Artificial Intelligence (AI) has emerged as a major disruptive and transformative force within the food and retail

industries, reshaping supply chains, quality metrics, and organizational operational paradigms. This study

evaluated the extent of AI implementation within the food industry based on software quality dimensions—

Functionality, Reliability, Usability, Efficiency, Maintainability, and Portability—and determined the core

operational challenges encountered during deployment. Utilizing a quantitative research design, data were

collected from food industry stakeholders and analyzed using frequency distribution, percentages, weighted

means, and One-Way Analysis of Variance (ANOVA). The findings revealed that AI implementation is highly

realized across key metrics, with Functionality scoring highest (Overall Weighted Mean = 3.29, Highly

Implemented) and Portability presenting stable operational compliance (Overall Weighted Mean = 3.17,

Implemented). Inferential testing verified that there are no statistically significant variances in the level of AI

The integration of Artificial Intelligence (AI) within the global food and retail infrastructure represents a

fundamental paradigm shift in corporate operations, driven by evolving market demands, operational cost

constraints, and complex consumer metrics. Beyond basic automated mechanisms, modern AI systems integrate

multi-dimensional algorithmic learning architectures, robotic process automation (RPA), the Internet of Things

(IoT), and natural language processing (NLP) to proactively handle data-heavy industrial demands (Russell &

Norvig, 2020). Industry forecasts project that AI systems within retail and production systems will exceed USD

100 billion by 2032, proving its long-term market influence (Premo, 2023).

In developing socio-economic environments like the Philippines, AI has gained considerable traction among

small and medium enterprises (SMEs) to reinforce organizational outputs and secure global competitive

empirical gap remains regarding how these software and operational models perform under industry-standard

frameworks, such as the ISO/IEC 9126 Quality Model. This research directly resolves this gap by thoroughly

assessing the practical integration of AI systems across six pillars: Functionality, Reliability, Usability,

Efficiency, Maintainability, and Portability. The ultimate objective is to analyze these concrete performance

dimensions and build a comprehensive, structurally sound AI Model that guides future strategic implementations

within the industry.

This study deployed a highly structured descriptive-quantitative research design to systematically evaluate the

actual level of AI software integration and map the localized operational hurdles within the target enterprise

ecosystem. Quantitative methodologies were selected to establish rigorous, standardized empirical parameters

operational scope and industry-respondent groups; Part II calculated specific integration parameters across the

six primary software attributes on a multi-point Likert configuration; and Part III documented structural and

technical issues.

Statistical evaluations integrated both descriptive metrics and inferential verification tools. Frequency and

percentage distributions mapped out organizational profiles and deployment tiers. Weighted means were

computed to provide explicit verbal interpretations of the current implementation stages. To test for variance

parity across demographic segments and institutional profiles, inferential processing via One-Way Analysis of

Variance (ANOVA) was executed with a formal statistical significance threshold set at alpha = 0.05.

1. Institutional Profile and Distribution Tiers

The software performance profiles were calculated through weighted mean evaluations across all six standard

quality criteria, identifying clear performance variations:

Table 1. ISO/IEC 9126 System Quality Integration Metrics Summary

promptly to real-time industrial inputs. Conversely, Portability recorded the lowest operational mean (Mean =

3.17), indicating that adapting AI structures to highly disparate external infrastructures remains a major technical

friction point (ISO, 2001).

3. Variance Mapping Across Demographic and Institutional Boundaries

To establish whether individual workforce characteristics or company scale significantly influence the real-world

execution of AI systems, comprehensive inferential testing was conducted.

Table 2. One-Way ANOVA Variance Mapping across Core Enterprise Demographics

an F-value of 1.531 with a p-value of 0.220. This mathematically confirms that AI software quality and

integration parameters remain consistent regardless of organization size or the workforce's age distribution.

Instead, success depends entirely on strategic alignment, organizational readiness, and targeted capital support.

 High Operational Realization: AI software systems deployed within the food industry exhibit a robust

functional architecture, particularly in technical functionality, workflow capability, and execution efficiency.

 Demographically Neutral Integration: Because comprehensive inferential analysis confirmed no

statistically significant variance across organizational scales or workforce groups (p > 0.05), AI integration

can be approached using standardized operational frameworks.

 Structural Implementation Hurdles: The true barriers blocking broad adoption are not the software

Based on these empirical findings, the study proposes a standardized AI Comprehensive Model constructed

across four key strategic quadrants:

Table 3. Comprehensive AI Model Optimization Framework