The onboarding decision trail may be useful; the customer's configuration is not automatically yours to share.

Start here

Does any of this sound familiar?

• Your implementation team repeatedly decides how to sequence setup, migrate a workspace, or recover from an integration mismatch.
• You can distinguish reusable internal guidance from tenant configuration, customer files, and user-specific activity.
• You want to test an AI onboarding assistant but are unsure whether real customer journeys are necessary.

Onboarding records mix general methods with a customer's environment. A company checklist explains prerequisites; a migration ticket may contain account names, schemas, permissions, and exceptions. The first may be reusable, while the second may remain subject to customer terms and privacy obligations.

A realistic task tests whether an assistant chooses a safe next step; a historical corpus contains real implementation records. You can evaluate a task without assuming customer journeys are licensed for training.

The problem

A repeatable workflow is not the same as a customer record

Evidence may span plans, support cases, configuration exports, notes, and analytics. A record can mix company recommendations, customer data, and third-party documentation. A broad export obscures boundaries and may conflict with service-use promises.

A setup marked complete may not mean the customer achieved its goal. Define the milestone, evidence source, and reviewer before using it as a successful answer; otherwise an AI task can reward the wrong process.

Reuse the method only after separating it from the customer's environment.

The solution

Design an onboarding task without exporting the journey

Choose a capability to test, such as setup prerequisites or safe migration order. Map records before selecting historical examples.

DataSupply partners only with labs that meet its top 0.01% credibility standard. We help assess whether a qualified buyer may be a fit and negotiate terms that reflect the data's potential value, including exclusivity where relevant. We also help you work through diligence questions about rights, privacy, security, and compliance, then present a high-level inventory of permitted records, not the dataset. Fit is specific to each situation; no buyer or value is guaranteed.

What to inventory before any buyer conversation

• Separate reusable process from instance data Separate company checklists and decision rules from tenant IDs, configurations, customer communications, and imported files. Record origin, owner, customer and third-party terms, purpose, and retention.
• Specify task and completion evidence Write a scenario with authorized inputs, starting state, constraints, and safe actions. Define completion, such as a verified integration test rather than a checkbox. Have an implementation specialist review the rubric and alternatives.
• Compare the task with permitted records For historical cases, map fields to authorization and remove unnecessary details before review. Record transformations and unresolved rights. Keep evaluation tasks, source records, and any training corpus distinct.

Set the boundaries before discussing access.

Review customer contracts, privacy notices, processing terms, IP, and vendor restrictions. Define purpose, access, retention, deletion, onward sharing, security, and derivatives. Require legal review and applicable authorization for sensitive data; removing names is not enough.

What could make a permitted example useful?

A specified task tests choices under realistic constraints; an internal playbook may be a lower-risk start. Neither proves a customer archive has a buyer or value. Account for curation, expert review, and rights-clearing.

A practical first step.

Ask implementation leads to outline a common decision using process steps only. Review rights and define a rubric before retrieving customer cases.

datasupply.ai can discuss possible fit and buyer questions without receiving your dataset. You decide whether to pursue any introduction. No buyer, license, or payment is guaranteed.

Documented example / what it proves

A public onboarding framework shows the value of phases and feedback

Microsoft's FastTrack checklist describes three phases for Microsoft 365 adoption: Envision, Onboard, and Drive Value, and provides checklist guidance for getting the Onboard phase underway. This is a public example of framing adoption as sequenced work rather than a single setup step. Read Microsoft FastTrack Onboard Checklist.

The phases can inform task design around preparation, implementation, and realizing value. They do not show that Microsoft licensed onboarding records for AI or authorize another vendor to use implementation data.

The important limit: Microsoft's checklist documents an adoption method, not a closed data license, AI training arrangement, or permission to reuse customer-specific onboarding records.