# legacy modernisation Published articles for legacy modernisation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why agentic AI starts with legacy modernisation DevFeed: [Why agentic AI starts with legacy modernisation]() Original publisher: [Read original article]() Author: Suzanne Angell Published: 2026-08-21T09:33:00Z Content type: opinion Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [AI Agent](), [legacy](), [legacy systems](), [data](), [Processes]() Tags: [agentic-ai](), [agents](), [ai-agents](), [artificial-intelligence](), [data](), [governance](), [integration](), [legacy](), [legacy-modernisation](), [operational-risk](), [processes](), [risk-management]() ### AI overview The article argues that organisations must address fragmented data, brittle processes, and outdated architectures before they can realise the full value of agentic AI. It explains that legacy systems may remain operationally critical, while accumulated technical, process, and organisational debt can prevent agents from accessing reliable information and executing workflows consistently. ### Source excerpt Organisations are increasingly excited by the potential of agentic AI, but many overlook the legacy obstacles that stand in the way. In this post, I explore why successful AI adoption depends on tackling fragmented data, brittle processes and outdated architectures, and why modernisation is often the most important step towards unlocking value from intelligent agents. ## AI success depends on data foundations DevFeed: [AI success depends on data foundations]() Original publisher: [Read original article]() Author: James Heward Published: 2026-08-14T14:19:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [data-architecture](), [AI Strategy](), [AI Development]() Tags: [agentic-ai](), [ai](), [ai-readiness](), [artificial-intelligence](), [data](), [data-engineering](), [governance](), [integration](), [legacy-it](), [legacy-modernisation](), [prototypes](), [quality](), [silos]() ### AI overview Strong data foundations--discoverable, high-quality, accessible, governed, and integrated--are presented as essential for reliable AI outcomes, especially as organisations adopt more autonomous agentic systems. ### Source excerpt As organisations invest in AI, many discover that their biggest challenges are not AI-related at all. In this post, I explore why strong data foundations, from quality and accessibility to governance and integration, are essential for turning AI ambition into reliable, production-ready outcomes. ## Four pillars of agentic AI success DevFeed: [Four pillars of agentic AI success]() Original publisher: [Read original article]() Author: Simon Sear Published: 2026-07-29T15:47:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Artificial Intelligence](), [Software Engineering](), [data](), [legacy](), [systems](), [context]() Tags: [accessibility](), [agentic-ai](), [ai](), [ai-accelerated-development](), [ai-agents](), [architecture](), [artificial-intelligence](), [context](), [data](), [data-architecture](), [data-governance](), [data-strategy](), [delivery](), [engineering](), [governance](), [legacy](), [legacy-modernisation](), [software-engineering](), [systems]() ### AI overview This article argues that organizations need strong foundations before scaling agentic AI. It identifies four pillars: modernizing legacy systems, building reliable data foundations and architecture, managing governance and risk, and democratizing access to data. ### Source excerpt Everyone is talking about agentic AI. That's not surprising. The promise is huge: AI agents that can plan, reason, use tools, work across systems and get real work done. In software engineering, that could mean faster delivery and better quality. In operations, it could mean complex processes moving with less manual effort, fewer handovers and better decisions.