AI Consulting for Chief Data Officers (CDOs)
AIDOLS helps Chief Data Officers make the data foundation AI-ready in 90 days — data quality, data contracts, lineage, and a first AI use case that proves the platform investment to the executive team. Every engagement integrates with the data stack you already run (Snowflake, Databricks, BigQuery, Fabric) and ships a production AI system that uses the data foundation rather than a parallel one.
What Chief Data Officers get from AIDOLS
Specific outcomes scoped against the metrics the Chief Data Officer office already tracks — not generic value props.
AI-readiness audit on the data layer your team already runs
AIDOLS Sprint engagements produce a data AI-readiness audit against your existing Snowflake, Databricks, BigQuery, or Fabric environment — quality, completeness, lineage, contracts, governance — with a remediation roadmap scoped against the AI use cases the executive team is asking for.
A first AI use case that uses your data platform, not a parallel one
Every Build engagement ships an AI system that uses the data platform the CDO already operates. The Build proves the platform investment to the executive team and produces the data contracts and lineage artifacts the next AI use case will reuse.
Data contracts and lineage that survive an AI deployment
AIDOLS produces data contracts (schema, semantics, SLAs) for every data source consumed by the AI system, plus lineage tracking from source to model output. CDOs end the engagement with the contract and lineage layer that subsequent AI use cases inherit, not retrofit.
DataOps that does not require a new tooling commitment
AIDOLS deploys against your existing dbt, Airflow, Dagster, or Fabric pipelines rather than asking the data team to adopt parallel tooling. CDOs avoid the modal failure of AI projects that fork the data layer and create reconciliation work the data team inherits indefinitely.
The 4 problems we solve for Chief Data Officers
The AI program assumes data is ready. Your audit says it is not.
Most enterprise AI programs are scoped against a use-case roadmap that assumes data quality, lineage, and access patterns are in place. CDOs know the assumption is wrong — actual data quality on critical AI use cases typically clears 60-75% of the bar required for production deployment, with the gap concentrated in lineage, contract definition, and SLA enforcement. The CDO has to either block the AI program (career risk) or absorb the data-layer remediation cost (unbudgeted).
AIDOLS Sprint engagements produce an AI-readiness audit on the specific data sources required for the priority use cases. The output is a scoped remediation plan — what to fix before the first Build, what can wait, what changes the contract terms with the source system. CDOs get a defensible plan that gates the AI program on actual data readiness rather than aspirational readiness.
Every AI team forks the data layer. You inherit the reconciliation problem.
The dominant pattern in AI development is for the AI team to extract data from the warehouse, transform it into a model-specific format, and store it in a parallel feature store or vector database the data team does not operate. The CDO inherits a reconciliation problem: the AI system's view of the data drifts from the canonical view in the warehouse, and downstream BI reports do not match AI outputs.
AIDOLS deploys against your existing data platform — Snowflake, Databricks, BigQuery, Fabric — and pushes feature engineering and lineage tracking into the same dbt or transformation layer the data team operates. Feature stores and vector databases, where required, are governed by the same data contracts and lineage as the warehouse. Downstream BI matches AI outputs by construction, not by reconciliation.
Data contracts are talked about. Almost nobody writes them down.
Data contracts (the formalized agreement between a source system and downstream consumers covering schema, semantics, SLAs, and breaking-change policy) are widely discussed and rarely produced. The CDO has the authority to require them but usually lacks the engineering capacity to author them at the scale the AI program demands — and source-system teams resist authoring contracts that lock their schema-evolution flexibility.
AIDOLS produces data contracts as a Build deliverable for every data source consumed by the AI system. The contract is co-authored with the source-system team during the Build and ships alongside the production AI system. Subsequent AI use cases inherit the contract instead of negotiating it from scratch — and source-system teams accept the contract because the terms are written against a specific consumer with a specific SLA.
The AI vendor wants to sell you a feature store. Your stack already has 90% of one.
Most AI platform vendors and large SIs recommend a dedicated feature store (Tecton, Feast, Hopsworks, SageMaker Feature Store) as part of the standard AI deployment. CDOs frequently find that their existing data stack — Snowflake with dbt, Databricks with Delta Live Tables, BigQuery with dbt — already covers 80-90% of feature store capability, and the remaining gap is closeable with engineering work rather than another vendor contract.
AIDOLS produces a feature-store decision artifact as part of Sprint engagements: where the existing data stack covers feature store needs, the AI Build deploys against the existing stack. Where genuine feature store gaps exist (real-time inference, point-in-time correctness at sub-second latency), the gap is scoped specifically rather than addressed by adopting an entire vendor platform.
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Start the Assessment →How a Chief Data Officer engagement typically works
Four steps from kickoff to production cutover. Fixed fee at every step.
