RUBIKSCORPAI R&D
From AI proof of concept to production: a roadmap with decision gates
An AI proof of concept answers a bounded question about feasibility. Production requires a maintained workflow with clear ownership, monitoring and recovery. Treating those as separate decisions keeps an impressive demo from becoming an unsupported commitment.
Replace a broad ambition with a testable question
“Use AI in the factory” does not identify a result someone can assess. A more useful question is whether a defined camera setup can count a particular product under specified operating conditions, or whether an assistant can find the applicable procedure without mixing document versions.
Write down what success would change for a person doing the work. An experiment that improves a model score but leaves the decision unchanged may not justify development. Include the current process so the proposed system has a meaningful comparison.
A reusable experiment brief
Use this as a discussion template. It does not imply that every project needs a model or that the result must be a production deployment. A documented decision to stop can be a useful outcome of research.
| Field | What to write |
|---|---|
| Question | One capability or uncertainty to investigate |
| User and decision | Who uses the result and what action follows |
| Baseline | The current process or a simple alternative |
| Data | Available examples, gaps, permissions and versions |
| Evaluation | Cases, metrics, critical failures and review owner |
| Limits | Excluded uses, conditions and actions |
| Decision gate | Evidence needed to continue, revise or stop |
| Handover | Artifacts and ownership if the experiment ends |
Move through explicit decision gates
These are stages of evidence, not a promised calendar. Data access, labeling quality and operating constraints determine how much work is needed. Do not present an arbitrary timeline as a guarantee before the scope is understood.
- Discovery: establish the problem, permissions, operating conditions and baseline.
- Prototype: test the smallest approach that can answer the question.
- Evaluation: examine representative cases, critical failures and practical constraints.
- Pilot: observe the approach within a bounded real workflow and a fallback process.
- Production decision: agree ownership, integration, monitoring and maintenance before expanding.
Worked example: a maintenance-document assistant
A team wants technicians to find the current approved procedure more easily. The baseline might be the existing search process. A first prototype can retrieve candidate passages and show their document revisions without generating instructions.
If retrieval is useful, a later experiment can test a draft summary that remains visibly tied to the approved source. Cases should include missing procedures, duplicate titles, obsolete versions and questions outside the assistant’s authority.
A production proposal needs an owner for publishing documents, a route for corrections and a fallback to the approved process. It also needs a clear boundary between locating information and authorizing a maintenance action. This is an illustrative project brief, not a claimed deployment.
Deliver evidence that remains useful after the demo
Keep the dataset inventory, data-use permissions, labeling instructions, configuration and evaluation outputs together. Record what was tested, what failed and which conclusions depend on a limited sample. Another team should be able to understand the result without relying on a meeting recording.
For a proposed release, include integration requirements, access boundaries, expected operating costs and recovery behavior. Name the owner who receives failure reports and the changes that require re-evaluation.
Research communication should distinguish a hypothesis, an experiment design, an observed result and an interpretation. Publishing only a favorable screenshot makes those distinctions impossible to assess.
When to stop or change direction
Pause when the required data cannot be used legitimately, labels remain inconsistent, the baseline is already adequate or a critical failure has no acceptable control. A smaller scope or a non-AI improvement may be more useful.
Agree a review point before committing to further work. The next step should follow the evidence and available resources, rather than a need to justify an earlier technology choice.
Common questions
Does a successful proof of concept mean the system is production ready?
No. Production also needs integration, ownership, monitoring, recovery and acceptance of the actual workflow.
Can an R&D project end without deployment?
Yes. A well-supported decision to stop, simplify or gather better data can be a useful result.
Do you publish completed research results here?
The research page identifies its status. We do not describe a proposed experiment as a completed result.
Sources and notes
- Google — Rules of Machine Learning
Practical guidance on baselines, data and production systems.
- NIST — AI Risk Management Framework
Voluntary guidance for identifying, measuring and managing AI risks.
- Google Cloud — Generative AI evaluation overview
Evaluation datasets, rubrics and metric types.
Read next
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Tell us the task, the information available and what a useful result would look like.