An Evolving Risk Landscape: Insights from a Decade of Surveys of Executives and Risk Professionals
Bibliographic record
Abstract
We report on the results obtained from ten annual surveys of global business executives on their perceptions of the most significant risks facing their organizations in the ensuing calendar year. These surveys of C-suite executives, directors and other risk professionals elicit their concerns about risks that may affect their organization’s success over the near-term horizon (i.e., the next calendar year). After a decade, we believe these results provide an opportunity to examine how the global risk landscape has evolved. In addition, two additional survey questions allow us to examine how these executives view the overall risk context and how enterprise risk management (ERM) is deployed and augmented in the face of an escalating risk environment. On average, we find that executives view the risk landscape they face as persistently risky over the ten-year period, even during the relatively robust economic environments for much of that time frame. Two industries report much more volatility in their risk environments, with respondents from the Healthcare sector and in Technology, Media and Telecommunications acknowledging the largest volatility. We also observe an increase in entities’ decisions to devote more time and resources to risk management over the ten-year period, suggesting that ERM has become an essential mechanism for organizational success. Our goal is to highlight the realities of constantly changing risk conditions and how context (e.g., industry and time) is an important distinguishing factor that affects an organization’s given risk profile, which is relevant to both executives and academics. Collectively, our findings emphasize the importance of understanding the ever-changing context of an organization’s environment, that risk identification must be an ongoing process, and that there is no “one-size-fits-all” approach to risk governance. We believe all this signals the importance of future research to help organizations respond with robust risk governance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".