Sales Assembly

As the President, I oversee many facets of the business including: Go-To-Market Strategy, Customer Success, Revenue Operations, Product Development & Delivery, and Business Operations.

My primary objective is to drive cross-functional alignment while delivering on business level KPIs (revenue and cash flow).  I do this by helping to enhance pipeline generation, optimize the sales process, implement best-in-class technology / systems, inform on the top ways we can utilize AI to make our business efficient, and ensure we hit our new business and retention goals.

President

Spiff Inc

Advisor

My Subscription Addiction

Chief Revenue Officer

Head of Revenue Operations - led the go-to-market strategy for G2.

Revenue Operations teams (Sales Operations, Sales Enablement, and Partnerships) are responsible for: sales strategy, sales forecasting and reporting, territory and quota planning, sales training, Salesforce administration, sales compensation management, pricing/packaging strategy and rollout, process improvement, and systems management.

The Strategic Partnerships team negotiated integrations with other B2B SaaS leaders as well as sold review syndication and data solutions.

Vice President of Revenue Operations and Strategic Partnerships

Built and ran the CSM team to ensure customer happiness and ROI.  
Led strategic partnerships in order to drive revenue and growth.

Director of Customer Success and Strategic Partnerships

Built and ran the sales team which is tasked with reaching out to all software vendors across the B2B ecosystem and providing them with relevant marketing solutions.

Director of Sales

Toodalu

Helped facilitate partnerships and sales for a disruptive mobile payment technology

Vice President of Business Development

U.S. Bank

Structured Finance - Modeling Analytics Department

AVP, Wealth Management Trust Manager

Bank of America

AVP, Transaction Manager

Proven GTM Leader having led teams across the revenue org: Sales, CS, Strategic Partnerships, and Revenue Operations.  I have extensive experience in growing B2B GTM teams - including being employee #3 at G2.

I am currently scaling Sales Assembly, which focuses on foundational skill development for GTM Teams.  Fostering an environment of learning and growth is essential to developing GTM talent within any successful SaaS organization.

I guarantee your CEO/board will ask you this one question in the next 3 months:

How are you improving GTM efficiency with AI and how are you measuring the impact?

AI is everywhere but actually implementing it successfully and tracking the impact are not trivial tasks.  

Here’s what separates the companies who see real ROI from those that are just freeing up 5 hours of their reps time each week only to have them binging on 67 TikTok videos:

1️⃣ They anchor AI to unit economics, not just model metrics

- Leaders measure cost-to-serve, contribution margin after AI compute/licensing, and payback time
- Most report 3–12 months to ROI when they track it properly

2️⃣ They run real experiments

- Instead of anecdotes, top performers use A/B tests and difference-in-differences in systems like Salesforce, HubSpot, or Zendesk
- To run a true experiment you need the basics (think back to HS science class):

*Define the Question (Hypothesis)

Example: “AI-assisted reps will close deals 15% faster than control reps.”  

*Identify Metrics and Success Criteria

Pick the primary metric (the one you’ll make a decision on).  Choose metrics that connect to outcomes: revenue, cost, time, or quality.

Define what success looks like before you start (≥10% improvement)

*Design the Experiment

Decide how you’ll test your hypothesis

Randomized A/B test? Difference-in-differences? Before/after?

Define your control group (no change) and treatment group (receives the intervention)

Make sure the groups are comparable (same time frame, customer mix, team skill, etc.)

*Run the Experiment

Implement your AI tool and let the test run long enough for statistical significance

Keep everything else constant like processes, timing, and incentives

*Analyze the Results

3️⃣ Function-level KPIs that drive demonstrable results to the bottom line:

Support/CX: AI-resolved tickets increasing w/ CSAT steady or rising

Sales: Rep throughput ↑, cycle-time ↓, win rate ↑

Marketing: Content output ↑ with quality intact, CPL ↓ while SQL rate steady

4️⃣ SMB vs Mid-Market vs Enterprise have different realities and expectations

SMB: Quick wins in chat & content automation - focus on cost-to-serve & response time

Mid-Market: Scale proven use cases and measure portfolio payback (overall improvement in revenue/retention with the same or less cost to serve + AI spend)

Enterprise: ROI hinges on ability to directly drive cost efficiencies, product improvements, and/or cost savings

5️⃣ Common pitfalls

No control groups → no attribution.  You cant just give everyone a tool and see if productivity improves, you need run a true experiment.

Hidden inference costs eating ROI - AI has costs other than $ 

Data/process debt blocking scale - you need to be ready to implement tools before they can be effective


The takeaway?

AI efficiency isn’t about how smart your model is, it’s about how well it moves the needle on cost, speed, and customer experience!

Brad Rosen

President @ Sales Assembly | GTM Leader | Coffee Fan

Your forecasting tool doesn’t matter.

I know… blasphemy on GTM LinkedIn. 

Everyone is obsessing over which tool will magically fix their forecast:

Clari? Great tool
Gong Forecast? Wonderful
Salesforce? Classic
HubSpot? Works well

But here’s the uncomfortable truth:

None of them actually work unless the underlying data is accurate.

