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By Kelvin Teo
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U.S. venture investment reached $144.9 billion across 3,644 deals in the second quarter of 2026, according to KPMG’s Venture Pulse report, with artificial intelligence accounting for a substantial share of the largest transactions.
Meanwhile, research into AI-native businesses, companies built around AI from the outset rather than adapting existing operations to use it, suggests that some are generating significantly more revenue per employee than traditional software companies, according to a 2026 Forbes analysis.
These findings raise a question with consequences for businesses across industries: if AI can take on more of the work involved in running a company, does growth still require larger workforces, more management layers, and increasingly complex operations?
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For decades, international expansion has generally meant adding employees, infrastructure, and management capacity as businesses enter new markets. That model remains necessary in industries where local expertise and human service are essential. But companies built around AI from the outset are testing whether technology can enable businesses to expand their capabilities without increasing organizational complexity at the same rate.
The early evidence is worth examining. The Forbes analysis highlighted the revenue-per-employee performance of certain AI-native firms compared with traditional public software companies. Another Forbes article examined research into more than 2,900 startups, finding that AI-native companies tend to have smaller workforces and fewer management layers.
These findings do not prove that smaller teams are always more productive, but they raise an important question about whether companies designed around AI can achieve scale differently from those adapting it to established systems.
AI and Global Growth
The challenge is particularly relevant to international commerce, where businesses must respond to different consumer preferences, pricing conditions, product availability, and purchasing behaviors.
In luxury retail, these demands are compounded by the importance of trust, personalization, and informed customer service. AI can help businesses process information across these areas, but its value depends on whether that information leads to better decisions.
The significance is not simply the size of the workforce, but how technology changes what a small team can manage.
AI integration can affect multiple areas of business operations, from pricing and product management to reporting and fulfillment. When incorporated into existing workflows, these systems can help process large volumes of information, identify patterns, and inform decisions. However, their effectiveness depends on the quality of the underlying data, the processes they support, and the level of human oversight involved.
The key consideration is whether automation improves how work gets done. Automating an inefficient process does not necessarily make it more effective. Businesses need to reconsider how work is structured, what information is required, where decisions should be made, and which activities benefit from human involvement. The objective should be to reduce unnecessary coordination while improving the quality and speed of operational decisions.
This becomes more complicated when a business operates across international markets. Consumer expectations, merchandising, pricing, and purchasing behavior can differ substantially between regions. A product that attracts attention in Europe may require a different approach in Asia, while local market conditions can influence how and when products should be presented.
AI can help businesses interpret these differences by bringing together information that might otherwise require extensive manual analysis. In international commerce, understanding these differences is essential to making informed decisions about product selection, pricing, messaging, and marketing. AI can make market-specific information easier to analyze, helping teams identify patterns across regions and respond more efficiently. But analysis alone is not enough. The information must still be translated into decisions that reflect the preferences and expectations of individual markets.
Rather than establishing entirely separate operational structures for every country, businesses may be able to use shared systems to identify market-specific requirements and respond more quickly. However, technology cannot replace local knowledge, cultural understanding, or regulatory expertise. Its effectiveness depends on the quality of the information it receives and the judgment of the people interpreting its recommendations.
The Human Factor
Human oversight is equally important in customer-facing work. Luxury purchases can involve considerable deliberation, and customers often expect personal attention. Customer service professionals can use AI to develop initial responses, analyze relevant information, and improve efficiency, while retaining responsibility for reviewing communications before they reach customers. Designers and engineers also play a role in determining how these systems operate and where intervention is necessary.
The broader principle is that AI should strengthen human expertise rather than eliminate it indiscriminately. A faster response is not necessarily a better one if it is inaccurate, impersonal, or poorly suited to a customer's needs. Businesses must decide where automation adds value and where human judgment remains central to the experience.
The implications extend beyond internal operations. As AI becomes more capable of connecting information about products, customers, and market conditions, it could influence how people discover products and make purchasing decisions. AI-enabled shopping concepts are one example of how capabilities traditionally used behind the scenes may eventually become part of the consumer experience.
Scaling With AI
For founders, the most consequential decisions may come before a company begins to scale. Businesses without legacy systems have an opportunity to determine which activities require human expertise, which can be standardized, and where AI can improve the flow of information. Organizational structure and technology architecture become connected decisions, shaping how the company responds to growth.
That does not mean every business should aim to operate with fewer employees. Lean structures can create capacity constraints, and operational mistakes can become more costly as transaction volumes increase. AI also introduces risks involving data quality, privacy, security, and accountability. These considerations must be addressed alongside potential efficiency gains.
Not every industry will adopt AI-native models at the same pace, and their success will depend on how effectively businesses manage complexity, maintain customer trust, and apply appropriate oversight. But the early evidence suggests a structural shift in how businesses can achieve scale, one where growth is increasingly driven by technological capabilities rather than organizational size.
The real test will be whether AI-native businesses can sustain that advantage as they grow, manage greater complexity, and maintain customer trust. Scale has traditionally been measured through revenue, workforce, and geographic reach. Increasingly, it may also depend on how effectively a company combines technology, human judgment, and operational discipline.
Kelvin Teo is founder and CEO of MAYFEYR, an AI-enabled luxury-commerce platform. All opinions are his own.
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