Most revenue leaders will tell you their team isn’t performing as well as it should. Fewer will acknowledge they helped design the system causing it.
Bad incentives are one of the most expensive and least examined problems in a scaling commercial organisation. They sit in the background, quietly shaping behaviour in ways that look rational at the individual level and destructive at the business level. And because the incentive structure is usually inherited rather than deliberately designed, nobody feels responsible for changing it.
That’s convenient. It’s also costing you money.
The revenue-only commission trap
When sales commission is 100% tied to revenue closed, you get exactly what you pay for: revenue closed. What you don’t get is customer fit, retention, or long-term value. Reps push the highest-value deals regardless of whether the customer will succeed with the product. Churn follows. NRR deteriorates. The CAC you spent acquiring that customer gets written off twelve months later while the rep who closed it is on to the next one.
The fix is a commission structure that reflects the full commercial picture: a meaningful base tied to revenue, weighted adjustments for retention and expansion, and a component tied to customer outcomes in the first 90 days. This aligns the rep’s incentive with what actually matters to your board — not just new ARR, but revenue that stays and grows.
Where AI helps: revenue intelligence tools can now analyse your historical sales data to identify which rep behaviours correlate with long-term retention versus early churn. That analysis — previously requiring a data analyst and several weeks — takes hours. Use it to design commission weights that reflect what produces durable revenue, not just what closes fastest.
The activity metrics trap
Measuring reps on calls made, emails sent, and meetings booked feels rigorous. It measures effort, not output. A rep making fifty calls a week to poorly qualified prospects and booking ten meetings that go nowhere is hitting their activity targets and destroying your pipeline quality simultaneously.
You’ve built a system that rewards looking busy. And your reps — being rational people — are looking busy. They’re not lazy. They’re doing exactly what you told them to do.
The fix is output metrics: pipeline generated per rep, conversion rate at each stage, revenue per selling hour. Harder to game, harder to measure, but they tell you something true about commercial performance rather than something comfortable about commercial activity.
Where AI helps: pipeline analysis tools now generate accurate conversion rate data by rep, by segment, and by deal type in real time. Managers no longer need hours in spreadsheets to understand where the pipeline is healthy and where it’s quietly rotting. That visibility makes output-based management practical rather than aspirational.
The individual performance trap
Most sales cultures over-index on individual performance. Leaderboards, stack rankings, President’s Club — all of it reinforces the idea that commercial success is a solo sport.
In a modern B2B revenue motion it isn’t. Enterprise deals involve SDRs, AEs, solution engineers, customer success, and often marketing. Mid-market deals close faster with tight coordination between prospecting and follow-up. The rep hoarding account intelligence to protect their position is actively reducing the team’s total output, even while climbing the individual leaderboard.
The fix is incentive structures that reward team output alongside individual contribution — shared elements tied to team pipeline, recognition systems that surface collaboration, and management that visibly rewards the rep who helps a colleague close as much as the one who closes their own deal.
Where AI helps: collaboration analysis tools can now identify where knowledge sharing is happening and where it isn’t — surfacing the invisible contributions that traditional incentive systems miss entirely and therefore never reward.
The vanity marketing metrics trap
Marketing teams rewarded for traffic, impressions, and MQL volume will produce traffic, impressions, and MQL volume. Whether any of it converts to revenue is, apparently, someone else’s problem.
This is where the misalignment between marketing and sales becomes most expensive. Marketing hits its targets. Sales complains about lead quality. Nobody changes anything because both teams are technically succeeding by the metrics they’re measured on. Meanwhile the pipeline is full of leads that don’t close and everyone blames everyone else.
The fix is tying marketing incentives to metrics that connect to revenue: MQL-to-SQL conversion rate, pipeline sourced and influenced, contribution to closed revenue. This forces a conversation about quality that the current structure carefully avoids.
Where AI helps: revenue attribution tools can now trace which marketing touchpoints contributed to closed deals with far more accuracy than the last-click models most companies still rely on. That data reframes the marketing conversation entirely — from volume to contribution. It also makes it significantly harder to hide behind vanity metrics when the pipeline is underperforming.
The system is the problem
Incentives are the operating system of a commercial organisation. If the operating system is wrong, it doesn’t matter how talented your people are or how sophisticated your tools are. The behaviour the system rewards is the behaviour you get.
Most scaling companies inherited their incentive structures from an earlier stage and haven’t revisited them since. AI has made it dramatically easier to analyse what the current incentives are actually producing and to model what different structures would produce instead.
The question is whether you’re willing to ask it: are your incentives designed to produce the commercial outcomes you actually want — or the ones that were easiest to measure when someone set them up three years ago?
Still here? Good. You might be exactly my kind of client.
