Make launch decisions evidence-based and embed risk accountability in everyday operations.
Methodology: The ZHICE Trusted AI Value MethodA model test score, a positive user experience and permission to launch are three different judgments. A system may answer accurately yet become unreliable when sources are outdated, permissions change or tool calls fail. It may save drafting time while increasing review effort. ZHICE develops evaluation practices across design, validation, operations and change, embedding governance in product accountability. Each application must specify intended and prohibited uses, acceptable errors and human takeover arrangements. Evaluation examines both outcomes and the process used to achieve them, feeding failures from real operations into the next round of improvement.
Evaluation begins with business definition. Business teams must specify acceptable outcomes, intolerable errors and situations requiring professional takeover; technical teams then organize samples and checks accordingly. ZHICE combines deterministic rules, professional judgment and user feedback, recording disagreements. Model grading can assist some evaluation tasks but must be calibrated against human criteria. The evaluation report’s purpose is not to collapse everything into a single score. It should explain the conditions in which the application works, where it fails and how those failures affect the launch decision.
Operating governance keeps change and accountability visible. Knowledge updates, prompt adjustments, tool substitutions and model upgrades can all affect task performance. ZHICE links significant changes to relevant regression checks and retains approval and handling records. Actual failures, human edits and takeovers feed the next evaluation, while costs and adoption effects enter product reviews. Teams should be able to restore, restrict, expand or exit on the evidence, rather than relying on vendor capability descriptions or treating a long absence of complaints as proof that risk has disappeared.
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| Governance area | Deviation to examine | Evidence to check | Accountability and improvement |
|---|---|---|---|
| Tasks and evaluation criteria | Generic model metrics replace business outcomes, averages obscure significant errors and samples lack representation. | Actual task segments, business criteria, boundary and failure cases, and records of expert disagreement. | Have business teams define acceptable outcomes, check significant failures separately and maintain samples over time. |
| Launch and residual risk | Completed testing is treated as permission to launch, while unresolved issues lack owners or explicit limits. | Validation reports, open issues, approvals, intended scope and human takeover arrangements. | Identify the launch decision owner and make specific remediation, restricted-use or deferral decisions for residual issues. |
| Version changes and regression | Knowledge or model updates test only new capabilities, leaving previously stable tasks and permission boundaries unchecked. | Change impact analysis, regression records, exceptional behavior and actual operation after approval. | Match quality, access and cost checks to the affected scope, retaining downgrade, pause and recovery paths. |
| Operational feedback and value reviews | Usage and satisfaction dominate; actual failures cannot be traced and cost changes do not inform investment. | Task failures, human edits, takeovers, issue closure and full operating cost records. | Assign ownership by issue type, return fixes to evaluation and periodically adjust task scope and resources. |
01Do evaluation samples cover failures that would actually change a launch decision, and are significant errors checked individually rather than offset by an average score?
02When model grading and professional judgment disagree, who explains the cause and decides which assessment to use?
03After changes to knowledge, prompts, tools or models, which tasks must be retested and who may downgrade, pause or restore use?
04Can an actual incident be traced to its task, version and key actions, and how does the team demonstrate that a repair resolved the problem without introducing regressions?
Three connected levels form the working approach. Select a stage to explore the detail.
Distinguish internal assistance, external outputs, business decision support and production-related uses. Identify risks such as information leakage, incorrect recommendations, unauthorized actions and business interruption.
Check whether samples represent actual tasks, cover different roles and exceptions, and use criteria confirmed by business experts. Look for bias from showcasing successful examples alone.
Identify owners for models, applications, knowledge, operations and business approvals. Examine how external service changes, vendor exit and data processing arrangements affect continuity.
Apply automatically verifiable rules to formats, permissions and task end states; expert review to facts and business applicability; and user research to collaboration. Model-based grading needs periodic human calibration.
Agree quality, risk, cost and operating conditions in advance. Specify how to narrow scope or continue remediation if standards are not met, and include model, prompt, knowledge and tool changes in appropriate regression checks.
Business owners are accountable for use and benefits, technology for operations and change, and risk and security functions for critical boundaries. Record disputed residual risks for a decision by an authorized owner.
Begin with offline evaluation and controlled trials, recording failures and human review findings. For high-impact uses, observe behavior through shadow operation or a restricted scope before deciding whether to enter live business processes.
Track quality deterioration, outdated knowledge, unauthorized attempts, human takeovers and cost changes in operation. Establish workable pause and downgrade arrangements, preserve evidence after incidents and review causes.
Conduct periodic reviews by product and role. Add new tasks, actual failures and business changes to the evaluation dataset, and adjust training, workflows and capability scope so governance evolves with the application.
Agree depth, data conditions and acceptance criteria at initiation. The engagement scope follows the tasks confirmed by both parties.
Suppose a business analysis assistant is moving from a small trial group to more departments. This illustrates evaluation and governance; it is not evidence of realized benefits.
Build evaluation cases from actual analytical tasks, distinguishing fact extraction, metric interpretation and recommendation development.
Work with business experts to check citations, calculations, explanations and access boundaries, recording the cost of human corrections.
Observe use and exception handling within a controlled scope, verifying that pause, downgrade and rollback arrangements work.
Have business and governance owners decide, on the evidence, whether to expand, limit use or continue remediation.
01Which failures would change the launch decision, and are they included in evaluation?
02When one model version improves, have other critical tasks regressed?
03Who can pause an unreliable application, and how will the business continue?
We translate management research and technological advances into working methods for energy businesses. The frameworks below are ZHICE's synthesis of the research and industry questions.
Application in the ZHICE approachApply trustworthiness and risk management across AI design, development, use and evaluation, specifying intended use, accountability and continuing monitoring.
Scope and limitationsA voluntary risk management framework, not proof of legal compliance for projects in China. Material described as a concept note on the 2026 critical infrastructure Profile page is not treated as a formally released standard.
Read the original researchApplication in the ZHICE approachIdentify risks specific to generative AI and incorporate trustworthy knowledge, information security, human review and operational feedback into application design.
Scope and limitationsA cross-industry risk guide. Controls must be selected for the task, data, people and system environment rather than applied mechanically.
Read the original researchApplication in the ZHICE approachDistinguish predefined workflows from agents in which models dynamically choose steps, keeping architecture complexity proportionate to task needs.
Scope and limitationsAn engineering practice article, not an industry standard. The official site notes that some tool information has changed; only its architecture selection principles are used here.
Read the original researchApplication in the ZHICE approachEvaluate task outcomes, action records and performance across repeated runs, combining automated checks, model-based assessment and human judgment.
Scope and limitationsGeneral agent engineering experience cannot replace on-site validation, safety assessment or professional judgment in the energy industry.
Read the original researchApplication in the ZHICE approachDraw on perspectives on differences in employee experience and learning effects to test AI outcomes by role, proficiency and task difficulty.
Scope and limitationsThis reference uses the final paper published in QJE in 2025. The study concerns customer support workers at one company; its average productivity change is not extrapolated into benefits for energy businesses.
Read the original researchEach practice examines diagnosis, solution design, implementation and delivery requirements in detail.
Turn a broad range of technical possibilities into prioritized business choices supported by evidence.
Diagnosis · Design · ImplementationExplore this practiceMake knowledge traceable, actions bounded and collaboration reviewable.
Diagnosis · Design · ImplementationExplore this practiceMake launch decisions evidence-based and embed risk accountability in everyday operations.
Diagnosis · Design · ImplementationExplore this practiceDiscuss the choices, constraints and evidence needed, then agree the engagement scope and work programme together.