Cosmo Tech | The Decision Twin Platform for Supply Chain, Finance & Asset Leaders at Fortune 500 Scale | Simulate Tomorrow. Decide Today.

Over the past 30 years, I’ve led sales teams at fast-growing Tech & SaaS companies — to rebuild and refine the strategies and systems so sales teams can scale and drive revenue growth.

In recent roles, I was able:

- double revenue while EBITDA grew 400%
- drive a +34% YoY revenue growth

Today I’m working with Cosmo Tech to help enterprises simulate the future for faster decisions, better systems, and maximum opportunity.

That means: 

- Quantum-level decision making
- Complex system resiliency
- Future-proof roadmap

“Steve is “Instant Offense” for sales. This is very rare and very valuable. He’s truly world class at quickly creating and running strategic, data-driven sales teams to drive growth. Great at strategy and wading into the minutiae to uncover and fix and optimize the team and process.

He’s a great leader with great judgment who makes it fun to work on the most difficult challenges. He and I have worked together on two companies that were great successes.”

— Jeff O'Mara, General Manager EKAHAU/ CEO, Healthentic.

Feel free to join my newsletter below — you’ll get all my best systems and strategies for scaling sales into the future. 

Join my newsletter at www.salesgss.com/newsletter

Trying to figure out where to start to double sales in 2025? Click on my "FREE Sales Accelerator Assessment" www.salesgss.com/assessment

Steve Litzow

A 17-year-old enterprise tech company went from $25M to $65M in revenue without changing their team, market, or product strategy.

Here's what actually drove the transformation:
Revenue: $25M → $65M (160% growth) 
Recurring Revenue: $5M → $30M (6× increase) 
EBITDA margins: 20% → 50% 
Product velocity: 1 release every 4 years → 4 per year

When I ask CEOs what they think enabled this kind of growth, they usually say: 
"Better talent? Bigger pipeline? New market positioning?"

None of the above.

It was implementing a High-Intensity Rhythm of Business (HI-ROB).

Here's what it looked like in practice:
Daily 15-minute standups focused on:
- Pipeline movement (what changed overnight)
- Yesterday's activity vs. plan
- Today's activity commitments
- What's closing today + backup options

Weekly cross-functional reviews where every team reports using the same metrics, same format, same cadence.

The insight most teams miss: Elite execution isn't about having better people. 

It's about building systems that surface problems in 24 hours instead of 30 days.

That time compression is what separates:
- Forecast accuracy from CFO discounts
- Pipeline visibility from month-end surprises
- Revenue growth from revenue hope

The companies scaling predictably right now aren't necessarily the ones with the best talent. 

They're the ones with the best operating systems. 

salesgss.com/newsletter

Your sales team has 200+ hours of PMF intelligence.
Your product team sees maybe 5% of it.

That gap is killing your velocity.

Sales hears every objection, every “not yet,” every competitive blindside.
Product never sees most of it.

That’s where PMF actually erodes — in the gap between Sales intelligence and Product velocity.

And the fix isn’t another roadmap meeting.

It’s a bi-weekly execution ritual:
Sales brings: Top 3 deal blockers
Product maps: Clear ship windows
CEO enforces: Ruthless scope discipline
15 minutes. Twice a month.

This loop eliminates the three silent killers:
- Roadmap drift
- Ship-date ambiguity
- Competitive feature surprises

One company I worked with went from 1 release every 4 years → 4 releases per year using this exact loop.

Result: 2× revenue and expansion from a niche tool to a mainstream IT platform.

The data backs it up:
Elite product teams ship with 76% success  --  Average teams sit at 51%

That 25-point gap compounds into years of competitive advantage (or decline).

With Q1 planning six weeks away, now is the moment to fix your Sales → Product loop before you lock next year’s roadmap.

Product-market fit isn’t a milestone — it’s a 90-day referendum.

Full framework + the Q1 implementation plan is in this week’s SalesGSS newsletter.
Link in comments. 👇

Couldn’t agree more with Phillip — the ability to anticipate, not just react, is what separates leaders today.

Simulation is already helping our customers cut decision cycles, reduce risk, and move with confidence.

CEO at Cosmo Tech I Global Business Leader I Chief Technology Officer

Enterprises today face a simple truth: disruption is no longer an occasional event, it’s a constant. Supply shocks, market volatility, and regulatory 

Open article: From Intelligent to Autonomous: How AI-Simulation Unlocks the Self-Learning Enterprise by Phillip Hazen • 2 min read

From Intelligent to Autonomous: How AI-Simulation Unlocks the Self-Learning Enterprise

The new advantage isn’t faster outreach — it’s faster proof.

✅ Days 0–7 (Sales): Define success metric + provision sandbox
✅ Days 8–21 (Product): Automate setup + guide onboarding
✅ Days 22–30 (CS): Deliver one-page ROI snapshot

Track what matters:
• TTFV (Time-to-First-Value) — leading indicator of conversion
• Activation → Expansion Ratio (≤60 days) — proof that value sticks

The teams that show ROI inside 30 days stay alive in deals CFOs can’t kill.

