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

I specialize in the messy middle of B2B SaaS scaling—taking companies from erratic growth to a disciplined, high-performance revenue engine.

Most sales teams don’t fail because they lack talent; they fail because of ambiguity.
When forecasts can’t be trusted, pipelines move only through founder heroics, and managers report activity instead of owning outcomes, you don’t need another playbook—you need an operating system.

My operating philosophy: Cadence | Ownership | Process

I install a repeatable flywheel of operational excellence:

Cadence
A rigorous Rhythm of Business—daily, weekly, and monthly operating cadence that surfaces execution risk early, before it becomes a board-level surprise.

Ownership
Managers own outcomes, not just activity. Forecasts are built bottom-up so leadership can commit without caveats.

Process
Qualification and execution are standardized before adding headcount, ensuring hiring is tied to capacity and real revenue impact—not hope.

The proof (Ekahau — SVP, Global Sales, 2019–2024)
	•	Revenue: Scaled $25M → $65M ARR in five years
	•	EBITDA: Increased $7M → $32M (margins ~20% → ~50%)
	•	Team: Built a 130-person global organization while reducing per-employee cost 25%

Selected wins
	•	Healthentic: Doubled ACV ($5M → $10M) and drove 10× pipeline growth through GTM transformation
	•	ControlCase: Delivered 34% YoY growth and 300% expansion revenue growth

Currently

Available for VP of Sales or Fractional GTM leadership with B2B SaaS companies $5M–$30M ARR. I also lead SalesGSS (revenue operating frameworks) and DigitalRegIntel (regulatory intelligence for AI/fintech).

The Senator perspective

Two terms as a Washington State Senator taught me the value of high-stakes negotiation and public accountability. I bring that same discipline to sales: clear ownership and execution when the pressure is real.

“Steve is ‘Instant Offense’ for sales—rare and valuable. World-class at creating strategic, data-driven sales teams.”
— Jeff O’Mara, CEO (Healthentic) / GM (Ekahau)

Steve Litzow

AI didn’t kill sales reps.
It killed excuses.

In 2026, AI isn’t a productivity tool — it’s a truth serum.

It exposes:
• Broken process
• Weak management
• Fake activity dressed up as “effort”

If your sales motion is tight, AI compresses time-to-decision.
If it’s sloppy, AI just produces more noise — faster.

Most $10M–$50M ARR teams are about to learn this the hard way.

This is Week 1 of the SalesGSS 2026 Operating Series.
Eight weeks. Eight uncomfortable truths.

AI didn’t come for your reps.
It came for your excuses.

Comment “2026” if you want the series.

VP of Sales / Fractional VP (B2B SaaS, VC & PE-backed) for $5M–$30M B2B SaaS | Scaled $25M → $65M ARR | SalesGSS

Your closers aren’t the problem.
Your committee is.

Roughly half of qualified B2B deals don’t lose.
They stall.
“No decision.”

At $10M–$50M ARR, the pattern is consistent:
• Strong champion
• Positive calls
• Deal “feels good”

But it’s single-threaded.

One relationship trying to carry internal selling across:
Finance. IT. Security. Procurement. Operations.

The closer is persuasive.
The organization isn’t aligned.

With 10+ stakeholders involved, persuasion no longer scales.

Elite closers in 2026 aren’t smoother.
They’re better at internal selling.

They multi-thread early.
They equip champions.
They sell the decision process.

Quick diagnostic:

Can your reps name five stakeholders—and each one’s priority—in their top deals?

If not, you’re optimized for persuasion when you need consensus.

Week 2 of the SalesGSS 2026 Operating Series.
(Week 1: AI doesn’t fix execution—it exposes it.)

Comment “2026” if you want the series.

If you can’t prove value in the first 60 days, you don’t have a deal.

In 2026, long sales cycles aren’t what kill deals.

Delayed proof does.

Buyers will wait longer to buy —
but they won’t wait longer to know if they made the right decision.

57% of B2B buyers expect positive ROI within 3 months — not at renewal.

If a champion can’t point to a concrete win inside 60 days:

Confidence erodes
Finance steps in
“Commit” turns into “let’s wait and see”

That’s why the strongest teams aren’t closing faster.

They’re proving faster.

They define Day-60 success before the contract is signed.
They scope for proof, not size.
They treat time-to-value as a deal quality metric, not a CS problem.

If value only shows up at renewal, the deal was insured by hope — not evidence.

