The complete pre-AI checklist for portfolio companies

If your AI plan assumes the data is ready, this is the list to clear before anyone signs. Each item is a question with a yes or no answer, and every “no” or “don’t know” is a risk you carry into the build, where it costs far more to discover and consumes time that could have gone to an initiative the data can support.

This checklist is designed for operating partners and portfolio company executives evaluating AI initiatives. It distils the common failure points in AI projects into a practical verification framework.

Use case definition

Specific use case identified. The AI application should be precisely defined, not a vague goal like leverage AI or improve operations. What specific prediction or decision will AI support? What input will it receive and what output will it produce?

Business value quantified. The expected financial impact should be calculated with explicit assumptions. How much revenue will be protected or generated? How much cost will be avoided? What is the confidence level in these estimates? Value should be quantified in terms that connect to enterprise value, not just operational metrics.

Success metrics defined. Beyond financial impact, what technical and business metrics will indicate success? What accuracy is required for predictions to be actionable? What adoption level is needed for value realisation? Define thresholds that distinguish success from failure.

Timeline realistic. AI projects routinely take longer than planned to reach production value. Does the timeline account for data preparation, model development, testing, integration, and organisational adoption? Is the timeline compatible with holding period constraints?

Organisational readiness confirmed. Will the business actually act on AI predictions? Is there commitment from the teams who must change their workflows? Have change management requirements been identified and planned for?

Data availability

Required data elements identified. What specific data fields are needed to support the use case? This should be a comprehensive list with enough specificity to enable verification, not a general category like customer data.

Data existence confirmed. For each required element, has the team verified the data actually exists in accessible systems? Do not assume based on system documentation. Verify against actual data.

Historical depth adequate. Does historical data extend far enough back to capture the patterns the model needs to learn? For cyclical phenomena, does data span multiple cycles? Are there discontinuities from system migrations that limit usable history?

Data accessible. Can the data be extracted from source systems in a reasonable timeframe? Are there technical, legal, or organisational barriers to access? Has access been tested, not just planned?

Labels available. For supervised learning, are there reliable labels for the outcome being predicted? If predicting churn, is there a consistent historical record of which customers churned and when? If predicting fraud, are there accurate labels for historical fraudulent transactions?

Data quality

Completeness assessed. What percentage of records have all required fields populated? Is coverage adequate across all segments the model must handle? Are there systematic gaps that would bias training data?

Accuracy validated. Has data accuracy been verified against authoritative sources or through business process review? Are there known accuracy issues that could impact model training?

Consistency confirmed. Is the same entity represented consistently across systems that must be integrated? Have definitions remained stable over the historical period being used for training?

Quality sufficient for use case. Given the specific AI application, are quality levels adequate? Different use cases have different tolerance for quality issues. Has quality been evaluated against use-case-specific requirements?

Remediation scoped if needed. If quality gaps exist, has the remediation work been scoped, budgeted, and scheduled? Is remediation a prerequisite for AI development or can they proceed in parallel? Are remediation timelines realistic?

Data integration

Cross-system requirements identified. Which systems must be integrated to support the use case? What data must flow between them? Have all required integrations been identified?

Entity resolution tested. Can records be reliably matched across systems? Has matching logic been tested on representative samples? What is the expected match rate? How will unmatched records be handled?

Definitions reconciled. Where systems use different definitions for the same concept, has reconciliation logic been defined and validated? Do reconciled values match business expectations?

Integration infrastructure exists. Is there a data pipeline capable of integrating data from required sources? Or must this infrastructure be built? If building is required, has this been included in timeline and budget?

Governance

Data ownership clear. Is there an accountable owner for each critical data element? Do owners understand their responsibility for quality? Do they have authority to enforce standards?

Quality monitoring in place. Are there automated checks that will detect data quality degradation? Who is responsible for responding to quality alerts? Have response procedures been defined?

Documentation adequate. Are data definitions, business rules, and system integrations documented sufficiently to support AI development and maintenance? Would a new team member be able to understand the data?

Privacy compliance confirmed. Does the intended use of data comply with privacy regulations and customer expectations? Has legal reviewed the AI application? Are there consent or notice requirements?

Resources

Technical capabilities available. Does the team have the data engineering and data science skills required? If not, has external support been secured? Are resources committed and available when needed?

Infrastructure adequate. Is computing infrastructure sufficient for model training and deployment? Are required tools and platforms available? If infrastructure must be provisioned, has this been included in plans?

Budget realistic. Does the budget account for data preparation, model development, integration, testing, deployment, change management, and ongoing monitoring? AI projects consistently exceed initial estimates. Has buffer been included?

Timeline has contingency. Is there buffer in the timeline for unexpected issues? AI projects rarely proceed exactly as planned. Has contingency been built in for likely delays?

Risk assessment

Failure modes identified. What could cause the project to fail? Data quality issues? Organisational resistance? Technical limitations? Integration challenges? Have potential failure modes been explicitly identified and acknowledged?

Mitigation planned. For each identified risk, what mitigation steps are planned? Who is responsible for monitoring risks? What triggers would indicate that risks are materialising?

Stage gates established. Are there checkpoints where the project will be evaluated against defined criteria? What criteria would trigger a pause, pivot, or discontinuation? Who has authority to make these decisions?

Exit criteria defined. Under what circumstances would the project be discontinued? Having explicit exit criteria prevents projects from continuing long after they should have been stopped. Have these criteria been agreed with stakeholders?

Using this checklist to audit your data before an AI investment

Review this checklist before committing significant resources to AI initiatives. Every unchecked item represents risk. The more unchecked items, the higher the probability of failure.

Do not proceed with AI development until critical items are verified. Data availability, data quality, and integration feasibility are critical. Proceeding without verification in these areas almost always leads to costly mid-project discoveries.

For items that cannot be verified, make explicit decisions about accepting risk or addressing gaps before proceeding. Document these decisions and ensure stakeholders understand the implications.

This checklist does not guarantee success. But each unchecked item is a risk you carry into the build, and each one you clear before signing is a risk you no longer have to explain to the board. The investment in verification is small compared to the cost of failed AI initiatives.