Read the full playbook → salesgss.com/newsletter

#SaaS #SalesStrategy #RevenueGrowth #SalesGSS

Great insight, Philipp.
Transforming Weibull and other reliability models into continuously learning systems through AI simulation is where asset management becomes truly prescriptive. It’s exciting to see this thinking gain momentum across the SAP ecosystem.

Yesterday I shared my first impressions on sessions about Predictive Maintenance, RCM, Asset Maintenance 5.0, and deep dives into data acquisition and diagnostic hardware at the Asset Performance conference. Today, I want to share one idea that really stuck with me—something that could be transformational if applied across the entire asset lifecycle:

Weibull distribution.
It’s a statistical tool that uses failure data from operations—data that’s often easy to get. It doesn’t need huge datasets, and because it’s based on probability distributions, it provides interpretable confidence intervals. That means maintenance experts can use it with confidence.
Of course, it comes with challenges: modeling expertise and thorough data cleansing are essential.

But the opportunities are huge. Startup Cosmo Tech showed a very innovative approach how operational data (e.g. failures, financials, risk) can be used to simulate different maintenance policies and predict their impact on CAPEX, OPEX, or any custom risk KPI you define is relevant for your business. This enables to test strategies before implementing them—using models they can actually understand.

My takeaway: Weibull distribution shouldn’t be a one-time tool for investment decisions. It should be part of a continuous loop:
- Define strategies based on simulations
- Implement and measure performance of such strategies
- Learn from cases where the policies been violates or fails, e.g. when using CBM or RUL predictions missed a failure beyond what is to be expected from a statistical model.

With continuous closed loops to monitor, improve, and optimize your maintenance strategies and operations, it would be possible to unleash the full power of Weibull distribution—or, as a matter of fact, any AI or model-based tool.

Thanks Olivier Blancke & Steven Coates for the insightful demo and nice discussion we had during the conference.

This is how leading utilities are using AI Simulation to prioritize investments, balance risk, and accelerate the energy transition.
Join Cosmo Tech and Groupe E on Dec 2 to see it in practice.

Join us for an insightful webinar with Cosmo Tech and Groupe E on December 2nd.

Ana Nazaré, Strategic Asset Manager at Groupe E, and Maxime Le Pendeven, Key Account Director at Cosmo Tech, will explore how Cosmo Tech AI Simulation empowers Groupe E to 𝘢𝘴𝘴𝘦𝘴𝘴 𝘢𝘯𝘥 𝘱𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘻𝘦 𝘵𝘩𝘰𝘶𝘴𝘢𝘯𝘥𝘴 𝘰𝘧 𝘳𝘦𝘪𝘯𝘧𝘰𝘳𝘤𝘦𝘮𝘦𝘯𝘵 𝘱𝘳𝘰𝘫𝘦𝘤𝘵𝘴, 𝘮𝘢𝘯𝘢𝘨𝘦 𝘶𝘯𝘤𝘦𝘳𝘵𝘢𝘪𝘯𝘵𝘺, 𝘶𝘯𝘤𝘰𝘷𝘦𝘳 𝘰𝘱𝘵𝘪𝘮𝘢𝘭 𝘵𝘳𝘢𝘥𝘦-𝘰𝘧𝘧𝘴 𝘢𝘯𝘥 𝘨𝘳𝘪𝘥 𝘧𝘭𝘦𝘹𝘪𝘣𝘪𝘭𝘪𝘵𝘺 𝘭𝘦𝘷𝘦𝘳𝘴 - 𝘢𝘭𝘭 𝘸𝘩𝘪𝘭𝘦 𝘢𝘭𝘪𝘨𝘯𝘪𝘯𝘨 𝘴𝘵𝘢𝘬𝘦𝘩𝘰𝘭𝘥𝘦𝘳𝘴 𝘢𝘳𝘰𝘶𝘯𝘥 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘤 𝘪𝘯𝘷𝘦𝘴𝘵𝘮𝘦𝘯𝘵 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴.

Whether you’re managing renewable energy plants, large industrial fleets, or complex infrastructure, this session is for you.

👉 Register here: https://hubs.ly/Q03Rxpwj0

#ai #simulation #assetmanagement #aip #digitaltransformation #innovation #energy #utility

Buyers don’t wait 90 days for ROI anymore.

The window for proof has collapsed — from quarters to weeks.

▪ 78 % of B2B buyers now expect measurable ROI within six months (G2)
▪ Deal slippage nearly doubled year-over-year (Kluster)
▪ Interactive demos lift conversions ≈ 25 % (Navattic)

If your product still takes three months to prove value, you’re already behind competitors who do it in 14 days.

👉 Stop selling platforms. Start selling proof.

This week’s SalesGSS issue breaks down how to:
• Re-engineer your first-value moment
• Turn demos into ROI trials
• Track the 3 KPIs boards now watch

Read here → salesgss.com/newsletter

#B2BSales #SaaS #SalesLeadership #GrowthStrategy

Most B2B tech CEOs think they have a meeting problem.
But the data says something very different.

Only 37% of meetings have an agenda.
Executives say 67% of them fail.
Teams waste 392 hours a year sitting in them.