This is Week 3 of the SalesGSS 2026 Operating Series.
Next week: why slow product cadence is becoming a silent deal-killer.

This week’s regulatory signal isn’t clarity — it’s formation.

In the U.S., digital asset market structure legislation stalled again. In the EU, regulators are already questioning AI Act transparency practices well ahead of enforcement.

Different jurisdictions. Different tools. Same supervisory pattern.

Expectations are forming before rules finalize — and long before penalties appear.

That’s what shows up later in:
- Board and audit committee questions
- nvestor and acquirer diligence
- Bank and payment-partner reviews

Not “what did the law eventually say?”
But “what did leadership understand and monitor as expectations evolved?”

I publish a short Digital Regulatory Intelligence brief each Tuesday that documents these early supervisory signals — not predictions, not legal conclusions — just what’s forming in real time.

Most regulatory risk doesn’t show up as a rule violation.

It shows up later as a question regulators now ask routinely in enforcement actions:

“When did you become aware of this risk?”

Across new digital regulations and recent enforcement matters, regulators have shifted from checklist compliance to supervisory posture — an expectation of continuous, documented awareness.

Exposure today isn’t just what you did.
It’s what you reasonably knew — and when you knew it.

That’s why this is increasingly a board-level issue, not a compliance exercise.

Curious how others are documenting supervisory awareness before it’s asked for.

When supervision exists only in email, it doesn’t exist at all.

This week didn’t bring a sweeping new regulation.
That’s exactly the risk.

Across the U.S. and EU, regulators continued applying existing authority while legislative clarity remains unresolved. That combination — active enforcement discretion + unresolved rules — is where most future compliance failures are born.

Fast-forward 24–36 months and the question won’t be:

“Was the guidance clear?”

It will be:
“What did you know — when did you know it — and how was that knowledge supervised?”

This week alone:

SEC and CFTC signaled regulatory coordination is moving forward without Congress
EU AI Act transparency expectations quietly locked in
FinCEN delayed rules while Treasury intensified enforcement
Enforcement volume dropped — but guidance-based expectations accelerated

None of this required a new rule. All of it creates exposure.

If your supervisory awareness lives in emails, Slack threads, or people’s heads, it won’t survive diligence, exams, or enforcement.

That’s why we published this week’s DigitalRegIntel Supervisory Brief — and why we believe supervisory awareness needs a system of record, not a newsletter.

Because when the question comes,
explanations don’t count. Records do.

This week’s brief formally documents each supervisory signal referenced above.

#RegulatoryCompliance #DigitalAssets #AIGovernance #RegTech #Compliance

Experienced large-scale finance transformation leader, supporting enterprises in translating transformation strategy into execution in complex, regulated environments.

My expertise spans finance transformation toward intelligent operating models, with AI and automation embedded selectively to create measurable, sustainable value while maintaining governance and control at scale.

I focus on simplifying complex finance environments, modernizing processes, strengthening data foundations, and embedding governance early to ensure resilience under regulatory pressure, organizational change, and growth.

I have led and contributed to multi-region programs, including post-ERP transformations and post-acquisition integrations, where consistency, control, and execution discipline are critical to success.

My background combines operational excellence and advanced technology. I hold a Lean Six Sigma Black Belt certification and bring hands-on experience across applied AI, RPA, operational analytics, and enterprise data environments, delivering improvements in financial performance, efficiency, and resilience.

I place strong emphasis on data governance and AI governance, with a focus on transparency, accountability, and decision integrity to support trust, compliance, and long-term performance.

I hold an MSc in Applied Artificial Intelligence and am currently pursuing an MBA with a focus on AI Project Management.

Joanna Miler

AI positioned as a governance or compliance solution needs a different evaluation lens
because the risk profile is fundamentally higher.

The market is crowded with AI tools claiming to enable governance, compliance, and data protection.

→ “Privacy-first”
→ “Security-first”
→ “Compliance-by-default”

These claims do not lower the bar.
They raise it.

When an AI tool handles sensitive or regulated data, approval is no longer technical.

It is a governance decision.

The bar for governance-positioned AI is higher, not lower.

If a tool:

→ Processes documents
→ Touches sensitive or regulated data
→ Positions itself as a compliance or privacy enabler

Then due diligence becomes mandatory, not optional.

