As of 13 August 2026, AI can forecast your sales for next quarter.

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, power-user skill, and roughly 1 hour until you can act on the result.

   How to actually do it

   1. Open your CRM and export historical closed-won and closed-lost sales for comparable past periods, including dates, values, products, segments and owners.
   2. Export the current open pipeline with opportunity values, stages, expected close dates, owners, probability fields and the last activity date.
   3. Open a spreadsheet, remove duplicate opportunities and closed deals, standardise currencies and dates, and mark missing, stale or unusually large records.
   4. Add a short note listing known changes for next quarter, such as pricing, capacity, territory ownership, campaigns, seasonality and planned leave.
   5. Paste the forecast period, cleaned historical data, pipeline export and change notes into the prompt, then ask the model to produce the base, conservative and upside cases.
   6. Check every total against the spreadsheet, compare stage conversion rates with your own historical results, and investigate any deal or assumption that materially changes the forecast.
   7. Send the forecast and its assumptions to your sales manager or finance colleague, then record which scenario will be used for targets, staffing or budget decisions.

   Prompt

   Forecast sales for the next quarter using the data below. Treat the data as the only source of truth and do not invent missing figures, deals, close dates or market conditions.
   
   Forecast period: [start date] to [end date]
   Currency: [GBP or other currency]
   Historical sales by month or quarter:
   [ paste data ]
   Current pipeline, with one row per opportunity and these fields where available: opportunity name or ID, value, currency, stage, probability currently used, expected close date, owner, segment, product and last activity:
   [ paste data ]
   Known changes next quarter, such as pricing, capacity, territory changes, campaigns or seasonality:
   [ paste notes ]
   
   Produce:
   1. A base-case forecast for the quarter and a monthly breakdown.
   2. A conservative case and an upside case, with the assumptions for each.
   3. A table showing how pipeline value is converted into expected revenue, including the conversion rate used for each stage or segment.
   4. A list of opportunities that materially affect the forecast and any missing or stale fields.
   5. A comparison with the historical periods supplied, clearly separating observed results from estimates.
   6. A sensitivity check showing which assumptions change the result most.
   7. A short list of actions to improve forecast accuracy before the quarter starts.
   
   Show calculations clearly. Use ranges where the data does not justify a single figure. State the forecast date and the data cut-off date as supplied above. Do not present a probability as a fact, do not use external market data, and label every assumption. End with the five checks a sales manager should complete before using this forecast in a target or budget decision.

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