This is the issue with every tool (and especially those with AI functionality).

If your data isn’t clean, structured, and governed, your “forecast” is basically a vibe check.

So before you go shopping for a new forecasting platform, fix these fundamentals first:

1) Clearly define your sales stages - not just catchy names

*Define the actual rules:

-Entry criteria
-Exit criteria
-Required actions
-Evidence needed to progress
-Rationale for why a deal belongs there

If a rep can’t explain why their deal is in that stage in 30 seconds, the stage isn’t defined well enough.

2) Set rules of engagement for your pipeline

Most pipelines are a graveyard of stalled deals because there’s no time-based discipline.

*Set the exact number of days a deal can sit in a stage.

If it doesn’t progress?

Move it back.

This type of rigor keeps your pipeline clean and prevents reps from inflating their numbers to look good.

3) Manager reinforcement

Forecasting is NOT “set a process and hope reps follow it.”

Forecasting IS:

-Weekly inspection
-Asking why a deal is in that stage
-Challenging assumptions
-Coaching to stage criteria
-Saying “this needs to move back” without flinching

Your reps should hear your voice in their head:

“Should this really be in this stage?”
“To advance, do we have explicit exec buy-in?”

If managers don’t reinforce it every week, it won't happen.

4) Stop accepting that everyone forecasts differently

No, it’s not “just Sally being a sandbagger.”
And it’s not “just Cade being overly optimistic.”

Those quirks destroy forecast accuracy.

When everyone follows the same process, with the same definitions, the one-off adjustments disappear.

So sure, buy the fancy tool - they’re great.

But a strong, disciplined, reinforced process, even if you forecast in spreadsheets, will outperform the most advanced AI forecasting product sitting on top of a sloppy process.

In similar words from Happy Gilmore:

"Tools don’t fix forecasts. Process does."

President @ Sales Assembly | GTM Operator | Sales, CS, & Rev Ops Leader | Coffee Fan

Your sales training is focused on the wrong things.

Sandler
MEDDICC
Challenger

All great frameworks, all are helpful.  But they are all fundamentally centered around what YOU want to uncover.

But here’s the thing:

Your buyer does not care about your acronyms.
They don’t care about your stages.
They don’t care about your process.

What they do care about is solving a business problem in a way that feels efficient, respectful, and aligned with how they make decisions.

If you want better win rates, shorter cycles, and higher trust… stop obsessing over the methodology and start training your reps on how buyers actually buy.

The problem is everyone buys differently.

Some buyers want 12 calls, full discovery, a written proposal, legal review, a security deep dive, and alignment with five stakeholders.

Others already know your tool because they back-channeled you with 3 trusted peers, they watched your demo library, they tested the free trial, and they’re basically ready to buy tomorrow.

But you’re forcing both groups into the exact same process.

No wonder deals fall apart!

The key is to move at the buyer’s speed.

If they want to move fast → move fast.
If they want to slow down and be methodical → slow down and be methodical.

Meeting the buyer where they are doesn’t mean skipping qualification or losing control.

It means:

*Setting the right expectations
*Guiding the process with them, not at them
*Aligning your motions to their internal decision path
*Supporting the way their org evaluates new tools

That’s the hard part.

That’s what separates good reps (who can run a textbook process) from great reps (who can run a flexible, buyer-first process without losing momentum).

Train your reps to understand buying behavior, not just sales behavior.

2023–2024: “What AI tools are we using?”
2024–2025: “How do we build AI into every process?”
2025+: “What’s the ROI of our AI investments?”

It’s not that companies are spending less on AI.
They’re actually spending more: Gartner reports that 80% of enterprises will double their AI spend by 2027!

But with every new AI tool, your CFO is quietly (or loudly) asking the same question:

“Is any of this actually working?”

That shiny outbound AI that promises 10x productivity 
 → Is it actually improving response rates?
 → Or is it just creating more noise and more unqualified meetings?

That custom research bot you built 
 → Is it accurate?
 → Does the team trust it?
 → And what happens with the hours it saves?  

These are no longer “innovation questions.”  They’re unit economics questions.

AI isn't exempt from scrutiny, it needs to earn its keep just like any other line item in the budget.

And the companies seeing real returns have one thing in common:

They start with business problems, not tools.

Before you implement anything AI-related, ask:

1️⃣ What problem are we solving?
 Low productivity? Missed opportunities? High operational cost?

2️⃣ How will we measure success?
 More meetings booked? Faster deal cycles? Higher CS capacity? Reduced cost-to-serve?

3️⃣ What is the expected ROI?
 Not theoretical. Actual dollars.
 (Example: McKinsey found companies using AI for sales saw 3–5% revenue uplift and 10–20% cost reduction in sales ops.)

4️⃣ Who owns the workflow change?
 AI doesn’t magically fix broken processes.
 It amplifies them, for better or worse.

Bottom line:

AI is incredible.
It’s transformational.
It’s the biggest unlock we’ve seen since the internet.

But it still has to justify itself.

The companies that treat AI like a strategic investment and not a shiny toy will pull away from everyone else.

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