Latest Articles
Practical perspectives on AI strategy, product development, and technical leadership.
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.
We've deployed Microsoft Copilot 365. Now what?
Deploying Microsoft Copilot is a logistics problem; extracting value from it is an organisational one, and most companies confuse finishing the first for starting the second.
Securing AI agents: Why tool use creates new attack surfaces
Securing AI agents requires treating the surrounding architecture as the threat surface, not the model itself, because authentication gaps, over-provisioned tool access, and prompt injection vulnerabilities combine to make your most capable agents your most dangerous ones.
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.
How AI Consulting Services Drive Digital Transformation
Most AI transformation programmes fail not because of technology, but because organisations treat it as a deployment problem rather than an organisational redesign challenge.
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.
What I've Learned Shipping AI Products That Most Consulting Advice Gets Wrong
Most AI product strategy advice fails in practice because the real challenges -- architectural rewrites, evaluation infrastructure, feedback loops, nonlinear timelines, and production edge cases -- only reveal themselves through hard-won experience of actually shipping AI systems.
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.
Why AI Startups Need a Different Kind of CTO, and What That Looks Like
AI-native startups fail when they hire traditional software CTOs who apply deterministic thinking to inherently probabilistic AI systems, creating a critical but overlooked leadership gap that costs companies months of misdirected effort.
The Investor's Technical Due Diligence Playbook: What Most AI Assessments Actually Miss
Current AI due diligence processes fail because they apply traditional software evaluation frameworks to fundamentally different AI risks, missing crucial questions about data provenance, model defensibility, and the intersection between technical depth and strategic positioning.
Best Consulting Firms for Defining Your AI Strategy in 2026: An Honest Buyer's Guide
This guide argues that choosing the right AI strategy consultant depends on matching your company's specific needs, size, and budget to the appropriate type of firm (whether global consultancies for enterprise credibility, specialist boutiques for technical depth, or independent consultants for focused expertise) rather than simply picking the most famous name.
The key metrics to measure the ROI of your LLM deployments
Most organisations measure LLM success using traditional software metrics whilst sitting on transformational cognitive infrastructure they barely understand how to evaluate properly.
EU AI Act Explained: Compliance Requirements and Business Impact
The EU AI Act's extraterritorial reach and risk-based classification system will reshape global AI development by creating competitive advantages for organisations that build regulatory compliance into their systems from conception rather than retrofitting it later.
Why Most AI Projects Fail Without Expert AI Consulting
Most organisations fail at AI because they mistake building models for building systems, burning millions on architectural decisions that doom projects from the start whilst ignoring the expertise gap that separates proof-of-concepts from production reality.
Building a Secure LLMOps Pipeline: From Development to Production
Most organisations treat LLM security like traditional DevOps while ignoring novel attack vectors through model weights, training data, and prompt injection that conventional tools cannot detect.
Why tokenization matters: CharGPT vs ChadGPT
GPT-4 struggles to count letters in "CharGPT" versus "ChatGPT" because tokenisation (the process of breaking text into processable units) fundamentally shapes what AI models can perceive, revealing why some companies' AI implementations fail at the architectural level rather than the reasoning level.
Thin wrapper or true AI? Technical due diligence for AI investments
Despite widespread AI claims in company pitch decks, 95% of generative AI pilots are failing, creating a massive gap between marketing promises and reality that requires rigorous technical due diligence to distinguish genuine AI capabilities from superficial implementations.
Best AI consultancies for 2026: navigating the agentic era
AI consulting in 2026 shifts from strategy to implementation as enterprises demand partners who can actually build and deploy working AI systems, not just create PowerPoint decks.
The business applications of reinforcement learning: why most enterprises are leaving billions on the table
Most companies are building static AI calculators when they could create adaptive systems that continuously optimise performance through environmental interaction—missing billions in potential value through reinforcement learning applications.
Finding a trusted AI consulting partner: beyond the marketing veneer
Most AI consultancies are selling yesterday's chatbots whilst genuine expertise lies in architecting sophisticated systems that exploit technical potential others cannot even perceive.
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-enhanced scenario planning: techniques for modern boardrooms
Modern boardrooms are squandering AI's potential in scenario planning by digitizing outdated methods rather than implementing sophisticated systems that explore true possibility spaces through causal inference, complex adaptive modeling, and counterfactual testing.
Cost-effective LLM implementation: when to fine-tune and when to prompt
Most companies are burning money on LLM implementations by defaulting to expensive fine-tuning when sophisticated prompting could achieve comparable results at a fraction of the cost and complexity.
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.
From guidelines to guardrails: operationalising AI ethics in product development
AI ethics must shift from performative checkbox exercises to embedded technical guardrails that transform ethical principles into operational constraints throughout the entire development lifecycle.
Hiring a fractional head of AI to complement your existing technical team
A fractional Head of AI bridges the critical gap between technical expertise and strategic AI leadership, enabling organisations to unlock their AI potential without the overhead of a full-time executive.
Self-improving systems: the AI architecture pattern everyone talks about, nobody builds
Despite the hype, truly self-improving AI systems remain theoretical due to fundamental technical and organizational barriers, with today's "self-improving" implementations being merely constrained optimization within predetermined parameters.
Parameter-efficient fine-tuning: what business leaders need to know
Parameter-efficient fine-tuning revolutionises AI model customisation by enabling comparable performance with just a fraction of the computational resources, offering businesses a strategic advantage over competitors using outdated, expensive full-model approaches.
