Every week, leadership teams review pipeline like it’s math.

If you’re in these meetings, you know the drill: how many deals are in commit and best case, which deals progressed stages last week, where did close dates slip – and so on.

Even if your sales team is diligently logging every deal and showing movement between stages, there’s still a good chance your CRM allows incomplete deals to move forward.

If that’s the case, your forecast isn’t math – and chances are it’s not accurate. Without required context, stage progression is proof that someone clicked and dragged a deal. That’s it.

Let’s look at why this happens and what to do about it.

Why Is Sales Velocity Often Inaccurate?

The fundamental issue here is simple: to have a reliable sales velocity, you need complete data, qualification integrity and demonstrable evidence when updating deal stages.

Practically, this comes down to clear process expectations and required fields that must be updated before a deal progresses or close dates move. If you’re already doing this, congrats – you’re in the minority and you can skip the rest of this article.

It’d be easy to pin this as a sales team problem, but the fact is, it’s a leadership team problem.

In our many years of experience working with sales and revenue leaders to develop trustworthy data, we’ve heard the common excuses (“we don’t want to slow the reps down,” “there’s bigger fish to fry,” “we’ll look at it next quarter.”)

Whatever the excuse, leaders ignore incomplete records. The signal they’re sending to the sales team is clear: data discipline is not a priority. So, they follow the signal. When it’s time to run through deals, they update deal stages and close dates based on the story they think they need to tell.

Here’s a walkthrough of how this can easily go off the rails:

What Fake Sales Velocity Looks Like in Practice

Let’s look at how this plays out in more concrete terms.

First, fake velocity won’t look suspicious or chaotic. Check the deals board and you’ll see forecast categories filling up and reports populating nicely. But if you’re checking this carefully and finding big gaps between your forecast vs actuals, you’ll see the cracks in the foundation.

Here are a few cracks we’ve spotted more than once:

  • Deals moved to proposal sent too early. Someone was eager to hit their proposal target, but deals in this stage have no documented business case, no quantified pain, no known reason why the buyer needs to act now.
  • Champion identified/decision-maker brought in – but not really. In some cases, deals hit a stage like this, but there’s no real access to anyone with actual authority. This is a regular offender.
  • Deals sit in any stage too long. The causes for fake sales velocity don’t always skew it in the faster direction. Perhaps the most common problem we see is deals that are effectively lost, but sales keeps updating the close date rather than let hit their close rate or because they’re not ready to accept it’s dead.

If you’re reviewing deals and not sure if you’re seeing cracks or not, make this even simpler. Look at deals that have advanced stages in the last week. If you can’t look at the deal and see why they would buy, from you, now, then you need to pull on that thread and understand if it actually advanced or just moved.

The Real Cost of Fake Velocity

Fake velocity might sound harmless. In the scheme of things, do we really need to worry about whether reps are filling in every field as long as they’re selling?

Short answer, yeah. Over time, the cost of fake velocity will start to pop up in lots of places.

  1. Forecast Instability

The quarter looks solid — until it isn’t.

Surprise misses are rarely surprises. They’re the downstream effect of deals that were never structurally sound but were allowed to age into “Commit.”

At the end of the quarter, suddenly what looked like a sure thing falls apart.

  1. Artificial Confidence

Pipeline coverage looks healthy. Stage distribution looks balanced. The dashboard says you’re on track.

But the system is measuring motion rather than conviction. And if that means you think you have a strong pipeline when you don’t, that creates real risk.

  1. AI Amplifies the Weakness

This is a big one, and we went in-depth on how AI exposes data trust problems.

AI tools will summarize, predict, score and recommend based on whatever is in your CRM.

If your CRM contains incomplete logic, unverified assumptions and false optimism, AI not only won’t fix it – it’ll magnify it by outputting convincing-sounding nonsense.

How to Fix Fake Sales Velocity

Here’s the good news: addressing this problem is simple. (Notice I said simple, not necessarily easy.)

You don’t need to over-engineer some elaborate infrastructure to fix this, but it requires some decisions and starts with documenting your process expectations.

Let’s break down what that should entail related to velocity.

  • When a deal is created, what information do you need collected to definitely know it is a real, justifiable deal (and not a glorified lead)? These tend to look like the questions above (why are they ready to buy, ready to buy now, ready to buy from you?)
  • At every progressive stage, what information do you need documented to know it truly belongs in that stage? Some are easy (quote signed = quote signed), some may need more nuance.
  • When a close date is pushed back, require the rep to include some form of explanation and evidence. (Not getting a response in a while doesn’t cut it.)
    Go deeper on why deal push rate is an underrated metric you should be tracking.
  • When an inactive deal goes well past its close date, set up automation to force a decision: either update the deal with a new close date + explanation, or this deal will move to closed lost.

Once those requirements have been defined, you can build the guardrails into HubSpot using required fields, deal rules and tools like Supered process boards.

Enforcement Is Not Friction

Back to the reasons this happens in the first place: enforcement is where this tends to break down. Many leaders resist stricter stage criteria because they fear slowing the team down.

That’s the wrong lens. Enforcement is clarity, not bureaucracy. Requiring context actually ensures that the system doesn’t fail you, which helps the business ultimately grow revenue, which helps the sales team make more money.

Fixing fake sales velocity is one step forward in establishing Data Trust. Learn more about Data Trust here.