Active deals and revenue
Open opportunities, contract stage, dollar value, next step, owner. The list you would lose money by forgetting.
How-to — — by Mahmoud Zalt
A lean way to track tasks, deals, and goals without living in Notion: lighter system, AI auto-updates from inbox and Slack, ten minutes weekly.
Every tracking tool starts the same way: a clean page, three columns, a sense of relief. Six months later the same founder has fourteen databases, half of them out of date, and a recurring guilt about the system they built to remove guilt. The tool is not the problem. The shape of the work is. Tracking that lives in a dedicated tool drifts because the updates happen somewhere else: a reply in your inbox, a deal in Stripe, a churn signal in Slack, a milestone slipping in a meeting. By the time you mirror that into the tracker, the day is gone. Then you build a richer template to fix it. Then the template needs maintenance. Then the tool becomes the work.
A tracker should answer one question: what will I forget that costs me money or trust if I forget it? Everything else is reaction work, and reaction work belongs in the channel where it lives, not in a second copy in a database. A reply to a customer email is reaction work. A signed contract is tracking work. A Slack ping is reaction work. A renewal date is tracking work. The split is brutal but freeing. Most founders track ten times too much because they confuse remembering with monitoring. You do not need a property to know a deal is hot, you need a follow-up scheduled. The smallest useful tracker covers five categories, and almost nothing else earns a row.
Open opportunities, contract stage, dollar value, next step, owner. The list you would lose money by forgetting.
Renewal dates, churn risks, NPS or sentiment notes, last touch. Anything that warns you before a paying customer leaves.
The two or three results that matter this quarter and the one move per week that advances them. Not tasks.
Things you said you would do for a customer, partner, or investor. The trust ledger. Missing one of these is expensive.
Notes, decisions, post-mortems, playbooks. Stuff you will want to find again in six months. Searchable, not pretty.
Yes, and this is where the model finally pays back. The reason trackers drift is that the human is the bridge between the channel and the database. Remove the human as bridge and the drift stops. An AI Employee can read your inbox, listen to meeting transcripts, watch named Slack channels, and write structured updates back into your tracker without you typing a row. The trick is not to ask it to track everything: ask it to handle the five categories above and stay out of the rest.
There is a quiet shift when this lands. The tracker stops being a place you visit to feel organised and starts being a place you visit to decide. Columns are filled in before you arrive, because something in your inbox an hour ago triggered the right row. You no longer scroll, you no longer rebuild, you no longer feel behind. The hardest part is letting go of the version where you maintain it by hand. Most founders need a single week of running the system in parallel before they trust it. After that, almost nobody goes back.
Once an AI Employee is doing the tracking, the next failure mode is feature creep. The founder who once built fourteen databases will try to add fourteen new automation rules. A simple system survives because the operator defends its simplicity, not because the tool enforces it. The next two sections are the discipline part: keep the system small over time, and run a ten-minute weekly review that flushes anything stale. Both are unglamorous and both are the reason this still works in month six.
Complexity in a tracker grows the same way complexity in a codebase grows: one well-meant addition at a time, each defensible on its own. The fix is a small set of rules you apply on the way in, not on the way out. New database only when an existing one cannot hold the new thing without losing meaning. New property only when you will filter by it more than once a week. New view only when an existing view actively misleads you. If those three gates hold, the tracker stays small. If you skip them, you will be back to fourteen databases inside a quarter.
Before adding a new database, view, or property, name the one you will retire. No additions without removals.
If a row has not changed in 90 days and is not a long-running commitment, archive it. Stale rows are noise, not history.
Prefer a paragraph note an AI Employee can read over an elaborate property structure no one updates.
Once a month, edit the rules the AI Employee follows. Do not hand-edit rows. Tune the system, not the symptoms.
Friday afternoon, late. The AI Employee has already run its weekly sweep. You open one page, not a workspace. You read the digest: new deals, customer signals worth noting, commitments due next week, weekly outcomes the system saw you hit or miss, knowledge entries the employee added. Two minutes adjusting anything the employee got wrong (usually nothing), three minutes pruning stale rows, five minutes deciding the one move that advances each goal next week. Ten minutes. No template rebuilding, no guilt. The numbers below are the rough shape of what changes once a system like this runs for a few months, drawn from founder workflow patterns we see at Sistava.
Not necessarily. Notion is one option, but the principle is bigger than the brand. The right tracker is whichever surface your AI Employee can read from and write to easily: a slim Notion, a Google Sheet, an Airtable, or the employee's own memory view inside Sistava. Pick the one with the lowest friction for you and stop debating the rest.
Yes. An AI Employee with access to your inbox, Slack, calendar, and CRM can detect events that match your rules (new deal, churn signal, milestone hit, commitment made) and write the right row into your tracker on a schedule. Your job becomes reviewing a digest, not typing cells.
Five categories: active deals, customer health, weekly outcomes against goals, commitments and promises, reusable knowledge. Anything outside those five is usually reaction work that belongs in the channel where it lives, not in a second copy of itself.
Sometimes, especially in the first week while rules are still loose. The fix is to review the digest on Friday, flag what got missed, and update the rules the employee follows. After two or three iterations the miss rate drops sharply. Keep humans for judgement calls, let the employee handle volume.
Once a week, ten minutes, during the Friday digest review. Archive anything stale, retire any rule the employee follows blindly, decide the one move per goal next week. Once a month, spend twenty minutes editing the rules rather than the rows. That is the entire maintenance budget.
If the tracking side of the workload finally feels light, the next obvious target is the inbox. Founders who get tracking under control usually discover email is the next biggest tax on their week, and the same approach (AI Employee reads the channel, surfaces what matters, leaves a short digest) works almost identically. The companion read below walks through that move in detail.
The honest summary of this entire piece: tracking is supposed to be a side effect of doing the work, not the work itself. The version of you that lives in Notion is not more organised, just more anxious about being organised. Shrink the tracker to five categories, let an AI Employee write the updates from the channels where the work already happens, and reclaim a Friday evening. The system is small enough to survive a busy quarter, simple enough to explain in one paragraph, and quiet enough that you forget it is running. The right tracker is the one that gives you back the time you used to spend feeding it, and almost everyone who tries this finds the database tab gets opened a lot less, and the business gets moved a lot more.