AI as a Decision Companion: Supporting Executive Pricing and FX Decisions in Global Enterprises Through LSTM Forecasting
Bibliographic record
Abstract
Global enterprises face increasingly volatile market conditions, with foreign exchange (FX) movements often forcing executives to make rapid pricing and strategy decisions under uncertainty. While artificial intelligence (AI) has transformed operational decision-making, its role in supporting board-level strategic choices remains underexplored. This paper examines how AI and advanced analytics can serve as a ‘decision companion’ for management teams and executives confronted with global shocks. Using Roblox Corporation as a case study, we apply a Long Short-Term Memory (LSTM) neural network to forecast bookings and simulate counterfactual scenarios involving euro depreciation and European price adjustments. The analysis reveals that a ten percent depreciation of the euro reduces consolidated bookings and profits by approximately six percent, and that raising European prices does not offset these losses due to demand elasticity. Regional attribution shows that the majority of the decline is concentrated in Europe, with only minor spillovers elsewhere. The findings demonstrate that AI enhances strategic agility by clarifying risks, quantifying trade-offs, and isolating regional effects, while ensuring that ultimate decisions remain with human executives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".