Glossary
F
14 entries beginning with F, the same definitions the articles use.
fast follower
A competitive strategy of letting early movers absorb the risk of a new technology, then observing what works and acquiring or replicating the capability later at lower cost; defensible in past technology waves but not for AI, where advantages compound.
Related: Planning a cloud migration? Here's what you should consider
Federated AI networks
Arrangements where models learn across organisational boundaries without sharing underlying data, requiring automated compliance checking at each node and raising data-sovereignty governance challenges.
Related: AI Centre of Excellence: Future-proofing Through Continuous Evolution
Federated Coherence Concept
An infrastructure principle centralising shared platforms, common tools and security standards for efficiency while federating unique needs and edge deployments, using standard composable components teams assemble into solutions.
First introduced in: AI Centre of Excellence: Building Capabilities That Scale With AI Adoption
Fine-tuning
Further training that shifts a model’s behaviour more substantially than surface controls, though it still operates on a base whose underlying alignment the organisation did not author.
Related: Rethinking Business Cases in the Age of AI: Building Your AI Business Case
FinOps
The practice of financial management for cloud spending, bringing engineering, finance and business teams together to manage cloud costs and value; a discipline underpinning cloud business cases and ongoing cloud economics.
See also:Cloud financial management
First-order effects
Direct, immediate and easily measured AI returns such as labour savings from faster claims processing, the gains that fit neatly into spreadsheets and board packs.
Related: AI’s Hidden ROI: Measuring Second and Third-Order Effects for Board Decisions
Five Pillars of AI Capability Asset
The five capability domains of an AI capability model that cut across every level of maturity: Governance and Accountability, Technical Infrastructure, Operational Excellence, Value Realisation and Lifecycle Management, and People, Culture and Adoption. Read together, they tell a Board not whether it has AI but whether it can run it.
Related: AI Strategy briefingAI Centre of Excellence: The Essential Functions of the Five Pillars
See also:AI Stages of AdoptionComplete AI Adoption Framework
Force multiplier
The AI CoE operating as an accelerator that concentrates organisational energy, building shared platforms and expertise, rather than a bureaucratic bottleneck dispersing effort across disconnected initiatives.
Related: AI Centre of Excellence: Building Capabilities That Scale With AI Adoption
See also:AI centre of excellence
Formal verification
The discipline of mathematically proving that a system satisfies specified properties across all possible inputs; mandated for catastrophic-failure aviation software under DO-178C/DO-333 and applied to prove regulatory compliance in financial services.
Related: The Accountability Gap: When AI Delegation Meets Human Responsibility
Forward deployed engineer
An engineer a technology provider embeds directly inside a client organisation to build AI where the work happens, delivering working systems alongside the people who use them rather than advising from outside. The industry’s move to forward deployment makes the layer above the engineering matter more, not less: the organisation still has to decide what gets remade, in what order, and who answers for the result.
Related: Remake
See also:PodAI centre of excellence
Foundation model
A large AI model trained on broad data that can be adapted to many tasks; Claude 3 Opus is described as Anthropic’s most intelligent, best-performing foundation model on highly complex tasks based on industry benchmarks.
Frontier capability
Access to the best available frontier AI models bought at hyperscale prices; combined with economical compute it typically requires surrendering sovereign control over the model itself.
Related: The Headroom Argument: Why AI Efficiency Means More Compute, Not Less
See also:Sovereign Control
Frontier model
A model at the leading edge of AI capability, typically trained at the largest scale and governed under the tightest jurisdictional controls; availability can be withdrawn by regulation as well as by commercial choice.
Related: Why Boards Need to Watch the EU's General-Purpose AI Code of PracticeThe AI Sovereignty Trilemma: When a Frontier Model Vanishes and Reality Bites
See also:AI Sovereignty Trilemma
Fundamental Rights Impact Assessment
A structured evaluation required for certain high-risk AI systems under the EU AI Act, examining potential effects on health, safety, fundamental rights, democracy, and the rule of law before deployment.
Related: Navigating the AI Regulatory Maze: A Boardroom Survival Guide
See also:EU AI Act
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