Prashant Dhingra: from enterprise AI strategy to production delivery.
A technology leader with 25+ years across Microsoft, Google, JPMorgan Chase, and DataKnobs: combining software engineering, machine learning, cloud, data products, GenAI, agentic systems, governance, and experimentation.
Career perspective
Experience across software, cloud, ML, finance, and AI products.
The value in a workshop comes from connecting executive choices to the engineering and operating realities that determine whether AI reaches production.
DataKnobs
AI/data product strategy and delivery across KREATE, KONTROLS, KNOBS, assistants, agents, data products, evaluation, and experimentation.
JPMorgan Chase
Leadership in machine learning engineering and data products across financial analysis, NLP, privacy, analytics, and enterprise use cases.
Data science leadership, secure ML experimentation, cloud AI, Industry 4.0, and predictive-maintenance initiatives.
Microsoft
Software, data and ML experience across Bing, Azure ML, SQL Server, and large-scale audience intelligence systems.
Core expertise
AI is a system, not a model.
Workshops connect the business problem, data/context, model behavior, workflow, controls, and operating model instead of treating GenAI as an isolated API call.
AI data products
Define product contracts, metrics, users, data quality, lineage, APIs, and AI-assisted decision experiences.
GenAI, RAG & context
Design retrieval, grounding, structured + unstructured context, prompts, models, tools, and evaluation.
Agentic systems
Plan, route, call tools, manage state, apply approvals, and build reliable multi-step workflows.
Governance & controls
Security, privacy, auditability, policies, human review, model/prompt change, and measurable runtime controls.
Experimentation & evaluation
Offline evals, A/B tests, model/prompt comparisons, quality thresholds, regression tests, and continuous learning.
Cloud & enterprise architecture
GCP, Azure, AWS, enterprise data platforms, APIs, deployment architecture, observability, and production operations.
Workshop options
Choose leadership alignment, technical enablement, or DataKnobs adoption.
The content is tailored to participant roles and the real decisions the organization needs to make.
Executive AI Workshop
For CAIO/CDO/CIO/CTO and product leaders: portfolio prioritization, architecture, governance, operating model, and roadmap.
- Use-case portfolio
- Reference architecture
- Governance/evaluation model
- Execution roadmap
AI / Agent / Data Product Workshops
For product, data, AI and engineering teams: build patterns, RAG, agents, evals, controls, and productionization.
- Hands-on architecture
- Implementation patterns
- Evaluation and release gates
- Delivery playbook
DataKnobs Product Workshop
For teams evaluating Kreate, KONTROLS, KNOBS and ABExperiment against enterprise needs.
- Platform deep dive
- Two solution walkthroughs
- Integration model
- Adoption blueprint
Facilitation approach
Move from concepts to decisions.
A good enterprise AI workshop should expose tradeoffs, surface assumptions, and produce artifacts that reduce the amount of rework after the meeting.
Frame the decision
Who is the user, what decision or workflow changes, and what measurable outcome matters?
Map context & architecture
Identify data, knowledge, models, tools, interfaces, constraints, integrations, and reusable capabilities.
Define acceptance
Specify quality metrics, failure modes, human review, governance, security/privacy, cost and reliability gates.
Design the operating loop
Clarify ownership, monitoring, evaluation, experimentation, incident response, and safe rollout/change.
Create the backlog
Turn the session into decisions, dependencies, owners, proof-of-value scope, and a production path.
Bring the hard AI decisions into one room.
Share the audience, goals, maturity, and one or two candidate workflows. We’ll recommend the right workshop structure.