But here’s the truth:

Meetings aren’t the problem.
Visibility lag is.

If you’re still discovering slippage at the end of the month, you’re already 30 days behind the teams you’re competing with.

Across the $25M–$65M companies I’ve worked inside, the pattern is identical:

Elite teams surface deal risk in 24 hours.
Average teams: 7–14 days.
Struggling teams: after the month closes.

This one gap explains revenue misses more than pipeline size, talent, or marketing.

The companies that scale consistently run a tight Rhythm of Business (ROB):

Daily (15 minutes):
• Pipeline movement
• Yesterday’s activity
• Today’s targets
• Deals closing today
• Backup if #1 slips

Everyone stands.
Cameras on.
Pipeline open.

Weekly:
Every function reports progress on the same goals, using the same metrics, in the same format.

No silos.
No spin.
No surprises.

Slippage is a visibility problem — not a talent problem.
Fix the cadence → fix the outcome.

If you want more on operating cadence, forecasting accuracy, and mid-market sales strategy, subscribe to my weekly newsletter - salesgss.com/newsletter.

The earliest sign your deal is slipping?
A single stakeholder goes silent for 11–14 days.

Across the teams I’ve led and advised, this Silent Thread Indicator is the #1 early warning signal.

When even one stakeholder goes dark for 14 days:
• Decision momentum breaks
• Champion confidence drops
• Parallel blockers emerge
• CFO/IT lose context
• Legal quietly resets the evaluation

Probability of stall increases 6–8X —
even if your champion absolutely loves you.

This is why single-threading kills Q4 forecasts.
You don’t lose because someone said “no.”
You lose because someone stopped saying anything.

Monday rule:
Any deal with a silent thread = at-risk deal.
Don’t wait for the stage to slip.
Re-engage immediately.
––
Full breakdown (and the Q1 2026 multi-threading plan) is in the new SalesGSS Newsletter issue.

linkedin.com/company/getdatabees/

JSESSIONID

li_at

Your prospects aren’t waiting 379 days because your category is complex.
They’re waiting because they can’t see momentum.

In 2025, PMF isn’t something you “achieve.”
It’s something your market re-evaluates every 90 days.

When you go two quarters without visible product news, buyers assume:
“Are they still investing?”
“Are they falling behind competitors?”
“Will this product still matter in 12 months?”

One quiet quarter doesn’t kill deals.
Two quiet quarters trigger the PMF death spiral:
longer cycles → lower win rates → silent champions → stalled pipeline.

Quarterly updates aren’t nice-to-have.
They’re your only defense against ambiguity.

If your deal cycles are extending while your roadmap isn’t shipping visibly every 90 days, that’s not a market problem — it’s a product velocity problem.

If you want the full framework, my latest SalesGSS newsletter is live - www.salesgss.kit.com

Most stalled deals don’t die in competition.

They die in conference rooms you’ll never enter.

Here’s what’s happening across $5M–$50M SaaS teams:
• Buyers spend 83% of the journey without you
• Buying committees: 10–11 stakeholders
• Only 9% of deals are multi-threaded
• Champion turnover: 81%
• Champion leaves → 80% deal loss rate

This is the Single-Thread Death Spiral:
One relationship in a 10-person committee is a gamble — not a forecast.

The simplest diagnostic I know:
How many unique stakeholders are engaged right now in your top 10 opportunities?

Benchmarks:
• 1–2 threads: Critical risk
• 3–4 threads: Baseline safety
• 5+ threads: Elite execution (6X win rate)

Thread count is now a leading indicator — not a “nice to have.”
If half your late-stage pipeline has fewer than 3 threads…
your Q4 number isn’t slipping — it’s already gone.
––
Full breakdown is in today’s SalesGSS newsletter -  https://lnkd.in/gZJeATBP

Marcos Rivera

Most B2B SaaS leaders build value.
But forget to capture it.

Let me guess:

- You’re guessing your price
- You’re losing revenue
- Scaling feels stuck

What if pricing could drive growth?

Here’s what I help you do:

→ Capture more value
→ Make pricing your edge
→ Build pricing confidence

Why should you trust me?

- 25+ years in tech pricing
- Ex–Head of Pricing at Vista Equity
- Priced 400+ SaaS products
- Author of *Street Pricing*
- Trusted by MBAs and SaaS CEOs

Beyond coaching, 

I’m an:

→ Investor 
→ Speaker
→ Entrepreneur 

passionate about helping businesses unlock their full revenue potential.

Ready to take control of your most powerful growth lever—pricing?

Checkout my featured section.

Founder and CEO

We help growth-stage B2B SaaS companies design the right pricing model to scale up

Ready to shift your pricing from guesswork to framework?

Your top 20% of customers are probably subsidizing your bottom 30%.
And you don't even know it.

See the #2 cause ↓

When we audit customer bases, we find this constantly: the customers who get the most value and never churn are often paying less than the customers who churn after 12 months.

Why?

Because most companies optimize pricing for customer acquisition volume, not for customer value fit.

