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Data and AI in Practice

A clinician holding a pen over patient notes during a consultation, the patient gesturing

Aug

NHS AI diagnostics don’t have an accuracy problem. They have a liability problem.

Just 28% of GPs use AI clinically, despite tools clearing regulatory approval by the hundred. What's holding them back is who carries the risk when an AI-informed decision goes wrong.

Manufacturing engineer working at a laptop on a factory floor

Aug

Manufacturing’s AI adoption isn’t slow. It’s normal speed, which is the actual problem.

AI use among US manufacturers has risen sevenfold in under three years, faster than the sector adopted robotics. The real barrier left is knowing where to point it.

Bank technology engineer reviewing printed system documentation beside server racks

Jul

By Data and AI in Practice, Financial Services No Comments

Banks’ AI Bottleneck Isn’t New. Regulators Named It in 2013.

Legacy core banking systems are blamed for stalling AI programmes. Regulators gave banks a decade to fix the same problem after 2008, and most still haven't.

Systems engineer checking equipment beside racks in a government data centre

Jul

By Data and AI in Practice, Public Sector No Comments

The UK’s public sector AI problem was never the money

Government has committed billions to AI and hit 38 of 50 roadmap targets. Adoption is still stuck at pilot stage, and legacy systems aren't the reason most assume.

Data and AI in Practice

NHS AI diagnostics don’t have an accuracy problem. They have a liability problem.

Manufacturing’s AI adoption isn’t slow. It’s normal speed, which is the actual problem.

Banks’ AI Bottleneck Isn’t New. Regulators Named It in 2013.

The UK’s public sector AI problem was never the money

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