AI Advisory
Strategic advisory for leadership teams navigating AI adoption. Covers capability building, vendor selection, internal team development, and how to distinguish substantive AI capabilities from marketing claims.
17 articles
What "sovereign AI" actually means, and when it matters
A clarifying primer that recasts "sovereign AI" not as a slogan but as control across four layers - data, compute, weights, governance - and shows that only a small, identifiable minority of enterprise workloads genuinely need more than the data layer existing tools already provide.
Inside sovereign AI governance: How it works when the frameworks meet reality
Sovereign AI governance fails not at the framework level but in the gap between documented controls and operational reality, where jurisdictional contradictions, supply chain dependencies, and untested incident response processes accumulate risk that compliance paperwork cannot see.
What every startup founder should ask when hiring an AI advisor
Hiring an AI advisor is a technical architecture decision disguised as a people problem, and most founders make it by selecting for exactly the wrong qualities.
AI adoption strategy: why centralise, decentralise, and wait-and-see all fail
Centralising, decentralising, or waiting on AI all fail for the same reason: organisations treat adoption as a deployment problem rather than an organisational learning challenge.
The AI Team Decision: When to Build Internally, When to Stay Fractional, and When to Do Both
Rather than treating AI resourcing as a binary "build or outsource" choice, companies should honestly assess their maturity and progress through a natural sequence — from fractional external leadership, to a hybrid model that builds internal capability, to a self-sufficient team — hiring only when clear readiness signals are met, since premature hiring is far costlier than most leaders expect.
What Happens After You Discover The AI Isn't Real: a Due Diligence Decision Framework
This article provides a practical framework for investors to categorise AI misrepresentation discovered during due diligence — from marketing exaggeration to outright fraud — and translate those findings into appropriate deal structures, remediation costs, and walk-away decisions.
The leader's guide to building AI aptitude in your organisation
Most organisations treat AI implementation like building rudimentary websites in 1995: functional but missing the architectural sophistication needed to exploit AI's genuine competitive potential beyond basic automation.
AI governance: what business leaders need to know
Most organisations are building AI compliance theatre whilst competitors build capability fortresses, treating governance as bureaucratic overhead rather than the competitive advantage that enables sustainable AI deployment.
Creating custom benchmarks that align with business objectives
Custom benchmarks, tailored to specific business objectives, are essential for driving meaningful performance insights and strategic success, far outperforming generic metrics.
Reinforcement learning: practical guide for business users
Reinforcement Learning is a pivotal AI component for business, enabling adaptive decision-making through interaction with environments, balancing exploration and exploitation, and offering benefits such as enhanced decision-making and increased efficiency, despite challenges like data requirements and ethical considerations.
Understanding the AI Skills for Business Framework — dimensions and implementation (part 2)
Successful AI adoption for SMEs requires focusing on five dimensions: privacy and stewardship, technical infrastructure, problem definition, problem solving, and evaluation.
Understanding the AI Skills for Business Framework — leaders guide and personas (part 1)
The AI Skills for Business Framework outlines four key personas—AI Citizens, AI Workers, AI Professionals, and AI Leaders—essential for organisations to effectively integrate AI.
Building your AI-first company
The rise of AI-first lean startups: rethinking organisational structure in the genAI era
What does a fractional CTO do?
A fractional AI CTO gives you senior AI leadership part-time: deciding where AI is worth building, where to buy instead, and how to govern it. What the role covers, when to hire one, and what it costs.
Conducting a data assessment
We outline our process for and benefits of conducting a data assessment prior to initiating any project.
Responsible and ethical AI — why does it matter?
Responsible AI is all the rage, but why should one care?
Recent trends in NLP
Examining some recent trends in NLP and AI, including transformer-based models, transfer learning, multimodal AI and conversational AI