Enable AI on enterprise data

Buying signal

Signals that the problem needs intervention.

• AI pilots demonstrate capability without a reliable baseline, evaluation set, or business acceptance owner.
• Enterprise data is incomplete, permission-sensitive, poorly labelled, or detached from the target workflow.
• Generated or predicted output has no defined human review, escalation, correction, or monitoring process.

Target business state

The business state the solution must produce.

• A clear invest, revise, or stop decision based on business-task performance rather than demonstration quality.
• Approved users receive AI assistance inside the target workflow with permitted data and explicit decision rights.
• The business can detect poor output, route consequential cases, withdraw the capability, and change models without losing operating accountability.

Solution boundaries

Decisions that bound the solution before estimation.

• Which measurable business task and user population justify this AI purchase, and what remains explicitly outside scope?
• Which data and model-processing paths are permitted for live use, evaluation, logging, feedback, and improvement?
• What task quality, handling effort, consequence, cost, uncertainty, and failure evidence leads to release, revision, suspension, or stop?

Solution package

What can be reviewed and accepted.

• Use-case investment brief defining the task, affected business result, users, current baseline, decision rights, prohibited behavior, and release boundary.
• Use-case data contract covering approved sources, permission enforcement, quality, lineage, retention, logging, and vendor processing.
• Representative evaluation pack with baseline comparison, acceptance thresholds, consequential cases, failure taxonomy, and cost model.
• Controlled workflow release design and implementation covering model or retrieval, integration, human review, abstention, fallback, and evidence capture.
• Release or stop recommendation plus operating runbook for monitoring, feedback, model or prompt change, incidents, suspension, and retirement.

Implementation path

A staged intervention instead of an uncontrolled leap.

1. Set the business baseline, consequence, owner, target users, decision rights, and stop conditions before selecting a model.
2. Approve the minimum data contract and build representative evaluation cases, including rare and consequential failures.
3. Compare options and prove the preferred design inside the real workflow with review, abstention, fallback, and cost evidence.
4. Issue an invest, revise, or stop decision; release under controlled access only when accepted, and require new evidence for expansion.

Measurable results

Measures tied back to the original problem.

• Accepted task quality against the current business baseline, segmented by representative and consequential case type.
• Elapsed handling time, human review effort, correction or rework, and cost per accepted task outcome.
• Abstention, override, escalation, harmful or unsupported output, access exception, and workflow fallback patterns.

Solution trade-offs

Trade-offs that may make another path more appropriate.

• Treating fluent output as accurate or suitable for the business decision.
• Exposing sensitive data through prompts, retrieval, logs, training, or vendor processing.
• Automating consequential action before review, appeal, accountability, and fallback are defined.

Decision questions

Buying questions to resolve before scoping.