Established technology companies are being urged to hold onto 19 operating habits from their startup years—direct customer contact, rapid prototyping, flat communication lines and hard limits on concurrent work among them—or risk slowing down as process layers accumulate, Forbes reported in a roundup of advice from its Technology Council, an invitation-only community of CIOs, CTOs and technology executives.
Why does this matter right now? Because the council members describe a specific failure mode: companies that grew on speed, then spent the following years building approval chains that quietly price agility out of the business. The advice is not sentimental nostalgia for garage days. It is an operational argument that scale should improve execution rather than distance a company from its users.
The concern comes down to these numbers. Each adds latency between learning and acting—exactly the interval where startups outperform incumbents.
Three Takeaways for Operating Executives
- Push decision authority down to the teams closest to the customer, reserving heavyweight review for costly, hard-to-reverse choices.
- Keep a hard work-in-progress limit: every new priority should displace an old one, because coordination—not effort—becomes the bottleneck at scale.
- Test before debating. Turn unresolved questions into small, fast, low-risk experiments rather than agenda items.
The Customer Distance Problem: Dashboards Versus Conversations
Leah Wang of Karpo led the list with the most fundamental habit: staying close to the raw customer experience. As organizations scale, she argued, dashboards and layers of interpretation obscure what people actually struggle with. Her prescription is direct—leaders keep speaking with customers themselves, and small teams get empowered to act on what they hear without a reporting chain in between.
Amy Gu of Dynamsoft applied the same logic to product delivery. Ship things that are not fully ready, she argued, and learn from real users instead of extensive internal reviews, because process exists to prevent mistakes but also prevents speed and discovery. Eva Rudin of Thales Cybersecurity framed the same discipline as a development tool: engaging a customer or partner early to confirm requirements puts healthy pressure on the build while surfacing external feedback on pricing, functionality and positioning.
But the balance sheet of attention tells a different story at scale. Vytautas Savickas of Oxylabs warned that the muscle for walking away weakens precisely when a company can finally afford to say yes to everything. When small, a firm cannot serve everyone, so it gets good at rejecting the wrong customers and use cases—and that focus, in his account, is half of why it survives. Those that continue turning away whatever does not fit preserve that sharpness well past the point where drifting would cost them nothing.
Where Hierarchy Creeps Back In
Rob Sloan of Zscaler located the delay in escalation itself. Issues get routed to the most senior person rather than the person best connected to the customer or the detail, which slows fixes and promotes permission-seeking over judgment. His remedy: push authority down and let the people closest to the problem make the call.
James Stanger of CompTIA described the best startups as having an “uncanny ability to throw unnecessary hierarchy to the wind and flatten communication lines”—how they home in on solutions rather than admiring the problem. Stephane Donze of AODocs extended the point across the org chart: if Joe from one team is afraid to talk to Mary from another because they report to different bosses, the company’s capacity to innovate is severely impaired.
Decision Discipline: Reversible Bets and the Work-in-Progress Cap
Nagesh Nama of xLM Continuous Intelligence drew the line that most scaling companies blur: reserve heavyweight reviews for decisions that are costly or difficult to undo, and let teams run small, bounded tests without routing every choice through approval layers. Melissa King of Paylocity warned that at scale, debate can become a substitute for discovery—established firms should convert unanswered questions into fast, low-risk experiments and let market evidence resolve what meetings cannot.
Akhilesh Sharma of A3Logics supplied the constraint that makes the rest workable: a hard work-in-progress limit. Early teams cannot pursue twenty top priorities, so starting one thing means stopping another. At scale, portfolios expand faster than capacity, and his conclusion is blunt—speed comes as much from limiting concurrency as from moving faster. Véronique Özkaya of DATAmundi paired that with the divestment half of the discipline: startups decide quickly both what to invest in and what to stop, because limited resources force the question.
Sergii Grushai of Peeklogic added a caution on the automation side. In mature organizations his team audits, roughly a third of flows either duplicate another or barely run, because large companies jump straight to automation and lock in their guesses. A month of running a new process manually first, he argued, is cheaper than a year of maintaining the wrong automation.
The Habits, Mapped to What They Protect
| Habit | What it protects at scale | Council contributor |
|---|---|---|
| Direct customer contact | Unfiltered signal obscured by dashboards | Leah Wang, Karpo |
| Test before debate | Discovery velocity vs. meeting paralysis | Melissa King, Paylocity |
| Reversible decision-making | Speed without heavyweight review overhead | Nagesh Nama, xLM Continuous Intelligence |
| Work-in-progress limits | Capacity vs. portfolio sprawl | Akhilesh Sharma, A3Logics |
| Manual-first processes | Against locking in wrong automation | Sergii Grushai, Peeklogic |
AI-First Thinking and the Blank-Slate Advantage
Tarek Nseir of Valliance identified the largest structural gap between startups and incumbents: new ventures have AI as the starting point rather than something bolted onto outdated processes, and that blank slate—no legacy systems standing between them and AI-driven gains—is a genuine edge. His recommendation is to begin every problem by asking where AI can make the biggest impact and build from there, a mindset he expects to define the future enterprise.
Yoav Amitai of Elsight bounded that ambition with a focus constraint: in the AI era, product-demand cycles, work methods and customer requirements have all shortened, so the one thing to preserve is agility within the boundaries of what the company actually solves. Getting lost trying to achieve everything, he noted, ends with achieving nothing. Alissa Knight of Assail supplied the cultural guardrail—process can quietly replace curiosity, and scale should give a company more resources to challenge the status quo, not more reasons to protect it.
Balancing Risk Appetite While Keeping Relentless Iteration Alive
Mandy Andress of Elastic put the risk trade-off plainly: young firms can run quickly precisely because they tolerate greater exposure, whereas mature enterprises move more cautiously to safeguard their standing, earnings and shareholders. Keeping innovation alive requires consciously balancing that risk appetite—and preserving “relentless iteration,” sharing early rough drafts for fast feedback rather than waiting for polish. Amy Brown of Authenticx added the retention mechanism: rituals created in early startup life become a touchstone to the mission, and as new employees join, those same rituals become part of their orientation. Merav Yuravlivker of Data Society offered the practical version—give teams a sandbox, clear data and security rules, and a few weeks to test; nobody writes a proposal, and the team reviews results together.
The throughline across all nineteen contributors is that none of these habits require abandoning scale. They require defending, on purpose, the behaviors that scale naturally erodes—starting with who is allowed to decide, and how quickly.
Disclaimer: The information provided in this article is for educational and informational purposes only and does not constitute financial advice.
