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

I'm a strategic technology leader passionate about reshaping how businesses evolve in a digital world. With deep experience across the automotive, wind power, chemical, and HVAC sectors, I’ve led 50+ large-scale transformation programs delivering measurable gains in cost savings, operational efficiency, and data-driven decision-making.

I translate advanced technologies into real business value. My focus is aligning digital strategy with core business goals, challenging the status quo, and enabling organizations to scale with confidence.

Key areas of impact:

🔹 Technology & Business Alignment: I bridge IT and business to ensure tech investments drive outcomes that matter.
🔹 Enterprise Digital Strategy: I craft and execute scalable technology roadmaps that unlock growth and agility.
🔹 Data-Driven Decision Making: I lead AI and analytics initiatives that turn complex data into predictive insights.
🔹 Cost Optimization & Efficiency: I drive automation, cloud adoption, and process reengineering to maximize ROI.
🔹 Leadership & Talent Development: I build and mentor teams that innovate boldly and deliver consistently.

Prabhakar V

𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲 𝗶𝗻 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝟰.𝟬

Automation scaled faster than coherence.

We’ve added sensors, dashboards, agents, and APIs—yet when a single constraint shifts on the shop floor, the system hesitates. Not because it lacks data, but because different layers are reasoning from different truths.

𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲 is the ability of an industrial system to stay internally consistent while reality changes.

It exists when:
• ERP expresses intent, not assumptions
• MES reasons in actions, not static routes
• Digital twins reflect the current state, not an idealized one
• Machines execute without being forced to compensate for planning gaps

This is why Industry 4.0 cannot be built bottom-up or top-down alone.
It must be built 𝘀𝘁𝗮𝘁𝗲-𝗳𝗶𝗿𝘀𝘁.

State-first means plans are derived from the live system state and customer intent—then orchestrated through MES, not hard-coded into machines.

In a coherent system, production plans are not defended—they are re-written.
Disruptions don’t trigger escalations; they trigger controlled re-composition.

𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀𝗹𝘆 𝗳𝗲𝗲𝗱𝘀 𝗯𝗮𝗰𝗸 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺’𝘀 𝘀𝘁𝗮𝘁𝗲, 𝗲𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗿𝗲𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝘄𝗵𝗲𝗻 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝘀𝗵𝗶𝗳𝘁𝘀.

Operational coherence is not a feature.
It is a design obligation.

Without it, every “smart” upgrade simply makes misalignment faster.

What’s the biggest coherence gap you’ve seen between planning and execution?

Digital Transformation Leader |Driving Enterprise-Wide Strategic Change | Thought Leader

𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝘄𝗶𝗻𝘀 𝗗𝗼𝗻’𝘁 𝗙𝗮𝗶𝗹 𝗮𝘁 𝗔𝗜. 𝗧𝗵𝗲𝘆 𝗙𝗮𝗶𝗹 𝗮𝘁 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻.

Most digital twin initiatives still treat 𝗕𝗜𝗠 (𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴) as a static artifact— a handover deliverable from design to construction.

That’s a fundamental mistake.

BIM is not the input to a digital twin.
BIM is the memory spine of the digital twin lifecycle.

A true digital twin evolves through three distinct phases:

𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗘𝗺𝗯𝗿𝘆𝗼 (𝗕𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝗮𝘀𝘀𝗲𝘁 𝗲𝘅𝗶𝘀𝘁𝘀)
At this stage, BIM is not geometry—it’s intent.
Design assumptions, performance targets, constraints, and trade-offs are encoded before concrete is poured or machines are installed.
Simulation here is about eliminating regret, not visual appeal.

𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝗕𝗼𝗱𝘆 (𝗪𝗵𝗲𝗻 𝘁𝗵𝗲 𝗮𝘀𝘀𝗲𝘁 𝗶𝘀 𝗮𝗹𝗶𝘃𝗲)
Once the physical factory or asset exists, BIM stops being static documentation and becomes a live reference model.
Sensor data, production states, maintenance events, and deviations continuously update the twin.
This is where most twins collapse—because models don’t evolve when reality does.

𝗧𝘄𝗶𝗻 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 (𝗪𝗵𝗲𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗲𝗱)
At maturity, the digital twin is no longer observing—it is governing.
Decisions are bounded, reversible, and confidence-scored.
When signals degrade, authority degrades automatically.
No heroics. No escalation theater.

What’s often missed is that BIM enables continuity across all three phases—
from planning → construction → operation → reuse or decommissioning—
without losing context or truth.

In smart factories, this lifecycle approach has shown measurable impact:

Lower WIP
Faster delivery
Fewer late-stage surprises

And most importantly, decisions that respect physical reality.

A digital twin without lifecycle continuity is just a dashboard with ambition.

The future isn’t more intelligence.
It’s more memory, more governance, and fewer resets.

How mature is your digital twin lifecycle today—design-time, run-time, or decision-time?