CDO + data-team kickoff
Joint workshop with the CDO, data engineering lead, and the use-case sponsor. Agree the priority use case, the data sources required, and lock the SOW including scope of data-layer remediation.
AI-readiness audit + data contract authoring
AIDOLS engineers audit the consumed data sources for AI readiness, co-author data contracts with source-system teams, and design the lineage layer from source to model output. Architecture decision records produced for the data team.
Build + dbt/transformation integration
Model and feature engineering built inside your existing dbt or transformation layer. Weekly review with the data team. Lineage and contract enforcement wired into CI for both data and model pipelines.
Production cutover + data-layer template
Production AI deployment using the canonical data layer. Data contract and lineage template extracted as reusable artifacts for subsequent AI use cases. Optional Scale retainer for ongoing operations and contract evolution.
Pricing model that fits a Chief Data Officer's budget cycle
Three transparent fixed-fee tiers — no time-and-materials drift, no partner-leverage uplift, no surprise change orders.
Best for: CDOs scoping data AI-readiness ahead of an AI program greenlight. Produces audit, remediation roadmap, contract template, and a Build SOW for a use case that uses the existing data stack.
- •Data + infrastructure audit
- •Prioritized opportunity map with ROI estimates per use case
- •Target-state reference architecture
- •90-day implementation roadmap with named owners
- •Executive readout deck
Best for: CDOs ready to ship a first AI use case that uses the existing data platform and produces the contract + lineage artifacts subsequent use cases inherit.
- •Production AI system shipped end-to-end
- •Data engineering, model development, MLOps deployment
- •Integration into existing systems
- •Post-launch evaluation harness + monitoring
- •100% ROI guarantee against a written KPI
Best for: CDOs operating production AI who want ongoing data contract evolution, lineage maintenance, and one feature release per month against the existing data stack.
- •Ongoing engineering retainer post-deployment
- •Model retraining, monitoring, drift detection
- •Incident response + evaluation harness updates
- •One new feature release per month
- •Replaces a $250K-$350K fully-loaded MLOps hire
AIDOLS vs Big Four for a Chief Data Officer
| Dimension | AIDOLS | Big Four |
|---|---|---|
| Pricing model | Fixed fee per tier; written, no T&M drift | Time-and-materials retainer; partner-leverage uplift |
| Data platform posture | Inside Snowflake/Databricks/BigQuery/Fabric you already own | Often recommends adoption of net-new feature store / data platform |
| Data contracts | Authored as Build deliverable; co-authored with source-system teams | Discussed in strategy phase; rarely authored |
| Lineage tracking | Source-to-model-output lineage in CI; matches BI by construction | Often introduces parallel lineage stack |
| Time to first use case | 90 days with data-layer remediation in scope | 12-18 months with separate data-platform program |
| AI-readiness audit | 2-3 weeks at Sprint pricing; scoped to priority use cases | 4-6 month enterprise data assessment |
| DataOps tooling | dbt/Airflow/Dagster/Fabric — your existing pipelines | Often layers proprietary orchestration vendor |
| Exit clarity | 60-day cancellation on Scale; data layer stays canonical | Annual prepayment; parallel data layer becomes permanent |
What a Chief Data Officer should ask before hiring an AI consulting firm
Seven questions to put on every shortlist call. Firms that cannot answer crisply on all seven are not engineering-first.
- Does the firm deploy against our existing data platform (Snowflake, Databricks, BigQuery, Fabric) or recommend net-new?
- Are data contracts authored as a Build deliverable, and co-authored with source-system teams?
- How is lineage tracked from source through transformation through model output, and does it match BI lineage by construction?
- What is the AI-readiness audit scope, and how is it sized to the priority use cases?
- Where does the firm recommend dedicated feature store / vector database vs. extending the existing data stack?
- How is the data-layer remediation scope baked into the Build SOW vs. a separate engagement?
- How many production AI systems has the firm shipped against canonical data platforms in the last 12 months?
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Run the ROI Calculator →Common objections from Chief Data Officers — and our honest answers
"Our data is not clean enough for AI yet."
A Sprint scopes the data-layer remediation specifically against the priority use case rather than against an aspirational enterprise data quality program. The output identifies what to fix before the Build, what can wait, and what contract terms need to change with source systems. CDOs gate the AI program on use-case-specific readiness, not enterprise-wide cleanup.
"AI teams always fork the data layer. We will just inherit reconciliation work."
AIDOLS deploys feature engineering inside your existing dbt or transformation layer and routes feature stores or vector databases (where required) through the same lineage and contract layer as the warehouse. Downstream BI matches AI outputs by construction. There is no parallel data layer to reconcile.
"We tried data contracts before. The source-system teams refused to author them."
