Glossary
A list of core AI automation terms and definitions.
1. Workflow Analysis
The structured review of how work moves through an organisation — mapping people, processes, and tools to identify friction points, inefficiencies, and automation opportunities.
At Brand Automation AI, it’s the foundation for any AI transformation, ensuring that automation begins with understanding, not guesswork.
2. Process Mapping
A visual representation of the steps, decisions, and data flows in a business process.
Used during workflow analysis to highlight redundancies, manual handovers, and gaps where automation could create measurable value.
3. Automation Opportunity
A task or process identified as a good candidate for automation — typically repetitive, rule-based, time-consuming, and low risk.
Opportunities are ranked by impact × effort × risk, helping teams prioritise high-ROI wins before tool selection
4. Integration Layer
The connective tissue between different software systems — enabling data to move automatically between platforms without manual intervention.
Common tools include Zapier, Make, and n8n, which are often configures to link CRMs, project management systems, and databases.
5. Tool Selection
The process of evaluating, comparing, and choosing the most effective AI or automation tools for a specific business context.
It considers fit, interoperability, compliance, and total cost of ownership, ensuring technology serves strategy, not the other way around.
6. Change Management
A structured approach to preparing and supporting people through organisational change — critical during AI adoption.
It includes communication, training, role redefinition, and feedback loops to ensure new systems are embraced, not resisted.
7. Human-in-the-Loop (HITL)
A design principle where human oversight remains central to automated systems.
Humans review, approve, or correct machine-generated outputs — maintaining accountability, judgement, and ethical control in AI-driven workflows.
8. Governance Framework
A set of policies, controls, and accountability measures that ensure automation is used responsibly and consistently.
Covers data handling, model usage, version control, and audit trails — aligning AI systems with legal, ethical, and brand standards.
9. Adoption Roadmap
A phased plan that guides how automation will be implemented, tested, and scaled across an organisation.
It includes pilot projects, success metrics, and training cycles — ensuring sustainable adoption rather than one-off deployments.
10. Intelligent Workflow
A business process that combines automation, data insights, and human judgement to deliver consistent, adaptive outcomes.
Unlike static workflows, intelligent workflows learn and evolve — forming the backbone of modern AI-enabled organisations.