01M
Map
the organization and its work
Guiding question
What is the organization responsible for, how does it operate and what outcomes matter?
Minimum evidence
Organizational picture · mandate · current-use map · stakeholder map
The MANUAL Method
Six stages from organizational context
to practical direction.
Each stage produces a useful record: an organizational picture, capacity profile, opportunity portfolio, chosen next step, operating practice or outcome report.
Understand before prescribing.
How the method is used
Understand the organization first. Choose technology only when its place is clear.
The method draws on technology-adoption research, implementation science, human-centred design, controlled experimentation, adult learning and responsible governance.
It guides an engagement. It is not a certification, a guarantee of results or a universal maturity score.
02A
Assess
adoption capacity
Guiding question
What conditions already support responsible use—and which are missing?
Minimum evidence
Capacity profile · target state · constraints · critical gaps
03N
Navigate
value, risk and route
Guiding question
Where can AI create value, where should it not be used and which opportunities deserve attention?
Minimum evidence
Prioritized portfolio · risk routes · act, prepare, explore, pause or decline decisions
04U
Undertake
the right next step
Guiding question
What action will resolve uncertainty or build the conditions for responsible use?
Minimum evidence
Research, consultation, process redesign, capability plan, prototype or bounded pilot
05A
Anchor
what proves useful
Guiding question
What must become standard for the improvement to endure?
Minimum evidence
Standard workflow · decision rights · job aids · training · ownership · support
06L
Learn
measure and renew
Guiding question
What changed, for whom, at what cost, and what should happen next?
Minimum evidence
Outcome report · adoption evidence · lessons · review cadence · next decision
A valid outcome
Do not use AI.
When it does not improve the work, the implementation burden outweighs the benefit, a simpler alternative is better or risk cannot be brought within an acceptable boundary, stopping is a successful decision.
The Adoption Capacity Map
Check six conditions before acting
A high overall score cannot compensate for one critical weakness. Unsafe data handling, unclear decision authority or an irreversible process can stop an opportunity.
Purpose & value
Is the intended outcome specific, worthwhile and connected to a real need or mandate?
Task & decision fit
Where do generation, retrieval, prediction, transformation and judgement sit in the work?
People, agency & inclusion
Who uses or is affected by the change—and do they have voice, time, skill and support?
Data, tools & infrastructure
Can information be used lawfully and safely, and can the tool be supported?
Accountability & assurance
Who remains responsible, what must be reviewed and how can concerns be challenged?
Learning, evidence & sustainment
Can the group test, measure, adapt, support and eventually retire the practice?
Three connected scales
A weakness at one scale can constrain the others.
No orbit diagram is needed: a person works inside an organization, and an organization depends on the institutions, infrastructure and relationships around it.
Can I use AI well in the work that matters to me?
Can we turn useful experiments into governed operating practice?
Can benefits diffuse fairly and capacity remain locally?
Value · risk · readiness
Every opportunity needs a route.
Pilot
Run a bounded test with comparison and stop conditions.
Prepare first
Fix the people, process, data or assurance constraint.
Park
Record what would justify reconsideration.
Simplify or skip
Improve the process another way—or stop.
See the full argument
From AI Interest to Adoption Capacity
Explore the research base, assessment architecture, evidence contract and detailed calls to action in New Manual’s interactive field guide.
Read the white paper→