About Human Productivity
As an AI Adoption Specialist, your job is to help enterprise teams work better with AI by changing how work gets done.
What You'll Be Doing
Workflow Discovery and Redesign
* Embed with client teams to understand how work actually happens, not how it's supposed to happen. Follow the time, find the friction, and surface the real bottlenecks rather than the ones on the process diagram.
* Redesign workflows around AI: accelerating what's already there, removing the drudgery, and unlocking things that weren't previously possible. Measure impact at the workflow level (cycle time, rework, quality consistency, throughput) and track adoption signals as evidence the change is sticking.
* Build momentum through short, focused engagements: co-labs, sprints, 1‐to‐1 and small group coaching that make progress visible fast.
Creating What Teams Need to Adopt AI
* Co‐create and ship assets teams actually reuse: prompts, custom instructions, skills, QA checks, evaluation prompts, and playbooks. If it isn't reusable, it isn't finished.
* Use AI‐assisted development tools (Claude Code, Codex, MCP) as your default for building small automations and integrations that remove friction, inside the platforms teams already use: ChatGPT, Adobe, Slack, Microsoft Teams. Connect tools and data sources so teams stop copy‐pasting and start flowing.
* Build to reflect how the client actually operates: their tone, constraints, risk posture, and governance expectations. Know the ceiling too. When off‐the‐shelf tools run out of road on reliability, integration, or scale, help shape the next step with Tomoro's AI Engineering team.
Coaching and Sustaining Capability
* Move people from "I should use AI" to "AI is my default first step" by working and building alongside them, not by putting them in a room with slides.
* Build trust quickly with people who are sceptical, overwhelmed, or both. The antidote isn't a workshop. It's doing real work together and letting the outcomes speak. Coach prompting and verification without being dogmatic, and build the champions and routines that keep improving after you leave.
* Help define the conditions for lasting adoption: new habits, processes, incentives, and ways of working. Where you find people who want to go further, help them grow into AI builders themselves.
Requirements
About You
You don't need to come from one specific background. We're looking for people who combine a few things that rarely sit together.
You might be a freelance AI practitioner who's been helping companies adopt AI tools and wants to do it at scale. You might come from an ops or enablement role inside a product company, where you've seen first‐hand how hard it is to change how people work. You might have an L&D or behavioural change background and have been pulling AI into your practice because it's clearly where the value is. Or you might be a domain expert – someone deep in a specific field who got frustrated with inefficient ways of working and taught yourself to use AI to do your job faster and better, and now you want to help others do the same.
What matters more than your title is how you work. These things are the most important attributes for you to be successful.
Being AI‐native
What Good Looks Like:
* You can go from messy input to a reusable asset quickly.
* You know when AI is helping versus confidently making things up.
* You naturally build simple feedback loops – checks, reviews, sampling – into workflows.
Focus on use and adoption
What Good Looks Like:
* You design 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 and behaviour change
What Good Looks Like:
* Teams become more confident because they're doing real work with AI, not watching slides or unrelated demos.
* You can coach prompting, review habits, and verification without sounding dogmatic.
* You build champions and routines that keep improving after you leave.
AI Tooling Literacy
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 and Resourcefulness
What Good Looks Like:
* You can research a workflow within a new domain quickly and demonstrate opportunities for AI.
* You naturally test, learn, and adapt.
* When something doesn't work, you try another approach without drama.
Benefits
* Generous holiday entitlement
* Private medical insurance
* Wellness plan
* Life policy
* Employee Assistance Programme
* Pension
* Access to global exclusive discount & savings platforms
* And more!
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