Bazigu, Rodgers2026-10-192026-10-192026-08-17https://ssrn.com/abstract=7289199http://hdl.handle.net/20.500.12280/3304Firms accumulating transactional, sensor, textual, and visual data frequently lack the analytical infrastructure to convert that data into decisions, a gap this paper examines through the case of a representative technology-enabled enterprise, InnovateCorp. The analysis develops a staged framework spanning four domains: predictive and prescriptive analytics for strategic decision-making; neural network architectures for predictive maintenance, inventory, and scheduling; natural language processing and conversational AI for customer engagement; and computer vision for quality inspection and retail intelligence. Rather than treating these as independent technology initiatives, the paper argues that they form concentric layers of a single organisational capability, one built on a unified data warehouse, staged deployment, and continuous human oversight rather than on the sophistication of any individual model. Reviewing evidence on ensemble methods, recurrent and convolutional architectures, transformer-based language models, and detection networks, the paper finds that the dominant constraint on value realisation is organisational rather than algorithmic: governance, data quality, and employee adoption determine outcomes considerably more than model selection. The paper concludes that mainstream, well-understood architectures, deployed conservatively and in sequence, offer InnovateCorp a more defensible path to sustained analytical advantage than pursuing technically novel but organisationally unproven alternatives.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Artificial intelligenceMachine learningDeep learningNatural Language ProcessingComputer visionAI governancePredictive maintenanceDigital twinsArticlehttp://dx.doi.org/10.2139/ssrn.7289199