Legacy ERP systems are becoming a major obstacle to AI adoption and business growth. Courtney Hounsell of Braintree explains why cloud migration alone is not enough, arguing that true ERP modernisation requires new architecture, real-time data integration and cloud-native capabilities. The article explores how modern ERP platforms enable AI-driven automation, predictive analytics and operational efficiency, while legacy infrastructure limits ROI, increases costs and prevents organisations from realising AI’s full business value.
Tag: enterprise AI
The risk of AI tunnel vision in IT efficiency
The risk of AI tunnel vision in IT efficiency explores why focusing solely on AI’s energy consumption overlooks the much larger challenge of improving efficiency across the entire IT estate. As data centre power demand rises in South Africa, organisations must adopt an end-to-end approach that optimises infrastructure, cooling, software, workloads, and data management. The article argues that sustainable IT efficiency is achieved by designing energy efficiency into every layer of traditional and AI infrastructure, enabling greater resilience, lower costs, and scalable digital transformation.
The evolving role of AI in business process outsourcing
The integration of AI into Business Process Outsourcing (BPO) has evolved from early excitement about its transformative potential to a more pragmatic approach focused on specific, high-impact use cases. While AI was initially viewed as a potential job disruptor, its role has proven to be an enabler rather than a replacement for human agents. AI’s greatest value in BPO is in automating repetitive, low-value tasks, like after-call summaries, which enhances operational efficiency and improves customer experience. However, the rapid adoption of AI has also revealed challenges, such as data privacy concerns, limitations in large language models (LLMs), and the importance of human oversight. Going forward, successful AI implementation in BPO will require a balance between innovation and caution, with a focus on enhancing human capabilities while addressing ethical and regulatory considerations in data management.