The curse of dimensionality: when more data becomes your enemy
The curse of dimensionality paradoxically undermines AI performance as data dimensions increase, creating mathematical conditions where distance metrics collapse and models fit noise rather than signal.
The future of AI for lawyers: transforming legal practice in the digital age
Legal AI is vastly underutilized, with true innovation lying not in basic document tools but in sophisticated neural-symbolic architectures that authentically model legal reasoning rather than merely mimicking paralegal functions.
Richard Sutton's bitter lesson explains why your AI solution feels shallow
Sutton's bitter lesson reveals that most AI implementations feel shallow because they prioritize domain expertise over computational scale, leaving roughly 80% of potential untapped.
Top AI consulting companies for 2025: the rise of boutique technical excellence
In 2025, boutique AI consulting firms are outpacing traditional giants by offering tailored, innovative solutions that meet specific client needs, reshaping the consulting landscape.
Beyond the hype: creating measurable ROI with LLM implementations
Despite their transformative potential, Large Language Models (LLMs) necessitate robust evaluation and strategic implementation to ensure they deliver real value rather than becoming a costly gamble.
Contextual chunking strategies that improve RAG performance
In the evolving AI landscape, mastering contextual chunking is essential for optimising Retrieval-Augmented Generation (RAG) performance.
Understanding how QLoRA works
QLoRA revolutionises the fine-tuning of large language models by combining quantisation and low-rank adaptation to significantly reduce memory usage while preserving performance, making advanced AI accessible to a broader range of users.
Small LLMs — why they matter
Small language models (SLMs), characterised by their efficiency and versatility, are emerging as pivotal tools for language processing, offering significant advantages in resource optimisation and accessibility, while challenging the dominance of larger models.
Diffusion models: a simple explainer
Diffusion models revolutionise generative AI by generating high-quality images, videos, and molecules through a dual process of noise addition and reconstruction, while raising significant ethical and computational challenges.
The Non-Executive Director's guide to assessing AI system performance
The guide equips Non-Executive Directors with essential frameworks and insights to effectively assess AI system performance, ensuring alignment with corporate objectives and ethical standards.
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.
Enterprise knowledge graphs as RAG foundations: implementation lessons
Enterprise knowledge graphs, enhanced by retrieval-augmented generation, are essential for transforming data silos into interconnected knowledge ecosystems, but their success hinges on data quality, scalability, security, and user-centric design.
Demystifying LoRA
Low-Rank Adaptation (LoRA) revolutionises the fine-tuning of large language models by enabling efficient model adaptation with minimal computational resources, while raising important ethical considerations.
Skills taxonomy for modern AI teams: beyond traditional data science
A modern AI skills taxonomy is essential for building versatile teams that go beyond traditional data science to include advanced technical, interdisciplinary, and ethical competencies for future innovation.
The fractional CTO's guide to building AI teams that deliver
The Fractional CTO's Guide highlights the crucial role of fractional CTOs in building high-performing AI teams that align with business objectives while fostering innovation and ethical practices.
The strategic value of a Non-Executive Director with AI expertise
The inclusion of Non-Executive Directors with AI expertise is essential for organisations to effectively navigate the complexities of modern business strategy and ethical considerations in an increasingly AI-driven landscape.
An executive’s guide to AI agents
AI agents are transformative software entities that enhance operational efficiency and decision-making in businesses by autonomously performing tasks and leveraging advanced technologies like generative AI.
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.
Common technical challenges fractional CTOs solve for startups
Fractional CTOs provide expert technical leadership to startups, addressing critical challenges like technical debt, scaling infrastructure, security, and cloud cost optimisation without the need for a full-time hire.
Your private LLM: deploying LLMs locally and offline using Ollama
Ollama enables local deployment of Large Language Models (LLMs), offering enhanced privacy, control, and efficiency for organisations seeking to harness the power of LLMs while maintaining oversight of their operational environment.
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.
Unlocking business potential with AI agents
AI agents are intelligent systems that autonomously handle tasks, enhancing efficiency and reducing costs.
Building your AI-first company
The rise of AI-first lean startups: rethinking organisational structure in the genAI era
Process reward models: a simple explainer
Process reward models (PRMs) train AI by providing feedback at each step of a task, enhancing understanding and problem-solving abilities.
Should I build my own large language model (LLM)?
Organizations considering building their own large language models (LLMs) should weigh the benefits of control and specialisation against challenges like high computational needs and expertise requirements.
What does a fractional CTO do?
A fractional AI CTO provides strategic guidance and technical leadership to help organizations assess, plan, and responsibly implement AI solutions.
Who are the best AI consulting firms in 2024?
When selecting an AI consulting partner, the right fit will depend on your organisation's unique needs, budget, and desire for a hands-on collaborative engagement.
Can you reason with LLMs?
Research from Apple reveals that large language models struggle with genuine mathematical reasoning and perform inconsistently on complex math problems
Understanding large language models: a group discussion analogy
By visualising the transformer as a dynamic conversation between human participants, we can grasp the core principles behind this influential neural network architecture.
Conducting a data assessment
We outline our process for and benefits of conducting a data assessment prior to initiating any project.
Delivery excellence through multi-disciplinarity and diverse teams
Unlocking the potential of AI through diverse teams: why it is crucial to employ a multidisciplinary team
Quantifying the benefits and risks of an AI deployment
Most AI business cases fail not because the technology doesn't work, but because organisations skip quantified baselines, ignore the full cost structure, and mistake efficiency metrics for financial returns.
Adversarial models: what are they and when should you use them?
A brief explanation of adversarial models and some potential use cases for them
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