You built pricing to attract as many customers as possible. 
So you attracted a lot of price-sensitive customers who were never going to be good long-term fits.

Meanwhile, your best customers, the ones who would happily pay 2-3x more because they're getting massive value, are grandfathered on old pricing.

The framework shows how to identify this pattern in your own customer base and fix it.

When we help clients migrate their high-value, under-monetized customers to pricing that reflects the value they're getting, we see 30%+ expansion revenue.

From customers who were already happy. 
They just needed a reason to pay more.

Are you optimizing your pricing for volume or value fit?

CEO of Pricing I/O • Award-Winning Author • Sought after Slayer of Bad Pricing

Years of calls, DMs, and pricing debates.
Finally in the same room.

Dan Balcauski, • Mark Stiving, Ph.D., and I have known each other for years through the pricing world. 

We've shared ideas, challenged each other's thinking, and watched the space evolve together.
But somehow, we'd never all been in the same place at the same time.

Until the Usage Economy Summit last week.

Pricing work can feel isolated. 
You're often the only person at your company who thinks about monetization strategy all day. 
The pricing community changes that.

These are the people who get it. Who've been in the trenches. Who know that pricing isn't just a spreadsheet exercise.

Good to finally make this photo happen.

This is Peter. He looks nice, but he challenges me all the time. 
And honestly? It makes our work better.

Here's the kind of thing we debate constantly:

We're reviewing a client case. High churn. Customers leaving after their first year. The executive team suspects it's a product problem.

The data tells a different story:
→ Legacy customers on old pricing? Staying longer. Making the most money. Seeing the most value.
→ New customers on "optimized" pricing? Churning at exactly 12 months. Citing ROI mismatch.

My take: "This is a pricing problem. The model doesn't align with value realization."

Peter's the first to push back on takes like this:

"Sometimes it really IS a product problem, Marcos. Sometimes the product just doesn't deliver enough value."

And he's right. Sometimes it is.

But here's how you know the difference:

If your legacy customers love the product and your new customers churn, it's not the product. It's the pricing.

The product didn't change. 
The pricing did.

The value is still there. 
Your pricing model just stopped aligning with how customers realize it.

This is what I love about our team at Pricing I/O.
We try to challenge ourselves:
→ Why that value metric?
→ What happens to expansion revenue?
→ Did you pressure-test this with sales?
→ I disagree with this segmentation, here's why.

It's not personal. It's rigorous.

Our best client work comes from these debates.

Because when smart people disagree about pricing strategy, that's where the real insights live.

The best pricing strategies aren't created in isolation.

They're forged when you put smart people in a room and force them to defend their thinking.

When you ask hard questions. 
Challenge assumptions. 
Stay willing to be wrong.

That's how you avoid the "drop decks and disappear" trap most consultants fall into.

If we can't defend a strategy to our own team, how can a client defend it to their board?

Across 400+ companies, this pattern repeats:
Pricing that worked at launch stops working as you evolve.

The winners don't treat pricing as a one-time project.
They build the muscle to test, learn, and adapt.

Do you have a process for pressure-testing big decisions?
Or are you making them in isolation?

Seat-based pricing is dying faster than most CEOs realize.

12 months ago: 50% of B2B SaaS used flat or seat-based models 

Today: Down to 27% 
Projected 2027: <10% 

The shift? 41% of companies are now using hybrid pricing models.

At SaaS Metrics Palooza, I walked through why this is happening, and the framework we use to build hybrid models that actually work.

Here's the reality:
AI changed who does the work.

It's not just humans anymore. It's systems completing tasks, generating summaries, resolving tickets, approving requests, all autonomously.

So how do you charge for that? By the person? The product? The outcome?

Answer: It depends on who's doing more of the work.

The BAM Framework - Base, Allowance, Meter 

These 3 layers create 5 different hybrid pricing models:

1/ Access Tiers (Base only) - Different AI complexity per tier, fair usage policy 

2/ Flat + Unit (Base + Meter) - Platform fee + outcome-based hybrid pricing 

3/ Flat + Limit + Unit (All 3 layers) - Most popular hybrid, includes volume + overages 

4/ Credit-Based (Base + Allowance) - PLG favorite hybrid, but don't fall into cost-plus trap 

5/ Pay-As-You-Go (Meter only) - Pure consumption, AI infrastructure 

The pattern from 400+ transformations: 
Companies using Flat + Limit + Unit (Model 3) are seeing the most traction. Why?

It gives customers predictability (base fee + included volume) while capturing expansion value (usage after allowance).

But, and this is critical, you need safety mechanisms.

→ Predict usage (dashboards, estimators) 
→ Prevent surprises (match reset timing to contracts) 
→ Protect customers (caps, true-up options, don't penalize usage) 

The companies winning with hybrid pricing aren't just picking a trendy model. They're designing the model that fits how their AI actually creates value.

Based on Tremont's research:
Hybrid models are capturing 3-4x more expansion revenue than traditional seat-based pricing.

The question isn't "should we go hybrid?"
It's "which hybrid model fuels OUR growth?"

Which model fits your product? Person doing the work or product doing the work?

