AI Consulting for Chief AI Officers (CAIOs)
AIDOLS helps Chief AI Officers ship the first 90 days of the CAIO mandate — AI operating model, vendor/build/buy framework, governance posture, and the first production deployment — without committing to a 12-month strategy engagement before any production system exists. Every engagement produces the artifact set the executive team and the board are already asking for.
What Chief AI Officers get from AIDOLS
Specific outcomes scoped against the metrics the Chief AI Officer office already tracks — not generic value props.
A defensible 90-day plan for the CAIO mandate
AIDOLS Sprint engagements produce the operating model, governance posture, vendor/build/buy framework, and prioritized use-case backlog the CAIO needs in the first 90 days of the mandate. Defensible artifact set ready for the next executive committee meeting.
AI organization design that scales from 0 to 20
AIDOLS produces the AI org chart, hiring plan, vendor mix, and the operating-model RACI for AI work — covering the realistic team trajectory from CAIO + 2 hires (year one) to CAIO + 15-20 hires (year three). Avoids the modal failure of over-hiring before use-cases are validated.
Governance and model lifecycle from sprint zero
AIDOLS architects governance — model registry, risk-tier classification, decision-log specification, drift monitoring, lifecycle policy — into every Build from sprint zero. CAIOs do not inherit a governance debt from production systems built before the function existed.
A working production system in the first 90 days
A Sprint scopes the priority Build; the Build ships a production system in 90 days. CAIOs walk into the next board meeting with a deployed system and a verified KPI rather than a 90-day status report on a multi-year program.
The 4 problems we solve for Chief AI Officers
The CAIO mandate is broad. The first 90 days will define whether the function survives.
The Chief AI Officer role is a recent (2024-2026) executive position with broad scope and limited established playbook. The first 90 days typically determine whether the function is treated as a strategic capability or as a centralized cost centre — the executive team is watching for evidence that AI investment translates to business outcomes inside one quarter, not one fiscal year.
AIDOLS Sprint engagements produce the artifact set the first 90 days demand: operating model, governance posture, vendor/build/buy framework, and a prioritized use-case backlog with a Build SOW for the highest-priority candidate. The Build ships a production system in the next 90 days. CAIOs end the first six months with a deployed system and a defensible roadmap, not a status report.
Build vs. buy vs. extend: every framework you have seen is vendor-biased.
Most build-vs-buy frameworks in AI consulting are produced by firms with a commercial interest in the buy decision (platform vendors), the build decision (large SIs), or the extend decision (Big Four advisory). CAIOs cannot rely on those frameworks unmodified — they need a vendor-neutral framework grounded in their specific use-case mix, data posture, and existing platform commitments.
AIDOLS produces a build-vs-buy-vs-extend framework as part of Sprint engagements, scored against your specific use cases and the platforms you already own (ServiceNow, Microsoft, Salesforce, SAP, Oracle). The framework is vendor-neutral by design — AIDOLS does not resell platform vendor licenses and has no commercial interest in any specific decision direction.
You have to defend AI talent strategy without a benchmark for an AI organization.
CAIOs face hiring pressure on a function that did not exist at most companies 24 months ago. The realistic team trajectory (CAIO + 2 in year one, CAIO + 8 in year two, CAIO + 15-20 in year three) is hard to defend against the dominant pattern of front-loaded hiring that creates capacity ahead of validated use cases. The result is a high-cost team with low utilization in the first 18 months.
AIDOLS produces an AI organization design as a Sprint artifact: realistic team trajectory matched to validated use-case backlog, vendor coverage to bridge capability gaps before in-house hires close, and an operating-model RACI that defines what AIDOLS, the in-house team, and the existing engineering org each own. The team scales against demonstrated demand rather than projection.
You inherit AI systems built before the function existed. Most have no governance.
CAIOs typically inherit a portfolio of 5-25 AI systems built across the company in the 18-36 months before the function was created — most without consistent governance, model registry, or decision-log specification. Retrofitting governance onto inherited systems is a 6-18 month program priced in millions, and most CAIOs do not have the budget to commission it as a standalone engagement.
AIDOLS Sprint engagements scope governance retrofit alongside new-system engagements: model inventory, risk-tier classification per inherited system, governance RACI, and a remediation roadmap sequenced against business risk. Build engagements ship the highest-risk remediation first and produce the governance template that applies to subsequent retrofits.
