Alignment where AI meets the business.
AI Concierge is the ongoing fractional CAIO engagement model, providing leadership across four focused project offerings. Each offering ends with a decision, a working capability, or a measurable change.
One connected path from discovery to value.
The journey starts by qualifying opportunities through Discovery, prioritizes investment through Strategy, validates the dependencies required for delivery, and moves through focused Execution from prototype to production. It ends by proving, improving, and scaling measurable business value, with AI Concierge providing leadership throughout.
AI Value Path
Workshop
Qualify opportunities
02StrategyAI Strategy& Roadmap
Prioritize investment
03ReadinessAI Readiness& Governance
Validate dependencies
04ExecutionAI Prototypesto Production
Build, validate, and deploy
Business Value
Prove, improve, and scale
Fractional CAIO
AI Concierge
A fractional CAIO who helps identify opportunities, set strategy, establish the foundation, and lead implementation.
AI Concierge is the ongoing engagement model above the four focused project offerings. Acting as an extension of the leadership team, the fractional CAIO connects opportunity discovery, strategy, governance, technology decisions, implementation leadership, and adoption through one accountable leader.
What this can include
- AI opportunity identification and prioritization
- AI strategy, roadmap, and investment planning
- Data, technology, governance, and operating foundations
- Business cases, budgets, and vendor decisions
- Solution implementation and adoption leadership
- Executive and board guidance
Best fit: Organizations that want ongoing senior AI leadership and implementation support without hiring a full-time CAIO.
01 Discovery
AI Discovery Workshop
The workshop is a guided process for uncovering friction and pain points across the business. It identifies broken or bloated processes, then determines where AI makes sense, where straightforward automation is the better fit, and where manual processing remains the right answer. When a solution is warranted, a preliminary buy-versus-build recommendation identifies a practical path to value for Readiness & Governance to validate.
The workshop brings leaders, employees, and technical teams together to test the business case before technical momentum takes over.
Baseline
Understand the current workflow, its cost, its friction, and the outcome it supports.
Test the economics
Compare expected value with the full cost of AI, integration, review, change, and maintenance.
Align
Identify the accountable owner, affected employees, technical team, risks, and adoption needs.
Decide
Make a go-or-no-go decision and place qualified opportunities on the shortlist for Strategy.
A useful outcome can be no. The right recommendation may be AI, simpler automation, process change, or no investment yet.
02 Strategy
AI Strategy & Roadmap
Prioritize qualified opportunities and turn them into an aligned AI strategy and investment roadmap.
This engagement typically follows the AI Discovery Workshop. We prioritize the qualified opportunities, define target outcomes and funding decisions, and create an investment roadmap that sequences decisions around value, capacity, and risk. Readiness & Governance then validates the delivery dependencies and finalizes the implementation plan.
What this can include
- AI strategy and operating principles
- Portfolio prioritization and funding decisions
- Strategic objectives and measurable outcomes
- Business cases, total solution cost, and success measures
- Investment roadmap with funding sequence and decision gates
- Leadership alignment on what to fund and why
Best fit: Businesses with a qualified opportunity shortlist that need an aligned strategy for what to fund, why it matters, and which investment decisions come first.
03 Readiness
AI Readiness & Governance
Validate the dependencies behind the investment roadmap and turn it into an implementation-ready plan.
Readiness is more than a technology assessment. We validate the people, process, data, technology, and governance assumptions behind the investment roadmap, expose gaps that could slow delivery or create risk, and sequence the work required before implementation begins.
What this can include
- Use-case-specific readiness assessment
- Data quality, access, privacy, security, and integration review
- Validation of preliminary buy-versus-build, platform, and vendor assumptions
- Responsible AI policies, risk controls, and review paths
- Operating model, decision rights, and accountable ownership
- Implementation-ready plan with dependencies, remediation work, owners, and sequencing
Best fit: Organizations with an approved investment direction that need to validate dependencies, close readiness gaps, and produce a plan that is ready for implementation.
04 Execution
AI Prototypes to Production
Move validated AI ideas through focused build sprints, from prototype to MVP to a production-ready solution.
Each sprint is organized around a business outcome, a working increment, and a decision about what comes next. Depending on the starting point, we can create a first prototype, advance an existing prototype into an MVP, or take a proven MVP through integration, production hardening, deployment, and rollout.
What this can include
- Sprint goals, scope, and measurable success criteria
- Workflow, user experience, and solution design
- Prototype build and business-value validation
- MVP development, integration, and user testing
- Production hardening across data, security, controls, and performance
- Deployment, adoption, monitoring, and support transition
Best fit: Businesses with a prioritized use case or an existing prototype that need a disciplined path to validate, build, and move into reliable production.
Choose the right starting point.
Discovery is usually the fastest way to move from AI speculation to an informed decision. If priorities are already clear, begin with strategy, readiness, or execution.