AI‑native startups are scaling faster than ever—some hitting milestones that traditional SaaS firms took years to reach. But things are starting to move even faster. A recent analysis by Stripe suggests AI startups reach $1 million in revenue in about 11.5 months compared with 15 months for the earlier top SaaS models. That velocity comes not just from better algorithms but from a fundamentally different organizational posture.
Meanwhile, many legacy firms are still navigating the early stages of adoption—pilots, governance debates, technical debt struggles—and too often fall short of meaningful impact. According to Boston Consulting Group, 74% of companies struggle to derive value from AI, with just 26% achieving scale beyond proof of concept.
The Senior Executive AI Think Tank brings together leaders immersed in machine learning, generative AI and enterprise AI applications. Their collective wisdom reveals that competing with AI challengers demands more than tech upgrades—it requires deep structural and cultural shifts. In this article, they explore those shifts and offer actionable strategies for traditional organizations to close the gap.
“Legacy companies must evolve from workflow‑ and process‑centric architectures to data‑centric ones.”
Data First, Everything Else Second
For legacy companies to compete with AI-native startups, they must rethink their entire operational backbone. Traditional organizations are typically structured around static workflows and rigid processes—but these frameworks were not built with AI in mind. AI-native firms, in contrast, treat data as the central product and build everything else around it. “Legacy companies must evolve from workflow- and process-centric architectures to data-centric ones,” says Prashant Kondle, Digital/AI Transformation Specialist at Ivis Technologies. “With data at the center, reorganizing workflows around AI becomes easier, and AI trained on curated internal data delivers far more relevant outcomes.”
Kondle also points to the need for cultural change: “Organizations must incentivize risk-taking with AI.” Rather than rewarding only error-free execution, he says, companies should value experimentation, iteration and fast learning cycles. “This is the cultural DNA AI-native startups already operate with.”
Build AI-Powered Generalists
Legacy firms often rely on deeply siloed teams of specialists—a structure that hinders agility. In contrast, AI-native startups empower employees to act as generalists who can rapidly pivot, adapt and scale solutions across business functions. “AI-native companies scale so quickly because their teams can adapt fast, pivot across functions and seize opportunities without being slowed by rigid roles,” says Mo Ezderman, Director of AI at Mindgrub Technologies. By breaking down functional boundaries and enabling hybrid skill sets through AI support, traditional firms can drastically improve their responsiveness and innovation.
“Traditional organizations will struggle to compete with AI-native startups due to their people and infrastructures holding them back.”
Flatten Hierarchies to Accelerate Change
For traditional companies, outdated organizational hierarchies and slow, bureaucratic decision-making are some of the biggest barriers to AI transformation. “Traditional organizations will struggle to compete with AI-native startups due to their people and infrastructures holding them back,” says Gordon Pelosse, Executive Vice President of Partnerships and Enterprise Strategy at AiCerts.
He stresses that AI is not a plug-and-play solution—it requires foundational change that must begin at the leadership level. That includes flattening hierarchies, empowering decision-makers closer to the front lines and championing continuous learning. “Many may resist,” he adds. “Unfortunately, they won’t survive unless they embrace change.”
Lead the Culture Shift, Not Just the Tech
Adopting AI is not simply a technical endeavor—it’s a people-centric transformation. “The real challenge isn’t just adopting AI; it’s bringing people along for the journey,” says Charles Yeomans, CEO and Founder of Atombeam. In any workforce, some employees eagerly embrace change while others resist it. Yeomans emphasizes that executive leadership must set the tone by modeling how AI improves efficiency and decision-making across the board. That means using AI tools themselves—and demonstrating their value in day-to-day operations.
Yeomans urges leaders to make the shift human-focused: “Companies that thrive will be those that treat AI adoption as a cultural shift centered on people, not just a technology rollout.” Building comfort and competence across the workforce ensures AI initiatives don’t stall due to fear or confusion.
Think Like a Startup (Within Your Company)
Startups succeed in AI because they operate at the intersection of speed and experimentation. “What drives AI companies’ success is not managing but leading, not postponing but executing,” says Soner Baburoglu, President of SonerB. Baburoglu suggests that traditional companies must adopt “startup within a startup” models—creating internal teams that are freed from legacy constraints and focused on delivering MVPs fast. Traditional firms stuck in cycles of risk aversion and long decision timelines will find themselves outpaced not by older rivals, but by agile startups founded just months ago.
