# The Development of an OpenAI-Based Solution for Decision-Making
## 4 Results
### Performance Metrics and System Efficiency  
To rigorously evaluate the system, we established key performance indicators (KPIs) with clear definitions and quantitative results. Each KPI is mathematically defined and benchmarked against conventional decision processes for context:  
* Decision Processing Time (\(T_{\textit{proc}}\)): Measured as the elapsed time from decision initiation to final resolution, i.e., \(T_{\textit{proc}}=T_{\textit{finish}}-T_{\textit{start}}\). In our case study, the average end-to-end decision cycle was ~30 min from proposal to recorded vote. This is a dramatic improvement over traditional methods (often 2-3 days via meetings and email), representing an 80% faster turnaround. The platform's real-time voting and automatedallying accelerated decision-making significantly. * Decision Accuracy (\(D\)): Defined as the proportion of AI-generated recommendations that matched expert-validated outcomes. \[D\ (\%)=\frac{Number\ of\ AI\ Recommendations\ matching\ Expert\ Decisions}{Total\ Number\ of\ Decisions}\ \times\ 100\] The AI system's suggestions aligned with human expert decisions about 85% of the time. This high accuracy indicates that AI support provided largely correct insights. Notably, after applying bias mitigation, alignment remained high (still ~85%) across demographic groups, indicating the model's fairness (as discussed in the fairness evaluation). * User Adoption Rate (\(U\)): Calculated as the percentage of target users actively using the system. \[U\ (\%)=\frac{Number\ of\ Active\ Users}{Total\ Number\ of\ Target\ Users}\ \times\ 100\] Within three months of deployment, adoption reached 90% at Dadword IT (employees regularly using the platform) and 85% at ZLabs. Such high adoption suggests strong user engagement and trust. Users found the platform intuitive and valuable, leading to broad participation in decisions. High engagement was also reflected in frequent logins and votes per user, indicating that the system became an integral part of the decision process. * Blockchain Transaction Efficiency: We assessed the ledger's performance by transaction latency and cost. Latency was measured as the time to record a vote on the blockchain. On a private PoA network, the average vote recording time was <20 s, with negligible variance, which introduced only minimal delay to the decision process. In contrast, public blockchains (e.g., Ethereum) typically handle fewer transactions per second (\(\approx\)15-30 TPS), leading to higher latency under load. Cost was evaluated by the fee to record decision data on the chain. By storing only hashed records, our approach minimized costs. For instance, recording a decision hash on Ethereum mainnet would incur roughly USD 1-10 in gas fees, whereas on a scalable network like Polygon it is around USD 0.001 (and essentially free on a private hyperledger). In our deployment, the effective cost per transaction was negligible since we used a private chain for votes. These efficiency metrics demonstrate that the AI-blockchain system not only improved decision quality and transparency but did so with fast performance and low overhead, outperforming conventional systems on speed and maintaining cost-effectiveness. Benchmarking against Traditional Processes: These quantitative results highlight the advantages over conventional decision-making. For example, 30 min decisions on our platform contrast with multi-day turnaround times in hierarchical or email-based processes. The AI system's 85% accuracy approaches expert-level decision quality, suggesting that the system's recommendations are typically as good as human judgments, with far greater speed. High user adoption (85-90%) within weeks signals greater enthusiasm than seen in many enterprise tool rollouts, indicating that users embraced this approach more readily than some traditional systems. Additionally, the low error rate in blockchain recording (<1% failed transactions) and the auditability of every vote provide reliability and transparency unmatched by off-chain methods.