A lot of AI reporting is a list of things the software did: records processed, drafts written, hours “saved.” Those numbers can move while the business stands still. If nobody had a better conversation, and nothing closed, the week was not improved. It was decorated.
The scoreboard worth keeping is smaller. Did revenue go up for reasons you can point to? Did people spend more of the week on customers instead of lists? Did the next decision get easier because the last one was measured?
Count the things a person would recognize
- Signals found — the market you actually saw.
- Opportunities prioritized — the set small enough to act on.
- Outreaches a person approved — nothing sent on autopilot.
- Revenue recorded — closed value someone logged, not a model’s guess.
Until a named workspace is publishing live totals, treat example numbers as an operating model: this is what the product is built to record. Do not present them as a customer case study.
Time is a mix, not a slogan
“Hours saved” is a weak claim if the hours just move into a different kind of busywork. The mix we care about is less list-building and drafting, more conversation and approval. That is a design for the week. It becomes a measured result when workspaces track time the same way they track wins.
Sharpness is the compounding effect
A business gets sharper when the next cycle starts with better context: what was approved, what closed, what to ignore. Tenant context should get more specific over time, not more generic. If the software cannot explain why it ranked something, it is not ready to prepare the draft.
If you want a first conversation about whether Mako would move those numbers for your company, start with how the business runs today — not with a tour of every capability.