Preparation plus a half-day or full-day session built around one active decision, not a generic AI presentation.
The session can select and rank use cases, define an AI product, design a consequential workflow, or resolve the evidence needed before a pilot or vendor moves forward.
Choose and rank the use cases, define the product or internal capability, assign an accountable owner, and decide what should be built, bought, tested, or stopped.
Define how evidence, people, systems, and AI must work together around one decision.
Determine what a pilot or vendor still must prove before buying, scaling, or launch.
Not every session should lead to a build. It should end with a ranked portfolio or product decision, an owner, and the evidence or action required next.
The team compares candidate workflows against the business result, feasibility, data, affected users, integration work, and consequence of failure.
Teams identify what AI should notice, assemble, recommend, prepare, initiate, execute, or never do without human authority.
The recommendation may be a pilot review, production validation, DCP shadow mode, a smaller experiment, or a decision to stop.
The people in the room should be able to discuss what AI may do, what evidence is missing, who owns the exception, and what would justify the next investment.
The content changes by role. Leaders make investment and authority decisions; product and technical teams design the workflow; reviewers and governance teams handle evidence, exceptions, and oversight.
Decide which uses deserve investment, who owns them, and whether the work has changed beyond individual prompting.
Allocate work between people and agents, define exceptions, and measure the full workflow outcome.
Determine what can move automatically, what requires review, how exceptions are handled, and what leadership must see before expansion.
Separate explainability from organizational authorization and design review that requires independent judgment.
Choose tasks that fit the tool, check the evidence, protect sensitive data, and know when the work needs a person.
Help platform and integration partners identify a real workflow, qualify the buyer’s problem, and know when GNS-AI should be involved.
Typical investment: $15,000–$30,000.
Includes preparation and a focused half-day or full-day session. Scope expands when multiple workflows, locations, or stakeholder groups are included.
Access, staff time, checkpoints, and the final decision are explicit from the start.
One 45-minute preparation call and a focused document request.
Half day or full day with the people who own the decision and the operating constraints.
GNS-AI captures the agreed workflow, authority, evidence gaps, and unresolved choices.
Leadership receives a recommendation for no further work, a Blueprint, Production Assurance, or a DCP Shadow Pilot.
The session ends with a decision and a working artifact, not a generic presentation.
Facilitated executive or cross-functional working session with a concise written decision record.
Approximately 4–8 pages, depending on the workflow and number of decisions resolved.
Executive sponsor, workflow owner, and the people responsible for AI, risk, operations, or delivery.
Which workflow moves first, who owns it, what evidence is needed, and which engagement should follow.
A redacted example can be walked through on a call. The artifact does not leave the live conversation.
The team should know what it is expected to decide or produce before the session begins.
The session begins with the decision or artifact the organization needs. The agenda, participants, and exercises are chosen around that output, so the meeting does not end with a slide deck and no owner.
The right group usually combines the business or mission owner with the people responsible for operations, AI or data, technology, risk, clinical judgment, procurement, or implementation. Attendance should reflect who can define the decision and act on the output.
Yes. The format can be adapted to healthcare, payer, federal, enterprise, or industry settings while keeping the session anchored to the audience’s decisions, operating conditions, and desired outcome.
Bring the decision the team needs to make, who should participate, and the outcome the session must produce.
Research and practical analysis on AI products, agentic systems, production assurance, governance, and decision control. Published by Dr. Amit K. Shah.