Seat_Based_Pricing

At Vista Equity Partners, I learned something that changed how I think about pricing forever.

It wasn't about finding the "perfect price point."

It was about understanding how pricing connects to valuation multiples.

Here's what that looked like in practice:

We'd acquire a portfolio company and immediately audit their pricing. Not to squeeze more revenue out of customers, but to understand if their pricing model supported the growth thesis.

The companies with tight discount controls, clear value-based packaging, and pricing aligned to customer outcomes? They consistently outperformed on valuation multiples.

The ones with ad-hoc discounting, feature-based tiers, and "we charge what competitors charge" mentality? They left millions on the table.

One pattern I saw repeatedly: the pricing model that works for a Series B company scaling the business is completely different from a $100M ARR company optimizing for profitability pre-IPO.

There's no one-size-fits-all.

That's why I started Pricing I/O. To bring that PE-level rigor to growth-stage SaaS companies, without the $1 million McKinsey price tag and 9 months of PowerPoint slides.

We deliver results in weeks, not quarters.
We work in the trenches, not from ivory towers.
We teach teams to own pricing, not create dependency.

Because pricing isn't a one-time project. 
It's a strategic function that evolves with your business.

What's one pricing lesson you wish you'd learned earlier?

63% of AI monetization barriers are pricing-related.
And here's the #1 question I get from CEOs: "Where does AI actually belong in our pricing?"

Most companies are guessing. Chargebee's research shows 29% are just building AI into the core without charging extra.

That's a mistake.

At SaaS Metrics Palooza, I broke down the framework we use with PE-backed companies to decide where AI belongs.

It's not about the technology. It's about the experience.

Three different AI experiences = three different packaging strategies:

1/ Underscore the Core - When AI reinforces what you already do
Slack started with AI as a $10/user add-on. A few months later? Built into Business+ with a 25% price increase.

Why? They realized AI was becoming core to the Slack experience—making search better, recaps faster, answers easier. Not a "nice to have" feature.

2/ Upgrade the More - When AI extends adjacent use cases
ClickUp Brain charges $28/user (their base plan is $7). That's 4x their standard pricing.

Sounds expensive until you realize it's replacing 3-4 other AI productivity tools at $25-30 each. For customers, $28 is a consolidation play and a bargain.

3/ Unlock the New - When AI creates entirely new workflows
Intercom's Fin resolves support tickets autonomously at $0.99 per resolution. 
New TAM, new use case, standalone product.

Those who package based on experience (not just "it's AI so charge more") capture 2-3x more value and see 40% less pricing pushback.

The mistake? Treating all AI features the same.

Your AI that makes existing workflows 10% faster ≠ Your AI that unlocks entirely new capabilities.

Where does your AI actually belong?

McKinsey published a 4000-word guide on AI pricing. 
They're right about the problem. Wrong about the solution.

After analyzing 150 AI vendors, they found that despite $4.4trillion in potential economic value from AI, less than 1% of software spend is actually going to AI products today.

Why? Three consistent blockers: only 30% of companies publish quantifiable ROI, CFOs can't forecast what they'll spend on AI quarter to quarter and pilots fail to scale without proper change management investment.

I agree with all of this. We're seeing the exact same patterns across our 400+ client benchmark.

But here's where McKinsey loses me.

Their solution: "Test with customers and iterate quickly." That's not a strategy. That's expensive guessing.

Here's what I told a $75M ARR CEO who was about to "test" three different AI pricing models:
"You can't test your way to the right pricing without first understanding which experience you're selling."

→ Are you underscoring your core product? 
(AI makes existing workflows faster/better) 

→ Upgrading adjacent use cases? 
(AI extends what you do into new territory) 

→ Or unlocking entirely new capabilities? 
(AI creates workflows that didn't exist before)

That decision shapes everything, your packaging structure, pricing metric, go-to-market motion, whether you embed or sell standalone.

McKinsey's "iterate quickly" approach skips this question entirely.
So companies end up testing random combinations. 

"What if we bundle it?" 
"Let's try consumption-based." 
"Should we do per-seat or per-action?"

Each test costs 3-6 months and thousands in opportunity cost. This is the difference between consultants who drop decks and consultants who work in the trenches.

A 200-page McKinsey deck will tell you that 41% of companies are moving to hybrid models and you should "test and iterate." Great. Now what?

Working in the trenches with your team means asking: What experience is your AI actually creating for customers? Then designing the pricing model that monetizes that experience.

Not copying what Salesforce or Snowflake is doing. Not testing five models and hoping one works.

Companies that got AI pricing right didn't test their way there. They designed the experience first, then built pricing around it.

→ Slack realized AI was becoming core to the messaging experience, built it into Business+ with a 25% price increase. 
→ ClickUp saw AI extending productivity workflows, charged $28/user as an add-on to consolidate other tools. 
→ Intercom built AI that automated support entirely, 99¢ per resolution, standalone product.

Three different experiences. Three different pricing strategies. All intentional from Day 1.

McKinsey is right that this is the biggest shift in software since SaaS. But "test and iterate" isn't how you capture the opportunity.

Context without expertise is aimless. Expertise without context is pointless. You need both.

