Australia AI Productivity Designer

About Human Productivity

Human Productivity is improvement in performance and outcome that comes from changing how work gets done with AI. Think improvements to cycle time, rework, quality consistency, throughput, and the range of work a team can reliably deliver.

In practice, we embed with client teams, observe real workflows, remove low-value steps, and introduce AI-enabled ways of working that people can repeat. The goal isn’t just “more AI usage.” The goal is five days of outcomes in three days of effort.

About you

You are an AI builder, but not the “train-a-model, ship-an-ML-platform” kind.

You’re able to build AI-native workflows. You can shape the routines, assets, and lightweight tooling that make AI adoption practical, repeatable, and safe for real teams. You combine technical fluency (so you can make things) with strong problem solving judgement (so you build the right thing) and coaching instincts (so change actually sticks).

Some weeks you’ll create a crisp set of workflow assets and coaching that unlocks immediate uplift. Other weeks you’ll identify a deeper need, partnering with our AI Engineers and Experience Designers to take it further.

What you’ll be doing

The examples below illustrate the kind of work you’d typically own. We don’t expect every successful candidate to have experience in all areas, if the role excites you and you can show a convincing mix of the capabilities below, we’d love to hear from you.

1) Workflow Engineering

Embed with client teams to observe and map how work actually happens.

Run discovery that gets to the heart of how time spent, where effort goes, and what’s more difficult than it should be.

Re-design workflows around AI: work alongside teams to re-think how their work gets done, from accelerating existing work, to removing drudgery or doing new things that haven’t been possible before.

Measure impact at workflow level (cycle time, rework, quality consistency, throughput) and identify adoption signals as supporting evidence.

Build momentum through short, focused interactions (co-labs, sprints, 1 to 1 and small group coaching) that make progress visible and quick.

2) Building Workflow Assets and Lightweight Tools

Co-create and ship assets that teams actually reuse: prompts, templates, Custom GPTs, QA checks, eval prompts, and playbooks.

Use AI-assisted development tools (think Codex or Claude Code) as your default way of building small custom solutions and automations that remove friction.

Create “minimum viable tooling” inside the tools teams already work in (ChatGPT, Adobe, Slack, Microsoft Teams).

Integrate tools and data sources, for example, building custom connectors, MCP servers and integrations to minimise manual effort and copy & paste for teams.

Build assets that reflect how the client operates, including tone, constraints, risk posture, and governance expectations.

Know the boundary: when off-the-shelf assistants hit the ceiling (reliability, governance, integration, scale), help shape the next step with AI Engineering.

3) Uplift Capability

Work alongside teams to turn everyday problems into clear steps and help teams to find the small, quick actions that drive outcomes.

Help people work towards becoming AI builder themselves, helping them to develop new knowledge, skills and abilities.

Build trust quickly with people who may be sceptical, overwhelmed, or both. Through coaching and facilitation, you can clearly communicate the possibilities for and usefulness of AI.

Work autonomously and make progress: find what’s needed, get the information, and make progress without waiting for perfect conditions.

Present clearly to stakeholders, helping to articulate outcomes and how ways of working, routines and incentives might need to change to adopt AI more effectively.

Indicators you’ll be a good fit

AI-native working

You default to AI. Not as a novelty, but as how you work. You iterate fast, verify intelligently, and use AI throughout your everyday work.

What good looks like:

• You can go from messy input to reusable asset quickly
• You know when AI is helping vs. confidently making things up
• You naturally build simple feedback loops (checks, reviews, sampling) into workflows

Focus on use & adoption

You care about whether something is useful and can and/ or will be adopted.

What good looks like:

• Your design and help people to establish workflows that reduce cognitive load, not increase it
• Assets and solutions you create are easy to reuse, easy to adapt, and hard to misuse
• You can explain why the workflow is designed the way it is

Coaching & behaviour change

You can help people build capability without turning it into “training theatre.”

What good looks like:

• Teams become more confident because they’re doing real work with AI, not looking at slides or watching unrelated demos
• You can coach prompting, review habits, and verification without sounding dogmatic
• You build champions and routines that keep improving after you leave

GenAI tooling literacy & advocacy

You love exploring AI tools and their application. You know what’s out there and how to apply it to real work constraints.

What good looks like:

• You can pick the right tool, model or pattern for the task and explain why
• You notice when the landscape meaningfully shifts and translate that into practical options
• You can explain technical concepts to non-technical clients without dumbing them down.

Autonomy & resourcefulness

You work things out. You don’t wait for permission or perfect information.

What good looks like:

• You can research a workflow within a domain fast and find/ demonstrate opportunities for AI quickly
• You naturally test, learn, and adapt
• When something doesn’t work, you try another approach without drama

• Generous holiday entitlement

• Private medical insurance
• Wellness plan
• Life Policy
• Employee Assistance Programme
• Pension
• Access to global exclusive discount & savings platforms
• And more!