Cognitive Augmentation gives your VeloIQ app five connected modules: define business goals and track their benefits, roll them into a balanced scorecard, propose and AI-forecast the strategies to achieve them, predict measure outcomes with real regression and machine-learning models, and capture what you learn along the way as structured knowledge.
$ veloiq extend-package cognitive_augmentation
Licensed by number of users · One installation-scoped key · Works on any VeloIQ host app
Strategy gets defined in a slide deck, tracked in a spreadsheet, and predicted in a notebook nobody else can open. By the time results come back, nobody remembers which decision they were supposed to validate.
Benefits and targets get defined once, then drift out of sync with the measures that are supposed to prove they happened. Nobody can answer "did this actually work?" without a week of manual reconciliation.
Predictive models get built for a single decision, then thrown away. There's no record of which model predicted which measure, using which scope, or how confident it actually was.
Each module stands on its own — together, they let a strategy manager move from "what are we trying to achieve" to "here's the forecasted plan and the knowledge it produced" without leaving the app.
Propose, analyze, evaluate and select complete, ready-to-implement business strategies for a measured goal. Strategic hypotheses forecast each scenario's potential results and trade-offs using natural-language sentences and RAG, so a manager can compare options and pick the strategy that actually achieves the goal — not just the one that sounds best in the room.
Define business goals and track their objectives, benefits and enablers, with planned and actual measures. Every benefit ties to the measures and targets that prove — or disprove — it actually happened.
Define scorecard perspectives and initiatives, associated directly to Benefit Realization Management elements — so the scorecard a leadership team reviews is built from the same data the benefits are tracked against, not a parallel copy.
Create predictive models and correlation analyses — automatically or guided by a data scientist — to predict a measure's outcomes from other measures, scoped by time period, item hierarchy and location hierarchy. Linear regression, decision trees and neural network regressors, trained with scikit-learn and linked back to the Benefit Realization objectives, benefits and enablers they reference.
Collect and define useful knowledge — typically inferred straight from Predictive Models, or registered directly by power users — so a hard-won insight about what drives a measure doesn't disappear into a chat log once the model that found it is forgotten.
Every module shares the same benefit-realization data model, so a target defined in brm, a perspective in bsc, a forecast in goalseeking, a prediction in predictivemodel, and the knowledge that comes out of it in knowledgeman are never three re-entries of the same fact — they're one fact, viewed five ways.
Licensed together as a single "Cognitive Augmentation" group, by number of users — see Licensing for the exact terms.
Benefit Realization Management — goals, objectives, benefits, enablers, planned & actual measures.
Balanced Scorecard — perspectives and initiatives tied to Benefit Realization elements.
Goal Seeking Scenarios — AI-forecasted strategic hypotheses to achieve a measured goal.
Predictive Model — regression, decision trees & neural networks, scoped by period, item and location hierarchy.
Knowledge Management — facts inferred from Predictive Models or registered directly by power users.
One Cognitive Augmentation license covers all five modules for your installation, capped at a maximum number of concurrent users — tracked in-memory, never bypassable via direct database access.
See Licensing →