Leadership Sensegiving
- Framing purpose
- Signalling legitimacy
- Modelling behaviour
- Setting provisional boundaries
A research-grounded model of how GenAI adoption actually unfolds: a recursive interpretive cycle of three phases, held inside two contextual fields that either propel or constrain it. Readiness is not a gate to pass before adoption begins — it emerges from use, and can stall or reverse.
Adoption is shaped less by strategy documents and more by an ongoing stream of leader signals: what is framed, modelled, permitted, and bounded. Leadership proximity magnifies these cues — silence does not stay neutral, it becomes an implicit signal that the topic is risky or premature.
The work here is to define why GenAI matters here, model use from the top, and set good-enough guardrails that evolve with learning.
Different groups form different meanings about GenAI at the same time. Trust, cultural norms, identity, and emotional response decide whether the leader's frame is taken up, contested, or ignored. Anxiety and curiosity coexist; "this replaces me" sits beside "this could help me".
This is where meanings diverge, converge, or fragment — and where the cycle either picks up momentum or stalls.
Confidence to experiment, informal diffusion, normalisation of use, acceptance of imperfection — these accrete through practice rather than arriving as a delivered state. Capability becomes organisational only when learning is shared: champions, show-and-tell, prompt libraries, playbooks.
Readiness can stall, fragment, or reverse if any of the above collapses. That is why the loop must close.
Time and capacity, data quality, infrastructure, governance capability. These are universal. Higher-perceived-success organisations did not eliminate them; they treated them as design parameters — scoping carefully, standardising tools, keeping humans in the loop.
Lower-success firms experienced the same conditions as blocking conditions, and deferred or restricted action. The constraint did not change. The relationship to it did.
Hallucinations, accountability, professional judgement. These do not get solved before adoption begins; they get held through validation routines, clear ownership of outputs, and explicit human-in-the-loop norms.
Governance, in this view, is a trust signal — not just compliance. Early guardrails enable safe experimentation. Their absence reads as silence.