Sales, artificial intelligence, sales organisation
Sales, artificial intelligence, sales organisation
Green KPIs, growing hidden costs
Green KPIs, growing hidden costs
KPIs can stay green while costs keep growing. Two mechanisms keep part of the sales team's work out of your systems. Your next AI project needs to understand that hidden workflow.
KPIs can stay green while costs keep growing. Two mechanisms keep part of the sales team's work out of your systems. Your next AI project needs to understand that hidden workflow.
‘My indicators look good.’ We hear this objection at almost every assessment review, and it is often true: customer satisfaction is stable, the pipeline looks healthy and activity is on target. An earlier article examined selling time lost to data entry. This one looks at the other side: costs that never appear because teams absorb the problems before they leave a trace.
A lead-scoring model amplifies the data it receives. An automated payment reminder speeds up collection on the invoices it is given, whether they are right or wrong. Every lead-to-cash process has two versions: the one recorded in your systems and the one your teams experience. A model learns from the version you give it. Disappointing projects often rely on the first.
The first mechanism is silent absorption. Your strongest employees shield customers from internal problems: they take the call from an unhappy customer, arrange the correction, negotiate the credit note and make sure the disruption stays out of sight. That professionalism is a real asset. It also creates a paradox: satisfaction stays high while, on conservative assumptions in the case examined, each account manager spends 2.5 hours a week absorbing problems. The CRM has no suitable category for those hours, so activity reports record them as ordinary customer relationship management.
A lead-scoring model amplifies the data it receives. An automated payment reminder speeds up collection on the invoices it is given, whether they are right or wrong. Every lead-to-cash process has two versions: the one recorded in your systems and the one your teams experience. A model learns from the version you give it. Disappointing projects often rely on the first.
The first mechanism is silent absorption. Your strongest employees shield customers from internal problems: they take the call from an unhappy customer, arrange the correction, negotiate the credit note and make sure the disruption stays out of sight. That professionalism is a real asset. It also creates a paradox: satisfaction stays high while, on conservative assumptions in the case examined, each account manager spends 2.5 hours a week absorbing problems. The CRM has no suitable category for those hours, so activity reports record them as ordinary customer relationship management.
Every lead-to-cash process has two versions: the one recorded in your systems and the one your teams experience.
Every lead-to-cash process has two versions: the one recorded in your systems and the one your teams experience.
Article
Article
Every lead-to-cash process has two versions: the one recorded in your systems and the one your teams experience.
Article
A further problem adds to the cost. Internal warnings often go unanswered, while a formal customer complaint gets things moving within hours. Teams draw a rational conclusion: a crisis is the fastest route to action. The cost appears later in customer churn. One priority rule can help: treat a salesperson’s internal warning with the same urgency as a customer complaint. That is an organisational decision you can make before investing in another tool.
The second mechanism is the use of unofficial tools: personal spreadsheets, separate calendars and, increasingly, consumer AI assistants used alongside company systems. Each workaround marks a point where the official workflow fails. Treat these workarounds as evidence, and together they become a requirements brief written by your users. The issue extends beyond compliance with tool policies: a lead-scoring model may train on neglected records, while a churn model cannot see problems that teams have deliberately absorbed without leaving a trace.
Before automating, make the actual workflow measurable. Track internal cycle times consistently and record a reason for every cancellation. These two practices can reveal, within weeks, some of the reality missing from your indicators. A four-week assessment, inviting everyone in scope to contribute anonymously, can then map the gap and estimate its cost conservatively. Our white paper sets out the full approach, from lead to cash.
A further problem adds to the cost. Internal warnings often go unanswered, while a formal customer complaint gets things moving within hours. Teams draw a rational conclusion: a crisis is the fastest route to action. The cost appears later in customer churn. One priority rule can help: treat a salesperson’s internal warning with the same urgency as a customer complaint. That is an organisational decision you can make before investing in another tool.
The second mechanism is the use of unofficial tools: personal spreadsheets, separate calendars and, increasingly, consumer AI assistants used alongside company systems. Each workaround marks a point where the official workflow fails. Treat these workarounds as evidence, and together they become a requirements brief written by your users. The issue extends beyond compliance with tool policies: a lead-scoring model may train on neglected records, while a churn model cannot see problems that teams have deliberately absorbed without leaving a trace.
Before automating, make the actual workflow measurable. Track internal cycle times consistently and record a reason for every cancellation. These two practices can reveal, within weeks, some of the reality missing from your indicators. A four-week assessment, inviting everyone in scope to contribute anonymously, can then map the gap and estimate its cost conservatively. Our white paper sets out the full approach, from lead to cash.
