AI Consulting
Perspectives on effective AI consulting: how to assess organisational readiness, design implementation roadmaps, and deliver value through hands-on technical work rather than theoretical recommendations. The emphasis is on building working systems over writing reports.
23 articles
Why I make clients pay for the part everyone wants for free
Cheap generation made working prototypes trivial; knowing what deserves to be built remains the only job that matters.
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.
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 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.
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.
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.
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.
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.
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 — 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.
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.
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.
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.