Knowledge · Practical guide
Production AI: what comes after the successful prototype
Production AI depends on integration, measurable quality, controlled actions and continuing responsibility for operation.
Digamma · Practical knowledge for process owners
Production AI repeatedly delivers a defined result in real processes. It connects to approved data and systems, is evaluated against representative cases, logs its actions and has explicit rules for approvals, exceptions, changes and responsibility.
Why a successful pilot is not yet production
A prototype tests whether a capability is possible. Production must also establish whether it works with real data, changing cases, access restrictions and economic limits.
Integration, data quality, ownership and exception handling are common gaps. Without them, impressive individual answers still leave the application short of an operational system.
Six controls for production readiness
Readiness is a set of technical and operational evidence, not a general impression of quality.
- Evaluation: representative cases and measurable acceptance limits.
- Permissions: minimum, role-based access to data and actions.
- Traceability: records of executions, steps, sources and results.
- Escalation: explicit handover for uncertainty, conflicts or high risk.
- Operation: monitoring for quality, runtime, errors, costs and availability.
- Change control: tested, versioned and approved releases.
Measures that matter in operation
Availability matters, but it does not establish that useful work is being completed. Assess correct results, processed volume, turnaround, manual handling time and the quality of exception handovers.
Also observe cost per case, model and tool usage and changes in the incoming cases. These measures help distinguish better performance from increased resource consumption.
Managed operations as continuing responsibility
Processes, data and models change. Incidents need attention, test cases need extending, rules need updating and new versions need a controlled release.
An operating model states who owns quality, who approves changes and who makes decisions during failures or risky cases. Responsibilities and service levels belong in the agreed scope.
Frequently asked questions
When is an AI application in production?
When it repeatedly produces an agreed result in real workflows and its integration, quality, access, exceptions and ongoing operation are explicitly managed.
What is the difference between a proof of concept and production?
A proof of concept demonstrates basic feasibility. Production also requires reliable integration, evaluation, permissions, monitoring, support and change control.
Why is continuing monitoring needed?
Because inputs, systems, models and the mix of cases change. Monitoring helps detect deteriorating quality, errors, rising costs and unusual exceptions.