Deal Guidance

Why it's needed

Deal odds are often fixed by stage and rarely updated based on actual patterns.

Low participation and completion rates cause data gaps, leading to inaccurate machine learning predictions.

What it does

Configure data connection
Deal scoring feature sent to teams
System learns to score deals by odds of winning
System learns behaviors of individuals
Teams and individuals track deals
System nudges users to improve odds
System tracks actions and outcomes

How it helps

1

Predictive diagnostic systems score deals and assign odds dynamically based on actual performance.

2

Personalized guidance system recommends next best actions for deals, recording behaviors and outcomes.

3

Once algorithms gain more consistency and accuracy, the system seeks to replicate what’s working across all deals to improve performance.

Sales Forecasting

Why it's needed

Sales cycle time-to-close is not calculated by most CRM systems.

Weightings are fixed by stage with no true-up to actuals.

People change their behaviors leading to distorted forecasts.

What it does

Data upload and configuration
Leaders send diagnostics to teams
Leaders build scenarios and see forecasts
System learns behaviors and odds of individuals
Teams and individuals build scenarios
System learns conversion rates by individual
System nudges users with new odds

How it helps

1

Sales people often hold back reporting bad news, which means sales forecasts are often overly optimistic.

2

We provide a scenario-based tool that lets sales people see both optimistic and pessimistic scenarios.

3

Personalized guidance system tracks progress-to-target of each person with personalized deal scenario tools.

4

OnCorps algorithms learn to optimize deal cycle times, conversion rates, and odds. Decision makers learn the deal scenarios that best meet targets.

Pricing Guidance

Why it's needed

Inconsistent behaviors can suppress margins, like applying blanket discounts across all products.

Systems can’t show sales people the pricing scenarios that are working on similar opportunities.

What it does

Data upload and configuration
Leaders send diagnostics to teams
Leaders build scenarios and see margins
AI auto approves or escalates
System enables pricing scenario building
System learns to optimize win rate and margins
System nudges users with next best actions

How it helps

1

Predictive dashboards identify where pricing can be improved.

2

Personalized guidance systems nudge changes that optimize margin.

3

Decision makers learn the pricing scenarios that win at high margin.

4

OnCorps algorithms learn to optimize supply and demand factors to boost margins.

Risk + Audit

Why it's needed

Most risk systems focus on controls but lack metrics to predict risky behaviors.

Few systems integrate all “lines-of-defense” to identify gaps in risks and control priorities.

Most internal audit groups lack predictive data on internal audit projects time-to-complete.

What it does

Risk and IA findings data analyzed
Leaders send risk diagnostics to 1st line staff
Leaders model risk mitigation scenarios
System predicts the odds of IA findings
Teams manage risks and projects
Staff invite others to diagnose risks
Traces all profiles and nudges for compliance

How it helps

1

Predictive diagnostic systems deliver a dashboard to integrate data on changing risks, controls, and behaviors.

2

Diagnostic systems can cascade down the three “lines of defense.”

3

Decision makers learn the areas most vulnerable to risk and actions needed.

4

OnCorps algorithms learn to predict focus areas that result in the most required actions.

Oversight + Reconciliation

Why it's needed

Risk thresholds are often fixed, based on historic distributions, leading to higher false positive rates during certain periods.


Anomalies are often addressed in the order received, making it difficult to allocate the right time by risk level.


What it does

AI-based scoring of transactions by materiality
Leaders send time diagnostics to analysts
Leaders model time management
System scores all transactions for risk/materiality
All decisions guided by system
System guides analysts based on score
Tracks time, urging more time on highest risks

How it helps

1

Predictive systems continuously track recent activity to ensure thresholds are adjusted to truly report anomalies.

2

Predictive diagnostic systems track time, risk, and experience to optimize oversight.

3

Personalized guidance system guides analysts through exceptions based on materiality and learns from their observations.

4

OnCorps algorithms learn to optimize time by scoring potential errors by risk. Decision makers learn to spend more time on high risk items and less time on low risk ones.