The discount death spiral: one deal at 30% off, another at 35%, your average hits 25%. 

Across 400+ SaaS companies, we see this pattern destroy margins and valuation multiples. 

Here's how PE-backed companies fix it with a 3-tier approval process. 

Full breakdown here 👆

McKinsey found that successful AI pilots require $3 in change management for every $1 in model development.

Most companies spend $0 on change management.
Then wonder why pilots don't scale to production.

After analyzing 150 AI vendors, McKinsey identified "sustained adoption" as the #3 blocker to AI monetization (behind value communication and price predictability).

Here's what's happening:

Companies invest heavily in building AI capabilities. Model development, infrastructure, data pipelines, millions of dollars.

Then they launch to customers and assume adoption will just happen.
It doesn't.

Without forward-deployed engineering, employee training, performance monitoring, and process integration, teams don't change their workflows. The AI sits unused. No ROI gets proven. Contracts don't renew.

The $3-for-$1 rule is McKinsey's finding from companies that successfully scaled AI pilots. For every dollar spent building the model, they spent three dollars on change management.

But here's what most pricing discussions miss:
Adoption friction isn't just an operations problem. It's a pricing problem.

This is the "L" in the REAL framework I shared at SaaS Metrics Palooza—Lift. How much effort does the customer need to invest to get value?

The more friction, the less pricing power you have.

→ High friction (3-6 months to value) means customers are skeptical. They want pilots. They negotiate hard. They're not willing to pay premium prices for something they're not sure will work.

→ Low friction (2-3 weeks to value) means customers see results fast. They're confident. They're willing to pay 2-3x more because the risk is lower.

Last month, I worked with a company that cut their AI onboarding from 12 weeks to 3 weeks through better change management, pre-built integrations, forward-deployed engineers, structured training.

Their close rates went up 40%. But they forgot to reprice.

They were still charging based on the old 12-week implementation timeline. Leaving money on the table.

Many companies forget to revisit pricing after they've reduced adoption friction. If you cut implementation time by 75%, you should be capturing more value, not the same amount.

Based on our 400+ client benchmark, companies that systematically reduce adoption friction and reprice accordingly capture 2-3x more revenue per customer than those who just focus on the product.

The pattern: 
Invest in change management → Measure time-to-value improvement → Reprice to reflect lower friction.

Adoption friction directly impacts pricing power.
Lower the friction, raise the price.

Are you investing $3 in change management for every $1 in AI development? 
Or are you building capabilities no one will adopt?

AIPilots

Your economics typically won't be as great out of the gate when it comes to AI, but the path to make it better over time is gonna be the key.

I shared this at SaaS Metrics Palooza because too many SaaS leaders are panicking about day-one AI pricing economics instead of building the right improvement path.

Here's the specific framework I shared:

Secondary limits that actually work:
→ Cap data sources (5 basic, 15 pro, unlimited enterprise)
→ Retention windows (30/90/365 days by tier)
→ Processing limits (hours per month, API calls)
→ Response time SLAs (24hr/4hr/1hr support)

The credit bonus math:
Instead of $1,200 ARR at $80/month (20% discount)
Do $1,200 ARR at $100/month + 20% usage credits

Same customer investment, but you protect pricing integrity and create margin expansion as they grow into those bonus credits.

Use this 3-step process as your 30-day action plan ↓

What secondary limit will you implement first?

Pricing

LLM costs have dropped 80% annually for two straight years.

Most AI companies responded by lowering prices or making features "unlimited."

McKinsey's research on 150 vendors shows that's the wrong move. 
Companies that maintained or raised prices despite falling costs saw 2-3x higher revenue growth than those who passed savings to customers.

Yet most are doing the opposite.

Jasper AI went from $10 per 5,000 words to unlimited generation included free. Sounds customer-friendly. But here's what that misses:
Your AI isn't getting cheaper. It's getting more valuable.

Two years ago, AI did single-step tasks, generate an email, summarize a doc, answer a question.

Today, AI agents execute multi-step workflows autonomously. Research across databases, draft outreach, update CRM, schedule follow-ups.

Your costs dropped 80%. 
Your complexity increased 10x.

Customers don't pay for compute. They pay for outcomes.

Adobe got this right. 
Started with embedded AI and simple features. 
Moved to standalone SKUs at higher prices as they added video generation, translation, and complex workflows.

Their costs went down. Their prices went up.

Q1 2025: $125M in AI revenue, expecting to double by year-end.

Only 16% of SaaS companies commercialized AI as standalone products. But those 16% report 2-3x higher customer traction and revenue.

The pattern across companies getting this right: costs fall, prices stay flat or rise with added capabilities. 

Use margin gains to add complexity, better integrations, multi-step workflows, higher quality.

Price based on value delivered, not costs incurred.

Based on our 400+ client benchmark, companies that maintain value-based pricing during falling infrastructure costs capture 4-5x more economic value.

Falling LLM costs are a margin opportunity, not a customer discount.

Question for your team: Are we pricing our AI based on the value customers get, or the costs we incur?