𝗦𝘁𝗿𝗼𝗻𝗴 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴.
𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗲𝗿𝗲 𝗱𝗲𝗽𝘁𝗵 𝗰𝗿𝗲𝗮𝘁𝗲𝘀 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲.

This comes from years in manufacturing setups, large SAP transformations, and analytics programs, environments where decisions don’t announce failure.
They erode trust quietly.

𝗥𝗶𝗴𝗵𝘁 𝗻𝗼𝘄, 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗯𝗲𝗶𝗻𝗴 𝗳𝗼𝗿𝗰𝗲𝗱 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗰𝗮𝗹𝗹𝘀 𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗲𝘆 𝗱𝗼𝗻’𝘁 𝗳𝘂𝗹𝗹𝘆 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 — 𝗮𝗻𝗱 𝗵𝗼𝗽𝗶𝗻𝗴 𝗯𝗿𝗲𝗮𝗱𝘁𝗵 𝘄𝗶𝗹𝗹 𝗰𝗮𝗿𝗿𝘆 𝘁𝗵𝗲𝗺 𝘁𝗵𝗿𝗼𝘂𝗴𝗵.
𝗜𝘁 𝘄𝗼𝗻’𝘁.

We still tend to frame leadership in binaries:
technical vs non-technical
specialist vs generalist

But effective leadership doesn’t live at the extremes.

It lives in the space where 𝗯𝗿𝗲𝗮𝗱𝘁𝗵 𝗶𝘀 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝗯𝘆 𝗱𝗲𝗽𝘁𝗵.

Breadth creates reach.
Depth creates trust.

The real skill is recognizing 𝘄𝗵𝗲𝗿𝗲 𝗱𝗲𝗽𝘁𝗵 𝗶𝘀 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗯𝗲𝗳𝗼𝗿𝗲 𝗱𝗲𝗰𝗶𝗱𝗶𝗻𝗴.

In complex systems:
→ You don’t need to write code, but you know where it strains under load
→ You don’t design machines, but you respect physical limits over plans
→ You don’t build models, but you understand where uncertainty compounds

A few lessons that stay with me:
• Credibility fades before authority does
• Simplification is useful — until it hides risk
• Saying “I don’t know” preserves trust — shallow certainty destroys it
• Leaders are tested not just on outcomes, but on how they arrive at decisions

Leadership isn’t choosing between depth or breadth.

It’s developing the 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘁𝗼 𝗺𝗼𝘃𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘁𝗵𝗲𝗺.

Too little depth creates noise.
Too much depth slows momentum.

𝗧𝗵𝗲 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝗶𝘀 𝘂𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 — 𝗮𝗻𝗱 𝘂𝗻𝗮𝘃𝗼𝗶𝗱𝗮𝗯𝗹𝗲.

When was the last time you realized you were leading with breadth, but not enough depth?

Follow me for grounded perspectives on leadership, AI, and systems under pressure.

𝗪𝗵𝗲𝗻 𝗮𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗙𝗮𝗶𝗹𝘀 𝗮𝘁 𝟮 𝗔𝗠, 𝗪𝗵𝗼 𝗧𝗮𝗸𝗲𝘀 𝗢𝘃𝗲𝗿?

No meeting.
No escalation call.
No panic.

Agent confidence drops below threshold.
Authority shifts.
The system already knows what to do.

That’s what governed autonomy looks like in real industrial environments.

Most “multi-agent systems” aren’t about agents coordinating.
They’re about governing autonomy under uncertainty.

Here’s how it would actually work on the shop floor.

Agents are continuously scored using hard signals:
• digital twin divergence (predicted vs actual behavior)
• statistical checks on agent outputs (drift, variance, violations)
• confidence decay as unexplained error accumulates

Agents aren’t trusted because they’re smart.
They’re trusted because their mistakes show up fast.

If a Process Agent degrades, decision rights may shift to a Quality or Planning Agent — but only within strict bounds.

If the agent hallucinates or its code misbehaves?
Different story.
There is no “AI replacing AI.”

Autonomy collapses downward:
• deterministic MES rules take over
• physical interlocks remain sovereign
• humans are escalated by policy — not hope

Conflicts are expected.
Quality may say stop. Planning may say continue.

A deterministic governance layer resolves it instantly:
safety > quality > throughput > cost.
No debate at runtime.

This is why some factories adapt to disruptions in minutes while others need days.
The difference isn’t better AI.

It’s better governance.

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗺𝗼𝗮𝘁 𝗶𝘀𝗻’𝘁 𝗺𝗼𝗱𝗲𝗹𝘀.
𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝗿𝗼𝘂𝗻𝗱 𝘁𝗵𝗲𝗺.

Genuinely curious where most teams are.

Ref : Supervisory control theory (Ramadge & Wonham), where autonomous components operate under a supervisory layer that restricts control under unsafe conditions.

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The Battle

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Prabhakar V

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