Head of AI & Data strategy, Revolutionizing GovCon at Procurement Sciences

I help Aerospace & Defense organizations modernize without losing what makes them mission-critical.

With 15+ years in A&D at Lockheed Martin and GDIT, I know the terrain: complex programs, siloed systems, tight compliance, and the high stakes of delivering at scale. Now at Salesforce, I work with public sector leaders to align technology with mission outcomes — using tools like Data Cloud, Mulesoft, Tableau, and Slack to make transformation not just possible, but practical.

My edge? I speak your language — program lifecycle, cybersecurity, workforce upskilling, supply chain visibility — and bring solutions that fit the reality of your environment, not fantasy decks.

Proud husband to an Army veteran, and someone who’s built digital strategy from the inside out.

Let’s connect if you’re navigating digital transformation in Aerospace & Defense — and want a partner who gets both the mission and the margin.

Anthony Brown

Dan Balcauski

B2B SaaS Pricing is having a moment. It seems like every day another article is published on Freemium or another pricing model “flavor of the month.” 

Many CEOs face the temptation to adopt a Freemium pricing model. This was the case during my business school internship over a decade ago. 

The CEO asked me to research if it was a good strategy.  I explored the ins and outs of this pricing model and looked for characteristics of when it worked and when it didn’t. 

The truth? It turns out that it works far less often than the hype would lead you to believe. In fact, it’s almost always a bad idea, and most often, the better answer is to offer new users a 14-30 day free trial instead of a Freemium model. 

With a Freemium model, there’s a real chance that you’re going to impose a significant hidden tax on your development team for every new feature you develop, slowing you down. 

It also creates a perpetual illusion of free users who will never convert. How many more times do you want to have the conversation, “Just maybe one more ‘play’ into that group will make the difference.” 

It also prevents you from doing the actual work of driving prospects through a well-designed funnel and understanding if you’re creating real, sustainable value for your users.  

Finally, people who push the “Freemium rules” narrative will claim that it helps reduce your CAC. In reality, you are often just playing shell games with your Income Statement. 

The good news is that there’s a golden opportunity that every B2B SaaS company has right now to build a pricing model that their customers (and your investors) will love that won’t distract you with a mirage of “free growth.”

Here are some things you can do TODAY to improve your pricing model:

*Segment your market by the value your product provides. Focusing on the outcomes different customer segments are trying to achieve can give you the best chance to create packaging that reduces your CAC, decreases your sales cycle, and dominate your market. 

*Leverage pricing metrics that align with the value you provide to customers that help them grow with you. Net Revenue Retention growth, anyone?

*Create, understand and communicate the differentiated value you provide to your customers over their next best alternative. Differentiated value and value communication are where the value-based pricing game is won.

I’m passionate about helping businesses by optimizing their pricing models. Reach out anytime if you’re interested in discussing pricing models related to your business.

Founder and Chief Pricing Officer

He told every CEO the same thing: "Don't create a category. Use what exists."

Then he broke his own rule.

Rohini Kasturi spent 20+ years in product leadership at Cisco, Juniper, Veritas, and SolarWinds. 
Now CEO of HG Insights, he found himself in the awkward position of having to create a category against his own advice.

3 lessons from Rohini's category decision:

1️⃣ Default to existing categories. Unless they undersell you.

Rohini's rule was simple: if Gartner already defined your market, use that category.

His logic? "If you name a dog a cat, you have to explain to everyone that cat is actually a dog."

Category creation is expensive. Exhausting. And usually unnecessary.

But at HG Insights, the existing categories didn't fit.


2️⃣ Test fit through elimination.

Rohini's team tried slotting themselves into every analyst category.

Sales intelligence? Only covered 25% of what they do.

Revenue data solutions? Only served rev ops personas.

Market research? Still too narrow.

Each time they asked: does this capture our full value?

Each time, the answer was no.


3️⃣ Tie your category to customer outcomes, not your capabilities.

