David Walter - Search Sensei

How NAB Established a Governed Knowledge Foundation for AI

A large archive-like environment with multiple rows of documents and systems converging into a single illuminated pathway leading forward, representing fragmented enterprise knowledge being unified into a reliable retrieval system.

AI ambition meets enterprise reality NAB had already begun exploring how AI could improve internal operations and support employees in navigating complex information environments. Teams were actively evaluating use cases and experimenting with tools—but quickly encountered a familiar constraint. The challenge was not the capability of AI systems. It was the condition of the organisation’s […]

Why Enterprises Should Fix Internal Knowledge Before Building Customer AI

A professional walking through a modern office transitioning from cluttered, fragmented information on one side to a clean, organized environment on the other, illustrating the need to fix internal knowledge systems before implementing customer-facing AI.

Many organisations rush to deploy customer-facing AI assistants. In practice, the most successful AI initiatives begin by fixing internal knowledge systems first.

Why So Many Enterprise AI Programs Produce Activity Instead of Operational Value

An abstract scene showing dense, glowing signals and activity dispersing outward without forming a clear structure or outcome, representing enterprise AI efforts that generate activity but fail to produce operational value.

Across large organisations, AI now has a strange status. It is no longer experimental in the old sense. Boards ask about it. Leadership teams issue directives. Business units are encouraged to identify use cases. In some organisations, there are formal programmes, steering groups, and internal working sessions dedicated to AI adoption. Yet for all of […]

Most Enterprise AI Projects Fail Before They Start

A partially constructed building where the upper floors feature modern glass offices and glowing AI elements, but the lower levels are incomplete scaffolding and exposed foundations, suggesting enterprise AI projects built before the underlying systems are ready.

Many enterprise AI initiatives stall before delivering real value. The problem is rarely the model. It’s governance, knowledge architecture, and how organisations manage their information.

What the Enterprise AI Stack Actually Looks Like in 2026

A cross-section of a layered enterprise system embedded in the ground, showing infrastructure and data systems at the base, organized knowledge systems in the middle, and a light AI interface layer at the top, illustrating how enterprise AI depends on underlying foundations.

Enterprise AI is not just about models and applications. It requires a layered architecture that includes governance, knowledge retrieval, and orchestration.

Why Your Employees Still Can’t Find Information (Even With AI)

An office worker navigating a maze-like environment formed by filing cabinets, screens, and document stacks, with multiple dead ends and no clear path, illustrating how employees struggle to find information even with AI tools.

Many organisations invest heavily in digital tools yet employees still struggle to find information. Here’s why knowledge fragmentation persists and what enterprises can do about it.

The CIO’s Dilemma: Everyone Wants AI, But Compliance Says No

A modern office scene showing a professional standing between two contrasting forces—one side representing innovation and AI momentum, the other structured controls and compliance barriers—illustrating the tension between ambition and regulation in enterprise AI.

Enterprise leaders are under pressure to deploy AI, but compliance and risk teams often slow progress. Here’s why that tension exists and how organisations can move forward responsibly.

What Actually Happens When AI Meets SharePoint

An office environment where multiple versions of documents and folders are scattered across interconnected systems, with overlapping and duplicated files creating confusion, representing how AI struggles with fragmented SharePoint content.

Many organisations assume AI tools will automatically unlock knowledge stored in SharePoint. In reality, fragmented document environments often create new challenges for enterprise AI.

5 Signs Your Organisation Isn’t Ready for Enterprise AI (Yet)

An incomplete or misaligned structure with missing or uneven components, representing an organisation that lacks the foundational readiness required to successfully implement enterprise AI.

Many organisations are eager to deploy AI, but underlying knowledge and governance challenges often prevent real progress. Here are five signs your organisation may not be ready for enterprise AI yet.

Why SharePoint Becomes the First AI Problem Enterprises Have to Solve

A central document system acting as a bottleneck where multiple information pathways converge and slow down, illustrating how SharePoint often becomes the first challenge enterprises must address when implementing AI.

Organizations rushing into AI often discover their biggest challenge is internal knowledge chaos. Here’s why SharePoint becomes the first AI problem enterprises must solve.