Smaller Companies Lead in Opening New Countries for AI Talent

Smaller enterprises with 500 to 749 employees are the most likely to open operations in entirely new countries to reach AI talent. Large enterprises with 1,000 or more employees instead focus on moving roles across countries where they already operate.

These patterns emerge from size-segmented findings in a Remote.com survey released on July 16, 2026, which examined how company scale influences responses to AI-driven changes in global hiring.

Survey Overview and Methodology

The survey gathered responses from 3,250 hiring leaders across eight markets: the UK, US, Spain, Singapore, Netherlands, Germany, France, and Australia. It targeted business and HR leaders with direct recruitment responsibility.

Overall, 97.7% of respondents indicated that AI adoption has altered where they hire talent. In addition, 72% reported missing business goals because of talent shortages. These statistics provide a baseline for understanding the broader impact of AI on recruitment strategies.

The methodology involved surveying leaders who have direct involvement in hiring decisions. This ensures the data reflects practical experiences rather than theoretical views. The markets selected represent a mix of established and emerging locations for tech talent.

When applying this data, consider whether your organization matches the respondent profile in terms of market presence and hiring focus. Companies outside these markets or not actively hiring for AI roles should treat the findings as directional rather than definitive.

Limitations include the self-reported nature of the responses and the focus on AI talent contexts. Results may not generalize to all firms or non-AI roles. No independent verification of the raw data is available in public sources.

A typical error is to apply the overall percentages without segmenting by company size. Another mistake involves assuming the survey covers all industries equally, when it emphasizes AI adoption specifically.

In a conditional example, a company could review the market list to see if their location is represented before applying the overall trends to their strategy.

Understanding the methodology helps in avoiding misinterpretation of the results. The focus on hiring leaders ensures the data is grounded in experience. Companies can use this to benchmark their own practices against the surveyed group. This benchmarking can reveal gaps in their current approach to AI talent location.

The eight markets were chosen to represent a diverse set of regions with varying levels of AI talent availability. This diversity helps in understanding global trends rather than regional ones. Respondents were selected based on their direct role in recruitment to ensure the answers reflect operational realities. This is important for practical application of the findings.

Overall Trends in New Country Operations

Forty percent of surveyed companies have opened operations in entirely new countries specifically to access AI talent. Another 58% are scaling hiring globally within existing markets.

This shift marks international hiring moving from an exception to a default approach. Nearly half of respondents expect the majority of their new hires to come from outside their primary country by the end of 2026.

The mechanics behind these trends involve AI talent being concentrated in specific regions that may not align with traditional company footprints. Companies must adapt by either entering new areas or optimizing current ones to meet demand.

Criteria for choosing between new country operations and scaling existing ones include assessing current talent availability in established locations and the urgency of AI skill needs. Organizations should evaluate the cost of setting up new operations against the benefits of accessing untapped talent pools.

Limitations of the trend data stem from the survey being limited to companies already engaged in AI hiring. This may overrepresent firms that have already begun adapting to location changes.

A practical example can be considered in a situation where a company decides to open in a new country after identifying a shortage in their primary market. They would first map talent distribution before committing resources.

Typical mistakes include underestimating the time required for new market entry or failing to account for regulatory differences across borders. Another error is ignoring the expectation that by end of 2026 many hires will be international.

The trend suggests that by the end of 2026, many organizations will have adjusted their global presence to accommodate AI needs. Monitoring this can help in strategic planning. Additional considerations involve the cost and time for opening new operations. Companies must weigh these against the benefits of accessing new talent sources.

Companies should also examine how AI changes the traditional hiring map that was drawn a decade ago. This examination helps in identifying which locations now offer better access to specialized skills.

Size-Based Differences in Expansion Strategies

Smaller enterprises (500–749 employees) lead in new-country openings at 43.2%. Mid-market firms (750–999 employees) follow at 38.5%, while large enterprises (1,000+) stand at 35.0%.

In contrast, 61% of large enterprises have moved roles to different countries within their existing footprint. The corresponding figure for smaller enterprises is 55.8%.

These differences highlight distinct playbooks based on company size. Smaller firms use new entries to build presence where needed, while larger ones leverage scale to redistribute within known environments.

Criteria for determining the appropriate strategy involve reviewing employee count and current international presence. Smaller companies should check if they have any existing operations before planning new ones.

Limitations arise because the data is self-reported and specific to the surveyed markets. Percentages reflect responses from companies already engaged in AI hiring and may not apply broadly.

In a conditional example, a company with around 600 employees might prioritize new country entry after reviewing their size category in the survey results.

Typical errors include misclassifying company size or overlooking the reshuffling option available to larger firms. Another mistake is not recognizing that mid-market percentages fall between the two extremes.

These numbers show a clear playbook difference based on size. Smaller companies build new markets, while large ones optimize within what they have. Criteria for choosing the strategy include your employee count and current number of countries operated in. If you have few countries, new entry may be necessary.

