Beyond AI Pilots, Artificial intelligence in banking is entering a more mature phase. Over the past several years, most institutions have focused on experimentation. Chatbots, copilots, document summarisation tools, coding assistants, and knowledge retrieval systems have demonstrated the potential of generative AI to improve productivity and enhance customer experiences. Yet despite significant investment and widespread interest, relatively few organisations have succeeded in

Enterprise AI is moving beyond its early chapters. The first phase was experimentation. Organisations tested generative AI in isolated pilots: chat interfaces, search tools, content generation, coding assistants, and internal productivity use cases. The second phase was feature inflation. Every SaaS vendor attached copilots, assistants, summaries, or “AI-powered” labels to existing products. The third phase is beginning to emerge, unevenly, and in early form. It is not abou

When SAP announced its expanded partnership with Anthropic at Sapphire 2026, the immediate reaction across the industry was predictable. Most observers interpreted the announcement as another example of generative AI being integrated into enterprise software platforms. But reducing the partnership to an “AI-enabled ERP” story misses the much larger structural transition now unfolding across the enterprise technology landscape. What SAP revealed is not simply the evolution of

In the first part of this analysis, we explored the recent signals suggesting that OpenAI and Anthropic are increasingly moving closer to enterprise deployment, operational integration, and AI-enabled services ecosystems. In the second part, we explored the deeper strategic fear potentially driving this evolution: the risk that LLMs themselves become commoditised infrastructure. But there may be an even larger transformation now emerging underneath the surface. The real endga

For decades, the digital economy ran on certainty. Traditional software systems followed fixed rules: if X happens, do Y. Payroll systems calculated salaries. Banking platforms processed payments. ERP tools matched invoices. Behind the scenes, logic was deterministic, repeatable, and auditable. Now a new model is emerging. Artificial intelligence is shifting software development from the classic Software Development Life Cycle (SDLC) toward an AI Development Life Cycle (AIDLC