CAIO, CDO, CTO, CIO, COO
Align AI investment with business strategy, risk, operating priorities, and measurable ROI.
Fractional AI leadership · 3-12 months
Senior AI leadership that ships.
Bring Prashant Dhingra into your leadership team to move from AI ambition to production: prioritize the right use cases, establish a reference architecture, deliver agents and AI-driven data products, and put the operating model around them.
ShipProduction-ready agents and data products
WeeksStart with a focused, high-ROI use case
Hands-onLead, design, and deliver with your team
What the engagement provides
Strategy tied to outcomesPrioritize AI use cases around measurable business value, not demos.
Architecture that can scaleData, retrieval, agents, evaluation, observability, security, and governance.
Delivery ownershipMove a flagship agent or AI data product from MVP into production.
Team capabilityLeave behind reusable patterns, templates, playbooks, and coaching.
Best fitExecutive Product + Data Engineering
For organizations that need AI ownership, not another experiment.
This model is designed for teams that already know AI matters but need a senior leader who can align executives, architecture, delivery, governance, and engineering around a practical roadmap.
AI product and data leaders
Accelerate agentic workflows, AI-powered data products, and a repeatable delivery portfolio.
Platform and engineering teams
Scale retrieval, tools, evaluation, observability, security, and reliability in production.
Need one concrete solution?
Start with a capability-led implementation.
If the immediate need is a single agent, assistant, governance workflow, or data product, start with the solution portfolio.
Need portfolio leadership?
Use the fractional model to own the system.
Best when multiple teams, use cases, governance decisions, architecture choices, and delivery priorities need one accountable senior leader.
Engagement options3 months Most popular6 months 12 months
Choose the horizon based on the transformation you need.
The engagement can span three months to one year. The difference is not simply duration; it is how much of the delivery system you want to establish.
Launch & Ship
Create fast momentum around a flagship agent or data product.
- •Use-case selection and success metrics
- •Architecture and build plan
- •MVP to production rollout
Scale & Systematize
Turn successful delivery into repeatable capability across multiple AI initiatives.
- •2-3 AI deliveries across agents/products
- •Evaluation and observability setup
- •Operating model and playbooks
Transform
Build a company-wide AI capability with portfolio, platform, governance, and team maturity.
- •AI product portfolio and quarterly roadmap
- •Platform, security, and governance maturation
- •Organization enablement and hiring guidance
Delivery model01 02 03 04
Leadership, architecture, and execution stay connected.
The engagement is explicitly delivery-driven rather than advisory-only. Work happens through weekly working sessions, executive alignment, and artifacts that accumulate into a repeatable operating system.
Assess & Align
Current state, constraints, stakeholders, target outcomes, and use-case priorities.
Design
Reference architecture, data/RAG design, build plan, and evaluation strategy.
Build & Ship
Hands-on execution with your team to deliver production outcomes.
Operate & Scale
Governance, cost controls, monitoring, and reusable delivery playbooks.
What remains after the engagement
Concrete deliverables: not just recommendations.
Typical deliverables
- ✓Use-case portfolio and prioritization scoring
- ✓AI agent and data-product roadmap: 90 days plus 2-4 quarters
- ✓Reference architecture for retrieval, tools, agents, evals, and observability
- ✓Reusable prompting, tool-schema, evaluation, and release templates
- ✓Governance guidance covering security, privacy, guardrails, and review workflows
Common starting projects
- →Customer-support agent with grounded retrieval and escalation
- →Operations agent for incident triage, runbooks, and knowledge updates
- →AI data product for standardized metrics, insights, and decision workflows
- →Internal analyst copilot for BI, SQL, and narrative reporting
- →Sales/marketing copilot for proposals and account research
Weekly working session
Architecture, prioritization, and delivery leadership.
Executive alignment
Monthly steering check-ins to track ROI, decisions, and risk.
Artifacts as you go
Roadmaps, architecture, playbooks, templates, and reusable operating guidance.
FAQ
How the fractional engagement works.
Is this advisory-only?
No. The engagement is designed to be delivery-driven. Prashant works hands-on with your team to ship.
What if we already have an AI team?
The engagement can accelerate that team with architecture, prioritization, quality practices, and executive alignment.
Do you work with our stack?
Yes. The approach is stack-agnostic and focuses on patterns such as retrieval, tools, evaluation, observability, governance, and delivery.
How do we start?
Start with a short discovery to select the highest-ROI use case, define success metrics, and agree on cadence.
Next step
Ready to move from AI plans to shipped capability?
Discuss your goals, constraints, current team, and which engagement horizon makes sense.