In as much detail as possible, describe the problem or experience related to your idea. Please provide the context of what you were trying to do and include specific examples or workarounds:
Enhance forecasting accuracy and responsiveness by incorporating corporate knowledge captured in unstructured text (e.g., management commentary, emails, reports, news summaries) as external explanatory factors in machine‑learning–based forecasts (AI Forecaster).
Textual data contains leading signals that are often not yet reflected in quantitative measures. By applying text‑based analytics—such as sentiment analysis, topic detection, and intensity scoring—these qualitative insights can be transformed into structured indicators (e.g., sentiment scores) and used as external drivers in forecasting models.
Examples of textual sources:
- Management and expert commentary
- Internal reports and narratives
- Strategic or operational updates
- External market and news summaries
How often is this impacting your users?
The current forecasting tool does not incorporate sentiment analysis or other qualitative signals into the model. As a result, expert judgment exists only in written form and cannot be systematically reflected in the forecast. This gap leads to frequent manual adjustments of the forecast outputs to compensate for insights that are not captured by the current setup.
Who is this impacting? (ex. model builders, solution architects, partners, admins, integration experts, business/end users, executive-level business users)
Because qualitative insights are not systematically incorporated, forecast outputs require manual intervention, reducing accuracy, consistency, and scalability. This limitation affects decision‑making quality across the entire company, as leadership relies on forecasts that do not fully reflect available corporate knowledge.
What would your ideal solution be? How would it add value to your current experience?
- Text Ingestion
Collect relevant internal and external textual sources. - Textual Analysis
Apply NLP techniques such as:- Sentiment analysis (positive / negative / neutral, intensity)
- Topic relevance scoring
- Signal frequency and trend detection
- Feature Engineering
Convert outputs into numerical indicators (e.g., sentiment index, volatility score). - Forecast Integration
Feed these indicators into ML forecasting models as external variables, alongside traditional drivers.
Please include any images to help illustrate your experience.
