The 3-Day Work Week

Deliver five days' worth of outcomes in three days' worth of effort.

We build AI-native teams that default to AI—not as a tool they sometimes use, but as the foundation of how they work.

What We Do

We help organisations achieve measurable productivity gains by changing how work gets done with AI.

This is not

An "AI strategy" engagement that ends with a document and a long implementation wait.

This is

Embedded delivery that changes behaviour and ships working assets in weeks.

Strategy tells you what to do. We change behaviour.

We embed with teams, redesign priority workflows, remove low-value "admin" work, and make the new behaviours stick through capability building and reinforcement.

The Outcome We're Driving

We're building AI-native teams. That shows up in three concrete shifts:

Faster & Better

Existing work becomes faster and better quality—less rework, more consistency.

Improvement

Work Disappears

Some work disappears entirely—automation, removal of unnecessary steps, better information flow.

Efficiency

New Work Becomes Possible

Prototypes, analyses, content, and decisions that were previously too slow or expensive.

Transformation

The third shift is where transformation happens. What exists afterwards is recognisably different—not just a better version of the same thing.

What AI-Native Means

"An AI-native employee isn't someone who 'uses AI.' It's someone who defaults to AI."

An AI-native employee treats AI as a first resort, not a last. They don't "ask it a question"—they run their work through AI to research, draft, analyse, rehearse, decide, document, and automate. Then they verify what matters before they ship.

What it is not:

  • Not "someone who writes emails with ChatGPT"
  • Not replacing judgement or accountability
  • Not uncontrolled copy/paste
  • Not a tool obsession

The AI-Native Operating Behaviours

AI-native employees reliably do these things:

1

Default to AI-first

Before going manual, ask "can AI help here?" and do a fast first pass.

2

Create clarity

Translate vague tasks into crisp goals, constraints, and definitions of "done".

3

Think with AI

Use AI to structure messy problems and test different approaches.

4

Iterate fast

Treat outputs as versioned drafts, not one-shot answers.

5

Verify intelligently

Know what must be checked: facts, numbers, policy, commitments.

6

Systematise & ship

Turn good work into reusable templates and workflows—then deliver.

Clarify → Create → Critique → Confirm → Compound ↻

Why Adoption Fails

Adoption fails for predictable reasons. We diagnose and reduce three barrier types:

Individual

Awareness, knowledge, ability. What's possible, how to steer tools, how to apply them reliably.

Technical

Capability limits, missing features, integration friction. Pricing constraints and tool gaps.

Cultural

Workflows that don't allow experimentation. Lack of time, misaligned incentives, and energy not directed at change.

This is why we don't just train. We remove friction, redesign workflows, and set the conditions for new habits.

How We Deliver

We focus on workflow-first: identify high-leverage workflows, implement AI-enabled versions immediately, and build the internal capability to sustain it.

Sandbox Experiences

Provide the permission and space to experiment, surface barriers fast, and generate high-value workflow candidates.

Accelerates discovery
+

Embedded Delivery

Work alongside teams, in the flow of work, to implement workflows, build reusable assets, and turn new behaviours into habit.

Produces measurable change

ChatGPT as the Wedge

ChatGPT is the entry point because it drives immediate value and behaviour change with minimal dependency. When off-the-shelf tools stop being sufficient—because reliability, governance, or scale matters—we bring in AI engineering to move from ad hoc usage to fit-for-purpose solutions.

Delivery frame: We typically use a four-week adoption sprint as a proven frame to build momentum. The constant is pace: value early, reinforcement quickly, and visible behaviour change.

What We Leave Behind

We leave behind changed work and a repeatable adoption system:

Workflow Assets

Templates, prompt patterns, quality checks, reusable workflows, and where relevant CustomGPTs and ChatGPT Apps.

Capability Layer

Champions and trainers, shared practice, real examples, and an operating rhythm that maintains conditions for AI use.

Accelerators

Guided UIs or helper tools that reduce the blank-page problem and make good usage easier to start and repeat.

How We Prove It Worked

We don't hide behind vanity metrics. We measure outcomes at workflow level:

Workflow Measures

  • Cycle time / time-to-complete
  • Rework reduction
  • Quality consistency
  • Variety of work completed

Delivery Measures

  • Solutions and prototypes produced with teams
  • How many get greenlit for further build
  • How many solutions persist and scale

Adoption Measures

  • Reuse of workflow assets and patterns
  • Value-per-token, not token volume
  • Sustained behaviour change over time

Ready to build AI-native teams?

Let's talk about where to start.