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Start the Assessment →How a Chief AI Officer engagement typically works
Four steps from kickoff to production cutover. Fixed fee at every step.
CAIO + executive kickoff
Joint workshop with the CAIO and the executive sponsor (typically the CEO or COO). Agree the 90-day mandate scope (operating model, governance, vendor framework, first Build) and lock the SOW.
Operating model + governance design
AIDOLS produces the AI operating model, governance posture, vendor/build/buy framework, prioritized use-case backlog, and AI org design. Concurrent build-team formation for the priority Build candidate.
First Build + governance application
Production Build of the priority use case, with governance documentation produced alongside the engineering work. Architecture decision records, model card, decision-log specification, drift monitoring plan all delivered.
Production cutover + governance template
Production deployment of first system. Governance template extracted as a reusable artifact for subsequent Builds and for governance retrofit on inherited systems. Optional Scale retainer for ongoing operations.
Pricing model that fits a Chief AI Officer's budget cycle
Three transparent fixed-fee tiers — no time-and-materials drift, no partner-leverage uplift, no surprise change orders.
Best for: CAIOs scoping the first 90 days of the mandate. Produces operating model, governance posture, vendor framework, AI org design, and a Build SOW for the priority use case.
- •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: CAIOs ready to ship the first production AI system inside the new function in 90 days. Includes governance template that applies to subsequent Builds.
- •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: CAIOs operating production AI who want a vendor coverage layer at $25K/month while in-house hires close, plus ongoing model lifecycle and governance updates.
- •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 AI Officer
| Dimension | AIDOLS | Big Four |
|---|---|---|
| Pricing model | Fixed fee per tier; written, no T&M drift | Time-and-materials retainer; partner-leverage uplift |
| Vendor neutrality | No platform reseller relationships; build/buy framework is vendor-neutral | Often platform-aligned; build/buy framework biased to vendor partnerships |
| First production system | Shipped inside first 6 months of CAIO mandate | 12-18 months of strategy work before first production |
| Governance posture | Built into Build deliverable; template reusable for retrofits | Separate $300K-$1.2M governance engagement |
| AI org design | Realistic trajectory + vendor coverage; no front-loaded hiring | Often recommends front-loaded $2M-$5M team buildout |
| Inherited-system retrofit | Roadmap as Sprint artifact; remediation in subsequent Builds | Standalone 6-18 month remediation program |
| Capability bridge | Scale retainer ($25K/mo) bridges in-house hiring | Multi-quarter retainer with no in-house transition plan |
| Exit clarity | 60-day cancellation on Scale; clean transition to in-house team | Annual prepayment; tribal knowledge stays with the firm |
What a Chief AI 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.
- What does the firm produce as the artifact set for the first 90 days of a CAIO mandate?
- Is the build-vs-buy-vs-extend framework vendor-neutral, or does the firm have platform reseller relationships?
- How does the firm scope governance retrofit on inherited systems vs. governance for new builds?
- What is the recommended AI organization trajectory, and how does it compare against revenue and validated use-case backlog?
- How does the firm bridge capability gaps while in-house hiring closes?
- How many CAIO-level engagements has the firm run in the last 12 months, and can I speak to two CAIO references?
- What is the cancellation clause if executive priorities shift mid-engagement?
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Run the ROI Calculator →Common objections from Chief AI Officers — and our honest answers
"I need a 12-month strategy engagement, not a 90-day sprint."
A 12-month strategy engagement produces a strategy document. A Sprint + Build sequence produces the strategy document AND a working production system inside the same 6 months. Most CAIOs find the latter easier to defend at the next executive committee meeting.
"My executive team will not accept an outside firm in the AI function this early."
AIDOLS works alongside the CAIO function rather than instead of it. Most CAIO engagements are explicitly co-sponsored by the CAIO and the CEO/COO, with AIDOLS positioned as a capability bridge and a build accelerator while the in-house function matures. The artifact set is owned by the CAIO and presented as the CAIO's plan.
"We need a Big Four firm for credibility with the board."
Big Four engagements typically take 12-18 months to produce a strategy document, with implementation handed off separately. An AIDOLS Sprint produces the equivalent artifact set in 2-3 weeks at $15K-$25K, with a Build SOW that ships a production system in the next 90 days. Most boards find the working system more credible than the strategy document.
"We have already started building an in-house AI team."
