In my MBA research with 18 UK SMEs, a recurring leadership instinct emerged when uncertainty intensified: the desire for longer-term strategy. Leaders looked for roadmaps, detailed implementation pathways, and defined end-states before committing to meaningful action.
This reflex is understandable. Strategy has traditionally been the mechanism through which organisations reduce uncertainty. But GenAI and agentic AI systems introduce a different operating environment — one where capabilities evolve rapidly, understanding develops unevenly, and use cases mature through practice rather than prediction.
Why long-term strategy struggles here
Although my research did not directly test planning cadences, the broader pattern was clear. Organisations that prioritised strategic completeness before action tended to stall. Plans risked becoming obsolete faster than they could be executed. Momentum slowed as leaders revisited assumptions rather than acted on them.
In contrast, organisations that maintained progress tended to operate in shorter interpretive cycles — framing near-term priorities, enabling bounded experimentation, and revisiting direction as learning accumulated.
The case for 90-day cycles
A roughly 90-day cadence has proven particularly practical in this context. Within these cycles, leaders identify a small number of exploratory focus areas, legitimise experimentation within agreed guardrails, and create structured moments for reflection and reframing.
At the close of each cycle, insights feed back into leadership sensegiving — refining priorities, resetting expectations, and shaping the next wave of exploration. This rhythm does not remove uncertainty. It makes uncertainty workable.
Rather than seeking certainty upfront, leaders treat direction as something that emerges through learning. Strategy becomes iterative, responsive, and grounded in organisational experience rather than abstract foresight.
What this looks like in practice
The insight here is not that long-term thinking disappears. It is that, under GenAI conditions, effective strategy becomes rhythmic rather than linear — shaped through repeated cycles of action, interpretation, and adjustment. Readiness grows not from static plans, but from the organisation's ability to move, reflect, and reframe deliberately over time.
The question to ask is not "What is our three-year AI strategy?" It is "What are we going to learn in the next 90 days, and how will we use that to shape the 90 days after that?"