Today, we’re speaking with Joaquin Perrone, a senior software executive who has focused on digitizing how blue collar workers are managed, paid, and scheduled. As Chief Revenue Officer for one of Europe’s largest technology groups, he leads the commercial strategy and innovation behind software systems that power the daily operations for millions of essential workers—from carpenters, plumbers, and cleaning crews, to healthcare professionals and energy infrastructure teams.
Earlier in his career, Joaquin founded a technology startup backed by some of the world’s leading venture capital firms, including Lightspeed Venture Partners and Sequoia Capital, and built multiple software businesses as part of BCG Digital Ventures.
Let’s start with the big picture. You often describe your field as “infrastructure software.” For people unfamiliar, what does that mean, and why is it so essential?
Infrastructure software is the invisible backbone that keeps the real economy functioning—things like payroll, invoicing, compliance, and workforce scheduling. In industries such as staffing, facility services, and trades, these systems coordinate millions of shifts, calculate taxes, and ensure people get paid correctly.
It’s not glamorous, but when these systems fail, businesses grind to a halt. That’s why I’m passionate about this space—it touches a huge share of the working population.
With that context, where does AI actually fit into this backbone?
It’s easy to assume AI will replace everything, but the reality is more nuanced. Generative AI excels at automating unstructured administrative tasks: summarizing work orders, drafting emails, or classifying documents. But for mission-critical processes—like payroll calculations or tax filings—everything must be fully deterministic.
You can’t hallucinate a payslip or approximate a tax rate. The most effective approach combines old-school digitization with intelligent augmentation—AI as a layer on top, not a substitute.
That makes sense. So even as AI accelerates, these barriers to entry remain quite high.
Exactly. In many of these industries, you have to obtain certifications, integrate with government tax authorities, and comply with complex regulations. Building that infrastructure can take years and significant capital.
That creates a paradox: the benefits of AI are often captured by established platforms that already hold the compliance and trust. This is why competition in critical systems doesn’t emerge overnight, even as tools become more powerful.
Yet you also see areas where AI adoption is much faster. What are those?
Marketing and recruiting have been transformed most rapidly. For example, recruiting automation is a huge lever in sectors with chronic labor shortages. AI can pre-screen candidates, draft personalized outreach, and even conduct structured interviews.
On the other hand, traditional marketing—like paid lead generation—matters less in these fields because demand often exceeds supply. So companies are focusing their AI investments on workflows that directly affect hiring and operational efficiency.
It feels like we’re seeing more founders recognize that potential. Many young entrepreneurs are buying traditional service businesses specifically to modernize them with AI. What’s your perspective on this trend?
It’s one of the most interesting developments right now. There’s a growing wave of “entrepreneurship through acquisition”—search funds, independent sponsors, even micro-PE—with a thesis that they can take an old-economy business and create transformative value through technology.
Sometimes that results in better margins and operational discipline. But increasingly, these founders also develop proprietary software in-house. Over time, that can evolve into standalone technology businesses, blurring the lines between services and SaaS.
You’ve had quite a journey—founder, head of product, now CRO of a major software group. How has that shaped how you think about innovation?
Early in my career, I co-founded a startup that raised funding from Lightspeed Venture Partners—one of the most respected investors in the world. That kind of validation feels great. But the truth is, when you’re speaking with actual customers, none of it matters. The only thing that counts is whether you’re solving a real problem in a way that’s valuable and scalable.
Too many founders chase the brand of the “successful startup”—focusing on decks, hype, or investor prestige. Nothing beats the moment when a customer is willing to pay—consistently—for something you’ve built. That’s what grounds you.
Today, I work in mature software companies where product-market fit was achieved years ago. The challenge is different: making smart, sustainable decisions every day—what feature truly creates long-term value, which sales channels scale over 3–5 years, and how to keep evolving without losing focus. That discipline is just as hard—and just as rewarding—as the zero-to-one phase.
How do you think the nature of software development is changing with the rise of LLMs and other AI systems?
We’re entering a new era where software paradigms are blending. Traditional, rule-based code still powers certified payroll engines or tax systems where precision is critical. Machine learning models add pattern recognition, predictions, and schedule optimization.
A third layer is emerging—LLMs that turn natural language into structured actions, letting users describe what they want instead of navigating complex interfaces.
Increasingly, real-world systems will combine deterministic logic for compliance, predictive models for optimization, and natural language interfaces for flexibility. The challenge—and opportunity—is orchestrating them to deliver reliable outcomes, where experienced software companies have a real edge.
If code generation becomes fully commoditized, what happens to the long-term value of software companies?
That’s the existential question a lot of founders are asking. When code itself becomes easy to produce, your competitive moat shifts to other assets:
- Deep domain expertise in how industries really work
- Regulatory and compliance infrastructure that is hard to replicate
- Trusted customer relationships
We’re also seeing a shift to “services as software,” where AI turns traditionally low-margin services—like onboarding or compliance checks—into scalable, high-margin offerings. That can fundamentally deepen your value proposition.
You’ve also been spending a lot of time in New York recently. How do you see the European and U.S. tech ecosystems differing?
They each have distinct strengths. In Europe, you see less flash and more focus on building highly specialized solutions—in regulated or niche industries where depth of expertise matters. In the U.S., there’s a greater appetite for bold, horizontal plays that aim to impact vast numbers of people and businesses. Both approaches can produce extraordinary companies, and the most exciting opportunities come from combining them.
Finally, for younger entrepreneurs thinking about building in this space—AI applied to mission-critical software—what should they keep in mind?
First, respect the complexity. These industries require deep expertise, strict regulation, and operational discipline. If you move fast and break things in payroll, people don’t get paid.
Second, focus on trust and reliability. AI doesn’t replace the need for solid software—if anything, greater power demands stronger guardrails and fallbacks.
Finally, don’t confuse novelty with value. The biggest wins come from deep integration, great UX, and solving essential problems at scale. It might not make headlines, but it’s impact that lasts—and there’s still massive opportunity ahead.
Joaquin, thanks for sharing such a thoughtful perspective.
My pleasure—Thank you.
