Can algorithms or AI be added to a system later?
Yes, and adding an algorithm does not always require waiting six months. Mature OCR, translation, or model APIs can be tested immediately on representative samples. Prediction, recommendation, and anomaly detection need a data-and-feedback loop when representative labels are missing. In every case, prove improvement over the current baseline before allowing automated decisions.
“Algorithm” can mean deterministic rules, a third-party AI API, traditional machine learning, a trained vision or prediction model, or an LLM application. Their data and risk requirements differ. Wavesteam first defines the input, user of the result, consequence of error, current human method, and objective historical evidence. Only a defined task can be estimated.
| Route | Prerequisite | Advantage | Limitation | Recommendation |
|---|---|---|---|---|
| Rule or statistical threshold | Clear rules and costly errors | Explainable, inexpensive, fast | Weak on complex long-tail cases | Retain as the baseline |
| Mature AI API | Representative input and acceptable provider terms | Fast validation without a training team | Provider controls versions, price, limits, data boundary | Prototype a low-risk task |
| Retrieval, prompting, or light tuning | Enterprise knowledge, questions, or labelled examples | Adapts to a specific context | Still needs permissions, evaluation, human fallback | Common for knowledge and documents |
| Trained prediction/recommendation/vision model | Representative features, labels, and continuing feedback | Optimizes for a defined business outcome | Bias, drift, training and operations cost | Invest after benefit over baseline is proven |
Readiness depends on coverage, not elapsed months. A hundred thousand identical daily records may be less useful than two thousand examples spanning seasons, refunds, exceptions, and regions. The first release should preserve raw input, business context, final human outcome, and time, plus definitions, provenance, authorization, and retention. “Future training” does not justify unlimited personal-data collection or copying client files into an external model.
Treat the algorithm as replaceable. A versioned service interface records rule/model version, input reference, result, confidence information, latency, and human correction. Timeouts, limits, and invalid output fall back to a rule or review queue. A model never overwrites original orders, device telemetry, or finance facts. High-risk actions add permissions, limits, and approval.
Evaluation starts from today's method. Classification reports precision, recall, and confusion by business class; extraction measures exact critical fields and review time; prediction uses time-based splits and decision benefit; recommendation tracks purchase, return, and diversity as well as clicks; generated Q&A measures answer and citation correctness, false answers instead of refusal, and severe failures. Averages must not hide high-risk categories.
The NIST Generative AI Profile helps address fabrication, privacy, bias, human dependence, and monitoring for generative systems. Traditional models still need suitable data splits, drift, and error analysis rather than an irrelevant LLM checklist. Personal information processing also follows China's Personal Information Protection Law.
Release in three stages: shadow output that does not affect work, decision support with human confirmation and reasons, then bounded automation after thresholds pass, severe errors are controlled, and rollback is proven. Every model or prompt update regresses against a frozen set and is staged with a path back.
Cost is calculated per successful business result, including model or compute, storage, review, monitoring, and error loss. If rules already meet the outcome safely, training a model may not pay. Wavesteam's AI cases and QuanYuTong case show application directions; a new project still needs client-sample evidence.