Good — AIDOLS works alongside the in-house team rather than replacing it. Most engagements are scoped jointly with the CAIO's direct reports, with AIDOLS shipping the first 1-3 production systems and bridging capability gaps under a Scale retainer while the team scales. Transition to in-house operations is part of the standard engagement design.
"Our governance posture is too immature for a production system."
A Sprint scopes the governance posture and the Build engagement applies it. The first Build ships with the governance template that applies to subsequent Builds and to inherited-system retrofits. CAIOs do not need a 12-month governance buildup before production work starts — governance and production ship together.
Three anonymized Chief AI Officer engagement patterns
Generic descriptors used to protect client confidentiality. Detailed reference architectures and named references available under NDA.
Mid-market financial services CAIO
New CAIO at a mid-market financial services company engaged AIDOLS in the first month of the mandate for a Sprint covering operating model, governance posture, vendor/build/buy framework, and AI org design.
Enterprise SaaS CAIO
CAIO at an enterprise SaaS company inherited 17 AI systems built across product and ops in the 24 months before the function existed. Engaged AIDOLS for governance retrofit roadmap + first Build.
Healthcare network CAIO
CAIO at a multi-site healthcare network engaged AIDOLS for a Sprint covering operating model, governance posture aligned to OCR HIPAA + provincial guidance, and AI org design across clinical and administrative AI use cases.
FAQs from Chief AI Officers
What does AIDOLS deliver in the first 90 days of a CAIO mandate?
AIDOLS Sprint engagements deliver the artifact set the first 90 days demand: AI operating model, governance posture, vendor/build/buy framework, AI organization design, prioritized use-case backlog with ROI projections, and a Build SOW for the priority candidate. The Build then ships a production system in the next 90 days.
Is the AIDOLS build-vs-buy-vs-extend framework vendor-neutral?
Yes. AIDOLS does not resell platform vendor licenses (ServiceNow, Microsoft, Salesforce, SAP, Oracle) and has no commercial interest in any specific decision direction. The build/buy/extend framework is scored against your specific use cases and your existing platform commitments — recommendations frequently land on extending platforms you already own rather than buying or building net-new.
How does AIDOLS scope governance retrofit on inherited AI systems?
AIDOLS Sprint engagements scope governance retrofit alongside new-system work: model inventory of inherited systems, risk-tier classification per system, governance RACI, and a remediation roadmap sequenced against business risk. Build engagements ship the highest-risk remediation first and produce a governance template that applies to subsequent retrofits.
How does AIDOLS approach AI organization design?
AIDOLS produces an AI org design with a realistic team trajectory matched to validated use-case backlog — typically CAIO + 2 hires in year one, CAIO + 8 in year two, CAIO + 15-20 in year three. Vendor coverage via Scale retainer bridges capability gaps before in-house hires close. The design avoids front-loaded hiring that creates capacity ahead of validated demand.
Can AIDOLS bridge capability while we hire the in-house AI team?
Yes. The Scale tier ($25K/month) provides ongoing engineering and operations capacity while in-house AI hires close (typically 4-9 months per senior hire). Most CAIO engagements run AIDOLS as a capability bridge for 6-18 months, then transition operations to the in-house team under a Scale wind-down.
How does AIDOLS work alongside an existing AI Center of Excellence?
AIDOLS works alongside an internal CoE rather than replacing it. Most engagements are scoped jointly with the CoE — AIDOLS ships the first 1-3 production systems and bridges capability gaps while the CoE matures, then transitions operations to the CoE under a Scale retainer wind-down. The CoE owns the artifact set and the operating model.
What is the typical AIDOLS engagement structure for a new CAIO?
The modal new-CAIO engagement is a Sprint in the first 30-60 days of the mandate to produce the operating model and governance posture, a Build at month 3-6 to ship the first production system against the priority use case, and a Scale retainer running 12-18 months to bridge capability while the in-house function scales. Total committed spend: typically $400K-$700K over 18 months vs. $1.5M-$3M for an equivalent in-house buildout.
How does AIDOLS pricing compare to Big Four for CAIO engagements?
Big Four CAIO-level engagements typically run $1M-$3M over 12-18 months and produce a strategy document with implementation handed off separately. AIDOLS prices Sprints at $15K-$25K, Builds at $75K-$150K with a 100% ROI guarantee, and Scale retainers at $25K/month — with deployed production systems and governance templates as the deliverables, not slide decks.
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 AI Officer mandate
Pick the path that matches the next decision your office has to make.