Futuring Your Strategy: An Unconventional Approach to Strategic Planning
Bibliographic record
Abstract
This article proposes an alternative, future-focused approach to strategic planning inspired by practices in futures studies and strategic foresight. Traditional strategic planning tends to rely on analyzing past trends and current conditions, limiting creativity and adaptive capacity. In contrast, a futurist approach frames strategy around exploring preferable futures and identifying discontinuities, uncertainties, and low-probability events that could significantly impact the organization. Key concepts from futures research like scenario planning, environmental monitoring, expert interviews and wild card tracking are presented as ways to conduct more expansive and long-term environmental scanning. The stages of developing strategic options, plans, and ongoing adjustments are also discussed through a futurist lens. Examples from Bell Canada and Meyer Turku demonstrate how these concepts have been successfully applied in practice. By cultivating foresight skills and habits of continual exploration, adaptation and stakeholder collaboration, organizations can develop strategic plans more resilient to an unpredictable future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".