Move from AI experiments to a governed AI portfolio.
A practical working session for senior leaders who need clarity on where AI creates value, how enterprise context and data should be organized, what architecture and controls are required, and what to do next.
Executive outcomes
Decision-ready clarity, not a technology tour.
The workshop is structured around the decisions leaders must make before an organization can scale AI responsibly.
AI opportunity portfolio
A prioritized view of use cases linked to business outcomes, users, data/context, risk, and reuse potential.
Reference architecture
A shared target picture for data/context, models, retrieval, agents, applications, evaluation, controls, and observability.
Governance & evaluation model
A practical set of quality measures, approvals, controls, monitoring, and change-management responsibilities.
Execution roadmap
A sequenced backlog for proofs of value, reusable platform capabilities, productionization, and organization enablement.
Five modules
A leadership-friendly path from value to production.
We can spend more or less time on each module depending on the organization’s maturity and the decisions that need to be made.
1. Prioritize the AI portfolio
Separate high-value use cases from demonstrations. Score opportunities by business outcome, data/context readiness, workflow fit, risk, and path to production.
2. Design the context and data layer
Map the enterprise context that agents and assistants need: structured data, documents, metrics, knowledge, permissions, lineage, and retrieval.
3. Choose the right AI pattern
Understand when to use predictive ML, LLM/RAG, assistants, tool-using agents, workflow automation, or a governed AI data product.
4. Build evaluation and governance in
Define quality metrics, test sets, human review, release gates, privacy/security controls, observability, and change management before scale.
5. Create the operating model
Clarify platform vs product ownership, reusable capabilities, vendor/model choices, cost controls, team skills, and how AI moves from pilot to portfolio.
Make it a working session
Bring your current initiatives and constraints.
The highest-value version of the workshop uses your real AI portfolio, data/context, governance constraints, and target workflows as the working material.
Before the session
Share 3–8 candidate use cases, existing platform/architecture context, key stakeholders, major constraints, and what leadership must decide.
During the session
Score use cases, map the context/data and AI architecture, identify reusable capabilities, define acceptance gates, and resolve the highest-impact open decisions.
After the session
Receive a concise artifact pack: prioritized opportunities, architecture view, governance/evaluation checklist, decisions, open questions, and next-step plan.
KREATE helps teams build AI/data products, KONTROLS makes governance and trust explicit, and KNOBS exposes measurable levers for selection, creation, control, evaluation, and continuous improvement. The workshop uses this framing as a practical decision model rather than a product pitch.
Facilitator
Prashant Dhingra
Technology and data-product leader with 25+ years across Microsoft, Google, JPMorgan Chase, and DataKnobs, with experience spanning enterprise AI, data products, cloud, governance, experimentation, and production ML/GenAI.
Use one workshop to align leadership before teams build in different directions.
Tell us the decisions you need to make, your current AI initiatives, participant roles, and target timeline. We’ll propose a focused agenda.