Why most enterprise AI strategies fail at the implementation stage - and what to do differently
The gap between a compelling AI strategy deck and working deployed systems is where most programmes lose their way. It is rarely a technology problem. It is almost always a governance, incentives or organisational capability problem.
AI in the energy transition: where the real commercial opportunities are in 2026
From smart grid optimisation to hydrogen production modelling and battery degradation prediction, the energy sector is generating some of the most significant applied AI opportunities of this decade.
What investors consistently miss in AI due diligence - a technology executive's perspective
Standard due diligence frameworks often miss the gap between an AI company's demo, its architecture and its ability to operate reliably after investment.
The non-technical founder's guide to not hiring a CTO too early
Hiring a full-time CTO at seed stage is one of the most common and costly mistakes in the AI startup playbook. The right technical leadership model changes as the company matures.
From smart grid to smart city: how AI is reshaping energy infrastructure decision-making
The convergence of real-time sensor data, machine learning and digital twin technology is changing how energy infrastructure is designed, operated and optimised.
What a code review actually tells you about an AI company - and what it does not
A codebase review is necessary but not sufficient in AI due diligence. The most important signals are often found in the relationship between code, data, team process and operating discipline.