The North Star
When AI Becomes a Shared, Subconscious Capability
The North Star is not a future full of autonomous machines. It is a future where people understand how to work with AI, trust the boundaries they operate within, and take responsibility for how it is used — without needing constant oversight, explanation, or approval.
This is the state the Agency of Agents model and ADAM are designed to reach. Not quickly. Not magically. But deliberately, through practice and shared ownership.
When AI Becomes a Shared, Subconscious Capability
The North Star is not a future full of autonomous machines.
It is a future where people understand how to work with AI, trust the boundaries they operate within, and take responsibility for how it is used — without needing constant oversight, explanation, or approval.
This is the state that Agency of Agents and ADAM are designed to reach.
Not quickly.
Not magically.
But deliberately — through practice, repetition, and shared ownership.
The North Star Is a Behavioural State, Not a Technical One
At the North Star, AI is no longer treated as:
- a specialist topic
- a central programme
- or something “owned by the AI team”
Instead, AI is treated in the same way organisations already treat:
- quality
- security
- financial responsibility
- operational risk
That is to say:
AI and automation are understood to be everyone’s responsibility.
This does not mean everyone builds AI systems.
It means everyone understands:
- how AI should be used,
- where it is appropriate,
- how it is governed,
- and how it evolves within their own area of responsibility.
When the Framework Disappears Into How Work Gets Done
In the early stages of adoption, the Agency of Agents framework is very visible.
People:
- refer to it explicitly,
- learn the language,
- and consciously apply the rules.
They ask questions like:
- Is this assistive or autonomous?
- Is this an FTE responsibility or an FTA?
- Has this behaviour been validated?
- Are we ready to evolve?
At the North Star, this thinking becomes subconscious.
Not because the framework has been abandoned —
but because it has shaped how people reason about AI.
Teams naturally:
- choose the lowest appropriate level of agency,
- default to augmentation before automation,
- validate behaviour before increasing autonomy,
- and retain human accountability without debate.
This is how frameworks succeed.
They stop being referenced — and start being lived.
ADAM at the North Star
From Guide to Shared Mental Model
ADAM plays a critical role throughout the journey — and a quieter one at the destination.
Early on, ADAM acts as:
- a teacher,
- a guide,
- and a translator of uncertainty into clarity.
People ask ADAM questions such as:
- “Is this a good AI use case?”
- “What agent level should we use?”
- “Are we moving too fast?”
ADAM answers using:
- the Agency of Agents model,
- the organisation’s current maturity level,
- validated patterns and agreed guardrails.
At the North Star, ADAM is still present —
but people need it less often.
Why?
Because ADAM’s logic has been absorbed into everyday decision-making.
ADAM becomes:
- a reference point,
- a sense-check,
- and a steward of consistency.
Not a gatekeeper.
Not a bottleneck.
No Central AI Team “Owning AI”
A defining feature of the North Star is this:
There is no single team responsible for “AI” across the organisation.
This is intentional.
A central AI team:
- cannot understand every domain,
- cannot scale decision-making,
- and unintentionally removes accountability from the people closest to the work.
At the North Star:
- each domain,
- each business unit,
- and each team
is responsible for:
- its own processes,
- its own outcomes,
- and its own mix of FTEs (people) and FTAs (agents).
AI is applied locally, where context lives —
but always within a shared model.
Shared Architecture, Shared Standards
While ownership is distributed, fragmentation is not allowed.
At the North Star, the organisation operates with:
- a shared reference architecture,
- standard patterns for agents and integration,
- common approaches to identity, data access, and monitoring.
Every solution:
- builds on the same foundations,
- speaks the same language,
- and respects the same boundaries.
This ensures that:
- innovation does not create chaos,
- autonomy does not create unmanaged risk,
- and scale does not create inconsistency.
The AI Steward
Leadership Through Enablement
At the centre of this ecosystem is a role — not a hierarchy.
The AI Steward.
The steward’s leadership style is deliberately subservient, similar to a Scrum Master.
They do not:
- approve every use case,
- control every system,
- or dictate every decision.
Instead, they:
- maintain the integrity of the model,
- evolve standards as reality changes,
- own governance mechanisms,
- and ensure ADAM reflects current understanding.
The steward sets:
- security policies,
- data retention standards,
- compliance and governance rules,
- and escalation paths.
And then steps back.
Their success is measured not by control,
but by how naturally the organisation operates within the model.
The Enabling AI & Technology Capability
Alongside the steward exists an enabling capability — much like modern IT.
This group:
- assists teams when specialist skills are required,
- integrates systems where complexity exists,
- provides shared services and tooling,
- and helps teams avoid known pitfalls.
They are not the owners of all AI.
They are:
- enablers,
- advisors,
- and builders when needed.
AI capability becomes part of the organisational fabric, not a silo.
Governance That Becomes Lighter Through Trust
At the North Star, governance still exists.
But it feels different.
Not because risk has disappeared —
but because behaviour has become predictable.
Governance becomes:
- embedded instead of imposed,
- preventative instead of reactive,
- understood instead of feared.
This is what it means when we say:
Governance becomes lighter.
Not weaker.
Not optional.
But aligned with reality.
Ownership Is the Difference Between Potential and Value
As organisations adopt AI and automation, one mistake appears again and again.
Everyone assumes someone else is responsible.
A central AI team.
A platform team.
A transformation programme.
A single “expert” role.
But AI does not create value simply by existing.
Value comes from:
- people using it,
- teams changing how they work,
- decisions being improved,
- and outcomes being owned end-to-end.
This is why Agency of Agents and ADAM are built on a simple principle:
AI and automation are shared responsibilities, not centralised tasks.
The Role of the AI Steward
The AI Steward exists to enable, not to own everything.
Much like a Scrum Master, the steward:
- maintains the framework,
- sets standards and guardrails,
- ensures security, data retention, and governance are in place,
- and keeps ADAM aligned with how the organisation actually works.
What the steward does not do is take responsibility away from teams.
They do not:
- deliver all AI solutions,
- approve every decision,
- or carry accountability for outcomes they do not control.
Their role is to make good behaviour easy and consistent, not to replace ownership.
Where Accountability Actually Lives
Accountability sits where the work sits.
With:
the team,
the domain,
the business unit.
Each area is responsible for:
- how AI is applied in their processes,
- how automation affects outcomes,
- whether value is actually realised,
- and whether the solution is being used in practice.
This includes:
- owning ROI,
- changing ways of working,
- and embracing the tools and patterns provided.
AI that is not used delivers no value — regardless of how advanced it is.
AI and Automation Are Corporate Assets
In many organisations, quality is treated as a corporate asset.
Everyone understands that:
- quality is not “owned” by one team,
- each person is accountable within their area of influence,
- and failure to act affects the whole organisation.
AI and automation are no different.
They are:
- shared capabilities,
- shared responsibilities,
- and shared opportunities.
Owning AI does not mean building systems.
Sometimes it is as simple as:
- using the tools provided,
- trusting the guardrails,
- adapting workflows,
- and being open to change.
Why This Matters
When ownership is unclear:
- adoption stalls,
- ROI is lost,
- trust erodes,
- and progress becomes fragmented.
When ownership is shared:
- AI becomes part of everyday work,
- outcomes improve,
- confidence grows,
- and governance becomes lighter because behaviour is predictable.
The difference is not technology.
The difference is ownership.
A Familiar Pattern
The following story captures what happens when responsibility is assumed — but never claimed.
