APPROACH
Decide where to invest, then land a capability the organization can use.
The diagnostic produces a decision package. Designing, testing, and implementing a capability is a separate engagement, delivered through Quantumize AI. Those are not the same commitment.
COMMERCIAL BOUNDARY
What the diagnostic delivers — and what it does not.
30-day diagnostic
An executive package: the operating problem, an investment sequence, a first-use-case brief, and the decisions needed to proceed. Production implementation is not included by default.
Decision package
Development and implementation
Focused AI development and Implementation and adoption support are separate engagements, once the use case is defined and ownership is clear. See engagement shapes.
Separate engagement
THE THREE STEPS
Diagnose. Sequence. Land.
01
Diagnose
Understand how the work happens today. Identify the decisions, information sources, exceptions, and people responsible. Look for the context that lives in someone’s experience rather than in the documentation.
02
Sequence
Choose what to address first, based on value, dependencies, and risk. Make the trade-offs explicit, including the work that should wait. Define a scope small enough to evaluate honestly.
03
Land
Build and introduce a focused capability with an accountable owner. Agree on how to test it, when people must review its output, and what the team needs to operate it. Implementation scope and timing are agreed separately from the diagnostic.
What I ask of an agentic system.
An agent can use information and tools to carry out parts of a workflow. The design has to make clear what it may access, which actions it can take, and where it must stop — so the team can move forward, not only so risk is contained.
These belong in the early conversation about value, scope, and what a successful first deployment makes possible next.
DESIGN
Five questions before expanding its responsibilities.
The task
What is the task, how will we judge whether it was completed correctly, and what will the team be able to do better if it holds?
Evaluation
Context
Which information is authoritative, and how should the system handle missing or conflicting context?
Sources
Actions
What can the agent change, which actions need human approval, and when is extra autonomy actually worth it?
Permissions
Failure
How can someone inspect a failure, correct it, and resume the work safely?
Recovery
Ownership
Who will maintain the workflow as the organization and its tools change?
Maintenance
Worked example: how those questions decided a real problem.
At MTY Food Group, standing up the data function meant making tacit operating language explicit before a system could help procurement or commercial teams. The context question — which information is authoritative, and who owns the result — was a leadership decision, not a technical footnote.
AI initiatives hit the same wall. A first use case creates value only when the language, the exceptions, and the owner of the result are named. That is what the diagnostic forces into the open before you fund development.
DELIVERABLES
The decision package, in four parts.
An executive diagnosis
The operating problem, current constraints, and ownership gaps.
Leadership
A ranked sequence
Proposed priorities, dependencies, and the reasons for the order.
Priorities
A first-use-case brief
Intended users, scope, data, risks, evaluation measures, and adoption requirements.
Use case
A leadership readout
The decisions needed to proceed and the commitments they require. Not a guarantee of production deployment within 30 days.
Decision package
Next step
Choosing a first use case, or clarifying a stalled pilot.