queue-depth thresholds
- Data drift, concept drift, retrieval failures, hallucination or quality signals, and abuse indicators
- Alerts, severity levels, on-call ownership, escalation contacts, and customer communications
- Containment, rollback, fallback operation, evidence preservation, and post-incident review
- Scheduled and event-driven evaluation after model, prompt, data, or integration changes
- Patch, upgrade, retraining, re-indexing, and approval procedures
- Exit and retirement criteria, including data deletion and user notification
Operational ownership must continue after the implementation team leaves. Put service levels, support hours, change approvals, audit access, and responsibilities for third-party outages in the statement of work.
Use readiness frameworks without mistaking them for proof
| Resource | What it is useful for | Limits to state explicitly |
|---|---|---|
| Microsoft AI Readiness Assessment | Prompts review of Business Strategy, AI Governance & Security, Data Foundations, AI Strategy & Experience, Organization & Culture, Infrastructure for AI, and Model Management. | The page states an assessment length of 45 minutes. That is a self-assessment duration, not the time required for provider due diligence or proof of delivery capability. |
| OECD SME AI Readiness Tool | Provides plain-language questions for small and medium-sized businesses in G7 countries. | The pilot questionnaire is described as taking approximately five minutes; its results are indicative, not an official OECD assessment or endorsement, and it is not specific to IT-services delivery. |
| NIST AI RMF and Playbook | Organizes trustworthiness and risk questions across Govern, Map, Measure, and Manage throughout design, development, use, and evaluation. | The framework is voluntary, and the Playbook says it is neither a checklist nor a complete sequence of steps. It does not certify a provider. |
| AI Engineering by Chip Huyen | Offers intermediate-to-advanced technical learning on foundation-model application development, evaluation, architecture, and deployment; O’Reilly describes it as a 534-page book published in December 2024. | It is supplemental team education, not an organizational readiness assessment or delivery certification. |
Framework outputs should be treated as prompts and records of evidence. None of the resources above establishes a validated readiness score for IT services companies.
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Run a project-specific due-diligence review
- Send a written scenario. Describe the client outcome, users, data classes, integrations, geography, impact, constraints, and target timeline. Ask every provider to respond to the same facts.
- Collect the use-case and architecture pack. Require the use-case brief, process map, data-flow diagram, model rationale, integration design, cost assumptions, and proposed acceptance criteria.
- Interview accountable owners. Speak with the business, technical, security, risk, and operations owners—not only sales or an innovation lead.
- Inspect evidence in a controlled session. Review a comparable project’s redacted evaluation report, deployment design, runbook, incident process, and change records. Do not accept confidential client material without authorization.
- Test a representative slice. Use client-approved sample data and edge cases. Measure agreed criteria and document limitations; do not treat a polished demo as a benchmark.
- Review contractual controls. Resolve data use, confidentiality, subprocessors, residency, audit rights, service levels, intellectual property, breach notification, and termination obligations.
- Record gaps and owners. For every missing control, document the remediation, owner, effort, dependency, deadline, and residual risk.
- Make a staged decision. Choose go, go with conditions, pilot only, or no-go. Define what evidence is required to move from one stage to the next.
Evidence pack to request
| Artifact | Questions it must answer |
|---|---|
| Use-case brief and success criteria | What outcome is being improved, for whom, by how much, and with what constraints? |
| Data inventory and access description | Who owns each source? Is access authorized? What are the quality, privacy, residency, and retention conditions? |
| Architecture and integration design | Where are models, data, tools, credentials, logs, and fallbacks located? How do systems authenticate and recover? |
| Named responsibility matrix | Who approves, operates, reviews, escalates, pauses, changes, and retires the system? |
| Security and governance review | Which threats, legal duties, privacy concerns, and residual risks were identified, and who accepted them? |
| Evaluation plan and test results | Which representative and adversarial tests were run, with which versions, thresholds, and subgroup results? |
| Deployment, monitoring, and incident procedures | How will the service be released, observed, contained, restored, communicated, updated, and retired? |
| Staffing and skills plan | Are qualified people available through implementation and ongoing operations, with backup coverage? |
Compare providers on evidence, not marketing maturity
When several providers are under consideration, compare them on the same practical axes:
- Evidence quality: Is material specific, dated, reproducible, and tied to a comparable project?
- Use-case fit: Does the proposed approach address the actual workflow, users, constraints, and failure costs?
- Risk exposure: Are important harms, threat scenarios, legal duties, and residual risks identified and owned?
- Data and integration effort: Are access, quality remediation, interfaces, security boundaries, and dependencies realistic?
- Operational ownership: Are monitoring, support, incidents, upgrades, and retirement funded and assigned?
- Evaluation strength: Are acceptance tests representative, versioned, independently reviewed where appropriate, and connected to a release decision?
You may use an internal 0–3 evidence scale for each axis—0 for no credible evidence, 1 for generic claims, 2 for project-relevant documented evidence, and 3 for demonstrated, reviewable evidence—but treat that as an internal decision aid, not an industry standard or certification score. A high total should not override a fatal gap such as unauthorized data access, an unowned high-impact decision, or no viable production operator.
Recognize common failure modes
“We completed the assessment, so we are ready”
Short questionnaires are useful for orientation. The Microsoft assessment states 45 minutes, and the OECD SME pilot states approximately five minutes. Neither duration represents the work needed to inspect a client’s data, architecture, controls, people, tests, and operating plan.
“The model is accurate on our demo”
A demo may use curated inputs and omit authorization, drift, rare cases, and operational failure. Require a representative, versioned test set and acceptance thresholds agreed before the result is shown.
“A human reviews everything”
Review is ineffective when volume exceeds capacity or reviewers cannot see evidence, override the system, or escalate. Quantify workload, authority, training, response time, and auditability.
“Security is covered by the cloud provider”
Cloud controls do not settle data rights, prompt and output retention, application authorization, insecure tool use, logging, or client-specific contractual duties. Assign controls across provider, client, and any model or platform subprocessors.
“The pilot will solve the details”
A pilot can reduce uncertainty, but it still needs a limited scope, approved data, safety boundaries, evaluation plan, stop conditions, and a decision about what evidence is required for production.
Make the go/no-go decision explicit
Approve delivery only when the proposed outcome, data authority, architecture, accountable owners, risk treatment, evaluation, and operating model are sufficiently evidenced for the project’s impact and constraints. A conditional approval should list every condition and its deadline; “we will address it later” is not a control.
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There is no published statistic establishing what percentage of IT services firms are ready to deliver AI projects, and no universal validated score that can replace this review. Readiness is demonstrated by project-specific evidence and sustained operational accountability, not by a badge, questionnaire result, or technology inventory.
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