Data contracts authored against an entire warehouse usually fail because source-system teams cannot estimate the cost of locking schema-evolution flexibility for unspecified consumers. AIDOLS authors contracts against a specific AI use case with a specific SLA — source-system teams can scope the cost concretely and typically accept the contract.
"Our AI vendor says we need a dedicated feature store."
Frequently you do not. AIDOLS produces a feature-store decision artifact at Sprint scope: where the existing Snowflake/Databricks/BigQuery/Fabric stack covers feature engineering, the Build deploys against it. Where genuine gaps exist (sub-second real-time inference, strict point-in-time correctness), the gap is scoped specifically rather than addressed by adopting an entire vendor platform.
"Our data team is at capacity. We cannot absorb another AI program."
Most AIDOLS Build engagements add roughly 4-8 hours per week of data-team review and contract authoring, not full-time data-team capacity. The Sprint scope explicitly identifies where source-system teams need to participate, and AIDOLS engineers do the data-engineering work inside your existing tooling rather than handing it to the data team to operate.
Three anonymized Chief Data Officer engagement patterns
Generic descriptors used to protect client confidentiality. Detailed reference architectures and named references available under NDA.
Mid-market financial services CDO
CDO at a mid-market lender engaged AIDOLS to scope a data AI-readiness audit ahead of an executive AI program greenlight. Use cases in scope: fraud detection, credit scoring, customer servicing.
Enterprise SaaS CDO
CDO at an enterprise SaaS company inherited 6 AI projects, each operating its own feature store and data extracts. Engaged AIDOLS to scope consolidation onto canonical Databricks platform.
Healthcare network CDO
CDO at a multi-site healthcare network engaged AIDOLS to scope AI-readiness audit covering 14 source systems feeding clinical and operational use cases, including PHIPA + HIPAA compliance review.
FAQs from Chief Data Officers
How does AIDOLS approach AI-readiness on existing data platforms?
AIDOLS deploys against your existing data platform — Snowflake, Databricks, BigQuery, Microsoft Fabric — rather than recommending net-new platform adoption. Sprint engagements produce an AI-readiness audit scoped specifically to the priority use cases: data quality, lineage, contract definition, and access patterns required for production deployment.
Does AIDOLS author data contracts as part of an engagement?
Yes. Data contracts are a standard Build deliverable for every data source consumed by the AI system. Contracts are co-authored with source-system teams during the Build and cover schema, semantics, SLAs, and breaking-change policy. Subsequent AI use cases inherit the contracts rather than negotiating them from scratch.
How does AIDOLS handle lineage tracking?
AIDOLS deploys lineage tracking from source through transformation through model output inside your existing dbt or transformation layer. Lineage matches downstream BI by construction rather than by reconciliation, and is wired into CI so contract violations and lineage breaks fail the build before reaching production.
When does AIDOLS recommend a dedicated feature store or vector database?
AIDOLS recommends dedicated feature store or vector database only where genuine gaps exist in the existing data stack — typically sub-second real-time inference latency or strict point-in-time correctness requirements that the warehouse cannot meet. In roughly 70% of Build engagements, feature engineering deploys against the existing Snowflake/Databricks/BigQuery/Fabric stack without dedicated feature-store vendor adoption.
How does AIDOLS coordinate with the in-house data team?
AIDOLS Build engagements add roughly 4-8 hours per week of data-team review and contract authoring, not full-time data-team capacity. AIDOLS engineers do the data engineering work inside your existing dbt/Airflow/Dagster/Fabric tooling, with weekly architecture reviews that include the data engineering lead and the use-case sponsor.
How does AIDOLS handle the data-layer remediation scope inside a Build SOW?
Data-layer remediation specifically required for the priority use case is scoped into the Build SOW with a fixed fee. Broader enterprise data quality work outside the priority use case is not — that scope is identified in the Sprint and flagged to the CDO with a separate roadmap rather than absorbed silently into the Build.
Can AIDOLS work with our existing data governance framework?
Yes. AIDOLS produces data contracts, lineage artifacts, and decision-log specifications aligned with whatever data governance framework you already operate (DAMA-DMBOK, Collibra, Alation, or in-house). The Build deliverable integrates with the existing framework rather than introducing a parallel governance layer.
How does AIDOLS pricing compare to a Big Four data + AI engagement?
Big Four data + AI engagements typically run $1M-$3M over 12-18 months and produce a target-state data architecture plus a separate AI implementation SOW. AIDOLS prices Sprints at $15K-$25K with an AI-readiness audit on existing platforms, and Builds at $75K-$150K shipping a production AI use case against the existing stack — typically 5-15x lower TCO with a deployed system as the deliverable.
AI consulting for other roles
AIDOLS works across the full executive stack. If your engagement spans multiple functions, we scope it jointly.
Three ways to start, scoped to a Chief Data Officer mandate
Pick the path that matches the next decision your office has to make.