Most B2B SaaS companies are about to copy their way into a churn crisis.
This is the conversation I'm having with CEOs and boards 3x per week.

I just watched a $50M ARR company copy a competitor's usage-based pricing model.
Six months later: CAC up 60%, churn jumped from 8% to 14%, sales cycles doubled.

What works at scale can destroy an early-stage company.

Before you change your pricing model, answer these three questions:

1. How do your customers actually consume value?
Not how you think. How they actually behave.

CRM where value scales with team size? Per-seat still works.
Data platform where value comes from queries? Usage-based fits.

2. What's your sales motion?
High-touch enterprise sales can handle complexity.
PLG with self-serve? If prospects can't understand their cost in 30 seconds, you've lost them.

3. What's your growth stage?
Series A proving PMF needs different pricing than $100M ARR pre-IPO.

The shift from license-based to outcome-based pricing is real.
But context without expertise is aimless, and expertise without context is pointless.

Just dropped a video about it ↓

https://lnkd.in/gNGqitFU

I break down the complete framework, why AI is accelerating this shift, how to model impact before launch, and how to test without blowing things up.

You don't hire consultants because you have a strategy.
You hire them because you don't.

A CPO told me recently:
"The team wants to start quickly. They have urgent things they're nervous about because nobody knows what they're doing, bluntly."

Not "we're pursuing a strategic initiative."
Just: nobody knows what they're doing and we're scared.

This is how every consulting project actually starts.
But nobody admits it.

Here's what really happens:

Your company launches an AI product. 
Pricing team guesses at a model. 
Three months in, churn spikes. 
Board asks questions. 
Nobody has answers.

Or: Competitor changes pricing. Sales scrambles. Everyone has opinions. Nobody has data. CFO demands a strategy by next board meeting.

Or: New product launching in 60 days. Pricing still says "TBD" in the deck. CEO keeps asking "what are we charging?" and the room goes quiet.

That's when you call.

Not because you woke up wanting to "optimize monetization strategy."

Because you're 30 days from a board meeting with no idea what to tell investors about your AI pricing.

Consultants sell transformation.
You're buying panic relief.

When you say "we need to move fast", you're not impatient. You're scared and running out of time.

When you say "the team is nervous", you're admitting you don't have confidence in your approach.

When you ask "how quickly can you start?", you're really asking: how soon can you stop us from making an expensive mistake?

The consultants who get it don't lead with "here's our transformation methodology."

They lead with "here's how we'll de-risk this for you in the next 30 days."

If you're evaluating consultants and the process feels like it's taking forever, this is why.

You need someone to de-risk the urgent thing. 
They're selling you a six-month transformation.

The best projects start by de-risking the urgent thing first. Building the transformation second.

When's the last time you were brutally honest with a consultant about why you're really hiring them?

"We're paying for 20 seats but only 5 people use it."

You're charging per seat. 
But your customers don't get value from the number of seats, they get value from tasks automated, data processed, or outcomes achieved.

This is value metric misalignment. And it's killing your renewals.

Real example: 
AI-powered task automation company charging $50/user/month. 
Customer signs up with 20 seats at $1,000/month. 
But only 5 people actively use it.

They're paying $1,000/month but perceiving $250 worth of value.

At renewal: 
"Why are we paying for 20 seats when only 5 people use this?" 
They churn.

The fix isn't lowering the price. It's changing the value metric.

→ If value comes from tasks automated, charge for tasks, like Zapier does. 
→ If value comes from data processed, charge for data volume. 
→ If value comes from outcomes, charge for outcomes.

When your pricing metric aligns with how customers actually consume value, they can justify the cost.

Across 400+ B2B SaaS companies we've worked with at Pricing I/O, this is the #1 cause of pricing-driven churn.

What's your pricing metric? 
Does it match how your customers actually get value?

$60K for 6 months of value? That's too expensive.
This is how customers justify churning, even when your product works.

I see this pattern constantly: products that require data integration, team adoption, or behavior change take 6+ months to deliver full value.

But companies charge full price from day one.

So at renewal, customers do the math: 
"I paid $60K but only got 6 months of real value. That's $10K/month. Too expensive."

And they churn.

Not because the product failed. 
Because the pricing model didn't account for the time it takes to realize value.

The carousel above shows three ways to fix this: ramp pricing, success milestones, or hybrid models.

The goal? 
Make it easy for customers to justify the cost at every stage of their journey

If it takes 6 months to see full value:
Don't charge full price from day one

The value may be real.
But it takes time to materialize.

If your pricing model doesn't account for ramp time, customers will churn before they see the full value.

Your best customers are paying you 40% less than your worst customers.
And that's why you have a churn problem.

I see this pattern constantly at Pricing I/O.

A CEO comes to us: 
"We've invested millions in product development. Hired more CSMs. Improved onboarding. But our churn rate is still 15%."

So we pull their data.

Legacy customers, 5+ years with the company, love the product, paying 40% less than new customers.

New customers? Churning after 12 months. 
Primary reason: "Can't justify the ROI."

Most CEOs think their legacy customers are just more loyal.
But that's not it.

Your legacy customers are paying less, but they understand exactly what they’re getting for the money.

