Direct answer
The decision in brief
An AI pilot becomes a production system only when a named business owner, an accountable workflow, representative evaluations, governed data and tool access, human escalation, operational monitoring, release controls, and a responsible operating team all exist. Model quality is one gate, not the entire gate. The safest path is to define the production decision first, test the highest-risk failure modes with representative data, and expand access only as evidence supports it.
Who this is for: Leaders whose AI demonstration works technically but has not earned approval, integration, or trust inside the operating workflow.
Decision framework
The AI production-readiness gate
A pilot should not advance because the demo was impressive. Each gate needs an owner and inspectable evidence.
| Gate | Question | Minimum evidence |
|---|---|---|
| Outcome | What decision or workflow improves? | Baseline and acceptance measure |
| Evaluation | How do we detect useful and unsafe behavior? | Representative cases and pass criteria |
| Authority | What may the system do by itself? | Human review and escalation rules |
| Access | Which data and tools are permitted? | Least-privileged identities and audit trail |
| Operations | Who responds when it fails? | Monitoring, runbook, rollback, and owner |
| Transfer | Who can maintain it after launch? | Code, configuration, knowledge, and responsibility accepted |
Deployment sequence
A controlled path from decision to evidence
- 01
Anchor the workflow
Name the user, decision, current baseline, exceptions, and business owner before selecting more technology.
- 02
Build the evaluation set
Represent normal cases, edge cases, prohibited outcomes, and the failures that matter operationally.
- 03
Constrain authority
Begin with read-only or human-approved actions and expand only when the evidence justifies it.
- 04
Integrate the real path
Connect identity, data, tools, logs, and escalation inside the approved client environment.
- 05
Exercise failure
Test provider errors, bad inputs, unavailable tools, incorrect outputs, rollback, and operator response.
- 06
Release and transfer
Launch under client authority with owners, runbooks, monitoring, known limitations, and a clear improvement loop.
Authority and ownership
Responsibilities must remain explicit
The client owns
- — Own the business decision and acceptable risk
- — Approve data, tools, identities, and release
- — Provide operators who can judge workflow correctness
Ten Mile Zone owns
- — Engineer the integrated production path
- — Create evaluation and operational evidence
- — Expose limitations and transfer maintainable implementation
Common failure modes
Where otherwise credible programs break down
Demo-driven requirements
A compelling conversation becomes the specification even though the real workflow contains permissions, exceptions, and consequences the demo never exercised.
Accuracy without consequence modeling
Averages hide the cases where a wrong answer, action, or delay causes material harm.
No operating owner
The build team launches a system that nobody is responsible for observing, correcting, or retiring.
Fit boundary
When forward-deployed engineering is the wrong choice
- The workflow has no accountable business owner
- The value cannot be measured even directionally
- The organization cannot provide lawful, governed access to required data
- A deterministic rule or conventional software feature solves the problem more safely
- The pilot depends on unrestricted autonomy before basic evaluation exists
Client participation
Who must participate
- Business owner for outcome and risk
- Operators who handle normal cases and exceptions
- Engineering owner for integration and reliability
- Security and data owners
- Release authority and ongoing service owner
Acceptance evidence
What proves the engagement worked
- Evaluation cases represent the intended workflow and material failures
- Permissions and tool actions match approved authority
- Logs make decisions and tool use reviewable
- Failure, escalation, and rollback paths were exercised
- A named team accepted the runbook and operating responsibility
Buyer checklist
Questions to resolve before proceeding
- Is the production workflow narrower than the original AI ambition?
- Are prohibited outcomes explicit?
- Does every tool action use an approved identity?
- Can an operator interrupt, correct, or escalate the system?
- Are provider and integration failures observable?
- Is there a release, rollback, and retirement decision owner?