AI-Enabled BPO Strategy: Moving Beyond Major Cities for Sustainable Growth

For decades, outsourcing has thrived in global hubs like Manila, Bangalore, and Mexico City. These cities became synonymous with the business process outsourcing (BPO) industry, offering dense talent pools, reliable infrastructure, and well-established ecosystems.

But the landscape is shifting. Artificial intelligence (AI) is no longer just a tool for automation, it’s redefining how outsourcing is delivered and where it can thrive. While AI enhances efficiency and reduces reliance on sheer workforce volume, geography still plays a critical role in cost structures, talent retention, and business resilience.

To stay competitive, AI-enabled BPOs must look beyond the major cities. The future lies in tapping into emerging labor markets, tier-two cities and regional hubs where untapped talent, lower costs, and new growth opportunities converge.

The Traditional Model – Strengths and Limitations

The rise of outsourcing in the late 20th and early 21st century was fueled by the advantages of concentrating operations in major metropolitan areas. Cities like Bangalore, Manila, and Mexico City offered everything providers needed to scale quickly: deep talent pools of educated workers, reliable internet and transportation infrastructure, and an ecosystem already familiar with serving global clients.

This model delivered undeniable benefits. Companies gained access to large, English-speaking workforces at competitive costs. Proximity to universities ensured a steady pipeline of skilled graduates. Established business hubs also made it easier to attract international investment and build partnerships.

Yet today, these strengths come with growing challenges. Costs in major cities are rising, squeezing the very savings that once made outsourcing attractive. Competition for skilled employees has led to high turnover rates and escalating wages. Infrastructure, once an advantage, is now strained under the pressure of rapid urbanization. For BPOs competing on price, speed, and quality, the traditional city-centric model is reaching its limits.

AI’s Role in Redefining Outsourcing

Artificial intelligence is changing the outsourcing equation. Tasks that once required large teams of entry-level employees – such as data entry, document processing, or basic customer inquiries – can now be handled more efficiently through AI-driven automation. This shift doesn’t replace outsourcing; it reshapes it.

By reducing dependence on sheer workforce volume, AI makes distributed models far more viable. Instead of concentrating thousands of employees in one city, AI-enabled BPOs can design leaner operations spread across multiple locations. Automation handles repetitive workloads, while human expertise focuses on judgment, creativity, and client-specific nuance.

The human-in-the-loop (HITL) approach ensures quality and adaptability. AI delivers speed and accuracy, but oversight from trained professionals remains critical for context, exception handling, and continuous improvement. The difference is that this human oversight no longer needs to be clustered in a handful of global hubs. With the right digital infrastructure, a skilled professional in a secondary city can deliver the same value as one in a major outsourcing capital, often at lower cost and with greater loyalty.

Unlocking Tier-2 and Emerging Locations

As AI makes outsourcing less dependent on massive labor hubs, new opportunities are opening up in tier-2 cities and emerging regions. These markets offer advantages that go beyond cost savings:

• Untapped talent pools – Smaller cities often have universities and technical schools producing skilled graduates, but limited local opportunities. Outsourcing brings high-value roles to these communities.

• Lower operating costs – Real estate, wages, and overhead are significantly cheaper compared to saturated hubs like Manila or Bangalore.

• Improved retention – With less competition for talent, employees in secondary markets are more likely to stay, reducing turnover and training expenses.

• Reduced infrastructure strain – Smaller cities can often support outsourcing operations without the bottlenecks caused by overcrowded urban centers.

Global examples are already taking shape. In the Philippines, providers are expanding into Iloilo and Davao, where talent is strong but competition is lighter than in Manila. In Eastern Europe, cities like Cluj-Napoca (Romania) and Kraków (Poland) have become outsourcing magnets, offering multilingual talent at lower cost than capitals. And in Latin America, countries such as Colombia and Mexico are seeing growth in secondary cities like Medellín and Monterrey, building on nearshore demand from North America.

For AI-enabled BPOs, this geographic diversification isn’t just a cost play. It builds resilience, expands access to niche skills, and positions providers to scale flexibly as client demands evolve.

Risk and Reward in a Distributed Model

Expanding beyond major cities comes with undeniable benefits, but it isn’t without challenges. A distributed model requires BPO leaders to balance opportunity with preparation.

Key risks include:

• Infrastructure readiness – Not every emerging city has reliable power, internet, or transport links.

• Training investment – New markets may need additional onboarding, upskilling, and cultural alignment to meet global client expectations.

• Operational oversight – Managing quality across multiple locations requires strong digital infrastructure and standardized processes.

But the rewards are significant:

• Lower vulnerability – Diversification reduces the risk of disruption if a single hub faces political instability, natural disasters, or infrastructure breakdowns.

• Stronger client appeal – Enterprises value providers with resilient, flexible footprints.

• Better workforce loyalty – Employees in smaller markets often show stronger commitment, translating to reduced attrition and higher service continuity.

Mitigation strategies make the model sustainable. Hybrid hubs  (combining established city centers with regional satellites) allow providers to scale without overreliance on one location. Partnerships with local governments and universities help secure infrastructure and training pipelines. Digital-first onboarding and AI-powered quality control create consistency across multiple geographies.

The outcome? A distributed, AI-enabled BPO that balances efficiency with resilience, offering clients both innovation and stability.

The Path Forward

Outsourcing is entering a new era; one where technology and geography are inseparable. AI is no longer an add-on; it is the backbone of next-generation BPO strategy. But even as automation transforms processes, the human element and where that talent is located will remain critical.

Over the next decade, expect to see outsourcing networks that look very different from today’s city-centric hubs. Instead of tens of thousands of employees concentrated in a handful of global capitals, AI-enabled BPOs will spread operations across multiple regions – smaller cities, nearshore locations, and even rural markets. This distributed approach will create ecosystems that are more resilient, more cost-effective, and more inclusive of diverse talent.

For enterprises, the shift means greater stability and access to specialized skills without the price tag of saturated hubs. For providers, it represents a chance to differentiate by building flexible, future-proof delivery models. And for employees, it opens opportunities in places where global outsourcing jobs were once out of reach.

The message is clear: the future of AI-enabled BPO will not be defined by mega-cities alone. It will be built on a smarter balance of automation, human oversight, and geographic diversification. The leaders who embrace this shift now will be the ones setting the standard for the next chapter of outsourcing.