Foresight-Informed Agile For Resilient Product Strategy
Bibliographic record
Abstract
Agile is today’s dominant operational framework in software product development due to its effectiveness at helping teams overcome dynamic and uncertain conditions. As external uncertainties increase, some organizations are also adopting Agile as a strategic approach. However, we hypothesize that while Agile contributes to adaptability, it is insufficient for an organization to be resilient against major disruptions. \nThis research project studies the relationship between Agile software development and the strategic resilience of small-to-medium-sized organizations in Ontario, Canada. Using a mix of surveys and interviews with product leaders, we found that many teams’ strategic capability to be resilient is limited due to Agile’s narrow attention and short-term focus, encouraging teams to be reactive rather than proactive. \n \nWe designed the Resilient Product Strategy Toolkit, which integrates proven Strategic Foresight practices with existing Agile and product management processes to help teams broaden their attention and increase their capability to manage uncertainty. This research design contributes to the plausibility of combining Agile and Strategic Foresight as an ambidextrous approach to enhance an organization’s strategic resilience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".