About the role

AI Operations Lead making AI work across the agency to improve workflow and decision-making processes. Accountable for outcomes with a focus on automation and AI enablement.

Responsibilities

You map how work actually flows across the agency — where time is spent, where handoffs break down, where decisions stall, and where the same effort repeats.
You bring process mining discipline to this analysis: decomposing workflows into their component steps, quantifying the cost of each, and identifying precisely where AI intervention creates the most leverage.
From the process analysis, you identify and prioritise the workflows best suited for automation.
You score opportunities by recoverable time and implementation effort, brief the implementation team to build in n8n, and sign off before anything reaches the people using it.
You are continuously scanning for AI opportunities that fall outside the automation backlog.
You prototype and test candidate solutions hands-on before recommending them, and you own the recommendation.
When a new need surfaces — from a team conversation, a champion observation, or your own analysis — you evaluate the right tool to address it.
You track usage and outcomes across everything deployed — automations, knowledge tools, AI-assisted workflows.
You measure what is working and what is getting traction, identify friction early, and direct iteration before patterns calcify.
You own the implementation of that recommendation.

n8n — workflow building, reviewing, and quality sign-off
Genuine working fluency across the current AI tool stack
Hands-on tool evaluation — prototypes against real content before recommending
Prompt engineering — design and evaluate for specific, production-grade use cases
Claude Projects and custom AI workspace configuration and deployment
NotebookLM and knowledge synthesis tools
Google Workspace AI (Docs, Meet, Gmail AI features)
Structured data layers: Google Sheets, Notion
API and webhook concepts in automation environments
Process mining — decomposes workflows into steps, identifies value loss, quantifies automation opportunity
Business operations experience — understands how agency functions actually work, not just how they are described
Workflow analysis and process mapping across complex, cross-functional operations
Leverage prioritisation — scoring and sequencing opportunities under time pressure
Technology selection judgement — matches tool to operational problem based on real testing
Brief writing — translates process analysis into tight, actionable implementation specs
Outcome accountability — tracks adoption and results, not just delivery
Change management — moves resistant teams from scepticism to habitual use
High-trust relationship building at every level of seniority
Clear written communication — briefs, process maps, recommendations, escalation documents