We Get People Hands On
Seeing, exploring, and most importantly, playing with AI is the only way to learn how to use it, how to optimise it, and how to re-think work with it.
Human Productivity with AI
We help organisations achieve measurable productivity gains by changing how everyday work gets done by empowering everyday workers to maximise the use of their AI tools.
What’s Different About Our Approach
We build capability by delivering in the flow of real work. We embed with teams and observe how work actually happens, redesign key workflows, remove low-value repetitive steps, and help teams build reusable assets that make the new way of working easy to repeat.
Most AI adoption fails in the gap between knowing and doing, so here’s what we do differently:
Embedding Is The Engine Of Results
We spend time with the people doing the work, understand the real constraints, and remove friction as we find it, not weeks later.
A Short, Focused Cadence Drives Momentum
Rapid half-day co-labs and two-week sprints create urgency, surface what is working quickly, and make it obvious what needs deeper build versus simple changes.
Quick Wins Matter, But We Don’t Stop There
Off-the-shelf assistants can hit an 80% ceiling when reliability, governance, or integration becomes the constraint, so we make that boundary explicit early.
We Optimise For Workflow Change, Not AI Activity
The goal is not more AI usage. The goal is less rework, higher quality, shorter cycle times, and new work made possible.
We Design For Compounding, Not One-Off Uplift
As fluency grows through use, teams identify new areas where AI can add value, creating a sustained flywheel of learning and capability.
The Outcomes We Drive
Our north star is the “3-day work week” — not literally working fewer days, but consistently delivering five days of outcomes in three days of effort.
In practice, that means freeing time so people can spend more of it on the things that require human judgement, creativity, relationships, and decision-making — with AI doing more of the drafting, searching, structuring, and repetitive transformation that currently consumes attention.
We build AI-native teams: teams that default to AI as part of how work gets done.
01
Some Work Disappears Entirely
Steps are removed through automation, better information flow, and less duplication.
02
Existing Work Becomes Faster and More Consistent
Less rework, fewer quality failures, more repeatability, and better outcomes at lower effort.
03
New Work Becomes Possible
Prototypes, analysis, content, and decisions that were previously too slow, too expensive, or too hard to prioritise.
What We Mean by AI-Native
An AI-native employee defaults to AI as the first step in day-to-day work to think, create, analyse, automate, and innovate, while staying accountable for verification, judgement, and outcomes.
They don’t just accelerate tasks. They build new, effective and more efficient workflows that compound performance over time. They are comfortable experimenting and iterating as part of their day-to-day work, continuously evolving their capability as AI evolves.
Default to AI-First Exploration
Start by testing whether AI can help, where it can help, and what good looks like.
Communicate Naturally with AI
Clarify goals, constraints, inputs, outputs, tone, and examples so work is easier to delegate and review.
Use AI as a Sparring Partner
Generate options, stress-test assumptions, compare trade-offs, and structure decisions without outsourcing judgement.
Iterate Fast
Treat outputs as drafts, version prompts and approaches, and improve quality through short rapid cycles.
Verify Intelligently
Know what must be checked, what can be sampled, and what can be guarded with quality controls and feedback loops.
Find New Ways to Create Value
Use AI to do work differently and unlock work that previously felt too slow, too expensive, or impossible.
“An AI-native employee isn’t someone who uses AI. It’s someone who defaults to AI.” - Elena Verner, Lovable
What AI-native is not:
- Someone who writes emails with ChatGPT.
- Someone who blindly copies and pastes AI generated content, outsourcing judgement and accountability.
- Treating AI as a single tool, rather than a set of collaborators and workflows.
- A side hobby for early adopters. This only works when it becomes normal team behaviour.
The Barriers We Address
Adoption fails for predictable reasons. We reduce friction across three types of barrier.
01
Knowledge Barriers
People need awareness, practical capability, and examples in their context, not theory.
02
Access Barriers
People need the right tools and environments. Missing tools, unclear permissions, and slow approvals stall progress.
03
Cultural Barriers
People need permission, incentives, and visible leadership signals that it is expected and safe to work differently.
What We Leave Behind
Changed work and a repeatable adoption system, not just recommendations.
Workflow Assets
Prompt patterns, templates, quality checks, reusable workflows, GPTs, and lightweight helper tools.
Capability Layer
Champions, shared practice, real examples, and an operating rhythm that keeps improvement continuous.
Cultural Accelerators
New ways of working, incentives, and principles that make the right behaviour the easy behaviour.
Opportunity Pipeline
Because we are embedded in real workflows, we surface higher-leverage opportunities across integration, automation, information flow, governance, and operating model. We capture these as clear options for leadership to evaluate and can help deliver them with an extended build team.
How We Prove It Worked
We measure outcomes at the workflow level, with adoption signals as supporting evidence.
Workflow Measures
- Cycle time
- Rework reduction
- Quality consistency
- Throughput and variety completed
People Signals
- Stronger practical AI capability in real work
- Higher confidence using AI in sophisticated ways
- Increased AI fluency and model literacy
Adoption Signals
- Reuse of workflow assets and patterns
- Evidence of AI-native behaviours in day-to-day work
- Tool activity tied back to workflow outcomes
Getting Started
We design experiences and engagements that help teams move from curiosity to confident, AI-native ways of working. These range from short, sharp sessions (as little as 60 minutes) through to multi-week embedded delivery, and can be run standalone, combined, or tailored into a bespoke programme.
01
AI Experience
Half day to full day
High-energy exposure to practical AI use cases and immediate opportunity mapping.
02
Masterclass
60 mins to half day
Focused deep-dives on prompt engineering, custom GPTs, evals, and safe adoption.
03
Co-lab
Half day to 2 days
Hands-on collaboration to build real micro-solutions and test utility in context.
04
Productivity Sprint
2 weeks
Outcome-driven sprint to redesign workflows, ship improvements, and set rollout priorities.
05
Workflow Re-engineering
4 to 12 weeks
Embedded productivity engineering that changes habits, systems, and measurable business output.
Common path: AI Experience -> Masterclass -> Co-lab -> Productivity Sprint -> Workflow Re-engineering.