Turn Business Models Into Data Flywheels
According to Mohan Krishna Mannava, Data and AI Leader at Texas Health, AI-native startups don’t just use AI—they are fundamentally built around it. “The real competition isn’t about technology, but about business models,” he says. These firms create self-reinforcing data loops: Every user interaction generates data, which improves AI performance, which in turn attracts more users and generates more data. This flywheel effect gives AI-native companies a growing, compounding advantage that traditional businesses can’t match with bolt-on solutions.
To compete, legacy firms must undergo a deep operational transformation. “They must reengineer their entire operation into a flywheel of their own,” Mannava asserts. “The most successful firms won’t just hire data professionals; they’ll transform into platforms that capture unique and proprietary data, using it as their most valuable asset.”
Move Fast, Upskill and Break Silos
Legacy organizations are often weighed down by complex approval chains, siloed departments and resistance to experimentation. “Encourage teams to test new ideas without fear of failure,” advises Roman Vinogradov, VP of Product at Improvado. He suggests companies adopt agile methodologies, streamline decision-making and empower cross-functional collaboration to accelerate AI-driven innovation. This culture of rapid iteration is what gives AI-native startups their momentum—and it can be replicated with the right organizational mindset.
Vinogradov also stresses the importance of education and upskilling. “Ensure employees understand AI tools and how they apply to their roles,” he says. Doing so not only boosts adoption but also democratizes innovation across the company.
From Data Chaos to Data Culture
“Traditional companies must evolve to compete with AI-native startups by fostering a data-driven culture, breaking down silos and embracing rapid experimentation,” says Suri Nuthalapati, Data and AI Leader for the Americas at Cloudera. These principles are second nature to AI-first firms but often foreign to legacy organizations still stuck in fragmented systems and slow delivery models. Suri emphasizes that without aligning leadership and integrating AI into core workflows, innovation efforts risk having minimal business impact.
“Legacy firms won’t compete with AI-native startups by bolting on tools—they must rewire their DNA.”
Treat AI as the Operating System, Not a Tool
The biggest mistake legacy companies make when approaching AI is treating it like a feature instead of a foundational shift. “Legacy firms won’t compete with AI-native startups by bolting on tools—they must rewire their DNA,” says Aditya Vikram Kashyap, Vice President of Firmwide Innovation at Morgan Stanley.
In his view, AI shouldn’t be a one-off innovation initiative; it must be the core architecture driving how the business operates, makes decisions and delivers value. That means embedding AI into strategy, processes and culture—essentially treating it as the enterprise’s operating system. “Those who make that leap won’t just catch up—they’ll lead,” Kashyap adds.
Next Steps for Legacy Leaders
- Treat data as the product, not just a byproduct. Reorient architecture so data is central and everything else is modular.
- Cultivate AI‑empowered generalists. Break role rigidity; encourage cross‑domain fluency supported by AI tools.
- Flatten hierarchies and empower teams. Shift decision authority downward to accelerate response loops.
- Lead by example and support the workforce. Executives must model AI use and invest in education and safety nets.
- Spin up internal startup units. Operate small, autonomous pods that follow MVP and customer feedback cycles.
- Build a data flywheel. Design operations so every interaction refines models, benefiting the business over time.
- Enable experimentation and agility. Train teams, break silos, adopt agile frameworks and normalize failure as learning.
- Foster a unified, data-driven culture. Break down internal data silos and modernize your tech stack to support real-time analytics and AI-driven decision-making.
- Rewire your enterprise’s DNA. Don’t treat AI as an add-on; restructure leadership models, operations and strategy to embed AI into the core fabric of your business.
Outpacing Disruption Starts From Within
Legacy firms ready to compete with AI‑native challengers must undergo deep structural and cultural shifts. It’s not enough to bolt on models—transformation must begin at the core: data architecture, organization design, leadership behavior and business model thinking. As the Senior Executive AI Think Tank has illuminated, the winners will be those who treat AI not as an add‑on but as the operating system of the enterprise.
The horizon is clear: Firms that can pivot their culture, embed continuous learning and design feedback loops will do more than survive—they’ll compete, lead and define what “legacy” means in the AI age.