What executive teams must verify before approval,

Data traceability

→ You can clearly explain where data is processed
→ You understand how it flows and under which legal basis
→ Transfers are lawful, certified, and defensible under GDPR and EU AI Act-style regimes

If you cannot explain this simply, you cannot approve the tool.

Verified integrations, not assurances

→ Identity, access, OAuth, and SSO are production-proven
→ Logging and audit trails exist in real customer environments
→ “In progress” and “under review” are risks, not features

Governance by default, not by exception

→ Data retention, encryption, breach notification, and continuity are transparent upfront.
→ Governance does not rely on custom clauses or post-demo discovery.

If governance needs exceptions, it is not governance by design.

Alignment between promise and reality

→ A tool claiming to reduce compliance risk must pass compliance scrutiny itself.
→ If it cannot survive the same review as your internal systems, risk is not removed.
→ It is merely relocated

AI accelerates decisions.
Executive accountability does not.

When things fail, regulators look to boards and leadership, not vendors.

The real risk today is not using AI.

It is delegating governance to tools that have not earned that responsibility.

For leaders accountable for data, compliance, and reputation, caution is not resistance.

It is what allows innovation to scale without breaking trust.

Finance Transformation Strategy | Intelligent Operating Models | Governed AI for Business Outcomes

Invoice disputes do not begin in SAP.

They begin quietly inside customer emails that finance teams usually ignore.

Below is a clean case card, written in simple, direct points.

Case: Early dispute detection using NLP on emails

Business problem:

• Invoice disputes appear late in SAP, after cash is already delayed.

• Finance teams only react once the dispute is officially logged.

Hidden risk signal:

• Customers express concern days earlier through email language.

• These messages reach AR teams but are treated as routine communication.

What NLP checked:

✓ Phrases indicating confusion or disagreement on charges.

✓ Mentions of incorrect pricing, missing credits, or contract mismatch.

✓ Negative or uncertain tone combined with billing keywords.

How the system worked:

• All inbound finance emails were scanned in real time.

• Each email received a dispute-risk score based on language and intent.

• High-risk emails triggered alerts before any SAP dispute was created.

Action taken early:

✓ Finance clarified invoices proactively
✓ Sales and billing aligned before escalation
✓ Customers received responses before frustration built up

Result:

• Fewer formal disputes in SAP
• Faster collections and improved cash flow
• Reduced friction between customers, sales, and finance

Core insight:

Disputes start as language, not transactions.

AI that listens early prevents problems that systems only see too late.

Where else in your Q2C flow are early signals being missed?

Finance teams do not move to the cloud because it is fashionable.

They move because fragmented ERPs, weak controls, slow closes, and regulatory pressure leave no alternative.

Once the move is decided, a tougher question appears.

Does the cloud provider actually matter?

The answer is yes, but not for the reasons vendors usually sell.

All three platforms offer the basics.

Compute. Storage. Security frameworks. Automation. ML.

The real differences show up in architecture, integration, automation, and governance.

Those choices quietly shape how finance actually operates under SOX, IFRS, and sector regulations.

What finance teams really get

AWS

→ High flexibility and event-driven automation
→ Strong for complex, multi-ERP environments
→ Fits cash application, reconciliation, credit scoring at scale
→ Best when customization and global control matter more than simplicity

Azure

→ Natural fit for Microsoft-centric finance teams
→ Tighter alignment with ERP, identity, access, and reporting
→ Strong auditability and access control
→ Works well under SOX-style internal control frameworks

Google Cloud (GCP)

→ Clear strength in analytics and data platforms
→ BigQuery and native ML enable fast insights
→ Strong for FP&A, forecasting, anomaly detection, and risk models
→ Suits finance teams driven by data-led decisioning

Where the differences really show up:

→ Credit risk and scoring
→ Cash application and reconciliation
→ Fraud and anomaly detection
→ Collections and dispute prevention
→ FP&A and scenario modeling

Different processes reward different strengths.

That is why many large organizations go hybrid by design, not by accident.

Finance transformation does not start with AI or tools.

It starts with architecture.

Cloud will not solve every problem.

But it gives finance something it has rarely had:

A consistent, scalable foundation to run the next decade of operations on.

AI stops being neutral the moment it touches money.

In Q2C, that moment comes faster than most teams expect.

When AI influences credit decisions, disputes, or collections, it becomes a decision system with legal, financial, and reputational consequences.

At that point, the real question is no longer:
“Does the AI work?”

It becomes:
“Are we entitled to let it decide?”

What many teams miss is this simple truth.

