A prototype proves that a technology can work. A production system must prove that a business can depend on it over time. The gap is usually less about model capability and more about whether objectives, data, workflows, accountability and operations form a complete loop.
QUESTION 01
Can business value be measured clearly?
Define the workflow to improve before choosing models or features. Useful measures include handling time, first-contact resolution, error rate, review volume and cost per task.
A prototype evaluated only by whether answers “look good” is not ready to support a production decision.
- Establish a pre-launch baseline
- Set targets and an observation window
- Measure both value and new operating cost
QUESTION 02
Are data, permissions and updates controlled?
A production application must know where information comes from, who can access it, when it changes and how it can be withdrawn. Knowledge assistants also need source traceability and inherited permissions.
- Connect only authorized sources
- Make answers traceable to evidence
- Isolate and audit sensitive data
QUESTION 03
How will output quality be evaluated continuously?
A passing test is not a guarantee of lasting quality. Models, prompts, knowledge and business rules change, so evaluation should combine fixed samples, real feedback and known failure cases.
- Cover normal, edge and high-risk queries
- Separate model quality from business outcomes
- Run regression checks after changes
QUESTION 04
Can it enter existing systems and workflows?
Value often appears only after AI connects to ticketing, CRM, ERP, knowledge or approval systems. Integration must cover identity, permissions, timeouts, retries and human takeover.
- Define what AI may read, write and never access
- Preserve human confirmation points
- Design recoverable failure states
QUESTION 05
Who owns long-term operation and risk?
Production requires a business owner, technical owner and content maintainer. The team must also know who decides, responds and reviews when quality, cost or availability changes.
- Define ownership and response paths
- Monitor quality, cost, adoption and risk
- Retire ineffective features and outdated knowledge
Make one workflow dependable before expanding.
A reliable path begins with a frequent, bounded and reviewable use case. Build a measurable loop there before extending the system to more users and systems.