Commands

Run a complete AI Strategy Sprint: maturity assessment, strategy document, investment case, and board presentation. Use when the CEO asks for an AI roadmap, the board wants a data strategy, or a new CDO needs to establish direction.

e.g. /cdaio:strategy [industry and company context, e.g. 'healthcare company, $2B revenue, 5000 employees']

Build a complete data governance framework: policies, stewardship program, RACI matrix, and compliance checklist. Use when starting governance from scratch or overhauling an existing program.

e.g. /cdaio:governance [industry, regulatory context, or specific governance needs]

Prepare for a board meeting: board deck, talking points, anticipated questions, and executive briefing. Use 1-2 weeks before any board or executive committee presentation.

e.g. /cdaio:board-prep [meeting date, agenda topics, or specific focus areas]

Full office activation for a new CDO/CDAIO. Produces a 90-day plan with quick wins, stakeholder mapping, team assessment, and strategic priorities. Use when starting a new CDO role or resetting after a mandate change.

e.g. /cdaio:first-90-days [company context, industry, or specific challenges]

Set up or run the quarterly operating rhythm: status reports, KPI dashboards, meeting agendas, and stakeholder communications. Use for ongoing operational cadence.

e.g. /cdaio:quarterly [quarter, focus areas, or specific reporting needs]

Build a Responsible AI and model governance program: AI model inventory, risk classification (EU AI Act), governance framework, bias testing protocols, and board reporting template. Use when preparing for AI regulation, managing AI risk, or establishing model oversight.

e.g. /cdaio:ai-governance [industry, AI maturity, or specific governance concerns]

Redesign the data and AI organization: current-state assessment, maturity-aligned target operating model, transition plan, and RACI. Use when restructuring a data team, scaling from small to large, or shifting between centralized and federated models.

e.g. /cdaio:org-design [current team size, org model, or specific restructuring goals]

Design a data quality improvement program: assess quality across critical domains, identify root causes, build technical remediation plan, quantify business impact, and create a prioritized improvement roadmap. Use when data quality is blocking analytics, AI, or regulatory compliance.

e.g. /cdaio:data-quality [specific quality issues, affected data domains, or business impact]

Run a data/AI cost optimization sprint: spend audit, waste identification, strategic prioritization, efficiency opportunities, financial modeling, and implementation roadmap. Use when the CFO demands budget cuts or data/AI costs are growing faster than value.

e.g. /cdaio:cost-optimization [current spend concerns, target savings, or specific cost areas]

Evaluate and select a data/AI technology vendor: requirements gathering, market scan, weighted scoring framework, compliance check, TCO analysis, and recommendation. Use when evaluating data platforms, MDM tools, ML platforms, catalogs, or any data/AI technology.

e.g. /cdaio:vendor-eval [technology category and context, e.g. 'data catalog for financial services, 500 users']

Run an AI/Data maturity assessment across 6 dimensions and 24 subdimensions. Scores current state, identifies gaps, and benchmarks against industry peers. Use as a starting point for any data/AI initiative.

e.g. /cdaio:assess [industry and company context]

Create a data governance policy document covering data classification, quality standards, retention, privacy, and AI governance. Tailored to industry and regulatory requirements.

e.g. /cdaio:policy [industry, regulatory context, or specific policy scope]

Create a board deck or executive presentation with consulting-grade formatting. Covers AI/data strategy updates, investment asks, progress reports, or any executive briefing.

e.g. /cdaio:deck [topic, audience, and key messages]

Design a target-state data and AI architecture blueprint with current-state assessment, technology recommendations, migration roadmap, and cost estimates. Covers all 8 architecture layers.

e.g. /cdaio:architecture [industry, current tech stack, or specific architecture focus]

Identify and score AI use cases for your industry. Produces a prioritized portfolio with feasibility-value scoring, 2x2 matrix, and first-wave recommendations.

e.g. /cdaio:use-cases [industry, business priorities, or specific domains to explore]

Benchmark your AI/data maturity against industry peers. Compares investment levels, capability scores, and leader-vs-laggard patterns. Use to build the business case or contextualize a strategy.

e.g. /cdaio:benchmark [industry, company size, or specific dimensions to benchmark]

Run a regulatory compliance assessment covering GDPR, CCPA, EU AI Act, HIPAA, DORA, and other applicable regulations. Identifies gaps and produces a compliance checklist with remediation priorities.

e.g. /cdaio:compliance [industry, jurisdictions, or specific regulations to assess]

Build a RACI matrix for any data/AI initiative. Clarifies Responsible, Accountable, Consulted, and Informed roles across stakeholders. Use for governance rollouts, AI projects, or organizational design.

e.g. /cdaio:raci [initiative name, scope, or stakeholder context]

Quality review any CDO Office deliverable against MBB style standards, design system, and output QA checklist. Use after producing any document, deck, or assessment. For board-facing deliverables, includes Board of Directors strategic challenge.

e.g. /cdaio:review [path to file or description of what to review]

Set up your project folder for the AI CDAIO Office. Creates context/ and deliverables/ directories, copies the client context template, and adds a .gitignore.

Full office activation for a new CDAIO. Every agent contributes to your onboarding plan.

CDO + Exec Comms + Analyst + Quality Reviewer produce a complete board package.

Head of AI + ML Lead + Data Engineer + Architect deliver a comprehensive AI strategy.

Head of Governance + Steward + Compliance build governance from scratch.

Chief of Staff + reporting roles establish ongoing cadence and dashboards.

AI/ML Lead + Compliance + Architect set up responsible AI and model oversight.

Program Manager + Head of AI + Chief of Staff restructure the data team.

Data Steward + Custodian + Data Engineer fix systematic data quality issues.

Chief of Staff + Data Engineer + Data Analyst cut data/AI costs.

Data Engineer + Architect + Compliance evaluate and select data/AI vendors.