AI governance in Q2C is sector-driven.

The same AI capability can be acceptable in one industry
and unacceptable in another
because accountability and impact are not equal.

How sector context changes what is allowed

Financial Services

→ Decisions must be explainable, auditable, and reversible
→ If you cannot defend it to regulators, it should not exist

Healthcare & Pharma

→ Governance prioritizes risk containment and human authority
→ AI can support decisions, not replace final control

Manufacturing & Industrial

→ Governance depends on causality and commercial fairness
→ Unexplained deductions or dispute outcomes destroy trust

B2B SaaS & Subscriptions

→ Governance sits at contracts, entitlements, and revenue recognition
→ A wrong collections action can trigger churn or misstatement

Retail & Distribution

→ Volume amplifies risk
→ Without thresholds, AI quietly erodes consistency and control

What invalidates AI here is not model accuracy.
It is missing sector-appropriate governance.

The same use case carries different consequences based on:

→ Regulatory exposure
→ Customer vulnerability
→ Auditability requirements
→ Tolerance for automation
→ Clarity of human accountability

That is why generic AI governance fails in Q2C.

The leadership question is not:

“Is the AI accurate?”

It is:

“Are we prepared to own the decisions this AI makes, in our sector, under our obligations, with our customers?”

Without that answer, AI has no legitimate place in credit, disputes, or collections.

The hardest trade-off I have managed was between customer empathy and collections performance.

For a long time, it felt like a forced choice. Be understanding and miss numbers. Push numbers and damage trust.

That framing was wrong.

The real issue was the absence of structure.

Here is what I saw repeatedly.

• Customers had real reasons for delays.
• Collections teams treated every delay the same.
• Empathy turned into open-ended patience.
• Performance pressure turned into friction.

Nothing improved because clarity was missing.

What we changed:

✔ We stopped treating all late payments as equal.
Cash-flow issue ≠ approval delay ≠ invoice confusion.

✔ We acknowledged the situation once, clearly and respectfully.
Empathy was explicit, not emotional.

✔ Every conversation ended with a next step.
Owner. Date. Action.

✔ We changed the question.
From: “Can you pay?”
To: “What needs to happen for payment to be released?”

What happened next:

✔ Customers felt respected, not chased.
✔ Collection cycles shortened.
✔ Escalations reduced.
✔ Trust actually increased.

The lesson was uncomfortable but important.

• Empathy without structure creates avoidance.
• Performance without empathy creates resistance.

The best results came from designing the middle ground.

Clear expectations. Human conversations. Firm timelines.

Where have you seen empathy help performance once structure was put in place?

If your product can be reverse-engineered by observing behavior, it is not a secret.

because AI has made behavioral cloning fast, cheap, and scalable.

AI does not need to break encryption or steal source code.

AI just needs to watch the system work.

→ Observe inputs
→ Observe outputs
→ Infer rules, thresholds, workflows, and decision logic
→ Replicate how you score fraud, route access, or prioritize alerts

What used to take months now takes weeks, and sometimes days.

That creates one uncomfortable question for product and security leaders.

→ What exactly is your IP protected by?

If your answer is any of these, your advantage is already shrinking:

✅ Features
✅ Workflows
✅ UI logic
✅ Decision behavior

In places like the EU, reverse engineering a lawfully acquired product can be lawful in many cases, especially for interoperability or error correction, as long as contract terms and confidentiality obligations are not breached.

AI does not change the legal baseline.

AI changes the speed and scale of exploitation.

So you are already choosing an IP posture, even if you are not calling it that.

Pick your posture:

Option 1: Feature-based IP

→ You win by shipping first.
→ You accept fast imitation as the cost of growth.

Option 2: Trade-secret-dependent IP

→ You hide logic, tuning, thresholds, and heuristics.
→ You bet they cannot reliably infer it from behavior or side channels.

Option 3: Evidence-based IP

→ You assume features and behaviors will be copied.

→ You compete on what cannot be cloned quickly:

✅ Auditability
✅ Data provenance
✅ Governance controls
✅ Certifications and trust posture
✅ Service quality and operational maturity

AI makes one thing clear.

If your “secret sauce” can be reconstructed from how the system behaves, it is not a secret.

It is a temporary asymmetry.

So the leadership question is no longer:
“Is our IP protected?”

It is:
“What would still differentiate us if someone copied our behavior, and we had to prove trust?”

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The Battle

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