As of 13 August 2026, AI can only partly project whether you will hit your sales target.

What to actually do

1. Hand it to a person

   The route this page recommends

   A person who owns the outcome does this end to end, worth it when the failure is dear.

2. Use a tool built for this

   Second choice

3. Do it yourself

   The distant third

   A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

   How to actually do it

   1. Open your CRM and export the current period's opportunities with deal name, value, stage, expected close date, probability, owner and last activity date.
   2. Open the target or quota record and gather the target period, sales target, closed sales so far, currency and any team or territory assumptions.
   3. Gather comparable historical reports showing stage conversion, average sales cycle, deal slippage, average deal size and results for similar periods, if your CRM contains them.
   4. Remove duplicate opportunities and mark closed, cancelled, stale or missing-value records before pasting the cleaned export and context into the prompt.
   5. Paste the prompt and data into a chatbot, then ask it to recalculate the base case after correcting any factual errors you find in its first response.
   6. Compare every figure and deal status in the forecast against the CRM, and compare its assumptions with your historical reports before accepting the scenarios.
   7. Send the forecast, assumptions and unresolved data gaps to your sales manager or sales operations colleague for a challenge of the probability range and the decision you plan to make.

   Prompt

   Project whether I will hit my sales target using only the information below.
   
   Target period: [start date to end date]
   Sales target: [amount and currency]
   Current actual sales: [amount and currency]
   Pipeline export:
   [paste CRM rows, including deal name, value, stage, probability if used, expected close date, owner and last activity date]
   Historical data, if available:
   [paste win rates by stage, average sales cycle, average deal size, slippage rate and comparable-period results]
   Business context:
   [paste any known changes to pricing, territory, capacity, seasonality, lead supply or sales process]
   
   First, identify missing, stale or contradictory inputs. Do not invent figures or fill gaps with industry averages. Then calculate a base-case projection from the supplied data and show the arithmetic. Give a conservative, base and optimistic scenario, state the assumptions for each, estimate the probability of hitting the target as a range rather than false precision, and list the deals or assumptions that have the greatest effect on the result. Separate booked revenue from forecast revenue. Finish with the five checks a sales manager should perform before using this projection in a decision. If the data is insufficient, say exactly what cannot be concluded.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.