Introduction
Enterprise AI projects have a failure rate that should concern every technology leader. Research consistently shows that the majority of AI initiatives stall before reaching production - not because the technology does not work, but because organizations skip the discovery work that determines whether a project is viable in the first place.
This checklist covers the eight areas you need to evaluate before committing budget and headcount to an AI implementation. Work through each section with your cross-functional team. Any area with unresolved blockers should be addressed before you move to a pilot.
Section 1: Data readiness
AI systems are only as good as the data they consume. Assess your data landscape honestly before evaluating models or platforms.
- Data inventory: Have you identified all data sources required for the target use case? Include internal databases, third-party feeds, user-generated content, and sensor data.
- Data quality assessment: What percentage of required data is clean, complete, and consistently formatted? Document known quality issues.
- Data volume: Do you have enough training data for your approach? Supervised learning models typically need thousands to millions of labeled examples.
- Data labeling: If labeled data is required, who will do the labeling? What is the estimated cost and timeline?
- Data freshness: How frequently does the data change? Does your pipeline support the refresh rate the model needs?
- Data access: Can your AI team access the required data without bureaucratic delays? Are there data sharing agreements that need to be established?
- Data lineage: Can you trace data from source to model input? This is critical for debugging, auditing, and compliance.
Section 2: Infrastructure requirements
AI workloads have different infrastructure demands than traditional applications. Evaluate your readiness across compute, storage, and networking.
- Compute capacity: Do you have access to GPU or TPU resources for training? What about inference infrastructure for production serving?
- Training environment: Where will model training happen - on-premise, cloud, or hybrid? Have you estimated costs for training runs?
- Serving infrastructure: How will models be deployed for inference? Do you need real-time serving, batch processing, or both?
- Data pipeline infrastructure: Can your current ETL/ELT infrastructure handle the volume and velocity of data the AI system requires?
- Monitoring and observability: Do you have tools to monitor model performance, data drift, and system health in production?
- Scalability plan: Can your infrastructure scale if the AI system succeeds and usage grows? What are the cost implications of scaling?
Section 3: Governance and ethics
AI governance is not optional in enterprise environments. Establish guardrails before building.
- AI ethics policy: Does your organization have a documented policy on responsible AI use? If not, who will create one?
- Bias assessment plan: How will you test for and mitigate bias in training data and model outputs?
- Explainability requirements: Do stakeholders or regulators require that AI decisions be explainable? What level of explainability is needed?
- Human oversight model: Where in the workflow will humans review AI outputs? What is the escalation path when the model produces unexpected results?
- Model versioning and rollback: How will you track model versions and roll back to a previous version if a new model performs poorly?
- Decision accountability: Who is accountable when an AI system makes a wrong decision? Document the chain of responsibility.
Section 4: Team skills and capacity
AI projects require specialized skills that many enterprise teams do not currently have.
- Skills inventory: Map your current team’s capabilities against project requirements. Cover data engineering, ML engineering, data science, MLOps, and domain expertise.
- Gap analysis: Where are the skill gaps? Can they be filled through training, hiring, or vendor partnerships?
- Dedicated capacity: Will team members work on the AI project full-time, or split between AI and other responsibilities? Part-time allocation is a common cause of delays.
- Domain expert availability: Can business domain experts commit regular time to guide data labeling, validate model outputs, and define success criteria?
- Organizational readiness: Is leadership prepared to invest in the iterative, experimental nature of AI development? Expectations of waterfall-style delivery will create friction.
Section 5: Vendor and platform evaluation
If you are using third-party AI services or platforms, evaluate them with the same rigor you apply to any enterprise vendor.
- Build vs. buy analysis: Have you determined which components to build in-house and which to source from vendors? Consider models, platforms, data labeling, and MLOps tooling.
- Vendor lock-in risk: Can you switch providers without rebuilding your solution? Evaluate data portability, model portability, and API compatibility.
- SLA and support: What uptime guarantees and support levels does the vendor provide? Are they sufficient for your use case?
- Pricing model: Is pricing predictable? Understand per-token, per-API-call, and compute-time pricing models and project costs at scale.
- Security and compliance certifications: Does the vendor hold certifications relevant to your industry (SOC 2, HIPAA, ISO 27001)?
- Data handling: Where does the vendor process and store your data? Does data leave your jurisdiction? Is your data used to train vendor models?
Section 6: Compliance and regulatory requirements
Regulated industries face additional requirements that must be addressed in the design phase, not after launch.
- Regulatory landscape: Which regulations apply to your AI use case? Consider GDPR, HIPAA, CCPA, the EU AI Act, and industry-specific rules.
- Risk classification: Under frameworks like the EU AI Act, what risk category does your use case fall into? High-risk applications have strict requirements.
- Consent and transparency: Do end users need to be informed that AI is making or influencing decisions? What consent mechanisms are required?
- Right to explanation: Do affected individuals have the right to understand how AI decisions are made? How will you provide this?
- Audit trail: Can you produce a complete record of what data was used, which model version made a decision, and what the inputs and outputs were?
- Cross-border data transfer: If data moves between jurisdictions, are appropriate transfer mechanisms in place?
Section 7: Success metrics and evaluation
Define what success looks like before you build anything.
- Business KPIs: What business metrics will the AI system improve? Be specific - “increase efficiency” is not measurable; “reduce claim processing time from 48 hours to 4 hours” is.
- Model performance metrics: Define acceptable thresholds for accuracy, precision, recall, F1 score, or other relevant metrics for your use case.
- Baseline measurement: Have you measured current performance without AI so you can quantify the improvement?
- A/B testing plan: How will you compare AI-assisted outcomes with the current process? Define test duration, sample size, and statistical significance thresholds.
- Monitoring cadence: How often will you evaluate model performance after launch? Daily, weekly, monthly?
- Degradation triggers: At what performance threshold will you intervene - retrain the model, fall back to manual processes, or escalate to leadership?
Section 8: Pilot scope definition
Start small, prove value, then expand. A well-scoped pilot reduces risk and builds organizational confidence.
- Use case selection: Have you chosen a use case that is high-value, low-risk, and has accessible data? Avoid starting with the hardest problem.
- Scope boundaries: Define exactly what the pilot will and will not do. Document these boundaries and share them with stakeholders.
- Timeline: Set a fixed pilot duration (8-12 weeks is typical). Longer pilots tend to drift in scope and lose momentum.
- Success criteria: What results would justify expanding the pilot to full production? Agree on these with leadership before starting.
- Kill criteria: What results would indicate the project should be stopped or pivoted? Having explicit kill criteria prevents sunk-cost thinking.
- Expansion plan: If the pilot succeeds, what is the path to production? Identify the next 2-3 use cases to tackle.
Using this checklist
Work through each section with your full project team - engineering, data science, legal, compliance, and business stakeholders. Mark each item as complete, in progress, or blocked. Items marked as blocked are risks that need a mitigation plan before you proceed.
The sections are roughly ordered by dependency: data readiness must be assessed before infrastructure can be planned, and governance must be established before compliance can be verified. However, many activities can run in parallel.
Planning an AI implementation in a regulated environment? CoreLine’s AI consulting for regulated industries helps enterprise teams navigate data readiness, compliance requirements, and vendor selection so you launch with confidence instead of costly rework.
