How AI Can Support Commercial Decisions: Pricing, Prioritization, Forecasting, and Discounts with Clearer Criteria
By Pedro Sonda, dooop
AI can support five recurring commercial decisions: which customers and opportunities to prioritize, what price to set, how much discount to grant, how much the company will sell, and where to allocate commercial effort. In each case, AI cross-references sales history, customer behavior, and market signals to suggest alternatives. The value depends on recommendations coming with explanations: the salesperson or manager needs to understand why the suggestion makes sense to follow it or to disagree with good reasons.
Updated October 2026.
Where the Value Lies
In McKinsey's global research, revenue gains attributed to AI appear most frequently in marketing and sales (McKinsey, 2026). BCG estimates that about 70% of AI's potential value lies in core functions, including sales, marketing, and pricing (BCG, 2025).
Interpretation: Commercial decisions have a direct effect on revenue and margin. Small improvements in frequent decisions can add up to significant results.
dooop Framework: The 5 Recurring Commercial Decisions
| Decision | How It Is Usually Made | How AI Can Support | Required Data |
|---|---|---|---|
| 1. Prioritize customers and opportunities | Salesperson intuition, client size | Score opportunities by probability of closing and potential value | History of won and lost opportunities in CRM |
| 2. Set price | Cost plus margin, fixed price list, competition | Suggest price ranges by segment, volume, and timing | History of prices charged, volumes, and results |
| 3. Grant discount | Case-by-case negotiation | Indicate if requested discount is within the pattern of similar deals | History of discounts and margins |
| 4. Forecast sales | Sum of salespeople’s expectations | Forecast based on history and opportunity stage, compared to salespeople’s forecast | Updated pipeline; conversion history |
| 5. Allocate effort | Distribution by territory or portfolio | Identify accounts with highest potential or risk of loss | Purchase data, interactions, and customer profile |
The Explanation Rule
| Recommendation Without Explanation | Recommendation With Explanation |
|---|---|
| "Prioritize customer X" | "Prioritize customer X: profile similar to customers who closed in the last six months, requested a proposal ten days ago, and has not responded" |
| "Suggested price: R$ Y" | "Suggested price: R$ Y, the median of deals closed in this segment and volume over the past year" |
| "Discount above standard" | "Discount 8 points above the average of similar deals; resulting margin below the minimum policy" |
With explanation, the recommendation becomes an argument. Without it, it becomes an order that the team either ignores or follows without thinking.
Risks of Commercial Decisions with AI
- Incomplete CRM data. If salespeople do not record lost opportunities, models learn from a distorted picture.
- Reinforcement of the past. Models trained on history tend to prioritize the type of customer the company has always served and may hide new markets.
- Automatic pricing and discounting without limits. Recommendations must respect commercial policy and be reviewed in cases outside the range.
- Association is not causation. Teams using AI report more revenue growth (83% versus 66%, according to Salesforce), but the research does not show that AI caused the growth (Salesforce, 2024).
How to Start
- Choose one decision among the five, preferably the most frequent with available data.
- Measure how it is made today and the result (conversion rate, margin, forecast error).
- Test the recommendation in parallel: compare what AI would suggest with what was decided before changing the process.
- Implement with explanation and monitor when the recommendation is followed and when it is not.
What to Do Now
- Audit CRM quality: Are lost opportunities, reasons, and values recorded?
- Choose the sales forecasting decision if management struggles with inaccurate forecasts; it is a good first case because the result is easy to compare.
- Define policy limits before any price or discount recommendation.
To map opportunities in commercial decisions, learn about dooop’s discovery at dooop.com.br/discovery-de-oportunidades-de-ia.
Frequently Asked Questions
Can AI set prices automatically? In some markets, such as e-commerce, dynamic pricing is already common. In consultative B2B sales, AI usually suggests ranges and the team decides within policy.
What is the difference between AI in sales and AI in commercial decisions? AI in sales focuses on salesperson productivity day-to-day. AI in commercial decisions focuses on choices that affect revenue and margin, such as price, priority, and forecasting. See How to identify AI opportunities in sales.
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About dooop
dooop is a consultancy based in Caxias do Sul (RS) that prepares leaders and organizations to transform artificial intelligence into business capability, with strategy, governance, and training.