Limitations are that the data is specific to AI talent and self-reported. It may not hold for other types of roles or different company types. Typical mistakes include not segmenting your company by size when applying the data. Also, confusing the percentages for new openings with those for reshuffling.

Further explanation involves how smaller companies may have less established presence, making new openings more common. Large companies can move roles without new setups. Companies should also consider the mid-market position as a bridge between the two approaches.

Rationale for Playbook Differences

Smaller firms often lack established international footprints, which makes entry into new markets necessary to secure AI talent. They can manage associated risks more affordably through available infrastructure such as employer of record services.

Large enterprises, already present in multiple locations, optimize by redistributing roles across those sites rather than building additional presence. This approach leverages existing compliance structures and reduces the need for new setup processes.

The rationale ties to infrastructure and risk management. Smaller companies benefit from tools that lower barriers to entry, while larger ones avoid duplication by optimizing what they have.

Criteria for selecting infrastructure include evaluating the affordability and compliance support available for new markets. Companies should compare options based on their risk tolerance and existing legal teams.

Limitations include that mentions of infrastructure come from the hosting company's perspective in a promotional context. Independent analysis of these tools is not provided in the survey.

A conditional example involves a smaller enterprise using compliance services to enter a new country without building full local operations from scratch.

Typical mistakes include assuming all smaller companies have the same access to infrastructure or that large firms never enter new countries.

The rationale is tied to existing infrastructure and risk levels. Smaller firms use tools to enter new areas, while larger ones avoid additional complexity. Criteria for deciding include assessing your current footprint and the availability of compliance tools. Smaller companies may prioritize tools that lower entry barriers.

Limitations include the promotional context for EOR mentions. The survey does not provide independent data on these tools. Typical mistakes include assuming that all smaller companies can easily use such infrastructure without checking costs. Large companies may also enter new countries if needed, but the data shows they prefer reshuffling.

Additional points include how risk management differs by size. Smaller firms may have less tolerance for high setup costs, leading to reliance on external infrastructure.

Implications for Hiring Speed and Compliance

Companies that open new operations can accelerate onboarding in previously inaccessible talent pools when using established compliance tools. However, each additional country multiplies the complexity of legal, tax, and operational requirements.

Organizations evaluating these options must weigh the speed gains against the administrative overhead that accompanies new market entry.

The implications extend to hiring timelines and regulatory adherence. Faster access to talent can support business goals, but increased complexity requires careful planning.

Criteria for balancing speed and compliance involve assessing the number of new countries planned and the availability of internal resources for management. Start with one new location to test processes.

Limitations of these implications are that the survey does not provide specific timelines or cost figures. Results are tied to AI talent contexts in the surveyed markets.

In a conditional example, a firm might experience quicker hiring in a new market but face delays in compliance setup if not prepared.

Typical errors include overestimating the speed benefits without accounting for setup time or underestimating the multiplied complexity with multiple new countries.

The implications for hiring speed are significant in competitive AI talent markets. Compliance complexity increases with each new location, requiring dedicated resources. Criteria for managing this include starting with one new country and building processes before expanding further. Assess internal capabilities for handling multiplied requirements.

Limitations are that specific speed or complexity metrics are not provided in the survey. The data is limited to the surveyed markets and AI contexts. Typical mistakes include underestimating the compliance complexity or overestimating the speed gains without proper planning.

In a conditional example, a company could achieve faster hiring in a new market but encounter delays if compliance is not addressed early. Further, the balance between speed and compliance requires ongoing evaluation as new countries are added.

Practical Next Steps for Talent Strategy

Review current hiring data to determine whether new-country expansion or internal reshuffling aligns with your scale and existing footprint. Assess infrastructure options for managing compliance before committing to new locations.

Monitor updates from primary sources such as the Remote.com analysis for ongoing shifts in AI talent location patterns.

Begin by categorizing your company size and mapping existing operations. Then compare the survey percentages to your situation to identify the most relevant playbook.

Next, evaluate talent shortages in current locations against potential new markets. Consider the criteria of urgency and resource availability when deciding.

Limitations in applying these steps include the single-source nature of the data and its focus on AI roles. Regular review of new information is necessary as trends evolve.

A conditional example can be considered when a company lists its current countries and matches them against the size-based data to choose between options.

Typical mistakes include skipping the initial review of company size or failing to monitor source updates for changes in patterns.

Begin the process by categorizing your company by employee number and listing your current countries of operation. Compare these to the survey findings to identify the matching strategy. Then, evaluate the talent needs for AI roles and the potential benefits of new markets. Use the criteria of size and footprint to guide the decision.

Next, research infrastructure options that fit your risk profile and budget. Test with a small scale if possible. Limitations in these steps include the evolving nature of the data and the AI-specific focus. Regular monitoring is essential.

Typical mistakes include not reviewing the data first or skipping the assessment of infrastructure. In a conditional example, a company could list their size and countries, then decide on expansion based on the playbook differences. Finally, document the decision process to allow for adjustments as new survey data becomes available.