When you lower the cost of asking questions, people stop asking questions shaped by the tool and start asking questions shaped by the decision they’re trying to make.

You can see this in how many of us learned to use tools like ChatGPT. Early on, it looked a lot like search—short, keyword-style prompts that mirrored how we’d always interacted with Google. Over time, that changed. People started using it to draft strategies, critique plans, role-play negotiations, and explore scenarios. The questions stopped copying the old interface and started reflecting the outcomes that actually mattered.

The same shift shows up when BevAlc teams start talking directly to their data through BevGenie.

At first, the questions mirror existing spreadsheets and dashboards. But very quickly, the conversation tilts toward decisions: What do I need to know to decide where to grow, who to call, whether a distributor is doing their job, or what just changed that can’t wait?

In our pilots, those decision-shaped questions keep converging on four core use cases:

1. Where should we grow next — and why?

   
   This is the first decision teams surface, regardless of size. Smaller brands use it to decide where to enter; larger brands use it to decide where to focus. Both are solving the same problem: where growth is actually showing up versus where it’s assumed. In BevAlc terms, this is choosing the next states/DMAs, deciding on-prem vs off-prem, and separating true demand from planned distribution.

   
   When teams can talk directly to their data, this decision becomes grounded in evidence rather than expectation.

   
   

   The decision questions teams ask:

   • Which states, regions, or DMAs should we enter next?

   • Which geographies are performing versus simply planned?

   • Should growth prioritize on-premise or off-premise?

   • Are chains or independents driving real velocity?

   • Where does demand show up in the data—and where does it not?

     
     

2. Which accounts should we go after next?
   
   

   Once direction is set, the next decision is execution at the account level. This is where most teams fall back on spreadsheets—sorting lists, filtering rows, and relying on experience to fill gaps. The limitation isn’t effort; it’s that the questions are constrained by the format.

   
   

   When teams can ask questions directly, the focus shifts from managing lists to prioritizing action. The output isn’t another report; it’s a call list.

   
   

   The decision questions teams ask:

   • Which specific accounts should sales focus on next?

   • What types of accounts are under-penetrated today?

   • Which accounts resemble places where we already win most often?

   • Where do distributor overlap and geography point to opportunity?

   • Who should we call next—and why?

     
     

   The goal is a 2-week call list—ranked outlets with a clear “why,” including look-alike accounts that resemble the places you already win.

   
   

3. Are our distributors actually performing?

   
   

   Distribution performance is uneven by nature, but it’s often evaluated late and indirectly. Teams can feel when something isn’t working; what’s harder is spotting where performance is slipping early enough to respond.

   
   

   Direct interrogation of performance data makes this decision concrete.

   
   

   The decision questions teams ask:

   • Which distributors are growing and which are flat?

   • Who is actively pushing the product versus maintaining it?

   • Where is velocity trending up or down?

   • Why is a distributor underperforming when the market isn’t?

   • Is this localized or systemic?

     
     

   Clear answers here change how teams engage distributors, shifting conversations from anecdotes to specifics and surfacing issues before they compound.

   
   

   It clarifies whether the issue is coverage (distribution), movement (velocity), or an execution gap (right accounts, wrong focus).

   
   

4. Are our distributors actually performing?

   
   

   This decision runs continuously in the background, even when it isn’t named. Teams aren’t looking for more metrics; they’re trying to determine whether something meaningful just changed and whether it warrants immediate action.This means spotting week-over-week shifts and generating early-warning flags by market, channel, and account type.

   
   

   When teams can work directly with their data, this becomes an explicit question instead of an implicit worry.

   
   

   The decision questions teams ask:

   • Is performance shifting in a way that matters?

   • Is this noise or the start of a real trend?

   • What changed recently that requires action now?

   • Where are early warning signals appearing?

   • What can’t wait for the next review cycle?

   
   

   This is the point where analytics moves from review to diagnosis—and from “what happened?” to “what do we do now?”

   
   

These use cases aren’t edge cases. They’re the decisions that recur at every scale of growth. Lower the cost of asking, and questions stop being shaped by tools and start being shaped by intent.

The result isn’t more analysis. It’s fewer steps between insight and action.