Machine Learning Model

A machine learning technique where a model trained on one task is repurposed as the starting point for a different but related task, significantly reducing the data and compute required for training. Transfer learning accelerates AI development timelines and reduces costs, making AI adoption more accessible to SMEs.

A machine learning technique that trains models across multiple decentralised devices or servers holding local data, without transferring the raw data to a central location. Federated learning addresses data privacy and sovereignty concerns by keeping sensitive data on-device while still enabling collaborative model improvement.

A business model in which a basic version of a product or service is offered free of charge while premium features, enhanced functionality, or expanded capacity are available for a subscription fee. The freemium model is prevalent in SaaS, enabling rapid user acquisition and product-led growth, with conversion rates from free to paid users typically ranging from 2% to 5%.

A dividend discount model that values a perpetual stream of cash flows growing at a constant rate, calculated as the next period's cash flow divided by the difference between the discount rate and the growth rate. The Gordon growth model is widely used to estimate terminal value in discounted cash flow analyses and requires that the assumed growth rate remains below the discount rate.

A chronological record of system activities, transactions, or document changes that provides a verifiable history of who did what, when, and why. Audit trails are essential for regulatory compliance, fraud detection, and internal controls, and are required by standards including SOX, GDPR, and ISO 27001.

A vehicle equipped with sensors, software, and AI systems that enable it to navigate and operate without direct human input. Autonomous vehicles represent a convergence of multiple high-value intangible assets, including proprietary algorithms, trained machine learning models, mapping data, sensor fusion technology, and safety validation datasets.

The process of further training a pre-trained machine learning model on a smaller, domain-specific dataset to adapt it for a particular task or industry. Fine-tuning allows organisations to leverage foundational models while creating proprietary, specialised AI capabilities that constitute identifiable intangible assets.

A subset of artificial intelligence in which algorithms improve their performance on a specific task through exposure to data, without being explicitly programmed for each scenario. Machine learning encompasses supervised learning, unsupervised learning, reinforcement learning, and deep learning techniques.

AI Integration Costs: The Hidden Expenses of AI Adoption

AI licensing fees represent only 20-30% of total AI adoption costs. The remaining 70-80% — data preparation, integration engineering, change management, and ongoing maintenance — are the hidden expenses that derail AI budgets and timelines.