Insights

Traditional consulting scales by adding bodies. The AI era demands something different — observable outcomes, operational governance, and evidence by design. Seven questions to ask any consulting partner to find out whether their delivery model has genuinely changed, or just their pitch.

Traditional consulting is structurally incompatible with the AI era. That's not commentary — it's a design problem. The pyramid was built to solve a production constraint that no longer exists. Here's why the economics have broken, and why the firms most threatened are the ones currently selling AI transformation.

The economics of consulting have shifted. AI has commoditised the output layer — frameworks, slide decks, operating model templates. What clients now pay a premium for is what AI cannot reliably provide in regulated industries: accountable delivery, operational integration, and evidence.

A BearingNode series on the AI era transformation of management consulting. Six questions. One uncomfortable answer. Written for CDOs, CIOs, and senior leaders in regulated industries who are tired of transformation programmes that end with a governance framework no one owns.

Whole-body thinking as an integration of head, gut, and heart. The head enables reason and analysis. The gut draws on experience and pattern recognition. The heart weighs values, consequences, and human impact. Visionary leaders embody this integration — not just in their own decision-making, but in how they frame problems for the people around them.

Better decisions don't come from more dashboards, faster AI, or bigger datasets alone. Barry Green, Advisory Board Member at BearingNode and co-author of Data Means Business, argues that good decisions require whole-body thinking — integrating logic, experience, intuition, ethical judgement, and organisational context.

Two words. Four meanings. None of them accidental. In the AI era, the premium has moved to trustworthy execution. BearingNode's name encodes exactly what modern data, analytics, and AI consulting must be.

Explore the unique economic characteristics of data and information assets, including their non-rivalrous nature, network effects, and strategic value creation in the digital economy.

Traditional project management fails AI projects not because it's badly executed, but because it's designed for the wrong world. Learn why Mediocristan governance can't handle Extremistan uncertainty.

CPS 230: When Operational Risk became everyone's problem: If there is one regulatory development that has fundamentally changed how APRA-regulated entities must think about operational risk, it is Prudential Standard CPS 230. It didn't introduce an entirely new concept, operational resilience has long been an expectation, but it did make the rules explicit, enforceable, and considerably harder to ignore.

A common mistake in data conversations is to treat governance and observability as interchangeable. They are not. D/I O11y overlaps with governance, compliance, management and strategy — but it is distinct from all four.

For most of human history, data was scarce. Our ancestors were “data foragers,” struggling to gather even a handful of observations to inform critical decisions. A farmer in 1900 relied on almanacs, folklore, and personal experience. Perhaps a few dozen data points, to decide when to plant crops.

Each quadrant of the uncertainty matrix splits into positive and negative scenarios — giving us eight distinct situations demanding different governance approaches. Traditional PMO works for four of them and actively harms the other four.

Daniel Rolles participated in FIMA 2025 at The Kia Oval in London, sharing insights on the convergence of Data Governance and AI Governance with industry leaders from across Europe's financial services sector.

Gen AI projects reveal inadequacy of inherited frameworks. Learn why 95% of GenAI pilots fail not from technical risks, but from unvalidated assumptions about adoption, workflow fit, and business value—and how to navigate uncertainty rather than just manage risk.

Both F1 racing and modern data platforms rely on telemetry to prevent disasters. The difference? F1 cars cost £15 million and get 300 sensors. Your data estate? Worth billions, gets a quarterly audit.

A day across London's data and AI ecosystem revealed the rapid convergence of enterprise data governance and AI engineering—from Collibra's OpenLineage commitment to AWS/NVIDIA RAG evaluation frameworks. These mirror-image problems are solving through shared observability infrastructure.

I challenged Claude Sonnet 4.5 to destroy our Data & Information Observability framework. What followed was one of the most intellectually challenging conversations about whether we're selling genuine innovation or just repackaging old ideas with new terminology.

It's encouraging to see open standards taking a prominent place at Big Data London. At BearingNode, we're invested in communities like OpenLineage and are thrilled to see the industry embracing open source solutions for next-generation data, analytics, and AI architecture.

As AI systems move from experimental projects to business-critical applications, organisations need comprehensive AI observability and governance frameworks that actually work in practice. Join BearingNode at Big Data London 2025.