Your new customers? 
They're paying more, but they can't connect what they pay to what they get.

When we run pricing surveys with churned customers, here's what they tell us:
"We weren't using enough of the product to make it worth it."
"We couldn't justify the cost."
"The ROI wasn't there."

These aren't product complaints. These are pricing complaints.

Your product didn't fail them. Your pricing model did.

If your legacy customers love the product and your new customers churn, it's not the product. It's the pricing.

The product didn't change. 
The pricing did.

Across 400+ B2B SaaS companies, I see this constantly: 
High churn isn't always a product problem. It's often a pricing problem.

Are your best customers paying you the least?

Building a SaaS product is the easy part.
The hard part? 

Knowing what to charge for.
Not how much. What.

I've worked with 400+ companies on pricing transformations, and the most common mistake isn't bad pricing, it's never defining the value metric in the first place.

They default to per-user because competitors do it. 
Or per-seat because the billing system made it easy. 
Or per-transaction because... honestly, no one remembers why.

None of those are strategies. They're inherited defaults.

Here's the question that actually matters: 
What is the thing that, when your customer gets more of it, they're clearly getting more value?

It's not always obvious.

For a fintech client, it wasn't users. It was connected bank accounts, that's what correlated with value delivered.
For others, it might be projects managed, workflows automated, or outcomes achieved.

The right value metric does three things: it scales with customer success, customers understand it intuitively, and it's predictable enough to forecast.

"Per user" used to check all those boxes. But AI is breaking that model.

When automation reduces headcount, when one person can do the work of five, charging per user starts to punish the efficiency you're selling.

I'm seeing companies rethink this now, before their customers start asking why they're being penalized for getting better at their jobs.

What's your value metric? 
And when's the last time you questioned whether it's still the right one?

A client who's known me for years just evaluated five other consultants before hiring me.
That should terrify you.

This week, a billion-dollar SaaS company closed with us on a pricing transformation. 
Their Chief Product Officer and I go way back, we worked together at Vista Equity Partners. 
He knows my work. Trusts me. We literally survived the trenches together.

But when his company needed to hire a pricing consultant, he still looked at six different options.
During our debrief, I asked what made the difference.
He said: "When I did the reference calls, I asked each vendor's clients a specific question: Would you use this consultant for any type of pricing problem, or just this specific one?"
For the other five consultants, clients said: "Actually, they're great at this thing, but not so much at that. For that other problem, I'd probably use a different vendor."
For us: "I'd use them for anything. And have, multiple times."
Here's the uncomfortable truth nobody's talking about:
Your relationship doesn't protect you anymore.
Your client can love you. Trust you. Have worked with you before. And they'll still compare you with five others.
Why?
Because they've been burned by consultants who solved one problem and disappeared. Who dropped 200-page decks and never showed up again. Who created dependency instead of capability.
So now they do real diligence. Doesn't matter who you are.
What decides the deal isn't your pitch. It's what your clients say about you when you're not in the room.
"Would you hire this consultant again? For a completely different problem? Or just this one?"
If the answer is "just this one," you're a disposable solution. An expensive band-aid.
If the answer is "for anything," you're a partner.
What's the difference?
Disposable consultants solve problems. Partners build capability.
Disposable consultants deliver a model. Partners teach the team to execute it independently.
Disposable consultants create dependency. Partners make themselves less necessary over time.
The executive said something else that stuck with me:
"We wanted to make sure we didn't bias our process by our relationship. So we did the diligence even though we already knew you from the Vista days."
Your relationship gets you on the shortlist.
What your former clients say about you is what closes the deal.
In 2025, everyone's making those reference calls. Everyone's comparing. And the question they're asking is:
"Do you make me better after you leave?"
If the answer isn't "yes," you're losing deals you thought were already yours.
When's the last time you asked your clients: "Would you hire me again for a completely different problem?"
If you're not sure of the answer, you already know.

"We're not using it enough to justify the cost."
When SaaS customers say this at renewal, it's not a usage problem. It's a pricing problem.

After analyzing hundreds of B2B SaaS churn calls, I've identified 3 customer churn red flags:

Red Flag #1: "We're not using it enough"
→ Your pricing metric doesn't align with usage patterns

Red Flag #2: "We can't justify the ROI"
→ Your pricing model doesn't account for value realization timeline

Red Flag #3: "It's too expensive for what we're getting"
→ Wrong customer segmentation driving churn

If you're hearing these in renewal calls, fix your SaaS pricing model, not your product.

Watch more insights here:
https://lnkd.in/g-7XkY9X

LLM costs dropped 80% for 2 years straight. Should you lower AI prices?

McKinsey analyzed 150 vendors: Companies that maintained/raised prices saw 2-3x higher revenue growth.

Why? Your AI isn't getting cheaper, it's getting MORE valuable.

18 months ago: Single tasks
Today: Multi-step autonomous workflows

Adobe's playbook: Costs ⬇️ → Prices ⬆️ → $125M Q1 revenue

What to tell your CFO: Customers pay for outcomes, not compute.

Source: McKinsey 2025 report

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