Artificial Intelligence (AI)

A type of neural network trained on vast corpora of text data, capable of generating human-like text, answering questions, summarising documents, and performing reasoning tasks. Large language models such as GPT and Claude represent significant R&D investment and are reshaping knowledge work, customer service, and content production across industries.

A mathematical model trained on data to identify patterns and make predictions without being explicitly programmed for each task. Machine learning models underpin many AI-driven business applications, from demand forecasting to fraud detection, and their development costs are increasingly recognised as intangible assets under IAS 38 when they meet the identifiability and future economic benefit criteria.

The computational expense of running a trained AI model to generate predictions or outputs in production. Inference costs directly impact the unit economics of AI-powered products and services, and are a key consideration in pricing, margin analysis, and the financial viability of AI deployments at scale.

The strategic adoption of digital technologies to fundamentally change how a business operates, delivers value, and competes. Digital transformation involves significant investment in intangible assets — including software, data infrastructure, process redesign, and workforce skills — and is a primary driver of productivity improvement in modern enterprises.

Technology that is owned exclusively by a company and not available to competitors, including proprietary algorithms, manufacturing processes, formulations, or technical architectures. Proprietary technology is a high-value intangible asset that creates barriers to entry and supports premium pricing.

The proportion of tasks, processes, or workflows within an organisation that are performed by automated systems rather than human labour. Automation rate is a key productivity metric, with higher rates typically correlating to improved operational efficiency, reduced error rates, and scalability — though the transition period often involves significant restructuring costs.

The process by which a smaller company with fewer resources successfully challenges established incumbent businesses, typically by addressing overlooked market segments or introducing fundamentally new value propositions enabled by technological or business model innovation. Disruption, as theorised by Clayton Christensen, occurs when incumbents focus on improving products for their most profitable customers while disruptors target neglected segments with simpler, more affordable, or more accessible offerings.

The process of breaking text, code, or other sequential data into discrete units (tokens) that serve as the input and output elements for large language models. Tokenisation determines how a model processes language and directly affects inference costs, since API pricing for large language models is typically based on token count.

AI Cost Reduction: How to Measure Real Operational Savings

AI vendors promise dramatic cost reductions, but actual savings depend on total cost of ownership, implementation quality, and measurement rigour. This article provides a practical methodology for measuring real AI-driven operational savings — net of all hidden costs.