Different leaders. Different decisions. One accountable build.
The buyer of an AI product feature has different questions from a CIO commissioning an internal AI system. A COO modernizing an operational workflow has a different approval process again. Marmation makes those differences explicit.
Who owns the decision?
Start with the consequence you answer for.
The technology comes after.
| Your responsibility | When the pressure appears | The decision to make | What to inspect |
|---|---|---|---|
| ProductCPO · VP Product · Product GM | An AI idea has attention. The roadmap still needs a defensible customer outcome. | Which capability belongs in the product? |
|
| TechnologyCIO · CTO · Head of AI | Business units want to move. Your team owns the data, identity and operating consequences. | What fits the environment you are accountable for? |
|
| OperationsCOO · VP Operations · Transformation | The applications work individually. Handoffs and exceptions still depend on people bridging the gaps. | Which part of the workflow needs to become dependable? |
|
A good buying conversation starts here.
The whole buying group needs a shared view of value, risk and what happens after the first release.
What result would justify the investment?
Establish the current baseline: completed tasks, review time, exceptions, rework or customer adoption. Agree what should change, who will measure it and what would count as a useful improvement. A credible business case starts there.
How will we know the system is right?
Ask for representative examples, ambiguous inputs and requests the system must refuse. Evaluate retrieval, permission correctness, product behavior and final transactions separately. Define acceptance before a polished demonstration shapes the discussion.
Who owns it after the demonstration?
Name the business decision owner and the technical operating owner. Identify who handles incidents, approves model or source changes, maintains dependencies and decides when the system should pause. Include the people who will support the result in the first architecture conversation.
Where will company information travel?
Trace information from the source through ingestion, indexes, inference, logs and backups. Check identity, retention, geography and permission changes at each boundary. A private model endpoint answers only one part of that question.
What will the full operating cost include?
Consider implementation, cloud and model usage, connectors, human review, support, evaluation and future changes. Compare those responsibilities with buying or extending an existing platform. Agree who controls usage and what happens when demand grows.
What changes if the first milestone works?
Define the next decision before expanding: release to a small group, test another source, add an integration or stop with the capability already delivered. A successful test opens a specific next step with its own acceptance criteria, operating owner and investment decision.
The reasoning in full.
How the buying situation shapes the work, the proof and a useful first conversation.
The situation behind the request
A product leader is measured by feature adoption and customer value. An IT leader must answer for data access and operational support. Operations has to keep a business process running. Procurement wants evidence that the investment is bounded and that the vendor can deliver.
Where the obvious solution falls short
Generic transformation language can hide these distinctions. It may describe everyone as a decision-maker but fail to address any one decision. A strong engineering engagement starts with the sponsor’s specific job and a shared definition of success.
How we would approach the work
Our buyer paths clarify typical constraints, proof requirements, project shapes and first milestones. They do not guarantee that any organization will be a suitable client; data access, technical competence and procurement remain critical.
What the engagement may include
- Product and Engineering leaders shipping AI-powered software.
- IT and AI leaders deploying company-specific AI.
- Operations leaders connecting fragmented workflows.
- Engineering leaders changing how teams build with AI.
The choices worth making explicitly
Do not confuse revenue band with buying intent. A smaller firm with a funded urgent need may be a better fit than a large enterprise with no executive sponsor or access to systems.
What we would test before calling it ready
Before proposal, confirm sponsor, meaningful problem, system access, scope ownership, risk and the first acceptance milestone.
A sensible first step
Choose the path that best describes who owns the business decision. Or send the project context directly.
Bring us the
difficult part.
A product idea, a broken workflow, scattered company knowledge or an integration others keep working around. Tell us what needs to change and what has already been tried.
Discuss a project