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Comparative Evaluation of AI Maturity Frameworks for Predictive Analytics in Food and Beverage Manufacturing SMEs in the UK

Author(s) Temitope Oluwafemi Oguntosin, Iveren Chimdimma Ukaa
Country United Kingdom
Abstract This paper evaluates the suitability of four prominent AI and analytics maturity frameworks- Microsoft, PwC Responsible AI, SAS Business Analytics Maturity, and Domino Data Science Maturity for guiding ERP-enabled predictive analytics in UK food and beverage manufacturing SMEs.
Motivated by the tension between generic, enterprise-oriented frameworks and the resource-constrained, ERP-dependent reality of SMEs, the study develops six context-specific evaluation criteria: Strategic Alignment (ERP-PA), Data Architecture and Governance, Technical Predictive Analytics Capability, SME Context and Scalability, F&B Manufacturing Fit, and a Responsible/Trustworthy AI Lens.
A qualitative documentary design is used, combining PRISMA-style corpus construction, structured data extraction, and multi-criteria scoring to compare the four frameworks systematically. Findings show that no single framework is fully fit-for-purpose. Microsoft and SAS provide relatively strong strategic and technical coverage but embed large-firm assumptions; PwC offers rich governance guidance but limited ERP-centric or sectoral specificity; Domino exemplifies advanced data-science operations yet lacks SME feasibility and food-sector alignment.
The paper contributes a transferable criteria-based lens and a transparent documentary Multi Criteria Decision Analysis (MCDA) approach for assessing AI maturity frameworks in specific organisational and sectoral contexts. Practical recommendations are offered for SME managers, ERP vendors, and policymakers on selectively combining and adapting existing frameworks rather than creating new ones.
Keywords SME, Beverage, United Kingdom, Artificial Intelligence, Decision Analysis
Field Sociology
Published In Volume 7, Issue 8, August 2026
Published On 2026-08-04
DOI https://doi.org/10.70528/IJLRP.v7.i8.2332
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