Currently building and scaling the next wave of AI-enabled services and products in the HubSpot ecosystem as founder and CEO of The RevOps Shop.

Expert operator providing sales automation, data, and marketing automation solutions for B2B companies looking to win more business, more often.

Trusted by America's leading sales and marketing executives to modernize and operationalize GTM best practices.

Michael Grinberg

CEO, Founder at RevOps.Shop | AI sales and marketing strategy expert for B2B companies looking to win more business, more often.

Director of Growth Operations | Church League Softball Benchwarmer | Win More Business More Often

I’m happy to share that I’m starting a new position as Director of Growth Operations at RevOps Shop!

Here’s a harsh truth about service businesses: You’re paid to be the learning arm for your clients. 

But how can you do this? By asking better questions.

The real leverage in consulting is the quality of the questions you ask.

Good questions do three things at once:

1. They expose what’s broken.

2. They reveal what the client has been avoiding.

3. They shift the conversation from tactics to truth.

Most people ask surface questions because they feel “safe.”

“How many leads do you want?”

“What’s your budget?”

Those questions gather information, but they don’t create breakthroughs.

Breakthrough questions feel uncomfortable. Even to the person asking them.

They sound more like:

“What decisions are you making with data right now?”

“Where do deals go dark and no one can explain why?”

“What are you over-investing in because it feels productive?”

If you want to ask better questions, here’s the work most people skip:

🔍  Understand the system before you touch the symptom.

If you don’t understand how revenue actually flows through a business, every question you ask will be tactical by default.

🤔 Study the executive point of view.

Not their job title. Their pressure. Their incentives. Their fears.

Great questions come from understanding what keeps them up, not what’s written in the org chart.

🥸 Stop trying to sound smart.

The moment your goal is to impress, your questions get worse.

❓ Ask questions you don’t already know the answer to.

If you’re just steering toward your solution, you’re selling.

If you’re genuinely curious, you’re consulting.

When a question is good, you can feel it in the room.

People pause.

They stop giving rehearsed answers.

They start thinking out loud.

That’s where real change starts. 🚀 

What’s a question you’ve asked, internally or externally, that has changed the outcome of a relationship for you?

Organizations are going to face a MASSIVE roadblock when it comes to implementing AI. Here’s my theory: 

I keep coming back to the pattern that played out 10 years ago with marketing automation.

When marketing automation tools first exploded, marketers could finally segment their database based on behavior, not just demographics.

The promise was simple: if someone visited your "dog products" page, you could tag them as a dog owner and send them targeted dog-related campaigns, even before they made a purchase.

But here's what happened:

The segmentation logic they were sold didn't work the way they expected.

Why? Because their website architecture was a mess. 

For behavioral segmentation to work, your URL structure had to be VERY clean. 

Dog products needed "/dogs/" in the URL path. 
Cat products needed "/cats/" in the path. 
Your title tags had to be consistent and logical.

Most companies didn't think about this when evaluating the software. 

So they'd buy the tool, try to implement the logic, and realize they need to completely restructure their entire site and potentially break their SEO in the process.

What was supposed to be a quick win became a 6-month upstream data cleanup project.

Right now, we're seeing the exact same dynamic with AI readiness.

Decision-makers are excited about AI agents. 

But there are problems that we’ll see:

Problem 1: Organizations that are AI-curious but don't even realize data quality is a prerequisite.

Problem 2: Organizations that know data is important but don't know how severe their own data issues are.

Problem 3: Organizations that know their data is messy but have been kicking the can down the road because they could work around it with manual processes.

Decision makers are asking themselves if this is going to be another situation where they buy the tool and then realize they need to spend 6 months fixing their foundation before they can use it.

For a lot of organizations, the answer is yes.

If you haven't started, you need to start fixing your data hygiene now. 

The infrastructure work isn't optional. 

Has anyone done a meaningful AI readiness workshop?

Most B2B teams think they’re competing against their direct competitors.

They’re not.

They’re competing against everyone with the same data.

The same ZoomInfo filters.

Same firmographics.

Same target accounts.

Same order of operations.

When everyone builds their list the same way, everyone reaches out in the same way, at roughly the same time. 

The competition stops being about product or pricing and turns into a race for attention.

That’s why most outbound feels harder than it should.

Signals are what change that dynamic.

Signals let you frame the WHY NOW according to your prospect’s POV.

They tell you who needs your attention now, not just who fits on paper.

When used by marketing, ads and emails convert at a higher clip. 

When used by sales, conversations last longer, and discovery calls are easier.

But here’s the part most teams underestimate.

Once you start building signal-based systems, the work stops being campaign work. 

It becomes infrastructure.

Point-in-time tactics can always be swapped.

Structural advantage can’t.

If your go-to-market motion is still built on static lists and generic sequencing, you’re not losing because your reps aren’t working hard enough.

You’re losing because you’re competing in the same lane as everyone else.

Change your data strategy, and you change your revenue performance.

#data #marketingops #signals

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