They landed on two finalists: "go-to-market intelligence" and "revenue growth intelligence."

The deciding question: What good is go-to-market intelligence if it doesn't help customers grow revenue?

One described what they do. The other described what customers get.

They chose the customer outcome.


Category creation is usually a mistake. But when existing categories undersell your value, staying in someone else's box is a bigger one.

🔗 Full episode of SaaS Scaling Secrets in comments

The Reality of Business Growth Projections

Anthropic ships faster than I can learn keyboard shortcuts.
If you're confused about which Claude to use, here's the field guide:

Claude on Web (claude.ai)

For people who think "API" is a type of beer. Tastes great, less filling.


Claude Desktop App

For people who close their browser at night like psychopaths. Now Claude lives in your dock.


Claude Code (Terminal)

For developers who think GUIs are moral failings. Point it at your codebase. Try not to mass-delete your repo.


Claude Code (VS Code Extension)

For devs who haven't seen sunlight outside their IDE since 2019. Your editor now has opinions about your architecture.


Claude Code (Desktop App)

Terminal vibes with training wheels. For people who want to feel like hackers but also want buttons.


Claude in Excel

For people who believe God created heaven, earth, and Microsoft Excel. In that order.


Claude Cowork

For people who spent $3,000 on a MacBook and refuse to open Terminal. Valid.


That's 7 Claudes. One AI.
Now you can explain this to your CEO.

"What problem does this solve?"
"I'll find the problem."
Zachary Garippa keeps hearing this exchange from founder friends.

They build "monster AI products." 
Demos so good you think, "Holy sh*t, I can't believe that exists."
Then they go hunting for a use case.

Zach runs Order.co, a procurement platform processing nearly $500M in annualized spend. His approach was the opposite:
Find the problem first. Then never stop solving it.

The market rewards building backwards. Investors love a killer demo.
That's the trap.

Any AI making generic assumptions will be commoditized. Zach used travel as an example: he flies early United, every time. 
An AI recommending late Delta flights? Dead on arrival. Doesn't matter how smart it is.

Generic is the enemy.
User specificity wins. "Best AI" claims without individual context won't get adopted.

🔗 Full episode of SaaS Scaling Secrets in comments

Everyone's racing to outcome-based pricing for AI agents.
Driving outcomes like support tickets closed per hour. 
Reduction in CAC from an AI SDR spamming inboxes.
But what if your desired outcome is spending MORE time on the phone with customers in their most anxious moments?

Ankit Jain is CEO of Infinitus Systems, Inc., a healthcare AI company, who has been building AI agents since 2019.

His answer isn't outcome-based vs. time-based. It's knowing when to use each.

Front office (patient-facing): Time-based is better.

Think about a nurse calling a patient after a procedure.

"You can hear the rush in the voice of the nurse, because the nurse is already thinking about the next patient."

That rush comes from scarcity. Human time is expensive.

AI flips the economics. You're substituting the cost of a nurse for the cost of an AI agent.
Suddenly an extra 15 minutes with an anxious patient costs almost nothing.

Time-based pricing lets the patient take as long as they need. No pressure to hit an outcome and move on.


Back office (pharmacy calling insurance): Outcome-based wins.

The goal is efficiency. Get the information as fast as possible.

If you pay by the hour, your team is incentivized to make phone calls and drag them out. Even when a faster API exists.

Outcome-based pricing rewards finding the fastest path.


The catch: buyers resist outcome-based pricing.

They're wired for "human labor budgeting." Hourly rates.
And outcome-based causes sticker shock. The per-unit price looks higher because you don't succeed 100% of the time.

Ankit's solution: start with time-based to benchmark. Then transition to outcome-based once they see the data.

🔗 Full episode of SaaS Scaling Secrets in comments.

taplio

The Battle

Anthony Brown

Dan Balcauski

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taplio

The Battle

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Dan Balcauski

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Dan Balcauski's LinkedIn Performance

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