Good evidence makes a better starting point.
An external success story can show what is possible. It cannot tell you what your business will earn. That takes a clear question and your own evidence.
Start with the sale, then work backwards.
Choose one commercial outcome: accepted quotes, repeat customers, revenue from reactivated accounts or relevant add-on sales. A response time or number of drafts is useful context, but neither proves that revenue increased.
Define the comparison before the pilot.
Record the starting situation, the customer group and a realistic sales cycle. Where practical, compare similar groups or periods. Account for changes in pricing, promotions, demand and seasonality so the AI does not receive credit for everything that changed.
Look at quality as well as revenue.
A higher order value is not a win if it comes from unsuitable recommendations. Monitor returns, complaints, discounts and customer preferences. The objective is business that customers have a reason to repeat.
Be honest about what the evidence supports.
A supplier’s case study is a reported result, not an independent experiment. A digital-buying survey describes preferences, not the effect of AI. We use both to ask better questions and make the limits explicit.
Decide what to do next.
At the agreed review, combine the commercial result, actual use and feedback from the team. Continue, adjust or stop. A pilot that rules out a weak idea should not be presented as a revenue success.
Sources used on this site
These organisations are independent sources, not Shape2Scale clients or partners. Source pages may change; their findings are not guarantees.
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